Optimal Stochastic Deployment of Heterogeneous Energy Storage in a Residential Multi-Energy Microgrid with Demand-Side Management

Zhengmao Li, Yan Xu, Xue Feng, Qiuwei Wu

Research output: Contribution to journalJournal articleResearchpeer-review


The optimal deployment of heterogeneous energy storage (HES), which mainly consists of electrical and thermal energy storage, is essential for increasing the holistic energy utilization efficiency of multi-energy systems. Consequently, this paper proposes a risk-averse method for HES deployment in a residential multi-energy microgrid (RMEMG), considering the diverse uncertainties and multi-energy demand-side management (DSM). Apart from the HES size, 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 (RES), power demands, outdoor temperature, and residential hot water needs, the multi-stage adaptive stochastic optimization (SO) 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.
Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Issue number99
Pages (from-to)1-1
Number of pages1
Publication statusAccepted/In press - 2020


  • Heterogeneous energy storage (HES)
  • Risk-averse deployment
  • Residential multi-energy microgrid (RMEMG)
  • Multi-energy demand-side management (DSM)
  • Multi-stage adaptive stochastic optimization

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