Optimal Stochastic Deployment of Heterogeneous Energy Storage in a Residential Multienergy Microgrid With Demand-Side Management

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 2; pp. 991 - 1004
• Main Authors: Li, Zhengmao, Xu, Yan, Feng, Xue, Wu, Qiuwei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Distributed generation; Energy conversion efficiency; Energy management; Energy storage; Energy utilization; Heterogeneous energy storage (HES); Integer programming; Investment; Linear programming; Microgrids; Mixed integer; multienergy demand-side management (DSM); multistage adaptive stochastic optimization; Optimization; Planning; Renewable energy sources; Residential energy; residential multienergy microgrid (RMEMG); Risk; risk-averse deployment; Smart buildings; Stochastic processes; Thermal energy; Uncertainty
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.2971227

The optimal deployment of heterogeneous energy storage (HES), mainly consisting of electrical and thermal energy storage, is essential for increasing the holistic energy utilization efficiency of multienergy systems. Consequently, this article proposes a risk-averse method for HES deployment in a residential multienergy microgrid (RMEMG), considering the diverse uncertainties and multienergy demand-side management (DSM). Apart from the HES size and location planning, its optimal investment phase is also determined by maximizing the system equivalent daily profit (EDP) and minimizing the risk. To handle the system uncertainties from renewable energy sources, power demands, outdoor temperature, and residential hot water needs, the multistage adaptive stochastic optimization approach is utilized. Then, through the constraint linearization and stochastic scenario sampling, the original nonlinear deployment model is converted to a mixed-integer linear programming one and tested on an IEEE 33-bus distribution network based RMEMG. The effectiveness of the proposed method is verified by comparing it with the existing practices. The comparison results indicate that the proposed risk-averse deployment method can effectively increase the system EDP and more immune to the uncertainties. Besides, this method can be practically applied for the emerging RMEMGs, such as smart buildings, intelligent homes, etc., which get long-term DSM contracts.