An optimization model for low carbon electricity transition planning: a case study from India

Introduction: Globally, achieving net-zero carbon is the new target set for energy systems. Consequently, the energy systems are undergoing a paradigm shift from least cost, robust, carbon-intensive, on-demand and firm power conventional systems to uncertain, intermittent, and variable renewable energy-integrated low-carbon electricity systems. Hence, the needs of planning and operations in transitioning electricity systems have changed from balancing “static supply with dynamic demand” to balancing “dynamic supply with dynamic demand”.

Materials and methods: This study proposes a novel linear programming-based optimisation model as a tool for planning electricity system transitions. The objective is to balance dynamic supply with dynamic demand at minimum cost and address the inherent challenges of renewable energy (RE) integration. It is designed to evaluate the technical characteristics of various supply technologies, their temporal and spatial resource potential, availability, and economics, thereby curating the least-cost generation mix at hourly resolution.

Results: The model has been validated on the electricity system of Karnataka, India, and the results were within an acceptable standard error of ±10%. The modelled system shows a lower economic cost of generation (Rs. 2.04/kWh) compared to the actual system performance (Rs. 2.13/kWh), in addition to higher utilisation of renewable resources than the actual system.

Conclusions: The advantages of our proposed model over its counterparts are flexibility, simplicity, inter-operability, less data-intensive, higher computational efficiency, scalability, and the ability to integrate resource planning and socio-economic and environmental aspects, making it a valuable tool for electricity transition planning, offering critical insights for policymakers and planners.