Co-optimized trading of hybrid wind power plant with retired EV batteries in energy and reserve markets under uncertainties

• Published in: International journal of electrical power & energy systems Vol. 117; no. C; p. 105631
• Main Authors: Zhan, Sen, Hou, Peng, Enevoldsen, Peter, Yang, Guangya, Zhu, Jiangsheng, Eichman, Joshua, Jacobson, Mark Z.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2020; Elsevier
• Subjects: FCR-N market; MILP; POWER TRANSMISSION AND DISTRIBUTION; Retired EV battery-storage system (RESS); Spot market; WIND ENERGY; Retired EV battery-storage system (RESS); MILP; Spot market; FCR-N market
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2019.105631

•A scenario-based stochastic programming is used to cope with the uncertainties.•A profitable solution to repurpose the retired EV batteries is proposed.•A two-stage optimization of sizing and operations is implemented with a MILP model. To be competitive in the electricity markets, various technologies have been reported to increase profits of wind farm owners. Combining battery storage system, wind farms can be operated as conventional power plants which promotes the integration of wind power into the power grid. However, high expenses on batteries keep investors away. Retired EV batteries, fortunately, still have enough capacity to be reused and could be obtained at a low price. In this work, a two-stage optimization of a wind energy retired EV battery-storage system is proposed. The economic performance of the proposed system is examined concerning its participation in the frequency containment normal operation reserve (FCR-N) market and the spot market simultaneously. To account uncertainties in the wind farm output, various electricity market prices, and up/down regulation status, a scenario-based stochastic programming method is used. The sizing of the equipment is optimized on top of daily operations of the hybrid system which formulates a mixed-integer linear programming (MILP) problem. Scenarios are generated with the Monte Carlo simulation (MCS) and Roulette Wheel Mechanism (RWM), which are further reduced with the simultaneous backward method (SBM) to increase computational efficiency. A 21 MW wind farm is selected as a case study. The optimization results show that by integrating with a retired EV battery-storage system (RESS) and a bi-directional inverter, the wind farm can increase its profits significantly when forwarding bids in both of the aforementioned electricity markets.