Integrated energy and ancillary services optimized management and risk analysis within a pay-as-bid market
Alberto Vannoni, Alessandro Sorce
Abstract
In liberalized electricity markets, trading energy between generators and consumers occurs primarily on the Day-Ahead Market (DAM) one day in advance. However, the scheduled programs may not comply with grid requirements or real-time conditions. To ensure grid stability and sufficient reserves, system operators procure resources on the Ancillary Services Market (ASM). With the increasing share of renewable energy sources , many programmable generators are shifting their business model, from generating energy at base load to providing grid services. In this context, a DAM-based traditional approach to dispatch scheduling, widely adopted by existing techno-economics analysis, may result significantly suboptimal. This paper presents a novel model for dispatch optimization maximizing profits simultaneously on both the DAM and ASM, utilizing a mixed integer linear programming (MILP) formulation and a machine learning algorithm considering a pay-as-bid pricing system and predicting the probability of offer acceptance based on historical data . The proposed framework is modular and flexible, allowing for separate use of the MILP dispatch optimizer and the machine learning offer acceptance prediction model. A risk propensity factor is defined and the impact on the optimal bidding strategy, the expected profits, and their variability, is studied. A Montecarlo approach is used to evaluate the profits probability density function . The performance obtained (i.e. 20 min to optimize one week of operation of a Combined Cycle Gas Turbine) allows in applying the proposed methodologies for both long term energy system planning and daily production offer scheduling.