Edgars Eisons

Transforming energy supply systems is essential for achieving a sustainable, carbon-neutral future. However, this transition substantially increases the complexity of long-term planning due to the large-scale integration of carbon-free energy sources, strong interactions among technical and economic subsystems, multiple stakeholders, and significant uncertainty. Comprehensive evaluation of alternative development pathways requires detailed power system and electricity market simulations across numerous scenarios, resulting in a major computational bottleneck. The objective of this paper is to develop and demonstrate an artificial intelligence-based surrogate modelling framework that enables the rapid evaluation of large numbers of long-term power system development scenarios while preserving the accuracy of detailed simulation models. The proposed framework combines stochastic scenario generation, detailed power system and electricity market simulations, and artificial neural network (ANN)-based surrogate models to approximate scenario-dependent performance indicators. By replacing repeated high-fidelity simulations with ANN surrogates, the methodology substantially improves computational efficiency while supporting uncertainty analysis, multi-scenario assessment, and multi-stakeholder decision-making. A case study demonstrates the effectiveness of the proposed framework for scalable, high-resolution strategic power system planning.

Transforming energy supply systems is vital for achieving a sustainable, carbon-neutral future. However, this transformation entails significant complexity, driven by the integration of carbon-free energy sources, tightly coupled technical and economic subsystems, and the need to ensure reliability, resilience, and affordability. Addressing these challenges requires advanced planning approaches capable of capturing uncertainty, multiple stakeholder perspectives, and system dynamics across temporal and spatial scales. In response, this article presents an integrated planning and operational framework that combines multi-actor, multi-criteria, and multi-stage stochastic optimization with surrogate modelling and artificial intelligence. The proposed approach enhances scalability, computational efficiency, and precision in the planning and operation of future energy systems. A case study illustrates its effectiveness in supporting high-resolution, uncertainty-aware decision-making.