Bi-level energy management and pricing for community energy retailer incorporating smart buildings based on chance-constrained programming

Abstract

To explore the interaction between community energy retailers and smart building (SB) consumers in the energy market, this paper proposes a bi-level energy management and pricing method for the community energy retailer incorporating SBs based on chance-constrained programming. Aiming at maximizing the profits of both retailers and consumers, a Stackelberg game model is established. At the upper level, the community energy retailer maximizes its profit by optimizing offering prices while ensuring the secure operation of the community distribution system. At the lower level, based on the detailed thermal dynamic model of buildings, consumers flexibly adjust the air conditioning’s energy consumption according to the offering price provided by the retailer to minimize the operational cost of SBs. Then, the chance-constrained programming is exploited to consider uncertainties of market prices, and the proposed bi-level optimization model is transformed into a mixed-integer linear programming problem using Karush-Kuhn-Tucker conditions, strong duality theorem, linearization, and deterministic transformations of chance constraints for solving. Numerical results show that the proposed approach can combine the flexibility of both supply and demand to benefit both retailers and customers. In addition, the economic impact of different confidence levels on the retailer and consumers is analyzed.