Many-criteria optimality of coordinated demand response with heterogeneous households. (15th September 2020)

• Record Type: Journal Article
• Authors: Zhou, Bin; Cao, Yingping; Li, Canbing; Wu, Qiuwei; Liu, Nian; Huang, Sheng; Wang, Huaizhi
• Is Part Of: Energy. Volume 207(2020)
• Publication Date: 2020-09-15
• Subjects: Coordination -- Demand response -- Multi-house energy management -- Many-criteria optimality -- Smart neighborhood -- Transformer; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118267
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper proposes a bilevel multi-house energy management (MHEM) framework to coordinate the residential demand response (DR) of heterogeneous households based on many-criteria optimality. In the upper level, the loss of life (LOL) cost of transformers is formulated into the DR cost model, and a stochastic scheduling is implemented to determine the optimum amount of transformer load deferment and curtailment. The lower level aims to optimally allocate the transformer load from the aggregator to individual households, and a many-criteria DR optimality model is proposed to maximize the multi-house benefits from DR while achieving coordination of the DR participation. Furthermore, a hypercube spatial transformation based classification and sorting scheme is developed to form an evolutionary many-objective (EMO) algorithm in order to solve the many-criteria decision making (MCDM) problem of coordinated DR with numerous participants. The performance of the proposed method was benchmarked and validated on different scaled neighborhood systems over a 24-h scheduling horizon, and comparative results demonstrated its superiority and optimality in solving many-house DR problems. Highlights: A house-aggregator-grid framework is proposed for the coordinated MHEM problem. The LOL cost function of transformer is incorporated into the optimum DR cost model. The multi-house DR coordination is formulated as a many-criteria optimality model. A hyperplane transformation based EMO method is developed to solve the MCDM problem. The peak demand can be flattened with the minimization of total DR scheduling cost.