Assessment of renewable energy-based strategies for net-zero energy communities: A planning model using multi-objective goal programming

Abstract Planning decentralised community-level hybrid energy systems has emerged as a solution to the various environmental and economic issues associated with conventional centralised energy supply systems. However, the optimal planning of community energy systems is a challenging issue due to the complexities, uncertainties, conflicting objectives, and high computational times in analysis. This study introduces a new multi-objective model based on weighted goal programming and grey pairwise comparison to assess renewable energy-based strategies in the case of net-zero energy communities. The problem was formulated to determine the optimal energy mix based on minimization of life cycle impacts and costs and maximization of renewable contributions and operational energy savings. To this end, binary integer and continues variables were applied on the code developed in a CPLEX environment. A pairwise comparison based on grey numbers was used to find the impacts of the goals in the objective function of the model under uncertainty. In addition to the grey-based weighting scenario, different weighting scenarios were employed to consider the importance of all goals on the system. These weighting scenarios were used to investigate the effects of changing decision priorities on the outcomes and on stakeholder interests at different levels. The developed goal-programming model was applied to the data of a case example and solved based on the weighting scenarios. Results indicated that the model is capable of finding the best possible strategies with the lowest total undesirable deviations from the desired levels of the goals compared to the literature of the decision-making techniques. The integration of maximum renewable energy (RE) supply in the energy mix with the locally available energy resources can deliver considerable benefits in terms of energy supply cost reduction as well as in mitigating life cycle environmental impacts. When environmental goals are prioritized, integrating low emissions RE as much as possible and excluding waste-to-energy technologies makes best sense, while under a pro-economic perspective, solar integration is comparatively discouraged. The findings of the study are expected to assist community developers and other decision makers involved in regional energy planning. The developed method will also be of use for those who are interested in the use of goal programming to solve complex planning issues involving numerous uncertain parameters.