An Aggregator-Based Market Modelling with an Impact of Risk Under Uncertainty

In the Electricity market the increased penetration of renewable energy sources (RES) and associated uncertainties impose challenges to determine the day-ahead distribution locational market prices effectively and also these uncertainties can jeopardize grid stability and reliability. RES aggregators compete to increase their profit but their intermittent nature adds financial risks to aggregators (As). The main objective of this paper is to model a day-ahead electricity market by considering RES aggregators as participants to trade energy effectively to maintain a dynamic energy balance. Instead of relying on existing probabilistic forecast methods to account for the variable uncertain nature of RES, this paper uses a novel data-driven forecasting method to predict variable RES power generation accurately. The proposed model follows a three stage approach. The first stage involves forecasting PV and wind output power with multiple scenarios. In the second stage, a scenario-based multi-aggregator market modelling is performed where aggregators submit their bids to the distribution network operator, who then clears the market by generating price signals. Uncertainties of RES aggregators lead to financial risk for aggregators. Hence, the third stage involves, risk assessment using value at risk (VaR) and conditional value at risk (CVaR) are applied to different scenarios for evaluating the potential portfolio losses within a specified time horizon and confidence level. To evaluate the effectiveness of the proposed model, it is tested on a modified 33-bus test system which shows effective energy trading at a distribution system with a considerable marginal range of voltage violations. The proposed novel three-stage model aims to improve the distribution level electricity markets efficiency and reliability, benefiting RES market participants and consumers alike.