Research article Topical Sections

A methodological Decision-Making support for the planning, design and operation of smart grid projects

  • Received: 26 March 2020 Accepted: 19 June 2020 Published: 13 July 2020
  • Planning and operation of Smart energetic have become more complex to analyse due to structural changes in the energy sector. The inclusion of distributed generation sources, generation with renewable sources, storage systems, and the dislocation of information between the different organization levels and actors lead to the inherent difficulty of defining appropriate models that help decision making. Nowadays, decisions in planning and operation are made level by level rather than integrated manner. To address this problem, this work proposes a multi-level methodological framework based on Key Performance Indicators and System of Systems concepts. Involving methods, both quantitative and qualitative, this work serves as guidance to managers, planners, or political decision-makers from any electrical enterprise to help them find suitable solutions for planning and operation of smart grid projects.

    Citation: Ricardo Echeverri-Martínez, Wilfredo Alfonso-Morales, Eduardo F. Caicedo-Bravo. A methodological Decision-Making support for the planning, design and operation of smart grid projects[J]. AIMS Energy, 2020, 8(4): 627-651. doi: 10.3934/energy.2020.4.627

    Related Papers:

  • Planning and operation of Smart energetic have become more complex to analyse due to structural changes in the energy sector. The inclusion of distributed generation sources, generation with renewable sources, storage systems, and the dislocation of information between the different organization levels and actors lead to the inherent difficulty of defining appropriate models that help decision making. Nowadays, decisions in planning and operation are made level by level rather than integrated manner. To address this problem, this work proposes a multi-level methodological framework based on Key Performance Indicators and System of Systems concepts. Involving methods, both quantitative and qualitative, this work serves as guidance to managers, planners, or political decision-makers from any electrical enterprise to help them find suitable solutions for planning and operation of smart grid projects.


    加载中


    [1] Lorena M, Lochinvar M (2016) A review of the development of Smart Grid technologies. Renewable Sustainable Energy Rev 59: 710-725. doi: 10.1016/j.rser.2016.01.011
    [2] Niesten E, Alkemade F (2016) How is value created and captured in smart grids? A review of the literature and an analysis of pilot projects. Renewable Sustainable Energy Rev 53: 629-638.
    [3] Doǧdu E, Murat Özbayoǧlu A, Benli O, et al. (2014) Ontology-centric data modelling and decision support in smart grid applications a distribution service operator perspective. 2014 IEEE International Conference on Intelligent Energy and Power Systems, IEPS 2014-Conference Proceedings, IEEE, 198-203.
    [4] EPRI (2011) Estimating the Costs and Benefits of the Smart Grid. A Preliminary Estimate of the Investment Requirements and the Resultant Benefits of a Fully Functioning Smart Grid.
    [5] Raza MQ, Khosravi A (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable Sustainable Energy Rev 50: 1352-1372. doi: 10.1016/j.rser.2015.04.065
    [6] Pasetti M, Ferrari P, Silva DRC, et al. (2020) On the use of LoRaWAN for the monitoring and control of distributed energy resources in a smart campus. Appl Sci 10: 1-27.
    [7] Talaat M, Alsayyari AS, Alblawi A, et al. (2020) Hybrid-cloud-based data processing for power system monitoring in smart grids. Sustainable Cities Soc 55: 102049. doi: 10.1016/j.scs.2020.102049
    [8] Martin-Utrillas M, Juan-Garcia F, Canto-Perello J, et al. (2015) Optimal infrastructure selection to boost regional sustainable economy. Int J Sustainable Dev World Ecol 22: 30-38.
    [9] Stephan Dempe (2002) Foundations of Bilevel Programming, Boston, Kluwer Academic Publishers.
    [10] Carli R, Dotoli M, Pellegrino R (2017) A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities. IEEE Trans Autom Sci Eng 14: 505-523. doi: 10.1109/TASE.2016.2593101
    [11] Lu J, Han J, Hu Y, et al. (2016) Multilevel decision-making: A survey. Inf Sci (Ny) 346-347: 463-487. doi: 10.1016/j.ins.2016.01.084
    [12] Li R, Wang W, Chen Z, et al. (2017) A review of optimal planning active distribution system: Models, methods, and future researches. Energies 10.
    [13] Mohammadi R, Mashhadi HR, Shahidehpour M (2018) Market-based customer reliability provision in distribution systems based on game theory: A Bi-level optimization approach. IEEE Trans Smart Grid 1-1.
    [14] Alaqeel TA, Suryanarayanan S (2018) A fuzzy Analytic Hierarchy Process algorithm to prioritize Smart Grid technologies for the Saudi electricity infrastructure. Sustainable Energy, Grids Networks 13: 122-133. doi: 10.1016/j.segan.2017.12.010
    [15] Iberraken F, Medjoudj R, Aissani D (2013) Decision Making on Smart Grids Projects Moving using AHP Method: The case of Algerian Network. IFAC Proc Vol 46: 543-548.
    [16] Personal E, Guerrero JI, Garcia A, et al. (2014) Key performance indicators: A useful tool to assess Smart Grid goals. Energy 76: 976-988. doi: 10.1016/j.energy.2014.09.015
    [17] Li Y, O'Donnell J, García-Castro R, et al. (2017) Identifying stakeholders and key performance indicators for district and building energy performance analysis. Energy Build 155: 1-15. doi: 10.1016/j.enbuild.2017.09.003
    [18] He Y, Wu J, Ge Y, et al. (2017) Research on model and method of maturity evaluation of smart grid industry. 633-642.
    [19] U.S. Department of Energy (2010) smartgrid.gov, Guidebook for ARRA Smart Grid Program Metrics and Benefits, 2010.
    [20] U.S. Department of Energy (2014) Smart Grid Status and Metrics Report, 2014.
    [21] GridPlus (2013) Supporting the Development of the European Electricity Grids Initiative (EEGI), 2013.
    [22] Salazar F, Fernando Martín, Maite Hormigo (2014) IDEAL GRID FOR ALL. Deliverable D7.1: KPI Definition, 2014.
    [23] Nordström L (2014) List of agreed KPIs with associated metrics and refined Smart Grids functionalities list. 2014.
    [24] Saaty TL (1978) Modeling unstructured decision problems-the theory of analytical hierarchies. Math Comput Simul 20: 147-158. doi: 10.1016/0378-4754(78)90064-2
    [25] Pham TN, Phan TT, Nguyen PT, et al. (2013) Computational Collective Intelligence. Technologies and Applications. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 8083: 603-611.
    [26] Dikmen I, Birgonul MT, Gur AK (2007) A case-based decision support tool for bid mark-up estimation of international construction projects. Autom Constr 17: 30-44. doi: 10.1016/j.autcon.2007.02.009
    [27] Spyridakos A, Siskos Y, Yannacopoulos D, et al. (2001) Multicriteria job evaluation for large organizations. Eur J Oper Res 130: 375-387. doi: 10.1016/S0377-2217(00)00039-4
    [28] Armacost RL, Hosseini JC, Pet-Edwards J (1999) Using the analytic hierarchy process as a two-phase integrated decision approach for large nominal groups. Gr Decis Negot 8: 535-555. doi: 10.1023/A:1008622202638
    [29] Yaman R, Balibek E (1999) Decision making for facility layout problem solutions. Comput Ind Eng 37: 319-322. doi: 10.1016/S0360-8352(99)00083-2
    [30] Zhu Q, Azar AT (Eds.) (2015) Complex system modelling and control through intelligent soft computations, Cham, Springer International Publishing.
    [31] Zhang G, Lu J, Gao Y (2015) Multi-Level decision making: Models, methods and applications.
    [32] Alekseeva E, Brotcorne L, Lepaul S, et al. (2019) A bilevel approach to optimize electricity prices. Yugosl J Oper Res 29: 9-30. doi: 10.2298/YJOR171115002A
    [33] Poursmaeil B, Ravadanegh SN, Hosseinzadeh S (2018) Optimal Bi-level planning of autonomous MGs. 3: 1-8.
    [34] Wu X, Conejo AJ (2017) An efficient Tri-Level optimization model for electric grid defense planning. IEEE Trans Power Syst 32: 2984-2994. doi: 10.1109/TPWRS.2016.2628887
    [35] Lin Y, Bie Z (2018) Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding. Appl Energy 210: 1266-1279. doi: 10.1016/j.apenergy.2017.06.059
    [36] Li R, Wang W, Chen Z, et al. (2018) Optimal planning of energy storage system in active distribution system based on fuzzy multi-objective bi-level optimization. J Mod Power Syst Clean Energy 6: 342-355. doi: 10.1007/s40565-017-0332-x
    [37] Huang W, Zhang N, Yang J, et al. (2019) Optimal configuration planning of multi-energy systems considering distributed renewable energy. IEEE Trans Smart Grid 10: 1452-1464. doi: 10.1109/TSG.2017.2767860
    [38] Liu Y, Yu N, Wang W, et al. (2018) Coordinating the operations of smart buildings in smart grids. Appl Energy 228: 2510-2525. doi: 10.1016/j.apenergy.2018.07.089
    [39] Cervilla C, Villar J, Campos FA (2015) Bi-level optimization of electricity tariffs and PV distributed generation investments. Int Conf Eur Energy Mark EEM 2015-Augus, 0-4.
    [40] Quashie M, Joos G (2016) Optimal planning of urban microgrids with an energy management system. Proc IEEE Power Eng Soc Transm Distrib Conf 1-5.
    [41] Chen J, Zhu Q (2017) A Stackelberg Game Approach for Two-Level Distributed Energy Management in Smart Grids. IEEE Trans Smart Grid 3053.
    [42] Ma T, Wu J, Hao L, et al. (2018) A real-time pricing scheme for energy management in integrated energy systems: A stackelberg game approach. Energies 11.
    [43] Evins R (2015) Multi-level optimization of building design, energy system sizing and operation. Energy 90: 1775-1789. doi: 10.1016/j.energy.2015.07.007
    [44] Sellak H, Ouhbi B, Frikh B, et al. (2017) Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support. Renewable Sustainable Energy Rev 80: 1544-1577. doi: 10.1016/j.rser.2017.07.013
    [45] Kumar A, Sah B, Singh AR, et al. (2017) A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable Sustainable Energy Rev 69: 596-609. doi: 10.1016/j.rser.2016.11.191
    [46] Yamakawa EK, Aoki AR, Siebert LC, et al. (2013) A Fuzzy-QFD decision making approach for selecting industry energy efficiency indicators. 2013 IEEE PES Conf Innov Smart Grid Technol ISGT LA 2013 0-4.
    [47] Heo E, Kim J, Boo KJ (2010) Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable Sustainable Energy Rev 14: 2214-2220. doi: 10.1016/j.rser.2010.01.020
    [48] Ishizaka A (2014) Comparison of Fuzzy logic, AHP, FAHP and Hybrid Fuzzy AHP for new supplier selection and its performance analysis. Int J Integr Sypply Manage 9: 1-22. doi: 10.1504/IJISM.2014.064353
    [49] Janjic A, Savic S, Janackovic G, et al. (2016) Multi-criteria assessment of the smart grid efficiency using the fuzzy analytic hierarchy process. Facta Univ-Ser Electron Energ 29: 631-646. doi: 10.2298/FUEE1604631J
    [50] Janjic A, Savic S, Velimirovic L, et al. (2015) Renewable energy integration in smart grids-multicriteria assessment using the fuzzy analytical hierarchy process. Turkish J Electr Eng Comput Sci 23: 1896-1912. doi: 10.3906/elk-1404-287
    [51] Shrestha R, Wagner D, Al-Anbagi I (2018) Fuzzy AHP-based siting of small modular reactors for power generation in the smart grid. 2018 IEEE Electrical Power and Energy Conference (EPEC), IEEE, 1-6.
    [52] Lazzerini B, Pistolesi F (2013) Efficient energy dispatching in smart microgrids using an integration of fuzzy AHP and TOPSIS assisted by linear programming. Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology.
    [53] He Y-G, Li Y (2016) Evaluation of power demand-side management factors in the new electric power system reform based on FAHP. 229-233.
    [54] Erbaş M, Kabak M, Özceylan E, et al. (2018) Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis. Energy 163: 1017-1031. doi: 10.1016/j.energy.2018.08.140
    [55] Zhao H, Guo S, Zhao H (2019) Comprehensive assessment for battery energy storage systems based on fuzzy-MCDM considering risk preferences. Energy 168: 450-461. doi: 10.1016/j.energy.2018.11.129
    [56] Wardayanti A, Zakaria R, Sutopo W, et al. (2018) Supplier selection model of the Lithium-ion battery using fuzzy AHP and analysis of BOCR. Int J Sustainable Transp Technol 1: 1-8. doi: 10.31427/IJSTT.2018.1.1.1
    [57] Ma Y (2018) Fuzzy AHP-based comprehensive evaluation for smart grid in energy internet systems. Int J Performability Eng.
    [58] Crowe TJ, Cheng C (1996) Using quality function deployment in manufacturing strategic planning. Int J Oper Prod Manag 16: 35-48. doi: 10.1108/01443579610114068
    [59] Kang X, Yang M, Wu Y, et al. (2018) Integrating Evaluation Grid Method and Fuzzy Quality Function Deployment to New Product Development. Math Probl Eng 2018: 1-15.
    [60] Haktanır E, Kahraman C (2019) A novel interval-valued Pythagorean fuzzy QFD method and its application to solar photovoltaic technology development. Comput Ind Eng 132: 361-372. doi: 10.1016/j.cie.2019.04.022
    [61] Saaty TL, Vargas LG (2012) Models, Methods, Concepts & Applications of the Analytic Hierarchy Process, Boston, MA, Springer US.
    [62] Kahraman C (2008) Fuzzy Multi-Criteria Decision Making, Boston, MA, Springer US.
    [63] Parmenter D (2007) Key Performance Indicators (KPI): Developing, Implementing, and Using Winning KPIs, Hoboken-USA, John Wiley & Sons, Inc.
    [64] Wang Y, Pan Z, Hao H, et al. (2015) Study on Multi-Objective effect evaluation system of smart grid construction. J Power Energy Eng 85-91.
    [65] Gaudo M, González R, Borroy S, et al. (2014) Smart grid technologies evaluation through KPIS. CIRED Work-Rome 01-05.
    [66] Gabbar HA, Labbi Y, Bower L, et al. (2016) Performance optimization of integrated gas and power within microgrids using hybrid PSO-PS algorithm. Int J Energy Res 40: 971-982. doi: 10.1002/er.3493
    [67] Gabbar HA, Abdelsalam AA (2014) Microgrid energy management in grid-connected and islanding modes based on SVC. Energy Convers Manage 86: 964-972. doi: 10.1016/j.enconman.2014.06.070
    [68] Nikolopoulos V, Mpardis G, Giannoukos I, et al. (2011) Web-based decision-support system methodology for smart provision of adaptive digital energy services over cloud technologies. IET Softw 5: 454. doi: 10.1049/iet-sen.2010.0008
    [69] Sanz R, Corredera Á, Hernández JL, et al. (2015) Towards the integration of monitoring systems to support the evaluation of nearly Zero Energy Buildings through Key Performance Indicators.
    [70] Minou M, Thanos G, Vasirani M, et al. (2014) Evaluating Demand Response Programs : Getting the Key Performance Indicators Right.
    [71] Filipowska A, Fabisz K, Hossa TM, et al. (2013) Towards Forecasting Demand and Production of Electric Energy in Smart Grids. Lecture Notes in Business Information Processing, 298-314.
    [72] Anadiotis G, Hatzoplaki E, Tsatsakis K, et al. (2015) A data model for energy decision support systems for smart cities: The case of BESOS Common Information Model, SMARTGREENS 2015-4th International Conference on Smart Cities and Green ICT Systems, Proceedings, 51-59.
    [73] Lugaric L, Krajcar S (2016) Transforming cities towards sustainable low-carbon energy systems using emergy synthesis for support in decision making. Energy Policy 98: 471-482. doi: 10.1016/j.enpol.2016.09.028
    [74] Carli R, Albino V, Dotoli M, et al. (2015) A dashboard and decision support tool for the energy governance of smart cities. 2015 IEEE Work Environ Energy, Struct Monit Syst EESMS 2015-Proc, 23-28.
    [75] Honarmand N (2015) Key performance indicators modeling for optimized microgrid configuration.
    [76] Picault D, Accouche O, Hadjsaid N (2014) Assessing and comparing smart grid demonstration projects. 1-5.
    [77] DOE (2016) Results from the Smart Grid Investment Grand Program, 2016.
    [78] Haines S (2000) The systems thinking approach to strategic planning and management, Boca Raton.
    [79] Centre TAPP (2019) Systems Thinking, 2019.
    [80] Cavalcante E, Cacho N, Lopes F, et al. (2016) Thinking smart cities as Systems-of-Systems. Proceedings of the 2nd International Workshop on Smart - SmartCities '16, New York, New York, USA, ACM Press, 1-4.
    [81] Pacheco FE, Foreman JC (2017) Microgrid reference methodology for understanding utility and customer interactions in microgrid projects. Electr J 30: 44-50.
    [82] FFIEC (2018) Systems Development Life Cycle, 2018.
    [83] Martín-Gamboa M, Iribarren D, García-Gusano D, et al. (2017) A review of life-cycle approaches coupled with data envelopment analysis within multi-criteria decision analysis for sustainability assessment of energy systems. J Clean Prod 150: 164-174. doi: 10.1016/j.jclepro.2017.03.017
    [84] Doorsamy W, Cronje WA, Lakay-Doorsamy L (2015) A systems engineering framework: Requirements analysis for the development of rural microgrids. Proc IEEE Int Conf Ind Technol, 1251-1256.
    [85] Zeng B, Wen J, Shi J, et al. (2016) A multi-level approach to active distribution system planning for efficient renewable energy harvesting in a deregulated environment. Energy 96: 614-624. doi: 10.1016/j.energy.2015.12.070
    [86] Jiménez MS, Filiou C, Giordano V, et al. (2012) Guidelines for Conducting a Cost-benefit analysis of Smart Grid projects, European Comission.
    [87] Li J, Li T, Han L (2018) Research on the evaluation model of a smart grid development level based on differentiation of development demand. Sustainability 10: 4047. doi: 10.3390/su10114047
    [88] Osorio-Gómez JC, Manotas-Duque DF, Rivera-Cadavid L, et al. (2018) Operational Risk Prioritization in Supply Chain with 3PL Using Fuzzy-QFD, 91-109.
    [89] Osorio-Gómez JC (2011) Fuzzy QFD for multicriteria decision making-Application example. Prospectiva 9: 22-29.
    [90] Hansen P, Jaumard B, Savard G (1992) New Branch-and-Bound rules for linear bilevel programming. SIAM J Sci Stat Comput 13: 1194-1217. doi: 10.1137/0913069
    [91] Talbi E (2013) Metaheuristics for Bi-level Optimization.
    [92] Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95: 649-655. doi: 10.1016/0377-2217(95)00300-2
  • Reader Comments
  • © 2020 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(3261) PDF downloads(269) Cited by(2)

Article outline

Figures and Tables

Figures(8)  /  Tables(9)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog