Review Special Issues

An overview of AC and DC microgrid energy management systems

  • In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing.

    Citation: Mohamed G Moh Almihat. An overview of AC and DC microgrid energy management systems[J]. AIMS Energy, 2023, 11(6): 1031-1069. doi: 10.3934/energy.2023049

    Related Papers:

    [1] Chris Thankan, August Winters, Jin Ho Jo, Matt Aldeman . Feasibility of applying Illinois Solar for All (ILSFA) to the Bloomington Normal Water Reclamation District. AIMS Energy, 2021, 9(1): 117-137. doi: 10.3934/energy.2021007
    [2] Austin Bushur, Kevin Ward, Tommy Flahaven, Tom Kelly, Jin H. Jo, Matt Aldeman . Techno-economic evaluation of installing EV and PV combined infrastructure on Academic Institution’s Parking Garages in Illinois, USA. AIMS Energy, 2019, 7(1): 31-45. doi: 10.3934/energy.2019.1.31
    [3] Alemayehu T. Eneyaw, Demiss A. Amibe . Annual performance of photovoltaic-thermal system under actual operating condition of Dire Dawa in Ethiopia. AIMS Energy, 2019, 7(5): 539-556. doi: 10.3934/energy.2019.5.539
    [4] Jin H. Jo, Kadi Ilves, Tyler Barth, Ellen Leszczynski . Implementation of a large-scale solar photovoltaic system at a higher education institution in Illinois, USA. AIMS Energy, 2017, 5(1): 54-62. doi: 10.3934/energy.2017.1.54
    [5] Laveet Kumar, Jahanzaib Soomro, Hafeez Khoharo, Mamdouh El Haj Assad . A comprehensive review of solar thermal desalination technologies for freshwater production. AIMS Energy, 2023, 11(2): 293-318. doi: 10.3934/energy.2023016
    [6] Nadejda Komendantova, Markus Manuel Schwarz, Wolfgang Amann . Economic and regulatory feasibility of solar PV in the Austrian multi-apartment housing sector. AIMS Energy, 2018, 6(5): 810-831. doi: 10.3934/energy.2018.5.810
    [7] Ali Boharb, A. Allouhi, H. El-houari, H. El Markhi, A. Jamil, T. Kousksou . Energy audit method applied to tertiary buildings: Case study of a University campus. AIMS Energy, 2022, 10(3): 506-532. doi: 10.3934/energy.2022025
    [8] Chiara D’Alpaos, Michele Moretto . Do smart grid innovations affect real estate market values?. AIMS Energy, 2019, 7(2): 141-150. doi: 10.3934/energy.2019.2.141
    [9] Fadhil Khadoum Alhousni, Firas Basim Ismail, Paul C. Okonkwo, Hassan Mohamed, Bright O. Okonkwo, Omar A. Al-Shahri . A review of PV solar energy system operations and applications in Dhofar Oman. AIMS Energy, 2022, 10(4): 858-884. doi: 10.3934/energy.2022039
    [10] Michael O. Dioha, Atul Kumar . Rooftop solar PV for urban residential buildings of Nigeria: A preliminary attempt towards potential estimation. AIMS Energy, 2018, 6(5): 710-734. doi: 10.3934/energy.2018.5.710
  • In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing.



    Abbreviations: CO: coal; DHW: Domestic hot water; DNE: discount net externality; 3E: energetic-economic and environmental; ESM: energy saving measurement; EP: Energy Provided; ES: Energy Saved during the System Life Cycle; ECD: The carbon dioxide emitted by specific resources; FPC: Flat plate collector; FF: fossil fuels; LED: light-emitting diodes; HPS: high-pressure sodium; NES: National Energy Strategy; NG: natural gas; OI: oil; PEM: The percentage in the energy mix of these specific resources; PV: photovoltaic; PD: The payback duration; PEF: primary energy conversion factor; RES: renewable energy sources; RECD: reducing carbon dioxide emission; SC: scenario

    The rapid population growth and urban expansion have resulted in an increase in worldwide energy demand. Because of the ever-increasing exploitation of energy resources, development should occur in tandem with energy production and consumption [1]. Thus far, the construction & buildings sector accounts for 32% of total global energy use. Furthermore, this sector accounts for roughly 20% of global greenhouse gas emissions [2]. Rising energy demand in developing nations has prompted increased attempts by numerous organizations to strike a balance between energy generation and energy consumption. Many studies that analyze the energy flow in buildings have been conducted in order to define the best course of action for energy efficiency and energy conservation activities. Energy efficiency is critical for rationalizing energy input, lowering energy costs and carbon footprint, and maintaining a comfortable environment in buildings [3]. While there are many ways to achieve energy efficiency for the building and construction sector, energy audits are considered the most efficient tool largely used to reach higher efficiency levels.

    To address this issue, governments all over the world have rolled out programs and governmental policies to increase energy efficiency in the construction and buildings sector [4]. In this context, Morocco is engaged in significant economic and social reforms [1], including a shift to green development [5]. Morocco approved a National Energy Strategy (NES) in 2009, with the goal of improving energy supply security and availability, as well as increasing broad access to energy at competitive costs [6]. To attain these goals, a range of energy policies have been supported, with the major emphases on energy source diversity, growth of the national potential in energy resources (particularly renewables), promotion of energy efficiency, and greatly advanced integration in the regional energy system. In addition to this energy plan, the Moroccan government has advocated an energy efficiency policy aimed at defining the roles of administrations and operators. The goals of this energy efficiency strategy are to establish an institutionalized public management system for energy efficiency concerns, as well as a proper legislative and regulatory framework, and to promote norms and standards. In this manner, Morocco's government also adopted Law 47-09 on energy efficiency [7]. According to Article 3 of the act, compliance with the standards related to urban planning, the "general building norms, " which specify the rules for building energy performance, are required. These rules focus on ensuring an appropriate building energy balance for each climatic zone, taking into account the lighting, orientation, insulation, and thermal fluxes of the building, as well as any renewable energy contribution in line with the building performance level improvement. Such new standards apply to both new construction and restorations. In 2014, Decree n.2-13-874 [8] was issued, developing the set of thermal construction regulations (RTCM) in Morocco and establishing them as mandatory in November 2015. This decree defined a set of minimum thermal technical building envelope criteria, such as external walls, roofs, and windows. Furthermore, based on climatic zoning, yearly maximum thermal needs for both cooling and heating energy consumption are established [9,10]. Morocco's 2015 energy strategy established a goal of 20% energy savings by 2030 [11]. In terms of energy labeling for electrical items and home appliances, current Moroccan rules make photovoltaic products and solar thermal systems mandatory [12].

    In a very specific context, educational buildings consume a significant portion of the energy utilized in the tertiary building sector, and their energy budget spending represents a significant financial burden for the country [13] and energy audit can help it achieve efficiency and reduce energy costs. In this sense, many energy audits for university buildings or higher education institutes, including campuses, have been completed and which has demonstrated that implementing energy efficiency measures can result in significant savings. As such, Hussain [14] conducted an energy assessment for a graduate engineering institution. The results showed that the potential energy savings from the action plan presented in the article were about 36% of total energy usage. Semprini et al. [15] did an energy audit for a graduate school of engineers and architects in Bologna. The results reveal estimated energy savings of 32% simply by improving heating energy efficiency. Singh et al. [16] conducted an energy audit for a Malaysian institution. The authors solely address electrical energy end-use and tackle the corresponding lighting and air conditioning action plan. It was discovered that the electrical energy-saving potential of the studied loads amounts to 10%. At the National Autonomous University of Mexico, Azucena Escobedo et al. [17] estimate energy consumption and associated greenhouse gas emissions for the buildings and infrastructures on the main campus (UNAM). The authors estimated the energy consumption in detail for each energetic element based on energy auditing levels Ⅰ and Ⅱ. The findings suggested that energy consumption could be 7.5% lower than in 2011 and CO2 emissions could be 11.3% lower than in 2011 if energy efficiency technologies are applied for retrofitting and taken into consideration for new buildings in lighting, refrigeration, and air conditioning; and a hybrid system (solar-electric-LPG) is used for water heating.

    Thewes et al. [18] provided the findings of an energy consumption study as well as prospective energy reductions for 68 school buildings. The study discovered that modest fixes such as insulation and air tightness may significantly cut energy demand. The authors calculated potential savings in the tertiary sector of 1% of the national annual fuel oil and gas consumption. Alajmi [19] conducted an energy audit to investigate energy conservation potential for an educational facility in a hot climate (state of Kuwait). The author discovered that the building's electrical and mechanical systems were not adequately operated or maintained. Saving up to 49.3% of the building's yearly energy consumption was attainable by making some adjustments, with a payback time of less than six months. In 2008, the city of Paris (France) initiated an energy-saving initiative in schools with the goal of reducing usage by 30% [20] More than 600 school buildings were involved in the initiative. The insulation and fenestration, as well as the heating systems, have been refurbished. By evaluating 100 schools, it was discovered that a saving of a total of 10 700 MWh of final energy end use, a reduction of 2300 t of carbon dioxide, and an annual savings of 85000? may be realized. Butala [21] did research on 24 ancient schools in Slovenia to increase their energy efficiency. The analysis revealed considerable energy losses (about 89% than standards). According to the report, the proposed action plan can decrease losses and allow structures to adhere to the specified values. Dimoudi and Kosterala [22] also explored the possible energy savings in Greek educational facilities. They found, using modeling studies, that boosting insulation levels can lower heating usage by 29%.

    On the other hand, Hamdaoui et al. [23] examined the energy consumption and environmental impacts of several construction scenarios for a Moroccan office building. The gathered data show that the best building option significantly reduces annual energy loads when compared to the baseline scenario. Estimates show that the annual drop in Agadir was around 20%, Tangier was about 48%, Fez was about 53%, Ifrane was about 56%, Marrakech was about 31%, and Errachidia was about 41%. According to Guechchati et al. [24], adding a 6 cm layer of extruded polystyrene insulation to the exterior of external walls can reduce the amount of heating and cooling that is required annually by 8.38 percent and 70.54 percent, respectively. Lafqir et al. [25] showed that combining wall insulation, roof insulation, and window type selection permits a thermal load reduction of more than 70% in all temperature zones of Morocco, with the exception of the cold one (Ifrane). The integration of renewable energy systems (RES) to achieve a net zero energy balance has been evaluated by several countries and areas. As a consequence, Good et al. [26] conducted a comparative analysis of the utilization of several solar energy solutions (solar thermal, photovoltaic PV), and photovoltaic-thermal (PV/T)) in order to attain a net zero energy balance for residential construction. The researchers found that a PV/T system could generate more energy than solar thermal collectors, and that a building composed solely of high-efficiency PV modules was the one that was most closely associated with a net-zero energy balance. Research on the impact of a building's passive characteristics on the structure's energy independence as a result of the addition of RES was done in Morocco by Chegari et al. [27]. The results showed that, especially in Ifrane city (cold zone of Morocco), thermal insulation had a significant impact on a building's capacity to produce its own energy in all the climates investigated. Given that this environment has a degree of energy independence that is 41.28 percent higher than the previous one. DHW production from solar thermal collectors, electricity generation from photovoltaic and small wind power systems, solar cooling (via absorption devices), and cooking using solar ovens are among the most often used techniques. However, in order to satisfy certain objectives (such as ones for the environment or the economy for a given climate), the NZEB, a building with the highest energy efficiency, requires examining a wide variety of feasible design possibilities [28]. Additionally, according to the Energy Efficiency Index (EEI) in kWh/m2/year, Adi Ainurzaman Jamaludin et al. [29] investigated the effectiveness of energy consumption at residential college buildings on the University of Malaya campus. As a consequence, the typical annual power consumption ranged from 24 to 120 kWh/m2.

    Minimal research has been done on energy diagnostics in educational institutions, according to the reviewed literature. The integration of RES, HVAC systems, and lighting systems are the efficiency improvements that are most frequently addressed while looking through the suggested action plans.

    The energy efficiency policy in Morocco grows up in the last decade by announcing a lot of policies for energy efficiency (EE). Indeed, Morocco's energy strategy has been developed in response to climate change specifically and because the country is an energy importer. It is centered on mobilizing Morocco's own natural resources, increasing the share of renewables in the energy mix, and making energy efficiency a national priority. In fact, the work focuses on the implementation of renewable energy resources (RERS) and energy efficiency (EE) as the major part of Morocco's energy strategy, as provided for in its National Plan for Renewable Energy and Energy Efficiency Plan. Morocco's energy strategy aims to save 20% in 2030 of total energy consumption [30,31]. Its adoption will allow for the creation of a diverse energy mix that will be optimized around certain technological choices that are both dependable and competitive.

    This approach, which has the primary goals of assuring supply security and general energy price optimization; mobilization of domestic energy resources, including the enormous renewable energy. For instance, in the building sector:

    ⅰ) A national energy efficiency program in the construction sector.

    ⅱ) Energy efficiency program for public buildings.

    ⅲ) Appliance labeling for EE, under the Renewable Energy and Energy Efficiency Law 2009;

    ⅳ) Low consumption light program in the public housing sector;

    ⅴ) Law 13.09 for integration of the PV system into the grid (interdiction for low voltage and without injection for medium voltage)

    The current research reported the energy saving potential on the building sector of Morocco, especially in public buildings, which represent huge energy consumption and has a lack at the level of the energy law (13.09) that has been published in this sense. It only allows connection to the electrical network for the medium voltage network without energy injection. The law also prohibits the connection and injection of energy into the low voltage network. In this context, the current study aims to analyze the use of different forms of energy, i.e., electrical from the grid and thermal from combustible in a specific building which is the university campus. Results of detailed energy diagnostics have been reported for 21 campuses in different locations (zone climatic) of Morocco. The work is showing a deep analysis of profile energy consumption as a typical profile energy consumption and behavior for the public building. In another more profound way, the study carried out aims to prove the shortcomings of the exit law which concerns the integration of RES in the building sector, in particular for low voltage. The study focuses on the energy potential of the integration of two simple RES systems on the energy system (electrical and thermal) of public buildings. In fact, this study examines the potential for energy saving and the environmental effects of integrating actions to improve energy consumption, including solar energy. 80% of the energy consumed in the campuses is designated to lightning and hot water. In actuality, the study sought to examine the RERS integration in the university's public campuses, specifically the photovoltaic (ESM1) and solar heating system for producing hot water (ESM2), in order to evaluate the system's techno-economic-environmental performance in terms of lowering building energy consumption, generating economic benefits, and lowering greenhouse gas emissions.

    Although poor works of the energy potential analysis saving in a specific building energy system as presented previously, most of them have a major drawback. Indeed, the current research presents a real study of the actual appliance of the Moroccan energy policy which is not found in the literature. The main contributions of this work are summarized as follows:

    - A level 1 and partial level 2 energy audits were carried out on 21 campuses in the different zone of Morocco by following the ISO 50002 method.

    - The study proves that the energy potential saving in the public building has a huge effect in the profile energy consumption and show how much law 13.09 is poor for the integration of RES system in the low voltage network (domestic application).

    - The paper thoroughly examined the integration and coupling of solar energy into a particular energy system building. It also studied the effect of the unsteady energy flow and low energy density make it difficult to collect, and convert, which is why the current paper has developed as a solution to maximize the mix of renewable source use and maximize its efficiency.

    - The viability and dependability of a built solar system (electrical and thermal) were proved in actual physical conditions and in a dynamic environment, including altitude, longitude, the direction and position of the sun, tilt, humidity, and real solar fluctuation.

    - Energy dashboard has been developed to monitor, manage and forecast the energy situation of each campus.

    The work structured after the introduction (section 1) as follows: section 2 presents the methodology follows in this deep analysis and data collection, section 3 presents general energy diagnostic for all campuses by presenting the detailed results of energy auditing for different forms and uses of energy; section 4 focuses on a detailed energy analysis of one campus such as a case study in order to assess how the energy is managed in this type of tertiary buildings; section 5 contains the discussion and projection analysis saving energy on the whole 21 campuses by implementation of the two energy measurement savings (ESM); the paper ended by a conclusion (section 6).

    One of the best ways to identify, evaluate, and enhance the energy performance of existing buildings is through an energy audit [32,33]. This multi-dimensional study's objectives are to identify strategies for enhancing energy efficiency and estimate the financial advantages. This research examines the majority of electricity-based consuming systems in the audited facility and is classified as a category Ⅱ energy audit by ASHRAE (American Society of Heating, Refrigerating, and Air Conditioning Engineers) [34]. The approach used in this study includes a number of phases that are explained below and illustrated in the following Figure.

    Figure 1.  Methodology and step of the study research and data collection.

    a) Step 1: Preliminary visit

    Numerous visits were made after goal-setting and planning to gather the information needed to drive the energy audit operations. The information below was put back together:

    ➢ Campuses' architectural plans

    ➢ The last three years' worth of electricity bills to:

    ✔ Study the variations in the monthly electricity consumption

    ✔ Define the energy consumption during each time slot;

    ✔ Verify the quality of the specified subscribed power;

    ✔ Calculate the transformer's maximum load rates;

    ✔ Assess the Displacement Power Factor (DPF) of the electricity system;

    ➢ Inventory of energetic system: external/internal lighting systems, boiler, pumps, air conditioner, lab tops, and etc.

    b) Step 2: Diagnosis and measurements

    In this stage, the electric installation on the campus will be examined for operational issues. It was feasible to identify the causes of energy losses and provide a suitable action plan using the measurement instruments. The following measurements were performed:

    ➢ The installation of an electrical network analyzer at the faculty's main circuit breaker. For 48 hours in a row, the analyzer continually recorded all of the electricity's characteristics. This measurement's objectives were to identify daily variations in power consumption and evaluate the quality of the electrical energy delivered;

    ➢ The operation of the capacitor banks placed in the transformer station was evaluated using the multi-meter clamp.

    c) Step 3: Data treatment and analysis

    After finishing step 2, we created an extensive energy audit report that included all the data gathered, all the investigations completed, all the suggestions, and all the planned actions. In this step all results are generated of the basis on the data treatment and solution proposed in this research paper present.

    d) Step 4: Decision-making

    Finally, based on the available financial resources and other technical restrictions, the research team helps the decision-maker validate and implement the retained energy efficiency initiatives.

    The data collected contains:

    - Electrical bills of 21 campuses

    - Combustible bills of 21 campuses

    - Measurements electrical of transformer that feed each campus

    - Data collected and treated from a questioning form developed which concerns the: scenario of use the equipment, inventory of the equipment and etc.

    This section presents a summary of energy audits for 21 university campuses. We undergo an energy diagnosis of different forms, in particular, the electricity consumption for different end uses (lighting, air conditioning and electric and electronic equipment like computers, Wifi modems for Internet, audio-visual and video conferencing technologies, and equipment for kitchen) and thermal energy for the production of hot water and heating.

    Six types of energy are used in the university campuses occupied by scholars. These are electricity used mainly for lighting and electrical equipment, fuel diesel, anthracite, fuel oil No. 7, propane and butane are used for the production of hot water (HW) and for catering, seven campuses use propane and three others use butane. Diesel fuel is also used for the car fleet in use. Only a few university campuses (five) are equipped with solar thermal collectors for the production of hot water.

    The electrical energy consumption of the 21 university campuses is illustrated in Figure 2a below represented more than 164767 MWh/yr in 2017 and 17331 and 17555, 3 MWh/yr for 2018 and 2019 respectively, with an increase of 1.3% between 2018 and 2019. The cost of this energy consumption changed from 1935056 $/year (2017) to 2406425 $/year (2019). In addition, the cost per kilowatt-hour varied from 0.104 $/kWh in campus 12 up to 0.3 $/kWh in campus 17. The national average was 0.136 $/kWh, against 0.131 $ /kWh in 2019, an increase of 3.9%. The reason for which the cost of kWh in Campus 17 is higher in comparison with other campuses is because the former has a low-voltage electricity subscription for the power supply of the entire campus and electricity consumption exceeds the third portion with regard to a reference set by local distributor.

    Figure 2.  a) Yearly electrical energy consumption in kWh; b) first Energy Index Consumption on kWh/bed/yr; c) second energy index consumption on kWh/room/yr.

    Further, the first index energy consumption on kWh/bed/yr indicates that the energy consumption ranged from 483,501 and 506 kWh/bed/yr for 2017, 2018 and 2019 respectively as illustrates in the Figure 2b. Knowing that the total number of beds is equal to 42987 beds in the whole campuses and is not changed during the period study. The high value of the current energy index is recorded for campus 21 around 1633 kWh/bed/yr, because it contains a lot of electrical equipment and also this campus is equipped with a small kitchen for 4 rooms. Besides, the second energy index (kWh/room/yr) ranged from 1660 (kWh/room/yr) in 2017 to 1750 (kWh/room/yr) in 2019 as illustrated in the Figure 2c. It should note that the number of beds per room varies from 1 bed per room to 11 bed per room with an average of 3.7 bed per room.

    Propane is used for catering in seven campuses such as campus 1, 11, 12, 13, 17, 19 and campus 21 as illustrated in Figure 3. The consumption for three Campuses (campus 12, 13 and 21) are at their own expenses, whereas for the remaining campuses, the propane is at the expense of private companies responsible for the catering service.

    Figure 3.  Propane consumption of the most consuming university campus.

    For the fuel oil No. 7, it is used for the production of hot water by a single campus which is the campus 5. The consumption was around 100 tons per year with an amount of 69571 $. Besides, the diesel is used exclusively for the production of hot water in eleven campuses as presented in the Figure 4, five campuses for car fleet in use and for both HW and car fleet in use in two campuses. Figure 4 below shows the diesel consumption by campus. Diesel consumption represents 631923 liters/yr, with a bill of 638148 $.

    Figure 4.  Yearly diesel bill and consumption.

    The consumption of diesel fuel in campus 7 and campus 6 represents 71.5% of the total consumption of the 12-university campus. Figure 5 below shows that diesel is used mainly for HW, with 90% of the total consumption. The diesel consumption for car use is limited to 2% whereas 8% of the consumption is mixed between HW and car use since the data provided by the university campuses do not allow a distinction to be made between consumption for HW and for the car fleet.

    Figure 5.  Distribution of the diesel consumption.

    For the lighting use, the study focuses on the outdoor lighting. Thus, the inspection of outdoor lighting consumption for 21 campuses represents 1211.2 MWh/yr which represents an average of 9% of the total electrical energy consumption.

    The number of watts per luminous tip is between 20 W and 1000 W. Outdoor lighting consists of several technologies: light-emitting diodes (LEDs), high-pressure sodium (HPS) and compact fluorescent lamps. Figure 6 below shows that the campus 12 has the highest lamp power intensity, followed by the campus 19 and 8 respectively. It is possible to decrease significantly the consumption by switching over to the LED bulbs instead of compact fluorescent bulbs.

    Figure 6.  Average lamp power (outdoor lighting).

    However, the consumption of thermal energy for hot water varies from one campus to another. The lowest ratio was obtained in the campus 19 and campus 2, as they use solar heating in parallel. This ratio is 40 kWh/student/year for the campus 21 and 65 kWh/student/year in the campus 2. For the other campuses, the ratio varies from 65.95 kWh/student/year in campus 4 to 1402 kWh/student/year in campus 7 only for hot water.

    Table 1.  Distribution of DHW and electricity consumption.
    Campus Diesel (DHW) Litre/yr Electricity kWh/yr Total DHW + Electricity kWh/yr DHW Percentage/Total %
    Camp 14 18 560 285 666 471 266 39%
    Camp 16 30 160 632 188 1 034 938 29%
    Camp 6 162 400 782 792 2 532 039 64%
    Camp 7 352 640 572 055 4 189 984 84%
    Camp 2 10 440 747 598 971 614 11%
    Camp 19 70 760 595 386 1 302 986 54%
    Camp 4 12 180 1 206 880 1 145 414 9%

     | Show Table
    DownLoad: CSV

    The pilot campus is a university campus with a capacity of 1100 people and 410 rooms. It is composed of Five pavilions (student accommodation); an Administration building; a Restaurant and refreshment cafeteria; Health center; Boiler room; Study rooms; Sports Hall; Multiservice room and staff accommodation.

    The campus has a low-voltage electricity subscription for the power supply of the entire campus. The price per kilowatt-hour is billed according to this bracket at 0.231 $ including tax. The price at low voltage is higher than at medium voltage in Morocco. Indeed, the average price per kilowatt-hour at medium voltage is 0.12 $/kWh including tax. The campus 17 electricity consumption in 2017 was 582631 kWh/yr, i.e., 130444 $/yr as illustrated in Figure 7.

    Figure 7.  Evolution of electricity consumption.

    Figure 7 shows the monthly consumption. It varies from 23,088 kWh in August to 63.567 kWh in January, with an average of 48.553 kWh. Summer consumption can be explained by the accommodation for 43 staff and the partial presence of administration staff.

    The campus has two diesel condensing boilers (107 KW for each one) for its hot water production needs. The diesel consumption for one typical year is illustrated in Figure 8. Diesel fuel is used to produce hot water using boilers. The quantity of diesel delivered annually is 26010 liters, or 22466 $/y.

    Figure 8.  Diesel fuel consumption.

    The water leaves the boiler at a temperature of 100 ℃ (measurement), while the boiler plug displays a maximum temperature of 90 ℃ and passes through an exchanger emerged in a storage tank. The water is stored in a tank of about 1000 liters (the thermometer indicates 60 ℃). The storage tank is fitted with an additional 9 kW electric heater. The boiler is supplied directly from the drinking water network. In fact, the showers only have a 1000-liter balloon which is used for shared showers for all the students on the campus.

    On the other hand, air conditioning as illustrated in Figure 9, is used in the offices, in the multiservice room (the room is used approximately 100 h/yr) and in the study room for the comfort of the occupants. The site is equipped with 19 split-system air conditioners. Consumption is estimated at 9.43 kWh/year, i.e., 1.6% of the site's total consumption. It was found that the study room doors stayed open while the heating was on, which increased the energy consumption of the air conditioner.

    Figure 9.  Inventory and energy consumption of air conditioning equipment.

    Taking into account the lighting time assumptions, power consumption for lighting represents approximately 283700 kWh per year, not counting conventional ballasts, or 49% of the campus total consumption. This consumption is distributed as follows:

    Figure 10.  Lighting inventory for the campus concerned.

    It should be noted that 56% of the lights installed are fluorescent and 35% are incandescent. These fixtures are energy consuming and should be replaced with LED fixtures as illustrated in the Figure 11 below.

    Figure 11.  Repartition of installed power by type of lamp technology.

    The table below summarizes the energy consumption of the pilot campus distributed from lighting (energy-intensive part) to office activities (minimum energy consumption), not to mention the production of hot water (thermal consumption).

    Therefore, on the basis on this detailed energy consumption situation, two Energy Saving Measures (ESM) was treated and analyzed in the paper.

    Table 2.  Summary of the distribution of energy consumption by use.
    Uses Consumption MWh/yr
    Lighting 283.7
    Office and administration 5.8
    Air conditioning/heating 9.4
    Kitchen 67.1
    Various: sport gym, health center, laundry 38.5
    Dorms 158.2
    Other: water pumping and garden work 20

     | Show Table
    DownLoad: CSV

    The technical opportunities for reducing energy consumption and expenditure identified on the case study include several measures; the main ones are grid-connected PV system for electrical energy production and solar thermal collector for HW production, which are being the subject of the technical and economic simulation below.

    Condition and data climatic

    Morocco has a significant solar and wind energy potential, as well as a strategic geographical location. Two significant renewable energy efforts, the Moroccan wind and solar projects, have been initiated in order to meet the national aim of raising renewable energy sources' proportion of the energy mix to 42% by 2020 [11]. Morocco, with its enormous sun resources (a potential of 2600 kWh/m2/year), providing a diverse variety of investment prospects in the thermal and photovoltaic solar energy sectors. Indeed, Table 3 below illustrates the climatic data such as average irradiation, temperature ambient and wind speed for each campus studied in the current paper.

    Table 3.  Climatic data of studied campuses.
    N℃ampus Average of solar radiation intensity (kWh/m2/day) Average of ambient temperature (℃) Average of humidity (%) Average of wind speed (m/s)
    Camp 1 5.44 20.3 51.7 2.49
    Camp 2 5.85 20.6 40 2.59
    Camp 3 5.19 19.2 59.2 1.79
    Camp 4 5.29 19.2 67.5 3.15
    Camp 5 4.83 18.3 81 2.18
    Camp 6 4.98 17.52 60.5 3.36
    Camp 7
    Camp 8
    Camp 20
    Camp 21
    Camp 9 5.01 17.51 81.3 2.99
    Camp 10 5.07 17.66 64.1 3.02
    Camp 11 4.83 18.31 81.1 2.18
    Camp 12 4.99 17.41 81.3 2.99
    Camp 13
    Camp 14 5.07 18.08 73.4 5
    Camp 15 4.99 17.41 81.3 2.99
    Camp 16 4.68 19.26 70.5 4.50
    Camp 17 4.99 17.41 81.3 2.99
    Camp 18 4.99 17.38 62.9 3.69
    Camp 19 4.98 17.52 60.5 3.36

     | Show Table
    DownLoad: CSV

    This part consists of setting up a photovoltaic installation connected to the local network to meet part of the electrical energy needs during the day.

    The daily electrical power profile reached an average power of 80 kW as Figure 12 shows. The installation of the PV generator in the roof of the building or on the ground closest to the TGBT is possible knowing that the campus has an available area of land of 6000 m2. It should be noted that the law limits medium voltage injection in the event of a surplus in Morocco.

    Figure 12.  Network analyzer result for one week of campus operation.

    The system proposed in this section is a hybrid system combining different sources, such as renewable energy systems and the national distribution network. These systems' main challenge is ensuring flexible energy management. The execution of the energy management plan must, in fact, balance supply and demand. These systems enable energy efficiency and lower power costs by ensuring both financial stability and environmental sustainability. As a result, restricting the regular usage of the national electrical grid, which often uses non-renewable sources, will assist the population's sustainable and social growth.

    However, grid-connected systems are intended to provide clean electricity to the power grid as illustrated in Figure 13. Using PVsyst and TRNSys platform simulation, the applied loads were the annual consumption of campus 17 (case study) taking into account the consumption in peak hours, while the climatic data for the specific location are collected and implemented. The grid-connected system turned out to be the most reliable option for the campus. The general configuration of the proposed system is described in Figure 12 for three scenarios (SC), i.e., the simulation for the ESM1 is conducted for 30 kWp, 60 kWp and 80 kWp respectively for SC1, SC2 and SC3.

    Figure 13.  Simplified of PV generator system.

    The simulated PV system for the campus (case study) does not have batteries, therefore the campus is connected to the grid (Figure 12), allowing it to work in diurnal intervals for self-consumption and reducing dependency on the utility grid.

    Figure 14 shows the electrical energy (kWh) consumption from the grid during three months of the winter season. Besides, the Figure shows the daily production profile for typical days in December, January and February for each scenario.

    Figure 14.  Monthly hourly sums of PV system production for the three scenarios for the winter season.

    Indeed, Figure 14 shows the energy demand in campus, showing how the demand is higher in comparison to the energy supplied by PV systems in this period of the year (peak energy). The energy demand, by the campus, is over 74373 kWh/month. On the other hand, for the best month in term of energy production for this season, the monthly PV system production achieved only 5380, 10850, and 14970 kWh/month for the SC1, SC2 and SC3 respectively, while the energy supply is 2399 kWh per month. The results from the Figure indicated that the PV system reached a rate of the self-consumption for this season is around 62% for the SC3, 46% for SC2 and 22% for SC1.

    The three scenarios (30, 60 and 80 kWp) have been developed and simulated to calculate the monthly and annual energy generation from the PV system. Overall efficiency is left to the default value of 85%. The results are summarized in Figure 15, where the monthly and yearly data are shown in Figures 14 and 15, respectively. The results show that scenario 3 exhibits the highest energy production while scenario 1 has the lowest energy generated. All proposed scenarios follow the same generation trend over the year, where it can be noted that the maximum monthly generation is obtained in May and June.

    Figure 15.  Monthly sum profile of PV system production for the three scenarios.

    The simulation results of the grid-connected system in campus 17 indicated that the highest solar radiation occurred in the location site during July, with an average of 1169 W/m2. During December, the lowest solar radiation intensity was at an average of 416 W/m2. Figure 15 shows the average monthly produced energy from the three scenario of the PV system in the campus under study. The average monthly hourly sums of energy production were 600 kWh for scenario 1 occurred in December while the highest 1000 KWh in July. The lowest energy production of the PV plant for scenario 2 is 1737 kWh was recorded in December and the highest 2000 kWh in May. For the third scenario, the minimum produced energy 1800 kWh was in January while the highest produced energy 2783 kWh in July. In fact, the energy saving by the implementation of the PV system production allows for saving about 51% for SC1, 84% for SC2 and 94% for SC3 on energy consumption as illustrated in Figure 16 bellow.

    Figure 16.  PV system solar fraction.

    Figure 17 demonstrated the results of economic saving on the bill of each scenario of the implementation of the current ESM1 into the campus energy consumption. The cost of installing the PV system was 900 $ per 1 KWp in Morocco including installation. Indeed, the cost of each scenario are 27000 $, 54000 $ and 72000 $ respectively for SC1, SC2 and SC3.

    Figure 17.  Earnings on Bill ($) for each PV system scenario: a) scenario 30 kWp; b) scenario 60 kWp; c) scenario 80 kWp.

    The bill analysis of the campus demonstrates an average of 10000 $ per month with 56000 kWh/month. The average price of the kilowatt hour is 0.189 $ per KWh. The Figure 17, presents the economic saving on bill associated with the realization of a PV plant on the campus. The results confirmed the profitability of a PV system at the campus case study, according to several indicators. NPV was positive and equal to 31013 $, 53628 $ and 64835 $ for the 30 kWp, 60 KWp and 80 kWp PV system, respectively. This finding could also be expressed as 2386 $, 4100 $ and 5000 $ profits on bill respectively for the SC1, SC2 and SC3. The values reported in this current analysis simulation indicate that the scenarios proposed can cover a rate of 24%, 41% and 52% for SC1, SC2 and SC3 of the bill energy of the campus as illustrated in the Figure 17.

    Starting with a hypothetical energy mix composed of only fossil fuels, RECD (reducing carbon dioxide emission) value was calculated by using the following Equations:

    RECD=ECDFFECDPV (1)
    ECDFF=(ECDOI×PEMOI)+(ECDCO×PEMCO)+(ECDNG×PEMNG) (2)

    The carbon dioxide emitted (ECD) by specific resources and PEM indicated the percentage in the energy mix of these specific resources.

    The subscripts refer to the relevant resource: fossil fuels (FF), photovoltaic (PV), oil (OI), coal (CO), natural gas (NG), the unit of gCO2 eq/kWh is mean that the equivalent quantities of CO2 in one kWh.

    According to the average values reported by [35], the following emissions data were proposed:

    ECDPV=42gCO2eq/kWh,ECDOI=824gCO2eq/kWh,ECDCO=1149gCO2eq/kWh,ECDNG=568gCO2eq/kWh.

    According to GSE data [36], the following percentages were calculated as follows:

    PEMOI =1%,PEMCO=21%,PEMNG=66%.

    In this way, the RECD value was calculated as 678 gCO2 eq/kWh, obtained as the difference between 720 gCO2 eq/kWh and 42 gCO2 eq/kWh. With respect to each scenario of PV system, an overall reduction of 31, 8 tCO2 eq/yr, 57, 12 tCO2 eq/yr and 88, 4 tCO2 eq/yr was calculated during the first year of activity as illustrated in Figure 18 for each scenario (30 KW, 60 KW and 80 KW). Assuming an externality value of 26 $/t CO2 eq, The DNE (discount net externality) value for the first year of activity was calculated as 827 $/year, 1486 $/ year and 2300 $/ year for the SC1, SC2 and SC3 respectively.

    Figure 18.  Carbon footprint of PV system, a) SC1; b) SC2 and c) SC3.

    The current campus uses the fuel for the production of DHW through boilers. The energy consumption of the boiler has been estimated at an efficiency of 90% while the production of hot water is at 55 ℃ as illustrated in the following table.

    Table 4.  DHW production from the boiler system of the campus.
    Diesel consumption (liter/yr) DHW production (m3/yr) Energy consumption (kWh/yr)
    106000 2100 107000

     | Show Table
    DownLoad: CSV

    Figure 19 shows the schematic diagram of Domestic hot solar water (DHSW). There are three major components in a DHSW: solar collectors, a hot storage tank and an auxiliary heater driven by electricity. When the outlet temperature of solar collectors (T1) is 10 ℃ higher than the water temperature in the hot storage tank (T2), the pump in the solar collection loop will be ON; and when the temperature difference between (T1) and (T2) is less than 3 ℃, the pump will be OFF. The three-way automatic valve is modulated to maintain the outlet temperature (T4) at 60 ℃ (temperature desired to use). If (T4) is less than 50 ℃, the auxiliary heater will start to provide 60 ℃ hot water to the demand use, and the auxiliary heater will be OFF when the output temperature (T4) is higher or equal to 60 ℃.

    Figure 19.  schematic diagram of a SHW system.

    To date, the most commonly used solar collectors in DHSW is Flat plate collectors (FPC) which use water as the energy carrier. The maximum efficiency of a well-designed FPC ranges from 40% to 60% with heat normally delivered between 50 and 75 ℃. In this study, a typical FPC with market average performance is selected. Table 5 below shows the characteristics of the solar collectors.

    Table 5.  Characteristics of solar collectors.
    Unit Value
    Optical efficiency 0, 8
    Loss coefficient first order (W/m2. K) 5
    Loss coefficient second order (W/m2. K) 0, 05

     | Show Table
    DownLoad: CSV

    Figure 20 shows the profiles of solar irradiation during a two-day time period in December and May for the FPC in the chosen site. One is a partially cloudy day (winter season), and the other is a sunny day (spring season). The outlet temperature of the solar collection loop follows the trend of solar radiation. During the first day (Figure 19a), when the cloud covers the sun, the solar side cannot provide 60 ℃ hot water; hence the auxiliary heater is on. And the auxiliary heater is also triggered when there is no solar energy available. During the second day (Figure 19b), due to the rich sunshine, the solar system can provide the hot water demand for a long period, and even store the extra energy in the storage tank for night use. This detailed energy profile makes it possible to consider control sequences close to reality and calculate the annual performance of the DHSW with improved accuracy.

    Figure 20.  Solar thermal system performance during two days of a): winter season (December); b) and spring season (May).

    The annual solar fraction and collector efficiency for the installation in the chosen campus as shown in Figures 21 and 22 are calculated based on simulation over a one-year period.

    Figure 21.  Monthly solar fraction and energy production of the SHW system.
    Figure 22.  Monthly thermal efficiency and solar fraction.

    In fact, Figure 21 presents the average daily energy power request from the hot water use, the solar energy production from the DHSW system. Indeed, the DHSW system achieved an average solar fraction of 55% during the worst climatic condition. Besides, the DHSW system has reached a maximum of 60% when the campus works on full time and at full capacity. In the contrary, the system achieved 100% in the summer season due to the high solar irradiance and a long day period. The overall trend of the monthly solar fraction is higher in the summer and lower in the winter where the largest difference was seen for campus (case study).

    From Figure 22, the results indicate that the thermal efficiency of the DHSW system ranged between 15% to 68% during the year which is very compatible with the bibliography work [37,38]. Indeed, the DHSW reached an average efficiency equal to 50% with a solar fraction of 55% which indicated that the system is very adequate to the campus for hot water production during its full capacity work.

    The study's second main focus is the economic analysis. In this study's economics, the entire cost of the solar system is covered upfront (i.e., no credit payments are assumed). The system's thermal performance degradation is expected to occur at a rate of 2% annually, the economic analysis's time horizon is 20 years, and all other percentage variables (inflation rates and market discount rates) are averages from the last decade. According to the bill that was paid, electricity costs $0.09 per kWh, and it is anticipated that supplemental diesel costs about $1, 1 per liter. A rate of energy was given throughout the life cycle of the solar thermal system (EP). This energy is primarily comprised of the energy needed for the building of the solar system, particularly the electric energy, which is calculated using a primary energy conversion factor (PEF). The mean PEF is assumed to be 2.7 for electric domestic use [39]. The EP during transportation and maintenance [38].

    The approach developed in this work gives a general estimation of the EP based on the bibliography data [39] and the results of the current system are illustrated in the Table 6 below.

    Table 6.  Calculation of the Energy Provided (EP) during the life cycle of the system in (kWh).
    Collector Storage Heat exchanger Total installation
    38902, 95 14773, 33 2586 56262, 28

     | Show Table
    DownLoad: CSV

    The payback duration (PD) shows how many years it takes the present DHW to create enough energy to match the energy used over its life cycle. The definition of PD is the (EP) over the Energy Saved during the System Life Cycle (ES). The PD stands for an essential need for both the installation's setup and operation. The following equation is used to compute the (PD):

    PD=EPES (3)

    The amount of energy saved was computed using a primary energy conversion factor based on the kind of fuel utilized, together with the effectiveness of the auxiliary heating system that the present solar system is replacing. Table 7 below shows the results of calculating the ES and PD for each installation. Every year of the installation's service life, the energy payback calculation has been taken into account as a constant. It was discovered that the electrical system efficiency for the existing system is around 56 MWh/year.

    Table 7.  Calculation of the energy provided (EP) during life cycle of the system in (kWh).
    System cost 156000 $
    Installation cost 17160 $
    Up-holding cost 6000 $
    Total Electricity saving 56.3 MWh/yr
    LCF saving based on electricity saving 11252 $

     | Show Table
    DownLoad: CSV

    The expensive shipping and delivery of the solar system component is the cause of the comparatively long payback time. With the anticipated increase in demand for solar products, these costs are anticipated to decrease. The life cycle savings, which indicates the money the owner would save by installing the solar system instead of buying power to meet his heat energy demands, is another element that highlights the importance of employing thermal solar systems.

    The extension of energy measurement saving in the whole campus on the basis of the results of current campus case study (camp 17) proves that the implementation of the two ESM1 and ESM2 (PV and solar thermal systems) can generate a very important potential of energy saving.

    In fact, for the PV system, the project involves installing a PV generator for self-consumption. The power to be installed should make it possible, initially, to fill a maximum of 20% of the consumption of the campuses. The power to be installed is summarized in Figure (23a) below.

    Figure 23.  Generalized PV Earnings on energy consumption for each campus.

    According to the simulation of the result previously for the campus case study, generalizing simulations have been done for the other campuses on the basis on the energy diagnostic presented previously. Figure 23 presented the results of the PV production for each campus with comparison with the energy consumption profile for each one of them. Also, the Figure contains the results of the sizing of PV generators and energy saving for each campus on the basis of the load curve found by the network analyzer. The profiles energy consumption ranged from 246, 3 MWh/yr (campus 14) as a minimum to 1183.7 MWh/yr as maximum (campus 18), due to the different capacity and surface of each campus and also due to many factors such as energy equipment and management way for each campus. Besides, the PV generators produced around 22% for the total energy consumption (48000 kWh/yr) for campus 14 and 224 MWh/yr for campus 12 which the power necessary for reached this energy saving is varied from 30 KW to 150 KW as illustrated in Figure (23a) and (23b).

    On the other hand, for the thermal action ESM2, only 5 campuses out of 21 have solar installations for the production of domestic hot water. The generalizing of the use of solar collectors to other campuses is addressed only for seven campuses that used the fuel for DHW production by using the boiler. The efficiency of the boiler was taken as 90% while the production of hot water should be at 55 ℃ as illustrated in the Table 8 below.

    Table 8.  Results of estimates of DHW consumption in campuses.
    Campus DHW diesel Litre/yr DHW diesel v DHW production at 55 ℃ m3/yr Current cold water consumption m3/yr
    Camp 14 16 000 160 000 3 100 35 299
    Camp 16 26 000 260 000 5 038 81 414
    Camp 6 140 000 1 400 000 27 129 166 827
    Camp 7 304 000 3 040 000 58 909 109 784
    Camp 2 9 000 90 000 1 744 70 057
    Camp 19 61 000 610 000 11 821 83 822
    Camp 4 10 500 105 000 2 035 74 853

     | Show Table
    DownLoad: CSV

    The hot water production of the seven campuses (see table below) was estimated at 109.7 m3/year, for a diesel consumption of 566.5 m3/year. The details of the estimates are recorded in the following table. The Campus 6 and 7 consume the largest quantities of water due to the high number of staff accommodation in these two campuses. The necessary investment was calculated assuming a price of 786 $ including per square meter, including installation.

    The production of DHW by solar thermal will provide 67% of needs as illustrated in the table below, i.e., the equivalent of 424, 5 m3/year of diesel. The corresponding saving is 417767 $ per year. The estimated savings to be made are presented in the following table.

    Table 9.  Savings from the use of ECS in campuses.
    Campus Heat production (kWh) Surface thermal collector system (m2) Diesel savings
    (litre/yr)
    Diesel savings
    ($/yr)
    Diesel savings (en%)
    Camp 14 107 900 90 11 989 113 29 67%
    Camp 16 176 100 146 19 567 131 33 68%
    Camp 6 955 600 778 106 178 1 081 52 68%
    Camp 7 2 027 100 1 650 225 233 2 294 23 67%
    Camp 2 62 100 47 6 900 63 20 69%
    Camp 19 419 900 342 46 656 475 23 69%
    Camp 4 71 400 54 7 933 79 33 68%

     | Show Table
    DownLoad: CSV

    To end, the execution of the ESM1 and ESM2 into the energy sector can achieved a very energy saving potential for the campuses. Indeed, without the execution of the ESM1 and ESM2, the electricity consumption performance indicator per bed varied from 167 kWh/bed per year to a maximum of 1342 kWh/bed/yr with an average of 435 kWh/bed per year in the whole campuses as illustrated in the Figure 24.

    Figure 24.  Performance indicator ratio in kWh/bed/year.

    Besides, the results of the current study indicated and prove that the integration of renewable energy into the energy system of the campuses by applying the ESM1 and ESM2 can reduce its energy consumption. In fact, the results prove that the performance indicator KWh/bed/year shows a very percentage of saving energy as illustrated in Figure 24 which is 248 kWh/bed per year which represents an average energy saving of 56%.

    Public buildings are energy-intensive consumers. The study supports the Moroccan government's exemplarity in terms of energy efficiency and sustainable development in the public building by presenting the results of a detailed energy performance analysis of more than 20 university campuses in Morocco in different locations (climatic zone), which concluded that approximately 80% of the energy consumed on university campuses is designated to electric equipment and hot water for sanitary use. The current study has analyzed the use of different forms of energy, i.e., electrical from the grid and thermal from combustible.

    The paper has studied the integration of two ESM, i.e., the PV and thermal system for a case study as a pilot campus detailed in order to generalize on all campuses. Indeed, three scenarios have been studied for ESM1 in order to find the optimal scenario. Besides, the ESM2 (thermal system) has been studied for hot water production by replacing the diesel boiler.

    The conclusions findings from the study are listed below:

    - The energy saving by the implementation of the PV system production in the pilot campus allows saving about 51% for SC1 (30 kWp), 84% for SC2 (60 KWp) and 94% for SC3 (80 KWp) on energy consumption.

    - The results of the bill analysis concerning the pilot campus reached an average of 10000 $ per month with 56000 kWh/month. The economic benefits results indicate that the PV scenarios studied can cover a rate of 24%, 41% and 52% for SC1, SC2 and SC3 respectively on the billed energy.

    - The implementation of solar thermal for hot water for the case study achieved an average solar fraction of 55% during the worst climatic condition. The ESM2 system has reached a maximum of 60% when the pilot campus works on full-time and at full capacity.

    - The Moroccan campuses (21) consume around 17443 MWh (electrical form) per year with a bill of 2406425 $ a year. The combustible consumption reached 632923 liters per year with a bill of 638148 $ a year. The results indicated that the energy consumption index has an average of 700 kWh/ped/yr which is equivalent to 100 $/bed, knowing that the number total of the bed is equal to 42978 and it's not changed during the study.

    - The extension of energy measurement saving in the whole campus on the basis of the results of pilot campus case study prove that the implementation of the two ESM1 and ESM2 generate a very important potential for energy saving. In fact, for the ESM1, the results prove that installing PV generators can reach a self-consumption of 20% of the total consumption of the campuses.

    - The energy consumption index per room has an average of around 1705 (kWh/room/yr). The results indicated that the integration of ESM1 and ESM2 allows for achieving an indicator of 1360 kWh/room per year. Knowing that the total room is equal to 11700 and the average rate of student occupancy per room is equal to 3, 7 bed per room.

    To end, the building sector is among the most energy-intensive sectors in Morocco with energy consumption of up to 33%, divided into 7% for commercial buildings and 26% for residential buildings. However, further effort work in this field is still needed to develop standardized procedures for decreasing the energy consumption and more exploitation of Renewable energy sources in this context.

    The authors declare that the research conducted and presented in the current paper and submitted to AIMS Journal, have not utilized AI tools in any stages of the research process.

    The authors whose names listed in the paper certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.



    [1] Schaab DA, Sauer A (2020) Stability implications for the design process of an industrial DC microgrid. 2020 International Conference on Smart Energy Systems and Technologies (SEST), 1–6. https://doi.org/10.1109/SEST48500.2020.9203022 doi: 10.1109/SEST48500.2020.9203022
    [2] Zhang D, Zhang Z, Ren Q, et al. (2022) Research on application mode of HYBRID microgrid AC-DC microgrid in large industrial enterprise park based on energy router. 2022 China International Conference on Electricity Distribution (CICED), 1715–1721. https://doi.org/10.1109/CICED56215.2022.9929073 doi: 10.1109/CICED56215.2022.9929073
    [3] Li Y, Sun Q, Dong T, et al. (2018) Energy management strategy of AC/DC hybrid microgrid based on power electronic transformer. 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2677–2682. https://doi.org/10.1109/ICIEA.2018.8398163 doi: 10.1109/ICIEA.2018.8398163
    [4] Kazemi M, Salehpour S, Shahbaazy F, et al. (2022) Participation of energy storage-based flexible hubs in day-ahead reserve regulation and energy markets based on a coordinated energy management strategy. Int Trans Electr Energy Syst 2022: 1–17. https://doi.org/10.1155/2022/6481531 doi: 10.1155/2022/6481531
    [5] Xia Y, Wei W, Yu M, et al. (2018) Power management for a hybrid AC/DC microgrid with multiple subgrids. IEEE Trans Power Electron 33: 3520–3533. https://doi.org/10.1109/TPEL.2017.2705133 doi: 10.1109/TPEL.2017.2705133
    [6] Li Z, Xie X, Cheng Z, et al. (2023) A novel two-stage energy management of hybrid AC/DC microgrid considering frequency security constraints. Int J Electr Power Energy Syst 146: 108768. https://doi.org/10.1016/j.ijepes.2022.108768 doi: 10.1016/j.ijepes.2022.108768
    [7] Calpbinici A, Irmak E, Kabalcı E, et al. (2021) Design of an energy management system for AC/DC microgrid. 2021 3rd Global Power, Energy and Communication Conference (GPECOM), 184–189. https://doi.org/10.1109/GPECOM52585.2021.9587523 doi: 10.1109/GPECOM52585.2021.9587523
    [8] Kang J, Fang H, Yun L (2019) A control and power management scheme for photovoltaic/fuel cell/hybrid energy storage DC microgrid. 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1937–1941. https://doi.org/10.1109/ICIEA.2019.8833994 doi: 10.1109/ICIEA.2019.8833994
    [9] Ferahtia S, Djeroui A, Rezk H, et al. (2022) Optimal control and implementation of energy management strategy for a DC microgrid. Energy 238. Available from: https://ideas.repec.org//a/eee/energy/v238y2022ipbs0360544221020259.html.
    [10] Ali S, Zheng Z, Aillerie M, et al. (2021) A review of DC microgrid energy management systems dedicated to residential applications. Energies 14: 4308. https://doi.org/10.3390/en14144308 doi: 10.3390/en14144308
    [11] Wu Y, Lau YY, Wu JA (2022) Integration of electric vehicles into microgrids: Policy implication for the industrial application of carbon neutralisation in China. World Electr Veh J 13: 96. https://doi.org/10.3390/wevj13060096 doi: 10.3390/wevj13060096
    [12] Konečná E, Teng SY, Máša V (2020) New insights into the potential of the gas microturbine in microgrids and industrial applications. Renewable Sustainable Energy Rev 134: 110078. https://doi.org/10.1016/j.rser.2020.110078 doi: 10.1016/j.rser.2020.110078
    [13] Torkan R, Ilinca A, Ghorbanzadeh M (2022) A genetic algorithm optimization approach for smart energy management of microgrids. Renewable Energy 197: 852–863. Available from: https://ideas.repec.org//a/eee/renene/v197y2022icp852-863.html.
    [14] Jung S, Yoon Y (2019) Optimal operating schedule for energy storage system: focusing on efficient energy management for microgrid. Processes 7: 80. https://doi.org/10.3390/pr7020080 doi: 10.3390/pr7020080
    [15] Albarakati AJ, Boujoudar Y, Azeroual M, et al. (2022) Microgrid energy management and monitoring systems: A comprehensive review. Front Energy Res 10. https://doi.org/10.3389/fenrg.2022.1097858 doi: 10.3389/fenrg.2022.1097858
    [16] Zahraoui Y, Alhamrouni I, Mekhilef S, et al. (2021) Energy management system in microgrids: A comprehensive review. Sustainability 13: 10492. https://doi.org/10.3390/su131910492 doi: 10.3390/su131910492
    [17] Brandao D, Santos R, Silva W, et al. (2020) Model-free energy management system for hybrid AC/DC microgrids. IEEE Trans Ind Electron PP: 1–1. https://doi.org/10.1109/TIE.2020.2984993
    [18] Prodanovic M, Rodríguez-Cabero A, Jiménez-Carrizosa M, et al. (2017) A rapid prototyping environment for DC and AC microgrids: Smart energy integration Lab (SEIL). 2017 IEEE Second International Conference on DC Microgrids (ICDCM), Nuremburg, Germany, 421–427. https://doi.org/10.1109/ICDCM.2017.8001079
    [19] Kim M, Choi BY, Kang KM, et al. (2020) Energy monitoring system of AC/DC hybrid microgrid systems using LabVIEW. 2020 23rd International Conference on Electrical Machines and Systems (ICEMS), 489–493. https://doi.org/10.23919/ICEMS50442.2020.9290836 doi: 10.23919/ICEMS50442.2020.9290836
    [20] Xiao J, Zhao T, Hai KL, et al. (2017) Smart energy hub—Modularized hybrid AC/DC microgrid: System design and deployment. 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 1–6. https://doi.org/10.1109/EI2.2017.8245453
    [21] Abdolrasol M, Mohamed A, Hannan MA (2017) Virtual power plant and microgrids controller for energy management based on optimization techniques. J Electr Syst 13: 285–294. Available from: https://www.proquest.com/openview/9aa5d28dce901943fd9de88988f32e42/1?pq-origsite = gscholar & cbl = 4433095.
    [22] Basantes JA, Paredes DE, Llanos JR, et al. (2023) Energy management system (EMS) based on model predictive control (MPC) for an isolated DC microgrid. Energies 16: 2912. https://doi.org/10.3390/en16062912 doi: 10.3390/en16062912
    [23] Freire VA, de Arruda LVR, Bordons C, et al. (2019) Home energy management for a AC/DC microgrid using model predictive control. 2019 International Conference on Smart Energy Systems and Technologies (SEST), 1–6. https://doi.org/10.1109/SEST.2019.8849077 doi: 10.1109/SEST.2019.8849077
    [24] Fathy Y, Jaber M, Nadeem Z (2021) Digital twin-driven decision making and planning for energy consumption. J Sens Actuator Netw 10: 37. https://doi.org/10.3390/jsan10020037 doi: 10.3390/jsan10020037
    [25] Thirunavukkarasu GS, Seyedmahmoudian M, Jamei E, et al. (2022) Role of optimization techniques in microgrid energy management systems—A review. Energy Strategy Rev 43: 100899. https://doi.org/10.1016/j.esr.2022.100899 doi: 10.1016/j.esr.2022.100899
    [26] Arrar S, Li X (2022) Energy management in hybrid microgrid using artificial neural network, PID, and fuzzy logic controllers. Eur J Electr Eng Comput Sci 6: 38–47. https://doi.org/10.24018/ejece.2022.6.2.414 doi: 10.24018/ejece.2022.6.2.414
    [27] Al-Saadi M, Al-Greer M, Short M (2021) Strategies for controlling microgrid networks with energy storage systems: A review. Energies 14: 7234. https://doi.org/10.3390/en14217234 doi: 10.3390/en14217234
    [28] Hu J, Shan Y, Xu Y, et al. (2019) A coordinated control of hybrid AC/DC microgrids with PV-wind-battery under variable generation and load conditions. Int J Electr Power Energy Syst 104: 583–592. https://doi.org/10.1016/j.ijepes.2018.07.037 doi: 10.1016/j.ijepes.2018.07.037
    [29] Nejabatkhah F, Li YR (2014) Overview of power management strategies of hybrid AC/DC microgrid. IEEE Trans Power Electron 30: 7072–7089. https://doi.org/10.1109/TPEL.2014.2384999 doi: 10.1109/TPEL.2014.2384999
    [30] Sahoo B, Routray SK, Rout PK (2021) AC, DC, and hybrid control strategies for smart microgrid application: A review. Int Trans Electr Energy Syst 31: e12683. https://doi.org/10.1002/2050-7038.12683 doi: 10.1002/2050-7038.12683
    [31] Singh P, Paliwal P, Arya A (2019) A review on challenges and techniques for secondary control of microgrid. IOP Conf Ser Mater Sci Eng 561: 012075. https://doi.org/10.1088/1757-899X/561/1/012075 doi: 10.1088/1757-899X/561/1/012075
    [32] Ramos F, Pinheiro A, Nascimento R, et al. (2022) Development of operation strategy for battery energy storage system into hybrid AC microgrids. Sustainability 14: 13765. https://doi.org/10.3390/su142113765 doi: 10.3390/su142113765
    [33] Allwyn RG, Al-Hinai A, Margaret V (2023) A comprehensive review on energy management strategy of microgrids. Energy Rep 9: 5565–5591. https://doi.org/10.1016/j.egyr.2023.04.360 doi: 10.1016/j.egyr.2023.04.360
    [34] Mohamed MA (2022) A relaxed consensus plus innovation based effective negotiation approach for energy cooperation between smart grid and microgrid. Energy 252: 123996. https://doi.org/10.1016/j.energy.2022.123996 doi: 10.1016/j.energy.2022.123996
    [35] Rangarajan SS, Raman R, Singh A, et al. (2023) DC Microgrids: A propitious smart grid paradigm for smart cities. Smart Cities 6: 1690–1718. https://doi.org/10.3390/smartcities6040079 doi: 10.3390/smartcities6040079
    [36] Yin F, Hajjiah A, Jermsittiparsert K, et al. (2021) A secured social-economic framework based on PEM-Blockchain for optimal scheduling of reconfigurable interconnected microgrids. IEEE Access 9: 40797–40810. https://doi.org/10.1109/ACCESS.2021.3065400 doi: 10.1109/ACCESS.2021.3065400
    [37] Wang P, Wang D, Zhu C, et al. (2020) Stochastic management of hybrid AC/DC microgrids considering electric vehicles charging demands. Energy Rep 6: 1338–1352. https://doi.org/10.1016/j.egyr.2020.05.019 doi: 10.1016/j.egyr.2020.05.019
    [38] Tummuru NR, Manandhar U, Ukil A, et al. (2019) Control strategy for AC-DC microgrid with hybrid energy storage under different operating modes. Int J Electr Power Energy Syst 104: 807–816. https://doi.org/10.1016/j.ijepes.2018.07.063 doi: 10.1016/j.ijepes.2018.07.063
    [39] Alluraiah NC, Vijayapriya P, Chittathuru D, et al. (2023) Multi-objective optimization algorithms for a hybrid AC/DC microgrid using RES: A comprehensive review. Electronics 12: 1–31. https://doi.org/10.3390/electronics12041062 doi: 10.3390/electronics12041062
    [40] Gabbar HA, El-Hendawi M, El-Saady G, et al. (2016) Supervisory controller for power management of AC/DC microgrid. 2016 IEEE Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 147–152. https://doi.org/10.1109/SEGE.2016.7589516
    [41] Silveira JP, dos Santos Neto P, Barros T, et al. (2021) Power management of energy storage system with modified interlinking converters topology in hybrid AC/DC microgrid. Int J Electr Power Energy Syst 130: 106880. https://doi.org/10.1016/j.ijepes.2021.106880 doi: 10.1016/j.ijepes.2021.106880
    [42] Lee H, Kang JW, Choi BY, et al. (2021) Energy management system of DC microgrid in grid-connected and standalone modes: Control, operation and experimental validation. Energies 14: 581. https://doi.org/10.3390/en14030581 doi: 10.3390/en14030581
    [43] Abbas FA, Obed AA, Qasim MA, et al. (2022) An efficient energy-management strategy for a DC microgrid powered by a photovoltaic/fuel cell/battery/supercapacitor. Clean Energy 6: 827–839. https://doi.org/10.1093/ce/zkac063 doi: 10.1093/ce/zkac063
    [44] Han Y, Ning X, Yang P, et al. (2019) Review of power sharing, voltage restoration and stabilization techniques in hierarchical controlled DC microgrids. IEEE Access 7: 149202–149223. https://doi.org/10.1109/ACCESS.2019.2946706 doi: 10.1109/ACCESS.2019.2946706
    [45] Yang F, Ye L, Muyeen SM, et al. (2022) Power management for hybrid AC/DC microgrid with multi-mode subgrid based on incremental costs. Int J Electr Power Energy Syst 138: 107887. https://doi.org/10.1016/j.ijepes.2021.107887 doi: 10.1016/j.ijepes.2021.107887
    [46] Liu X, Zhao T, Deng H, et al. (2022) Microgrid energy management with energy storage systems: A review. CSEE J Power Energy Syst, 1–21. https://doi.org/10.17775/CSEEJPES.2022.04290 doi: 10.17775/CSEEJPES.2022.04290
    [47] Xia Y, Wei W, Yu M, et al. (2017) Decentralized multi-Time scale power control for a hybrid AC/DC microgrid with multiple subgrids. IEEE Transactions on Power Electronics 33: 4061–4072. https://doi.org/10.1109/TPEL.2017.2721102 doi: 10.1109/TPEL.2017.2721102
    [48] Manbachi M, Ordonez M (2019) Intelligent agent-based energy management system for islanded AC/DC microgrids. IEEE Transactions on Industrial Informatics 16: 4603–4614. https://doi.org/10.1109/TII.2019.2945371 doi: 10.1109/TII.2019.2945371
    [49] Arunkumar AP, Kuppusamy S, Muthusamy S, et al. (2022) An extensive review on energy management system for microgrids. Energy Sources Part Recovery Util Environ Eff 44: 4203–4228. https://doi.org/10.1080/15567036.2022.2075059 doi: 10.1080/15567036.2022.2075059
    [50] Cecilia A, Carroquino J, Roda V, et al. (2020) Optimal energy management in a standalone microgrid, with photovoltaic generation, short-term storage, and hydrogen production. Energies 13: 1454. https://doi.org/10.3390/en13061454 doi: 10.3390/en13061454
    [51] Kumari N, Sharma A, Tran B, et al. (2023) A comprehensive review of digital twin technology for grid-connected microgrid systems: State of the art, potential and challenges faced. Energies 16: 5525. https://doi.org/10.3390/en16145525 doi: 10.3390/en16145525
    [52] Muqeet HA, Javed H, Akhter MN, et al. (2022) Sustainable solutions for advanced energy management system of campus microgrids: Model opportunities and future challenges. Sensors 22: 2345. https://doi.org/10.3390/s22062345 doi: 10.3390/s22062345
    [53] Baharizadeh M, Karshenas HR, Guerrero JM (2018) An improved power control strategy for hybrid AC-DC microgrids. Int J Electr Power Energy Syst 95: 364–373. https://doi.org/10.1016/j.ijepes.2017.08.036 doi: 10.1016/j.ijepes.2017.08.036
    [54] Pratomo LH, Matthias LA (2022) Control strategy in DC microgrid for integrated energy balancer: Photovoltaic application. Iran J Energy Environ 13: 333–339. https://doi.org/10.5829/ijee.2022.13.04.02 doi: 10.5829/ijee.2022.13.04.02
    [55] Volnyi V, Ilyushin P, Suslov K, et al. (2023) Approaches to building AC and AC–DC microgrids on top of existing passive distribution networks. Energies 16: 5799. https://doi.org/10.3390/en16155799 doi: 10.3390/en16155799
    [56] Qu Z, Shi Z, Wang Y, et al. (2022) Energy management strategy of AC/DC hybrid microgrid based on solid-state transformer. IEEE Access 10: 20633–20642. https://doi.org/10.1109/ACCESS.2022.3149522 doi: 10.1109/ACCESS.2022.3149522
    [57] Irmak E, Kabalcı E, Kabalci Y (2023) Digital transformation of microgrids: A review of design, operation, optimization, and cybersecurity. Energies 16: 4590. https://doi.org/10.3390/en16124590 doi: 10.3390/en16124590
    [58] Khubrani MM, Alam S (2023) Blockchain-Based microgrid for safe and reliable power generation and distribution: A case study of saudi arabia. Energies 16: 5963. https://doi.org/10.3390/en16165963 doi: 10.3390/en16165963
    [59] Azeem O, Ali M, Abbas G, et al. (2021) A comprehensive review on integration challenges, optimization techniques and control strategies of hybrid AC/DC microgrid. Appl Sci 11: 6242. https://doi.org/10.3390/app11146242 doi: 10.3390/app11146242
    [60] Kumar AA, Prabha NA (2022) A comprehensive review of DC microgrid in market segments and control technique. Heliyon 8: e11694. https://doi.org/10.1016/j.heliyon.2022.e11694 doi: 10.1016/j.heliyon.2022.e11694
    [61] Chen J, Alnowibet K, Annuk A, et al. (2021) An effective distributed approach based machine learning for energy negotiation in networked microgrids. Energy Strategy Rev 38: 100760. https://doi.org/10.1016/j.esr.2021.100760 doi: 10.1016/j.esr.2021.100760
    [62] Khan R, Islam N, Das SK, et al. (2021) Energy sustainability–survey on technology and control of microgrid, smart grid and virtual power plant. IEEE Access 9: 104663–104694. https://doi.org/10.1109/ACCESS.2021.3099941 doi: 10.1109/ACCESS.2021.3099941
    [63] Pabbuleti B, Somlal J (2020) A review on hybrid AC/DC microgrids: Optimal sizing, stability control and energy management approaches. J Crit Rev 7: 376–381. Available from: https://www.semanticscholar.org/paper/A-REVIEW-ON-HYBRID-AC-DC-MICROGRIDS%3A-OPTIMAL-AND-Pabbuleti-Somlal/db68b9f4b88a82fd5707656b721f96976acb8176.
    [64] Gutiérrez-Oliva D, Colmenar-Santos A, Rosales E (2022) A review of the state of the art of industrial microgrids based on renewable energy. Electronics 11: 1002. https://doi.org/10.3390/electronics11071002 doi: 10.3390/electronics11071002
    [65] Haidekker M, Liu M, Song W (2023) Alternating-Current microgrid testbed built with low-cost modular hardware. Sensors 23: 3235. https://doi.org/10.3390/s23063235 doi: 10.3390/s23063235
    [66] Arif S, Rabbi A, Ahmed S, et al. (2022) Enhancement of solar PV hosting capacity in a remote industrial microgrid: A methodical techno-economic approach. Sustainability 14. https://doi.org/10.3390/su14148921 doi: 10.3390/su14148921
    [67] Zhao T, Xiao J, Koh LH, et al. (2018) Distributed energy management for hybrid AC/DC microgrid parks. 2018 IEEE Power & Energy Society General Meeting (PESGM), 1–5. https://doi.org/10.1109/PESGM.2018.8586403 doi: 10.1109/PESGM.2018.8586403
    [68] Garcia-Torres F, Zafra-Cabeza A, Silva C, et al. (2021) Model predictive control for microgrid functionalities: Review and future challenges. Energies 14: 1296. https://doi.org/10.3390/en14051296 doi: 10.3390/en14051296
    [69] Francis D, Lazarova-Molnar S, Mohamed N (2021) Towards data-driven digital twins for smart manufacturing. In: Selvaraj, H., Chmaj, G., Zydek, D., Proceedings of the 27th International Conference on Systems Engineering, ICSEng 2020. Lecture Notes in Networks and Systems. 182: 445–454. https://doi.org/10.1007/978-3-030-65796-3_43
    [70] Rosero DG, Sanabria E, Díaz NL, et al. (2023) Full-deployed energy management system tested in a microgrid cluster. Appl Energy 334: 120674. https://doi.org/10.1016/j.apenergy.2023.120674 doi: 10.1016/j.apenergy.2023.120674
    [71] Leonori S, Paschero M, Frattale Mascioli FM, et al. (2020) Optimization strategies for Microgrid energy management systems by Genetic Algorithms. Appl Soft Comput 86: 105903. https://doi.org/10.1016/j.asoc.2019.105903 doi: 10.1016/j.asoc.2019.105903
    [72] Vásquez LOP, Ramírez VM, Thanapalan K (2020) A comparison of energy management system for a DC microgrid. Appl Sci 10: 1071. https://doi.org/10.3390/app10031071 doi: 10.3390/app10031071
    [73] Islam H, Mekhilef S, Shah NBM, et al. (2018) Performance evaluation of maximum power point tracking approaches and photovoltaic systems. Energies 11: 365. https://doi.org/10.3390/en11020365 doi: 10.3390/en11020365
    [74] Shafiullah M, Refat AM, Haque ME, et al. (2022) Review of recent developments in microgrid energy management strategies. Sustainability 14: 14794. https://doi.org/10.3390/su142214794 doi: 10.3390/su142214794
    [75] Çimen H, Bazmohammadi N, Lashab A, et al. (2022) An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring. Appl Energy 307: 118136. https://doi.org/10.1016/j.apenergy.2021.118136 doi: 10.1016/j.apenergy.2021.118136
    [76] Elsied M, Oukaour A, Gualous H, et al. (2014) An advanced energy management of microgrid system based on genetic algorithm. 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), 2541–2547. https://doi.org/10.1109/ISIE.2014.6865020 doi: 10.1109/ISIE.2014.6865020
    [77] El Makroum R, Khallaayoun A, Lghoul R, et al. (2023) Home energy management system based on genetic algorithm for load scheduling: A case study based on real life consumption data. Energies 16: 2698. https://doi.org/10.3390/en16062698 doi: 10.3390/en16062698
    [78] Ali M, Hossain MI, Shafiullah M (2022) Fuzzy logic for energy management in hybrid energy storage systems integrated DC microgrid. 2022 International Conference on Power Energy Systems and Applications (ICoPESA), 424–429. https://doi.org/10.1109/ICoPESA54515.2022.9754406 doi: 10.1109/ICoPESA54515.2022.9754406
    [79] Bianchini I, Kuhlmann T, Wunder B, et al. (2021) Hierarchical network management of industrial DC-microgrids. 2021 IEEE Fourth International Conference on DC Microgrids (ICDCM), 1–6. https://doi.org/10.1109/ICDCM50975.2021.9504619 doi: 10.1109/ICDCM50975.2021.9504619
    [80] Ahmed M, Abbas G, Jumani T, et al. (2023) Techno-economic optimal planning of an industrial microgrid considering integrated energy resources. Front Energy Res 11: 12. https://doi.org/10.3389/fenrg.2023.1145888 doi: 10.3389/fenrg.2023.1145888
    [81] Dzyuba A, Solovyeva I, Semikolenov A (2022) Prospects of introducing microgrids in Russian industry. J New Econ 23: 80–101. https://doi.org/10.29141/2658-5081-2022-23-2-5 doi: 10.29141/2658-5081-2022-23-2-5
    [82] Ghasemi M, Kazemi A, Mazza A, et al. (2021) A three‐stage stochastic planning model for enhancing the resilience of distribution systems with microgrid formation strategy. IET Gener Transm Distrib 15. https://doi.org/10.1049/gtd2.12144 doi: 10.1049/gtd2.12144
    [83] Han Y, Shen P, Coelho E, et al. (2016) Review of active and reactive power sharing strategies in hierarchical controlled microgrids. IEEE Trans Power Electron 32: 2427–2451. https://doi.org/10.1109/TPEL.2016.2569597 doi: 10.1109/TPEL.2016.2569597
    [84] Nardelli P, Hussein M, Narayanan A, et al. (2021) Virtual microgrid management via software-defined energy network for electricity sharing: benefits and challenges. IEEE Systems, Man, and Cybernetics Magazine 7: 10–19. https://doi.org/10.1109/MSMC.2021.3062018 doi: 10.1109/MSMC.2021.3062018
    [85] Borisoot K, Liemthong R, Srithapon C, et al. (2023) Optimal energy management for virtual power plant considering operation and degradation costs of energy storage system and generators. Energies 16: 2862. https://doi.org/10.3390/en16062862 doi: 10.3390/en16062862
    [86] Lombardi P, Sokolnikova T, Styczynski Z, et al. (2012) Virtual power plant management considering energy storage systems. IFAC Proc Vol 45: 132–137. https://doi.org/10.3182/20120902-4-FR-2032.00025 doi: 10.3182/20120902-4-FR-2032.00025
    [87] Jithin S, Rajeev T (2022) Novel adaptive power management strategy for hybrid AC/DC microgrids with hybrid energy storage systems. J Power Electron 22. https://doi.org/10.1007/s43236-022-00506-x doi: 10.1007/s43236-022-00506-x
    [88] Bhattar CL, Chaudhari MA (2023) Centralized energy management scheme for grid connected DC microgrid. IEEE Syst J 17: 3741–3751. https://doi.org/10.1109/JSYST.2022.3231898 doi: 10.1109/JSYST.2022.3231898
    [89] Balapattabi S, Mahalingam P, Gonzalez-Longatt F (2017) High‐gain–high‐power (HGHP) DC‐DC converter for DC microgrid applications: Design and testing. Int Trans Electr Energy Syst 28. https://doi.org/10.1002/etep.2487 doi: 10.1002/etep.2487
    [90] Modu B, Abdullah MP, Sanusi MA, et al. (2023) DC-based microgrid: Topologies, control schemes, and implementations. Alex Eng J 70: 61–92. https://doi.org/10.1016/j.aej.2023.02.021 doi: 10.1016/j.aej.2023.02.021
    [91] Kang KM, Choi BY, Lee H, et al. (2021) Energy management method of hybrid AC/DC microgrid using artificial neural network. Electronics 10: 1939. https://doi.org/10.3390/electronics10161939 doi: 10.3390/electronics10161939
    [92] Friederich J, Francis DP, Lazarova-Molnar S, et al. (2022) A framework for data-driven digital twins of smart manufacturing systems. Comput Ind 136: 103586. https://doi.org/10.1016/j.compind.2021.103586 doi: 10.1016/j.compind.2021.103586
    [93] Bazmohammadi N, Madary A, Vasquez JC, et al. (2022) Microgrid digital twins: concepts, applications, and future trends. IEEE Access 10: 2284–2302. https://doi.org/10.1109/ACCESS.2021.3138990 doi: 10.1109/ACCESS.2021.3138990
    [94] Yu P, Ma L, Fu R, et al. (2023) Framework design and application perspectives of digital twin microgrid. Energy Rep 9: 669–678. https://doi.org/10.1016/j.egyr.2023.04.253 doi: 10.1016/j.egyr.2023.04.253
    [95] Sifat MdMH, Choudhury SM, Das SK, et al. (2023) Towards electric digital twin grid: Technology and framework review. Energy AI 11: 100213. https://doi.org/10.1016/j.egyai.2022.100213 doi: 10.1016/j.egyai.2022.100213
    [96] Nasiri G, Kavousi-Fard A (2023) A digital twin-based system to manage the energy hub and enhance the electrical grid resiliency. Machines 11: 392. https://doi.org/10.3390/machines11030392 doi: 10.3390/machines11030392
    [97] Essayeh C, Raiss El-Fenni M, Dahmouni H, et al. (2021) Energy management strategies for smart green microgrid systems: A systematic literature review. J Electr Comput Eng 2021: e6675975. https://doi.org/10.1155/2021/6675975 doi: 10.1155/2021/6675975
    [98] Kannengießer T, Hoffmann M, Kotzur L, et al. (2019) Reducing computational load for mixed integer linear programming: An example for a district and an island energy system. Energies 12: 2825. https://doi.org/10.3390/en12142825 doi: 10.3390/en12142825
    [99] Lagouir M, Badri A, Sayouti Y (2019) Development of an intelligent energy management system with economic dispatch of a standalone microgrid. J Electr Syst 15: 568–581. Available from: https://www.proquest.com/openview/74e70711c074dc123b54081dab08fa5f/1?pq-origsite = gscholar & cbl = 4433095.
    [100] Bishnoi D, Chaturvedi H (2021) Emerging trends in smart grid energy management systems. Int J Renewable Energy Res (IJRER) 11: 952–966. Available from: https://www.ijrer.org/ijrer/index.php/ijrer/article/view/11832.
    [101] Dwivedi SD, Ray PK (2022) Energy management and control of grid-connected microgrid integrated with HESS. 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), 1–6. https://doi.org/10.1109/ICICCSP53532.2022.9862374 doi: 10.1109/ICICCSP53532.2022.9862374
    [102] Rahman M, Hossain MJ, Rafi F, et al. (2016) A multi-purpose interlinking converter control for multiple hybrid AC/DC microgrid operations. 2016 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia), Melbourne, VIC, Australia, 221–226. https://doi.org/10.1109/ISGT-Asia.2016.7796389
    [103] Yang P, Xia Y, Yu M, et al. (2017) A decentralized coordination control method for parallel bidirectional power converters in a hybrid AC/DC microgrid. IEEE Trans Ind Electron 65: 6217–6228. https://doi.org/10.1109/TIE.2017.2786200 doi: 10.1109/TIE.2017.2786200
    [104] Helal S, Hanna M, Najee R, et al. (2019) Energy management system for smart hybrid AC/DC microgrids in remote communities. Electr Power Compon Syst 47: 1–13. https://doi.org/10.1080/15325008.2019.1629512 doi: 10.1080/15325008.2019.1629512
    [105] Kumar S, Chinnamuthan P, Krishnasamy V (2018) Study on renewable distributed generation, power controller and islanding management in hybrid microgrid system. J Green Eng 8: 37–70. https://doi.org/10.13052/jge1904-4720.814 doi: 10.13052/jge1904-4720.814
    [106] Senfelds A, Bormanis O, Paugurs A (2016) Analytical approach for industrial microgrid infeed peak power dimensioning. 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 1–4. https://doi.org/10.1109/RTUCON.2016.7763140 doi: 10.1109/RTUCON.2016.7763140
    [107] Mosa MA, Ali AA (2021) Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique. Electr Power Syst Res 192: 106971. https://doi.org/10.1016/j.epsr.2020.106971 doi: 10.1016/j.epsr.2020.106971
    [108] Du H, Zhang X, Sun Q, et al. (2019) Power management strategy of AC-DC hybrid microgrid in island mode. 2019 Chinese Control And Decision Conference (CCDC), 2900–2905. https://doi.org/10.1109/CCDC.2019.8833467 doi: 10.1109/CCDC.2019.8833467
    [109] Dalai SK, Prince SK, Abhishek A, et al. (2022) Power management strategies for islanding and grid-connected DC microgrid systems with multiple renewable energy resources. 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT), 1–6. https://doi.org/10.1109/GlobConPT57482.2022.9938187 doi: 10.1109/GlobConPT57482.2022.9938187
    [110] Kim TG, Lee H, An C-G, et al. (2023) Hybrid AC/DC microgrid energy management strategy based on two-step ANN. Energies 16: 1787. https://doi.org/10.3390/en16041787 doi: 10.3390/en16041787
    [111] Ullah S, Haidar A, Zen H (2020) Assessment of technical and financial benefits of AC and DC microgrids based on solar photovoltaic. Electr Eng 102: 1297–1310. https://doi.org/10.1007/s00202-020-00950-7 doi: 10.1007/s00202-020-00950-7
    [112] Ellert C, Horta R, Sterren T, et al. (2017) Modular ICT based energy management system for a LVDC-microgrid with local PV production and integrated electrochemical storage. 2017 IEEE Second International Conference on DC Microgrids (ICDCM), 274–278. https://doi.org/10.1109/ICDCM.2017.8001056 doi: 10.1109/ICDCM.2017.8001056
    [113] Senfelds A, Apse-Apsitis P, Avotins A, et al. (2017) Industrial DC microgrid analysis with synchronous multipoint power measurement solution. 2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe), 1–6. https://doi.org/10.23919/EPE17ECCEEurope.2017.8099322 doi: 10.23919/EPE17ECCEEurope.2017.8099322
    [114] Sarda JS, Lee K, Patel H, et al. (2022) Energy management system of microgrid using optimization approach. IFAC-Pap 55: 280–284. https://doi.org/10.1016/j.ifacol.2022.07.049 doi: 10.1016/j.ifacol.2022.07.049
    [115] Dey P, Chowdhury MdM (2022) Developing a methodology for reactive power planning in an industrial microgrid. 2022 IEEE Region 10 Symposium (TENSYMP). https://doi.org/10.1109/TENSYMP54529.2022.9864406 doi: 10.1109/TENSYMP54529.2022.9864406
    [116] Zhou Z, Xiong F, Biyao H, et al. (2017) Game-theoretical energy management for energy internet with big data-based renewable power forecasting. IEEE Access 5: 5731–5746. https://doi.org/10.1109/ACCESS.2017.2658952 doi: 10.1109/ACCESS.2017.2658952
    [117] Sood VK, Ali MY, Khan F (2020) Energy management system of a microgrid using particle swarm optimization (PSO) and communication system. In: Ray P, Biswal M., Microgrid: Operation, Control, Monitoring and Protection, Singapore, Springer, 263–288. https://doi.org/10.1007/978-981-15-1781-5_9
    [118] Wei B, Han X, Wang P, et al. (2020) Temporally coordinated energy management for AC/DC hybrid microgrid considering dynamic conversion efficiency of bidirectional AC/DC converter. IEEE Access 8: 70878–70889. https://doi.org/10.1109/ACCESS.2020.2985419 doi: 10.1109/ACCESS.2020.2985419
    [119] Zafir S, Muhamad Razali N, Tengku J (2016) Relationship between loss of load expectation and reserve margin for optimal generation planning. J Teknol 78. https://doi.org/10.11113/jt.v78.8783 doi: 10.11113/jt.v78.8783
    [120] Diewvilai R, Audomvongseree K (2022) Optimal loss of load expectation for generation expansion planning considering fuel unavailability. Energies 15: 7854. https://doi.org/10.3390/en15217854 doi: 10.3390/en15217854
    [121] Li J, Cai H, Yang P, et al. (2021). A Bus-Sectionalized hybrid AC/DC microgrid: Concept, control paradigm, and implementation. Energies 14. 3508. https://doi.org/10.3390/en14123508. doi: 10.3390/en14123508
    [122] Pabbuleti B, Somlal J (2022) A hybrid AC/DC microgrid with multi-bus DC sub-grid optimal operation. Int J Intell Syst Appl Eng 10: 1–7. Available from: https://ijisae.org/index.php/IJISAE/article/view/2353.
    [123] Nguyen DH, Banjerdpongchai D (2016) Iterative learning control of energy management system: Survey on Multi-Agent System Framework. Eng J 20: 1–4. https://doi.org/10.4186/ej.2016.20.5.1 doi: 10.4186/ej.2016.20.5.1
    [124] Jasim AM, Jasim BH, Bureš V (2022) A novel grid-connected microgrid energy management system with optimal sizing using hybrid grey wolf and cuckoo search optimization algorithm. Front Energy Res 10. Available from: https://www.frontiersin.org/articles/10.3389/fenrg.2022.960141.
    [125] Li J, Cai H, Yang P, et al. (2021) A Bus-Sectionalized hybrid AC/DC microgrid: Concept, control Paradigm, and Implementation. Energies 14: 3508. https://doi.org/10.3390/en14123508 doi: 10.3390/en14123508
    [126] Yu D, Gao S, Zhao X, et al. (2023) Alternating iterative power-flow algorithm for hybrid AC–DC power grids incorporating LCCs and VSCs. Sustainability 15: 4573. https://doi.org/10.3390/su15054573 doi: 10.3390/su15054573
    [127] Kassa Y, Zhang J, Zheng D (2020) Optimal energy management strategy in microgrids with mixed energy resources and energy storage system. IET Cyber-Phys Syst Theory Appl 5: 80–85. https://doi.org/10.1049/iet-cps.2019.0035 doi: 10.1049/iet-cps.2019.0035
    [128] Zagrajek K, Paska J, Sosnowski L, et al. (2021) Framework for the introduction of vehicle-to-grid technology into the polish electricity market. Energies 14: 3673. https://doi.org/10.3390/en14123673 doi: 10.3390/en14123673
    [129] Katche M, Makokha A, Zachary S, et al. (2023) A comprehensive review of maximum power point tracking (MPPT) techniques used in solar PV systems. Energies 16: 2206. https://doi.org/10.3390/en16052206 doi: 10.3390/en16052206
    [130] Li Z, Tan, Ren J, et al. (2020) A two-stage optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets. Processes 8: 107. https://doi.org/10.3390/pr8010107 doi: 10.3390/pr8010107
    [131] Kantor I, Robineau JL, Bütün H, et al. (2020) A mixed-integer linear programming formulation for optimizing multi-scale material and energy integration. Front Energy Res 8. Available from: https://www.frontiersin.org/articles/10.3389/fenrg.2020.00049.
    [132] Ravichandran A (2016) Optimization-based microgrid energy management systems. Available from: https://www.semanticscholar.org/paper/Optimization-based-Microgrid-Energy-Management-Ravichandran/bc20a7cd69a6ce34652304ae2bcfd5499f534608.
    [133] Zia MF, Elbouchikhi E, Benbouzid M (2018) Microgrids energy management systems: A critical review on methods, solutions, and prospects. Appl Energy 222: 1033–1055. https://ideas.repec.org//a/eee/appene/v222y2018icp1033-1055.html
    [134] Naeem A, Ahmed S, Ahsan M, et al. (2016) Energy management strategies using microgrid systems. 2016 Conference: 2nd International Multi-Disciplinary Conference, 1–8. Available from: https://www.researchgate.net/publication/326534831_Energy_Management_Strategies_using_Microgrid_Systems.
    [135] Rodriguez-Diaz E, Palacios-Garcia EJ, Anvari-Moghaddam A, et al. (2017) Real-time Energy Management System for a hybrid AC/DC residential microgrid. 2017 IEEE Second International Conference on DC Microgrids (ICDCM), 256–261. https://doi.org/10.1109/ICDCM.2017.8001053 doi: 10.1109/ICDCM.2017.8001053
    [136] Spiegel M, Veith E, Strasser T (2020) The spectrum of proactive, resilient multi-microgrid scheduling: A systematic literature review. Energies 13: 4543. https://doi.org/10.3390/en13174543 doi: 10.3390/en13174543
    [137] Shahzad S, Abbasi M, Chaudhary M, et al. (2022) Model predictive control strategies in microgrids: A concise revisit. IEEE Access 10: 122211–122225. https://doi.org/10.1109/ACCESS.2022.3223298 doi: 10.1109/ACCESS.2022.3223298
  • This article has been cited by:

    1. Samir Idrissi Kaitouni, Ibtihal Ait Abdelmoula, Niima Es-sakali, Mohamed Oualid Mghazli, Houda Er-retby, Zineb Zoubir, Fouad El Mansouri, Mohammed Ahachad, Jamal Brigui, Implementing a Digital Twin-based fault detection and diagnosis approach for optimal operation and maintenance of urban distributed solar photovoltaics, 2024, 48, 17550084, 100530, 10.1016/j.ref.2023.100530
    2. Samir Idrissi Kaitouni, Mouatassim Charai, Niima Es-sakali, Mohamed Oualid Mghazli, Mohammed El Mankibi, Sung Uk-Joo, Mohammed Ahachad, Jamal Brigui, Energy and hygrothermal performance investigation and enhancement of rammed earth buildings in hot climates: From material to field measurements, 2024, 315, 03787788, 114325, 10.1016/j.enbuild.2024.114325
    3. Niima Es-sakali, Jens Pfafferott, Mohamed Oualid Mghazli, Moha Cherkaoui, Towards Climate-Responsive Net Zero Energy Rural Schools: A Multi-Objective Passive Design Optimization with Bio-Based Insulations, Shading, and Roof Vegetation, 2025, 22106707, 106142, 10.1016/j.scs.2025.106142
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4352) PDF downloads(355) Cited by(4)

Figures and Tables

Figures(11)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog