A multi-objective stochastic framework for optimal operation of a hybrid energy system considering cost-emission optimization

• A stochastic multi-objective framework is proposed for operation of a hybrid system. • Operation cost and CO 2 emissions are minimized for optimal operation. • Three-point estimation method is used to model load uncertainty. • For robust operation, a scenario-based objective function is developed. • Multi-objective exploration-exploitation method is introduced to find Pareto front. Optimal operation of hybrid systems is a challenging optimization problem in power systems. In such a problem, to ensure robust operation against load variations, it is vital to incorporate load uncertainty into the operation problem. This paper proposes a multi-objective stochastic framework for optimal operation of an on-grid hybrid system with photovoltaic (PV), biomass and battery storage considering load uncertainty. To optimally determine the power exchange with the electricity grid, a scenario-based objective function is defined which integrates three-point estimation method (3PEM) within the multi-objective stochastic framework to model the random behavior of load. As two conflicting objectives, the operating cost and CO 2 emissions are simultaneously minimized using two optimization methods. For the first-time, multi-objective exploration-exploitation (MO-E2) is developed and applied for optimization of the hybrid energy system and the results are compared with those obtained by multi-objective crow search algorithm (MO-CSA) under deterministic and stochastic scenarios considering different reliability levels. For the case study, results demonstrate the efficiency of the proposed multi-objective stochastic framework which leads to greater flexibility and better-informed decision-making. Under the deterministic full-load satisfaction, when the min-emission solution is considered, MO-E2 achieves a 20.7% reduction in CO₂ emissions compared to the min-cost solution. For the minimum-cost solution on the stochastic Pareto front, when 5% unmet load is allowed, compared to the deterministic scenario, the operating cost and CO 2 emissions increase by 13% and 4.6%, respectively.