An upsurge in the electricity demand seems inevitable due to the large-scale deployment of electric vehicles (EVs). Demand response, which is a potential avenue to curb this demand, aims at reduction of power generation costs and electricity bills by allowing control of electricity consumption through electricity prices. This study proposes a holistic approach to combining the behaviour of EV users and customers with other elastic loads participating in demand response to make the scenario more realistic. In this study, various cases with single and multiple utility companies (UCs), which try to set the prices in such a way so as to maximise their profits, have been considered. A Stackelberg game model has been designed to address this conflict of interests between the UCs and the customers. This study considers different utility functions for different types of customers in order to meet their energy requirements meanwhile maximising the profits of the UCs at the Stackelberg equilibrium. The impact of the increase in competition is also studied.
The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intraday markets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market.
In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.
System operators have the option to trade balancing reserves among countries and operators. In order to trade balancing reserves with other system operators the markets should be harmonized. While the spot and intraday markets are already harmonized within the Nordics, the balancing markets still display differences. The differences can be subtle, yet they may play a significant role for the planning, operation, modelling and control of the power system. In this paper, we conduct a thorough literature review on Nordic balancing markets and summarize the market rules and requirements. This review can help operators and modellers to better represent the Nordic power system.
In this paper, three single-stage stochastic programs are proposed and compared for optimal dispatch by a System Operator (SO) into balancing markets (BM). The motivation for the models is to represent a possible requirement to undertake system balancing with increasing amounts of intermittent renewable generation. The proposed optimization models are reformulated as tractable Mixed Integer Linear Programs (MILPs) and these consider both fuel cost and intermittency cost of the generators, when the SO activates the up-or down-regulation bids. These three models are based on the main approaches seen in practice: dual-imbalance pricing, single imbalance pricing and single imbalance pricing with spot reversion. A scenariogeneration algorithm based on predictive conditional dynamic density distributions is also proposed. We perform a comparative analysis of these three proposed models in terms of how they help the SO to optimize their balancing market actions considering intermittent-renewable generators. The single imbalance pricing is found to be the most market efficient.
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