Co-optimized bidding strategy of an integrated wind-thermal-photovoltaic system in deregulated electricity market under uncertainties

Clean Energy sources, such as wind and solar, have become an inseparable part of today's power grids. However, the intermittent nature of these sources has become the greatest challenge for their owners, which makes the bidding in the restructured electricity market more challenging. Hence, the main goal of this paper is to propose a novel multi-objective bidding strategy framework for a wind-thermal-photovoltaic system in the deregulated electricity market for the first time. Contrary to the existing bidding models, in the proposed model, two objective functions are taken into account that the first one copes with profit maximization while the second objective function concerns with emission minimization of thermal units. The proposed multi-objective optimization problem is solved using the weighted sum approach. The uncertainties associated with electricity market prices and the output power of renewable energy sources are characterized by a set of scenarios. Ultimately, in order to select the best-compromised solution among the obtained Pareto optimal solutions, two diverse approaches are applied. The proposed bidding strategy problem is being formulated and examined in various modes of joint and disjoint operation of dispatchable and non-dispatchable energy sources. Simulation results illustrate that not only the integrated participation of these resources increases the producer's expected profit, but also decreases the amount of the produced pollution by the thermal units.© 2019 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model

Renewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In the current paper, a three-stage stochastic multi-objective offering framework based on mixed-integer programming formulation for a wind-thermal-energy storage generation company in the energy and spinning reserve markets is proposed. The commitment decisions of dispatchable energy sources, the offering curves of the generation company in the energy and spinning reserve markets, and dealing with energy deviations in the balancing market are the decisions of the proposed three-stage offering strategy problem, respectively. In the suggested methodology, the participation model of the energy storage system in the spinning reserve market extends to both charging and discharging modes. The proposed framework concurrently maximizes generation company’s expected profit and minimizes the expected emission of thermal units applying lexicographic optimization and hybrid augmented-weighted ∊-constraint method. In this regard, the uncertainties associated with imbalance prices and wind power output as well as day-ahead energy and spinning reserve market prices are modeled via a set of scenarios. Eventually, two different strategies, i.e., a preference-based approach and emission trading pattern, are utilized to select the most favored solution among Pareto optimal solutions. Numerical results reveal that taking advantage of spinning reserve market alongside with energy market will substantially increase the profitability of the generation company. Also, the results disclose that spinning reserve market is more lucrative than the energy market for the energy storage system in the offering strategy structure.© 2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed