Integrated modeling and optimization for environmental and social sustainability in multi-energy systems

The optimization of Multi-Energy Systems (MES) has traditionally been centered around economic objectives and the minimization of operational greenhouse gas emissions (GHG). However, the broader environmental and social impacts of these systems, especially those related to their life cycle and beyond mere GHG emissions, necessitate a more holistic optimization approach. This study introduces a novel and comprehensive objective function, the Inclusive Wealth Index (IWI), for optimizing Multi-Energy Systems. The IWI is defined as the weighted sum of three types of capital—human, natural, and produced—thereby integrating societal and environmental considerations into the optimization process in a comprehensive manner. By conducting a life cycle assessment (LCA) of the technologies and energy carriers within the MES, their implications on human and natural capitals are evaluated, while produced capital is assessed through investments in infrastructure and technology manufacturing, directly influencing economic growth and societal well-being. Utilizing mixed-integer linear programming (MILP) in a Python framework with the Gurobi solver, this research optimizes the design and operation of an MES to both maximize the IWI and reduce overall costs. A reference case is considered, where electricity and heat are supplied through the grid and natural gas boilers, respectively. The optimization of a grid-integrated case study, featuring photovoltaic modules (PV), heat pumps (HP), internal combustion engines (ICE), boilers (BOIL), electrical (EES), and thermal energy storage (TES) demonstrates that focusing solely on cost minimization results in a 46% savings compared to the reference case, yet it adversely impacts the IWI, reducing it to -0.03 points when all capitals are equally weighted. Prioritizing IWI maximization, on the other hand, substantially elevates the index to 0.114 points but incurs costs 42% higher than those associated with the cost-minimization scenario. Through multi-objective optimization that balances cost and IWI objectives, the study reveals that significant enhancements in societal wealth are attainable with low expenses, achieving a notable improvement in IWI of 0.056 points alongside a cost reduction of 41% compared to the reference case, and only 8% higher than the cost-minimization scenario. This research also underscores the critical importance of balanced capital weighting in optimizing MES for sustainable development, paving the way for energy systems that strategically integrate economic, environmental, and social considerations.