Abstract
Reliability option (RO) is a capacity adequacy mechanism and it is coupled with electricity market, carbon emission trading market and trading green certificate market. This article explores the impact of implementing the RO mechanism in a multi-market trading context on wind power investment equilibrium and user side cost, under different demand elasticities and the degrees of competition on the generation side. We introduce a novel multi-agent deep reinforcement learning algorithm, leveraging the twin delayed deep deterministic policy gradient, aimed at obtaining equilibrium. Initially, we propose a method for calculating the comprehensive income of power producers, integrating risk preferences using Conditional Value at Risk. Subsequently, we employ a decentralized partially observable Markov decision process to emulate multi-agent investment patterns. During each exploration phase, wind power output scenarios are meticulously crafted via Latin hypercube sampling. Evaluations of our algorithm underscore its superior convergence and discerning rationality. Numerical simulations reveal that in areas with high demand elasticity, the RO mechanism primarily reduces user side risk without much impact on equilibrium. Yet, in regions with low demand elasticity, its role in influencing wind power investment and user side risk hinges on the degree of market competition and the designated wind power RO sales ratio.
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