Risk-constrained optimal bidding and scheduling for load aggregators jointly considering customer responsiveness and PV output uncertainty

• Main Authors: Hongtao Shen, Peng Tao, Ruiqi Lyu, Peng Ren, Xinxin Ge, Fei Wang
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Energy Reports
• Subjects: Load aggregator; Demand response; Bidding; Real-time pricing; Uncertainty
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352484721004868

Recent years have witnessed a growing trend in the participation of renewable energy on the generation side and relatively high peak loads on the demand side, which makes it gradually challenging for traditional methods concentrating separately on the generation side to maintain system balance. Load resources, with potentialities to provide faster and more economical responses to balance signals, are able to make contributions to system balance through demand response(DR) programs in addition. With the reformation and development of the electricity market, load aggregators(LAs) pear as representatives of small-scale customers and generations to meet response limitations and participate in DR programs. The LA in this paper, which aggregates residential customers and a PV system with battery energy storage(BES) units, balances the power by optimal scheduling and bidding in Day-ahead(DA) and real-time(RT) markets based on real-time electricity price(RTP). And the objective of this paper is to maximize the profits of LA. A majority of papers use deterministic methods for the modeling of renewable generations and residential loads. However, influenced by multiple factors, the actual renewable outputs and residential responsive loads towards real-time electricity prices are unavoidably uncertain. These uncertainties bring risks to LA’s scheduling and bidding strategies, which results in the reduction of LA’s profits. A stochastic model based on the scenario generation method is adopted to reflect the uncertainties of customers’ responsive loads and the PV system’s outputs. The objective profit function turns out to be a risk function influenced by uncertain factors and the risk control method conditional risk at value(CVaR) is integrated to obtain optimal solutions for this maximization problem. Case studies have verified the effectiveness of the proposed strategy.