Abstract
Existing studies have developed some advanced building side controls that enable renewable energy sharing and that aim to optimize building-cluster-level performance via regulating the energy storage charging/discharging. However, the flexible demand shifting ability of electric vehicles is rarely considered. For instance, the electric vehicle charging will usually start once they are plugged into charging stations. But, in such charging period the renewable generation may be insufficient to cover the EV charging load, leading to grid electricity imports. Consequently, the building-cluster-level performance is not optimized. Therefore, this study proposes a coordinated control of building prosumers for improving the cluster-level performance, by making use of energy sharing and storage capability of electricity batteries in both buildings and EVs. An EV charging/discharging model is first developed. Then, based on the predicted future 24 h electricity demand and renewable generation data, the coordinated control first considers the whole building cluster as one ‘integrated’ building and optimizes its operation as well as the EV charging/discharging using genetic algorithm. Next, the operation of individual buildings in the future 24 h is coordinated using nonlinear programming. For validation, the developed control has been tested on a real building cluster in Ludvika, Sweden. The study results show that the developed control can increase the cluster-level daily renewable self-consumption rate by 19% and meanwhile reduce the daily electricity bills by 36% compared with the conventional controls.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agency USIE (2010) United States—Policies and legislation. Available at http://www.ieahev.org/by-country/united-states-policy-and-legislation/. Accessed on 25 Oct 2019
Agency SE (2017) Förslag till strategi för ökad användning av solel [Draft strategy for increased use of solar electricity]. Available at https://energimyndigheten.a-w2m.se/Home.mvc?ResourceId=5599. Accessed on 19 Dec 2019
Barone G, Buonomano A, Calise F, Forzano C, Palombo A (2019) Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies. Renew Sustain Energy Rev 101:625–648
Cao X, Dai X, Liu J (2016) Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Build 128:198–213
Cooper AI, Schefter KE (2018) Electric vehicle sales forecast and the charging infrastructure required through 203. Edison Electric Institute
Dallinger D, Gerda S, Wietschel M (2013) Integration of intermittent renewable power supply using grid-connected vehicles—A 2030 case study for California and Germany. Appl Energy 104:666–682
Fachrizal R, Munkhammar J (2020) Improved photovoltaic self-consumption in residential buildings with distributed and centralized smart charging of electric vehicles. Energies 13
Fan C, Huang G, Sun Y (2018) A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level. Energy 164:536–549
Ferroamp (2018) The energyhub system. Available at https://static.ferroamp.com/files/brochure/en/Ferroamp%20Brochure%20English%202018.pdf. Accessed on 10 May 2019
Gao D-C, Sun Y (2016) A GA-based coordinated demand response control for building group level peak demand limiting with benefits to grid power balance. Energy Build 110:31–40.
Geth F, Willekens K, Clement K, Driesen J, Breucker SD (2010) Impact-analysis of the charging of plug-in hybrid vehicles on the production park in Belgium. In: Melecon 2010—2010 15th IEEE mediterranean electrotechnical conference, pp 425–430
Huang P, Sun Y (2019a) A clustering based grouping method of nearly zero energy buildings for performance improvements. Appl Energy 235:43–55
Huang P, Sun Y (2019b) A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level. Energy 174:911–921
Huang P, Sun Y (2019c) A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty. Renew Energy 134:215–227
Huang P, Huang G, Sun Y (2018a) Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements. Appl Energy 213:486–498
Huang P, Wu H, Huang G, Sun Y (2018b) A top-down control method of nZEBs for performance optimization at nZEB-cluster-level. Energy 159:891–904
Huang P, Lovati M, Zhang X, Bales C, Hallbeck S, Becker A et al (2019) Transforming a residential building cluster into electricity prosumers in Sweden: Optimal design of a coupled PV-heat pump-thermal storage-electric vehicle system. Appl Energy 255:113864
Kara EC, Macdonald JS, Black D, Bérges M, Hug G, Kiliccote S (2015) Estimating the benefits of electric vehicle smart charging at non-residential locations: a data-driven approach. Appl Energy 155:515–525
Klein S, Beckman W, Mitchell J, Duffie J, Duffie N, Freeman T et al (2004) TRNSYS 16–A TRaNsient system simulation program, user manual. University of Wisconsin-Madison, Solar Energy Laboratory Madison
Lovati M, Zhang X, Huang P, Olsmats C, Maturi LJB (2020) Optimal simulation of three peer to peer (P2P) business models for individual PV prosumers in a local electricity market using agent-based modelling. Buildings 10:138
Lu Y, Wang S, Sun Y, Yan C (2015) Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming. Appl Energy 147:49–58
Luthander R, Widén J, Munkhammar J, Lingfors D (2016) Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment. Energy 112:221–231
Merrill L, Bridle R, Klimscheffskij M, Tommila P, Lontoh L, Sharma S et al (2017) Making the switch: From fossil fuel subsidies to sustainable energy. Nordic Council of Ministers
Merten J, Guillou H, Ha L, Quenard M, Wiss O, Barruel F (2012) Solar mobility: Two years of practical experience charging ten cars with solar energy. In proceedings of the 5th International conference on integration of renewable and distributed energy resources, Berlin, Germany (pp. 4–6)
Odonkor P, Lewis K (2015) Adaptive operation decisions in net zero building clusters. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 57076:pp. V02AT03A029. American Society of Mechanical Engineers
Palacios-Garcia EJ, Chen A, Santiago I, Bellido-Outeiriño FJ, Flores-Arias JM, Moreno-Munoz A (2015) Stochastic model for lighting’s electricity consumption in the residential sector. Impact of energy saving actions. Energy Build 89:245–259
Palacios-Garcia EJ, Moreno-Munoz A, Santiago I, Flores-Arias JM, Bellido-Outeirino FJ, Moreno-Garcia IM (2018) A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector. Energy 144:1080–1091
Prasad A, Dusparic I (2019) Multi-agent deep reinforcement learning for zero energy communities. In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) (pp. 1-5). IEEE
Salom J, Widén J, Candanedo J, Sartori I, Voss K, Marszal A (2011) Understanding net zero energy buildings: evaluation of load matching and grid interaction indicators. In: Proceedings of building simulation, pp 2514–2521
Shariful Islam M, Mithulananthan N, Quoc HD (2019) Coordinated EV charging for correlated EV and grid loads and PV output using a novel, correlated, probabilistic model. Int J Electr Power Energy Syst 104:335–348
Shen L, Li Z, Sun Y (2016) Performance evaluation of conventional demand response at building-group-level under different electricity pricings. Energy Build 128:143–154
Song C, Qu Z, Blumm N, Barabasi A-L (2010) Limits of Predictability in human mobility. Science 327:1018–1021
Sun Y, Huang G, Xu X, Lai AC-K (2018) Building-group-level performance evaluations of net zero energy buildings with non-collaborative controls. Appl Energy 212:565–576
Taşcıkaraoğlu A (2018) Economic and operational benefits of energy storage sharing for a neighborhood of prosumers in a dynamic pricing environment. Sustain Cities Soc 38:219–229
Usman M, Knapen L, Yasar A-U-H, Vanrompay Y, Bellemans T, Janssens D et al (2016) A coordinated framework for optimized charging of EV fleet in smart grid. Procedia Comput Sci 94:332–339
Ustun TS, Zayegh A, Ozansoy C (2013) Electric vehicle potential in Australia Its Impact on smart grids. Ind Electron Mag IEEE 7:15–25
van der Kam M, van Sark W (2015) Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study. Appl Energy 152:20–30
Widén J, Wäckelgård E (2010) A high-resolution stochastic model of domestic activity patterns and electricity demand. Appl Energy 87:1880–1892
Xylia M, Silveira SJEP (2017) On the road to fossil-free public transport: The case of Swedish bus fleets. Energy Policy 100:397–412
Zhang S, Huang P, Sun Y (2016) A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties. Energy 94:654–665
Zhang B, Li Q, Wang L, Feng W (2018) Robust optimization for energy transactions in multi-microgrids under uncertainty. Appl Energy 217:346–360
Zhang S, Cheng Y, Olaide Oladokun M, Wu Y, Lin Z (2020) Improving predicted mean vote with inversely determined metabolic rate. Sustain Cities Soc 53:101870
Zhao Y, Lu Y, Yan C, Wang S (2015) MPC-based optimal scheduling of grid-connected low energy buildings with thermal energy storages. Energy Build 86:415–426
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Huang, P., Zhang, X. (2021). Genetic Algorithm for a Coordinated Control to Improve Performance for a Building Cluster with Energy Storage, Electric Vehicles, and Energy Sharing. In: Zhang, X. (eds) Data-driven Analytics for Sustainable Buildings and Cities. Sustainable Development Goals Series. Springer, Singapore. https://doi.org/10.1007/978-981-16-2778-1_15
Download citation
DOI: https://doi.org/10.1007/978-981-16-2778-1_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2777-4
Online ISBN: 978-981-16-2778-1
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)