Leveraging sanitized data for probabilistic electricity market prediction: a Singapore case study

Abstract

In deregulated electricity markets, predicting price and load is a common practice. However, market participants and shareholders often seek deeper insights into other system statuses associated with price prediction, such as power flow and market share of generation companies (GenCos). These insights are challenging to obtain using purely data-driven methods. This paper proposes a physics-based solution for the probabilistic prediction of market-clearing outcomes, using real sanitized offer data from the National Electricity Market of Singapore (NEMS). Our approach begins with approximating the generator offers that have been historically cleared. Using this pool of offer data, we propose a probabilistic market-clearing process. This process allows for the probabilistic prediction of market prices. By considering the power system network and its constraints, we also naturally obtain probabilistic predictions of power flow and market shares. We validate our approach using actual NEMS data. Our findings show that while the overall performance of price prediction is comparable to existing methods, our proposed method can also provide probabilistic predictions of other associated system operating conditions. Furthermore, our method enables scenario studies, such as the impact of demand-side participation and the penetration of rooftop photovoltaic (PV) systems on the Uniform Singapore Energy Price (USEP).