Combining Energy System Planning with Carbon Emissions Trading Optimisation for Decarbonisation of Emerging Economies

Abstract

This study integrates scalable carbon trading models into an energy planning framework to address carbon emissions challenge associated with increasing energy consumption in emerging economies. The models utilise mixed-integer nonlinear programming (MINLP) formulations to optimise power generation, emissions, and costs. A case study is demonstrated on an ASEAN (Association of Southeast Asian Nations) country, Malaysia. Results reveal that carbon trading enhances both financial gains and environmental sustainability, with direct optimisation (where emission rights or carbon prices are variables) approach proving more effective in maximising carbon markets’ efficacy, leading to better cost to emission ratios when compared to indirect optimisation (with emission rights prices as parameters, estimated via demand-supply curve). For emissions minimisation, direct optimisation resulted in cost to emissions ratios about 1.3 to 2.1 times more than those for indirect optimisation for three out of four studied periods. The study highlights the potential of coordinated emissions trading and optimal investment decision-making, providing valuable insights for energy planning in Malaysia which is aiming for net-zero target by year 2050.