Optimal Sizing of PV and BESS for a Smart Household Considering Different Price Mechanisms

The study on the sizing of renewable energy generation systems and energy storage systems together in a household considering different price mechanisms can further promote the development of the home energy management system (HEMS). In this paper, a HEMS expressed as a bi-level model is provided to investigated capacity allocation strategy of the photovoltaic (PV) and battery energy storage system (BESS) in a smart household considering: 1) the impact of electricity price mechanisms which include the time-of-use pricing (TOU), the real-time pricing (RTP), and the stepwise power tariff (SPT); 2) the effect of subsidies of PV; and 3) the uncertainty in the PV output and seasonal load profiles. Then, the hybrid approach which combines the cataclysmic genetic algorithm and the DICOPT solver in GAMS is employed to find an optimal solution. Finally, six cases with different price mechanisms and approaches, as well as the sensitivity analysis of optimal solution to subsidies are presented. Results indicate that, with the subsidies, only the PV system needs to be equipped in a household under the SPT, while the PV system and BESS need to be equipped together under the RTP and TOU. Only when the subsidies of PV reach a certain level will the installation of PV be considered.

[1]  Panos M. Pardalos,et al.  A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches , 2018, Appl. Soft Comput..

[2]  Mosayeb Bornapour,et al.  Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties , 2016 .

[3]  Hamidreza Zareipour,et al.  Home energy management incorporating operational priority of appliances , 2016 .

[4]  Yan Gao,et al.  Real-time pricing scheme based on Stackelberg game in smart grid with multiple power retailers , 2017, Neurocomputing.

[5]  Farhad Shahnia,et al.  Plug in electric vehicles in smart grids: Energy management , 2015 .

[6]  J. Kleissl,et al.  Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems , 2013 .

[7]  Hsiao-Dong Chiang,et al.  Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data , 2017, IEEE Transactions on Power Delivery.

[8]  Yongqing Xiong,et al.  Government subsidies for the Chinese photovoltaic industry , 2016 .

[9]  G. Shafiullah,et al.  An analysis of the time of use electricity price in the residential sector of Bangladesh , 2017 .

[10]  Yonghong Kuang,et al.  Smart home energy management systems: Concept, configurations, and scheduling strategies , 2016 .

[11]  Olivier Deblecker,et al.  Optimal design and techno-economic analysis of an autonomous small isolated microgrid aiming at high RES penetration , 2016 .

[12]  Yongjun ZHANG,et al.  Energy hub modeling to minimize residential energy costs considering solar energy and BESS , 2017 .

[13]  Hedayat Saboori,et al.  Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels , 2017 .

[14]  Abhisek Ukil,et al.  Hybrid Optimization for Economic Deployment of ESS in PV-Integrated EV Charging Stations , 2018, IEEE Transactions on Industrial Informatics.

[15]  Xiaofeng Yin,et al.  Optimal battery sizing of smart home via convex programming , 2017 .

[16]  Reza Hemmati,et al.  Technical and economic analysis of home energy management system incorporating small-scale wind turbine and battery energy storage system , 2017 .

[17]  Peng Li,et al.  Optimal operation modes of photovoltaic-battery energy storage system based power plants considering typical scenarios , 2017 .

[18]  Yongjun Zhang,et al.  Real-time optimal reactive power dispatch using multi-agent technique , 2004 .

[19]  Xueying Yu,et al.  How real time pricing modifies Chinese households’ electricity consumption , 2017 .

[20]  Canbing Li,et al.  Multiobjective Model of Time-of-Use and Stepwise Power Tariff for Residential Consumers in Regulated Power Markets , 2018, IEEE Systems Journal.

[21]  Olivier Deblecker,et al.  Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule , 2018 .

[22]  Canbing Li,et al.  A New Stepwise Power Tariff Model and Its Application for Residential Consumers in Regulated Electricity Markets , 2013, IEEE Transactions on Power Systems.

[23]  Kenji Asano,et al.  Effects of local government subsidy on rooftop solar PV in Japan , 2017, 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA).

[24]  Joao P. S. Catalao,et al.  A new perspective for sizing of distributed generation and energy storage for smart households under demand response , 2015 .

[25]  Hamidreza Zareipour,et al.  Home energy management systems: A review of modelling and complexity , 2015 .

[26]  Sun Luping,et al.  The Stepwise Pricing Mechanism Research of Residents’ Living Power , 2011 .

[27]  Oğuz Solyalı,et al.  Optimal sizing of stand-alone photovoltaic systems in residential buildings , 2017 .

[28]  Kyung-Bin Song,et al.  An Optimal Power Scheduling Method for Demand Response in Home Energy Management System , 2013, IEEE Transactions on Smart Grid.

[29]  Vincent W. S. Wong,et al.  Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid , 2013, IEEE Transactions on Smart Grid.

[30]  Bo Zhao,et al.  Operation Optimization of Standalone Microgrids Considering Lifetime Characteristics of Battery Energy Storage System , 2013, IEEE Transactions on Sustainable Energy.

[31]  Elias B. Kosmatopoulos,et al.  A roadmap towards intelligent net zero- and positive-energy buildings , 2011 .