Withholding strategies for a conventional and wind generation portfolio in a joint energy and reserve pool market: A gaming-based approach

• Published in: Computers & chemical engineering 2020-03, Vol.134, p.106692, Article 106692
• Main Authors: Tsimopoulos, Evangelos G., Georgiadis, Michael C.
• Format: Article
• Language: English
• Subjects: Capacity withholding; Conventional and wind generation portfolio; Energy dispatch; Energy-only markets; LMP’s; MPEC; Reserve deployment; Wind uncertainty
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2019.106692

•Novel bi-level optimization framework for a strategic producer whose generation portfolio consists of conventional and wind power production.•Initial bi-level model is efficiently reduced into an MILP model.•Proposed algorithm derives optimal scheduled thermal and wind energy as well as reserve deployments.•It also provides optimal offers based on the endogenous formation of local marginal prices.•Two applications illustrate the achievement of higher profits for the strategic producer. This work considers a strategic producer whose generation portfolio consists of conventional and wind power production. Based on the single leader-follower game, a bi-level complementarity model is constructed to derive optimal capacity withholding strategies for this portfolio in a pool-based market. The upper level of the model represents the maximization of the strategic producer’s expected profits while the lower level represents the security-constrained economic dispatch conducted by the independent system operator. The market clearing scheme refers to energy-only markets optimizing jointly scheduled energy and reserves through a two-stage stochastic programming. The first stage illustrates the day-ahead market clearing and the second stage illustrates the balancing market clearing taking into consideration the wind generation uncertainty. With the use of the Karush-Kuhn-Tacker optimality conditions the initial bi-level model is recast into a mathematical programming with equilibrium constraints model which is then reduced into an equivalent mixed integer linear programming using the strong duality theorem and disjunctive constraints. The proposed algorithm derives optimal scheduled thermal and wind energy as well as reserve deployments. It also provides optimal offers based on the endogenous formation of local marginal prices under network constraints and different wind energy penetration levels.