A two-stage operation optimization method of integrated energy systems with demand response and energy storage. (1st October 2020)

• Record Type: Journal Article
• Authors: Zhang, Lizhi; Kuang, Jiyuan; Sun, Bo; Li, Fan; Zhang, Chenghui
• Is Part Of: Energy. Volume 208(2020)
• Publication Date: 2020-10-01
• Subjects: Integrated energy system (IES) -- Two-stage operation optimization method -- Demand response (DR) -- Energy storage -- Genetic algorithm (GA) -- Stochastic dynamic programming (SDP); Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118423
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: This paper presents a two-stage operation optimization method of an integrated energy system (IES) with demand response (DR) and energy storage. The proposed method divides the optimal scheduling problem of the IES into two optimization problems, including demand-side and supply-side optimization problems. An interactive mechanism between the customer demand and operation scheme is established. In the first stage, a genetic algorithm (GA) is used to optimize electricity, cooling, and heating demand curves within the comfort requirements of customers. In the second stage, stochastic dynamic programming (SDP) is applied to determine the optimal energy production and storage scheme based on the demand curves generated by GA. The results of the second stage are then fed back to GA to re-optimize the demand curves. The optimization process loops until the optimal demand curves and operation scheme are obtained. The proposed method gives full play to the advantages of GA and SDP, so as to increase the possibility of finding the global optimal solution. Case studies are performed on a hotel in northern China to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed method obtains an efficient and cost-effective operation strategy and reduces the operation cost by 3.6% in comparison with the traditional GA method. The proposed method will help decision-makers determine operation schemes of IESs and can also help consumers to gain more profits. Highlights: A two-stage operation optimization model including demand-side and supply-side optimization models is constructed. Genetic algorithm and stochastic dynamic programming method are organically combined for optimization. Integrated energy system accounts for renewables, demand response, comfort, and energy storage. Proposed method can reduce operation cost by 3.6% in comparison with traditional genetic algorithm.