Stochastic optimization of system configurations and operation of hybrid cascade hydro-wind-photovoltaic with battery for uncertain medium- and long-term load growth

The implementation of a large-scale energy infrastructure, relying exclusively on 100% clean and renewable sources, effectively meets the rising regional energy demands and mitigates climate change impacts stemming from fossil fuel consumption. However, challenges arise due to uncertainties in renewable energy outputs and the dynamic nature of medium- and long-term electricity demands, affecting energy supply reliability, as well as considerations of investment and economic performance. To tackle these issues, a stochastic two-stage optimization method is proposed for refining system configurations in a hybrid cascade hydro-wind-photovoltaic system with a battery. This approach includes optimizing the scheduling and management of energy production plants. Uncertainties related to water inflows, wind and solar powers, and electric demands are modeled using a scenario generation method. Temporal correlation degrees are introduced to improve the precision of Latin hypercube sampling. Addressing nonlinear aspects, the linear relaxation method, based on the McCormick envelope, deals with nonlinear elements in cascade hydro power plants. A mixed-integer linear programming approach is then used to obtain optimal solutions for the hybrid system. Results show that the proposed scenario generation method enhances computational efficiency. Cover ratios for sampling and clustering improve by 2.24% and 3.60%, respectively, and average errors of temporal correlations decrease by 0.81% for sampling and 0.52% for clustering. Key parameters like electric growth, cost parameters, and initial battery state, be considered for optimal system configurations and strategies. The levelized cost of electricity of the optimum system configurations is about 0.060$/kWh when the peak electric load is 10,000 MW. The life cycle CO2 emission is about 9.49 kg CO2-eq/MWh. Both electricity cost and environmental impact will have dramatic growth because of the integration of EES with large capacity when the electric load continuously increases.