An optimization strategy for managing surplus electricity through P2G pathways

Author(s): Lingyi Gu, Jeeyoung (Julie) Kim, Joohyung Ko, Azadeh Maroufmashat, Michael Fowler, Ali Elkamel


Ontario has a high dependency on nuclear and renewable energy, with more than 85% of its electricity supplied from non-carbon-based sources. Due to the intermittency and inflexibility of the generators, surplus electricity (SBG) is generated when supply exceeds demand. This paper aims to develop a mathematical model for power-to-gas (P2G) energy system, converting SBG into hydrogen to be used by end-users. The four end-users studied were renewable natural gas sector (RNG), hydrogen-enriched natural gas sector (HENG), mobility sector and industry sector. Two scenarios were considered for the model: Scenario 1, where a single pathway was considered for implementation and Scenario 2, where the four pathways were integrated into one system for a combined installment. The objective of this model is to minimize the total cost and maximize emission offset with a lifetime of 20 years. The modelling and optimization were carried out in Python using mixed integer linear programming (MILP) based approach. The Scenario 1 results showed that replacing industry feedstock by hydrogen is more cost-efficient than other sectors. The Scenario 2 results showed that the system can reduce 470,595 tonnes of CO2 per year, which is 95% of the maximum achievable offset and utilize 57% of the surplus electricity.