Research article Topical Sections

Evaluation of renewable energy deployment scenarios for building energy management

  • Received: 09 April 2016 Accepted: 28 September 2016 Published: 10 October 2016
  • According to International Energy Agency (IEA), 35% of total energy is consumed in buildings. Proper management of building energy would effectively improve fossil fuel consumption by integrating Renewable Energy Sources (RES). This paper introduces novel methodology to deploy Renewable Energy Sources (RES) for buildings. The developed methodology composed of two steps: evaluation of RES deployment to a building and evaluation of load-generation scenarios in buildings. At first, the proposed algorithm obtains information about building facilities and structure that can be used to deploy PV, wind turbine and gas generator. Solar and wind profiles are analyzed and integrated with building energy model, which is used to evaluate potential energy generation scenarios. The second step includes the evaluation of different supply—generation scenarios based on load profiles and solar and wind generation profiles. This step will include the minimization of energy loss and will seek effective utilization of generated energy. A case study of domestic home in Toronto, Canada, was chosen as an example to demonstrate the proposed algorithm. Results are shown and analyzed which demonstrate the different scenarios generated for the selected case study based on loads and generation profiles.

    Citation: Hossam A. Gabbar, Ahmed Eldessouky, Jason Runge. Evaluation of renewable energy deployment scenarios for building energy management[J]. AIMS Energy, 2016, 4(5): 742-761. doi: 10.3934/energy.2016.5.742

    Related Papers:

  • According to International Energy Agency (IEA), 35% of total energy is consumed in buildings. Proper management of building energy would effectively improve fossil fuel consumption by integrating Renewable Energy Sources (RES). This paper introduces novel methodology to deploy Renewable Energy Sources (RES) for buildings. The developed methodology composed of two steps: evaluation of RES deployment to a building and evaluation of load-generation scenarios in buildings. At first, the proposed algorithm obtains information about building facilities and structure that can be used to deploy PV, wind turbine and gas generator. Solar and wind profiles are analyzed and integrated with building energy model, which is used to evaluate potential energy generation scenarios. The second step includes the evaluation of different supply—generation scenarios based on load profiles and solar and wind generation profiles. This step will include the minimization of energy loss and will seek effective utilization of generated energy. A case study of domestic home in Toronto, Canada, was chosen as an example to demonstrate the proposed algorithm. Results are shown and analyzed which demonstrate the different scenarios generated for the selected case study based on loads and generation profiles.


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    [1] International Energy Agency (2013) Transition to Sustainable Buildings: Strategies and Opportunities to 2050.
    [2] Canada's Energy Future 2016, Energy Supply and Demand Projections to 2040, Executive Summary. National Energy Board, 2016. Available from: www.neb-one.gc.ca/energyfutures.
    [3] Schultz CC, PE, CEM, CXA, and Scott G. (2011) Energy conservation in Existing Buildings. Engineered Syst 28: 34-40.
    [4] Difsa K, Bennstamb M, Trygga L, et al. (2010) Energy conservation measures in buildings heated by district heating—A local energy system perspective. Energy 35: 3194-3203. doi: 10.1016/j.energy.2010.04.001
    [5] Smeds J, Wall M (2007) Enhanced energy conservation in houses through high performance design. Energ Buildings 39: 273-278. doi: 10.1016/j.enbuild.2006.07.003
    [6] Kim D, AP L (2010) Optimizing Cost Effective Energy Conservation Measures For Building Envelope. Energ Eng 107: 70-80.
    [7] Pitts A, Saleh JB (2007) Potential for energy saving in building transition spaces. Energ Buildings 39: 815-822. doi: 10.1016/j.enbuild.2007.02.006
    [8] Mousa M, Akash B (2001) Some prospects of energy saving in buildings. Energ Convers Manage 42: 1307-1315. doi: 10.1016/S0196-8904(00)00140-0
    [9] Chedwal R, Mathur J, Das Agarwal G, et al. (2015) Energy saving potential through Energy Conservation Building Code and advance energy efficiency measures in hotel buildings of Jaipur City, India. Energ Buildings 92: 282-295. doi: 10.1016/j.enbuild.2015.01.066
    [10] Busch JF, Pont PD, Chirarattananon S (1993) Energy-efficient lighting in Thai commercial buildings. Energy 13: 197-210.
    [11] Saberbari E, Saboori H (2014) Net-Zero Energy Building Implementation through a Grid-Connected Home Energy Management System. The 19th Electrical Power Distribution Conference (EPDC2014), 35-41
    [12] Hernandez P, Kenny P (2010) From net energy to zero energy buildings: Defining life cycle zero energy buildings. Energ Buildings 42: 815-821. doi: 10.1016/j.enbuild.2009.12.001
    [13] Marszal A, Heiselberg P, Bourrelle J, et al. (2011) Zero Energy Building—A review of definitions and calculation methodologies. Energ Buildings 43: 971-979. doi: 10.1016/j.enbuild.2010.12.022
    [14] Givler T, Lilienthal P (2005) Using HOMER software, NREL,s Micropower Optimization Model, to Explore the Role of Gen-sets in Small Solar Power Systems. Case Study: Sri Lanka, National.
    [15] Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001) EnergyPlus: Creating a new-generation building energy simulation program. Energ Buildings 33: 319-331. doi: 10.1016/S0378-7788(00)00114-6
    [16] Mishra A, Irwin D, Shenoy P, et al. (2013) GreenCharge: Managing Renewable Energy in Smart Buildings. IEEE J Selected Areas Commun 31: 1281-1293. doi: 10.1109/JSAC.2013.130711
    [17] Runge J, Gabbar H (2014) Solar Windows Control System for an Apartment Building in Toronto with Battery Storage. International Conference on Power Engineering and Renewable Energy (ICPERE), Bali.
    [18] Gabbar H (2009) Engineering design of green hybrid energy production and supply chains. Environ Modell Softw 24: 423-435. doi: 10.1016/j.envsoft.2008.08.006
    [19] Gabbar H, Eldessouky A (2015) Energy Semantic Network for Building Energy Conservation and Management, Intelligent Industrial Systems. DOI 10.1007/s40903-015-0023-8.
    [20] Hussain S, Gabbar H, Musharavati F, et al. (2013) Key performance indicators (KPIs) for evaluation of energy conservation in buildings. Smart Energy Grid Engineering (SEGE), 2013 IEEE International Conference, Oshawa.
    [21] Feed-in Tariff Program, Independent Electricity System Operator (IESO) (2009) Available from: http://fit.powerauthority.on.ca/fit-program.
    [22] Zidan B, Shaaban M, El-Saadany E (2013) AppLong-term multi-objective distribution network planning by DG allocation and feeders’ reconfiguration. Electr Pow Syst Res 105: 95-104. doi: 10.1016/j.epsr.2013.07.016
    [23] Zou K, Agalgaonkar AP, Muttaqi KM, et al. (2012) Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties. IEEE T Sust Energ 3: 112-123.
    [24] Spertino F, Di Leo P, Ilie I, et al. (2012) DFIG equivalent circuit and mismatch assessment between manufacturer and experimental power-wind speed curves. Renew Energ 48: 333-343. doi: 10.1016/j.renene.2012.01.002
    [25] wind power program. Available from: http://www.wind-power-program.com/turbine_ characteristics.htm.
    [26] Paiva L, Rodrigues C, Palma J (2014) Determining wind turbine power curves based on operating conditions. Wind Energy 17: 1563-1575. doi: 10.1002/we.1651
    [27] Weather Data, Wind Speed and Solar Radiation Data. University of Toronto, Department of Geography (2012) Available from: http://www.utm.utoronto.ca/geography/resources/ meteorological-station/weather-data.
    [28] Abdelsalam A, El-saadany E (2013) Probabilistic Approach for Optimal Planning of Distributed Generators with Controlling Harmonic Distortions. IET Generat Transm D 7: 1105-1115. doi: 10.1049/iet-gtd.2012.0769
    [29] Thevenard D, Pelland S (2013) ESTIMATING THE UNCERTAINTY IN LONG-TERM PHOTOVOLTAIC YIELD PREDICTIONS. Sol Energ 91: 432-445. doi: 10.1016/j.solener.2011.05.006
    [30] Karki R, Hu P, Billinton R (2006) A Simplified Wind Power Generation Model. IEEE T Energ Convers 21: 533-540. doi: 10.1109/TEC.2006.874233
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