A risk-aware bidding model for virtual power plants: Integrating renewable energy forecasting and carbon market strategies

Abstract

Integrating renewable energy resources (RES) into the energy market through a virtual power plant (VPP) framework is an effective strategy for reducing carbon emissions while enhancing system efficiency, reliability, and cost-effectiveness. However, RES-based power generation is inherently uncertain due to weather variability, making it crucial to incorporate uncertainty modelling. Additionally, carbon emissions can serve as a revenue source through carbon reduction policies such as carbon taxes and cap-and-trade schemes. An alternative approach to carbon reduction is the uplift payment scheme, which promotes a more carbon-efficient energy market (EM). This study introduces a novel bidding model within a VPP environment that leverages Extreme Gradient Boosting algorithm (XGBoost) algorithm to predict RES generation, addressing uncertainty through advanced forecasting techniques. The associated prediction risks are quantified using the Conditional Value at Risk (CVaR) method. Furthermore, the proposed bidding model is integrated with the carbon market, incorporating various carbon reduction policies to determine carbon credit prices dynamically. In addition to this, the proposed model is also optimized with a very new meta-heuristic algorithm called White Shark Optimizer (WSO) Algorithm to check the possibility of convergence of the model. A comprehensive comparative analysis is conducted to evaluate the performance of the proposed approach. The model’s effectiveness is demonstrated through case studies, illustrating its potential to optimize bidding strategies while mitigating risks associated with RES uncertainty and carbon pricing fluctuations. By integrating advanced forecasting methods, risk assessment, and carbon market mechanisms, this work contributes to the development of a more sustainable, reliable, and economically viable energy market.