Evaluating market designs in power systems with high wind penetration

Abstract

In this paper we assess the incidence of strategic behavior of demand and supply units in market set ups which should enable the efficient in-feed of renewable energy sources. Therefore, a sequence of energy and reserve power auctions is modeled in an agent-based framework in order to approximately come up with the drawbacks of the different market designs. The main focus lies on the reserve power market. The generation companies as well as representative consumers are facilitated with Q-learning in order to exploit market flaws. The representative network includes a renewable in-feed, which is characterized by low marginal generation costs. In terms of congestion management, we assume in a first step uniform marginal pricing, in a second step nodal pricing for the energy power auction.Index Terms-Electricity Market, market design, renewable energy in-feed, demand side participation, multi-agent system.

Continuous Reinforcement Algorithm and Robust Economic Dispatching-Based Spot Electricity Market Modeling considering Strategic Behaviors of Wind Power Producers and Other Participants

“…Paschen [34] analyzed the dynamic behavior of day-ahead EM prices in Germany due to structural shocks in wind and solar power by using a dynamic structural vector autoregressive model. Similar studies can also be seen in [35,36], but researches in [29][30][31][32][33][34][35][36] regard the wind power or other renewable powers as an exogenous random variable so that strategic bidding behaviors of wind or other renewable power producers as well as impact of the EM bidding process on WPPs are neglected in those literatures.…”

“…Zamani-Dehkordi et al [32] studied the impact of a proposed wind farm project on wholesale and retail electricity prices by using EM models based on nonparametric regression algorithms. In [33], by using the Q-learning algorithm, Haring et al proposed a multiagent EM approach to analyze the effects of renewable power uncertainty on the spot EM bidding progress. Salehizadeh and Soltaniyan [2] modified the multiagent EM approach through the fuzzy Q-learning algorithm, by which the effects of renewable power uncertainty on the spot EM bidding progress was also studied within a continuous market state (wind power) space, but discrete action spaces.…”