Windfall profit-aware stochastic scheduling strategy for industrial virtual power plant with integrated risk-seeking/averse preferences

The increasing penetration of renewable energy in power grids introduces higher levels of uncertainty, while current decision-making models typically favour either a risk-averse or risk-neural strategy, and the research works related to windfall profit-aware risk-seeking strategies are quite limited. In this paper, a novel concept of integrated risk-seeking/averse preference is proposed, and a windfall profit-aware stochastic scheduling model for an industrial virtual power plant (IVPP) is developed based on this type of risk preference, which can realize the joint management of potential high profits and extreme losses. Firstly, the potential best- and worst-case results are incorporated into the holistic decision-making framework, which are quantified by the value at best (VaB) and the conditional value at risk (CVaR) measures, respectively. Next, the windfall profit-aware stochastic scheduling model is developed for the optimal operation of IVPP in day-ahead and real-time electricity markets, where multi-type flexible resources, such as energy conversion and storage devices, industrial production workstations, material storages and financial instruments, are utilized to improve expected profits and minimize the potential risks. Specifically, two types of credit-based virtual transactions, increment and decrement bids, are employed by the IVPP to increase its trading flexibility in electricity markets. Finally, simulation studies are conducted for a multi-energy IVPP to validate the proposed windfall profit-aware framework and model, showcasing that the risk-seeking and risk-averse preferences of decision-makers can be fully satisfied simultaneously under actual market environment. Moreover, risk parameters can be adjusted accordingly to manage windfall profits, expected profits, and extreme losses flexibly in electricity markets.