SUMMARY In this paper, we present a method for assessing the impacts of demand–response (DR) programs on the load profile and the market prices, which can be recognized directly in terms of the demand elasticity (DE). The method simulates the effects of the DE arising from the DR programs on the liberalised wholesale electricity market. The influence of DR programs on the DE in the market is estimated, and then the impact of the DE on the load profile and the market prices is simulated using the day‐ahead market‐simulation tool by calculating a new market equilibrium point. The model is more suitable for initial planning stages of the DR programs and could be used to assess what levels of elasticity would be necessary to achieve the desired levels of DR and savings. The proposed method was illustrated on the centralized Slovenian electricity market. The results indicate that an increased DE leads to a lower demand for electricity, leading to significant reductions in the electricity price on the market. The method provides policy designers and investors in DR programs with a quick and effective way to estimate the market effects of these programs. Copyright © 2012 John Wiley & Sons, Ltd.

With the integration of distributed renewable energy sources (DRES) and active demand (e.g., units providing demand response, DR) in the distribution grid, the importance of monitoring the network conditions, managing the line congestions and observing the voltage levels is increasing. The distribution system operator (DSO) needs a mechanism, such as the traffic light system, to screen and approve the proposed operation schedules of the flexible active resources in the distribution grid. Their aggregated control will require the aggregators to employ advanced scheduling algorithms. The DR scheduling algorithms can be set to pursue various goals, for example, maximization of profit or cost reduction, grid support, or provision of the ancillary services. In the paper, we present a new DR scheduling approach suitable for the aggregation agent using approximate Q‐learning (AQL) algorithm scheduling. We present the AQL algorithm and the associated assumptions used in simulations on a real‐world low‐voltage (LV) grid model, comparing the AQL approach results to those of the economic scheduling and the energy scheduling approaches. Our assumption was that the AQL approach could outperform the energy or economic approaches as the AQL agent would be able to learn to avoid the scheduling penalties. The results of our research show that the aggregator agent using the economic approach shows the best economic performance, but causes the most schedule violations. The energy scheduling approach improves the network voltage profile but lowers the aggregator's profit. The AQL approach results in the agent's economic performance between the former two approaches with minimal schedule violations, confirming our research hypothesis. This article is categorized under: Concentrating Solar Power > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Efficiency > Economics and Policy

No abstract

No abstract

The paper presents an agent-based approach to model the flexibility of the demand-side. It uses Q-learning algorithm to model a behavior of a demand-side agent, so to investigate the elasticity of the demand to the change in price. Often, market simulation models assume that the demand elasticity is known, however due the lack of data this elasticity is not easy to determine. The objective of this paper is to evaluate the flexibility of the total system demand, and the shift in the consumption with the price, i.e. increase in the demand when the price is low, and a decrease in the demand when the price is high. The here presented model of a demand-side agent is incorporated into the market simulator with double-sided auctions, and is tested on the Slovenian market. However, this approach can be used to estimate flexibility in any system for which the forecasted demand data and generation offers are know