Optimal scheduling of a hybrid AC/DC multi-energy microgrid considering uncertainties and Stackelberg game-based integrated demand response. (November 2022)

• Record Type: Journal Article
• Authors: Cai, Yao; Lu, Zhigang; Pan, Yao; He, Liangce; Guo, Xiaoqiang; Zhang, Jiangfeng
• Is Part Of: International journal of electrical power & energy systems. Volume 142:Part B(2022)
• Publication Date: 2022-11
• Subjects: Hybrid AC/DC multi-energy microgrid -- Optimal scheduling -- Uncertainties -- Stackelberg game -- Integrated demand response; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108341
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A novel hybrid AC/DC MEMG structure with the employment of SST is designed. The Stackelberg game-based IDR considering different DR behaviors is integrated into the scheduling model. The cooperation of TARO method and reserve allocation is proposed to handle the uncertainties of RESs and loads. A solution method using C&CG algorithm and CPLEX solver is designed to solve the two-level scheduling model. Abstract: The emergence of solid-state transformers (SSTs) enriches the system structure and operation mode of the multi-energy microgrid (MEMG). This paper depicts a novel hybrid AC/DC MEMG structure based on a three-stage SST, and proposes a two-level optimal scheduling model considering generation and load uncertainties, as well as Stackelberg game-based integrated demand response. In the upper level model for the energy hub operator (EHO), the pricing strategy and equipment dispatch are optimized to maximize profit, and the two-stage adaptive robust optimization and reserve allocation are used to address the uncertainties of renewable generation and loads, respectively. In the lower level model for the demand response aggregator (DRA), the response strategy of different loads, including shiftable, adjustable and replaceable loads, is optimized for surplus maximization. To solve the proposed scheduling model, the two-level optimization problem is transformed to a single-level min–max-min problem according to Karush-Kuhn-Tucker conditions, then it is divided into two subproblems and solved by the column-and-constraint generation algorithm and CPLEX solver. Furthermore, a case study is carried out to validate the effectiveness of the proposed model, and it shows that the total benefit of EHO and DRA can be improved by 5.75% due to the demand response strategy.