Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology

Abstract

Developing hybrid renewable energy multi-energy system at the community level (CMES) is crucial for sustainable development and society. There are considerable resources in community users that can be activated by demand-side management (DSM) and combined with CMES for smart and clean energy supplies. This work focuses on optimal multi-timescale CMES management at energy and power levels considering source-load interaction. Firstly, the flexible community building and EV loads are comprehensively modeled, including the subjective psychological factors of community users' willingness to participate in DSM activities. Then, a bilevel collaborative strategy is proposed to manage CMES. The upper-level unites all schedulable resources to actively control CMES and flexible loads to achieve optimal multi-energy balances. The lower-level forms hybrid virtual electrical storage to passively adapt to instantaneous power rebound/decline events. These two levels are bridged through chance-constrained programming, achieving risk avoidance. Finally, the performances, including community users, the grid, and CMES itself, are evaluated, as well as influences of confidence level and user attitudes (in DSM activities). Simulation results show that the proposed strategy with a high confidence level avoids the lack of flexibility and load interruption and reduces cost and grid interaction fluctuations by 5.75 % and 59.48 %, respectively. DSM can support the efficient and flexible operation of CMES by providing equivalent energy storage with a capacity/power of up to 657 kWh/350 kW. On the other hand, the high subsidy price reduces CMES's interests, and users' benefits will be weakened accordingly, while users actively involved in DSM create a win-win situation with CMES.