A price decision approach for multiple multi-energy-supply microgrids considering demand response. (15th January 2019)

• Record Type: Journal Article
• Authors: Li, Bei; Roche, Robin; Paire, Damien; Miraoui, Abdellatif
• Is Part Of: Energy. Volume 167(2019)
• Page Start: 117
• Page End: 135
• Publication Date: 2019-01-15
• Subjects: Price -- Multi-energy-system -- Hydrogen storage system -- Demand response -- Optimization -- Neural network; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.10.189
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Multi-energy-supply microgrids covering different types of demands are expected to play an important role in smart grids. Local generation, energy storage systems, and renewable energy sources can form load service entities, which can provide ancillary services to the utility grid and to consumers. On the other hand, microgrids can also sell energy to load service entities or the utility grid to obtain profits. But how the load service entities can decide the electricity selling price to multiple microgrids, and how the microgrids can decide the electricity selling price to load service entities are problems. In this paper, we present a price decision method for multiple microgrids considering demand response. Mixed integer linear programming is used to control the operation of each microgrid, and is also used to operate the load service entities. A genetic algorithm is used to search for the best price for each microgrid and the load service entities. The combined method is deployed in a decentralized way, namely, each microgrid runs its own operation problem. The simulation results show that the new searched price works better than a basic time of use price, which can reduce the operation cost of the whole system. The searching method is compared with a method based on the Cournot model. At last, a large system is tested, in which 4 load service entities, 16 microgrids and an IEEE30-node network are considered. In order to reduce the searching time, a neural network model is presented to estimate the operation of the whole system. Based on the neural network model, the prices are obtained, and the results show that the prices based on neural network model are better than with the time of use price. Highlights: A decentralized price decision approach for UG-LSEs-MGs is presented. MILP is used to control the operation of MG based on DR and the operation of LSE. MGs/LSEs can buy and sell electricity to LSEs/MGs. GA is used in ISO to search for the best price for LSE and each MG. A neural network model to estimate the performance of the whole system is proposed.