Generation and Balancing Capacity in Future Electric Power Systems-Scenario Analysis Using Bayesian Networks

This paper examines the evolution of the Finnish electric energy system up to 2035, focusing on the likelihood of different development paths. The primary contribution of this paper is the development of an extensive Bayesian Network, designed to model and analyze the evolution of power generation capacity mix, assess the likelihood of different grid management scenarios, and understand the causal relationships underlying these scenarios. A target optimization was carried out using the constructed Bayesian Network to explore possibilities to minimize grid management complexity. The results of the optimization reveal that the authorities and stakeholders should prioritize increasing demand response, gas power, and battery storage capacities. These mature technologies are well-suited to guarantee energy adequacy during peak consumption periods, which in Finland typically occur during consecutive cold, dark and windless winter weeks. Although this study focuses on the evolution of the Finnish power grid, the constructed Bayesian Network approach is broadly applicable and can be utilized to explore causal relationships in other countries by employing the designed questionnaire and engaging a panel of experts specific to the country’s energy infrastructure.