## 1 Introduction
### Related Works
Simulation comparisons revealed that this method effectively handled uncertainties, enhancing energy storage utilization and operational economy compared with traditional centralized storage systems. In [33], the study optimized hybrid RESs with PV and backup storage, comparing PV/battery systems with off-grid PV/hydrogen configurations for remote locations. Using PSO in MATLAB, the research showed PV/battery systems were more economical with decreasing reliability and increasing interest rates. The PV/battery setup required 44.8% less solar panel capacity than PV/hydrogen. In [34], a comprehensive framework for optimizing urban energy systems with renewable sources was introduced. Applied to a small neighborhood in Beijing, this framework combined rooftop PV and waste biomass potential assessment, multi-perspective optimization, and an analysis of energy policy impacts. The study revealed that fully utilizing biomass and PV could meet 73% of local electricity demand at a cost of 0.1030 $/kWh and 0.5416 kg/kWh in carbon emissions. EVs are increasingly vital for sustaining energy balance. Properly managing the energy of these vehicles as well as that of charging stations is essential [35; 36]. In [37], a new approach for designing and optimizing a PV/Wind Hybrid RESs for an EV charging station and a university shopping complex in India was introduced. It proposed a two-stage framework that considered uncertainties in renewable energy generation and utilized the Ebola optimization search algorithm (ESOA). The first stage involved modeling the hybrid system components to minimize levelized power supply price, while the second stage used ESOA for optimization. The results showed a 15% reduction in levelized power supply price and a 10% improvement in system reliability, offering insights into optimal distributed generation sizing in SWHS. In [38], a distributed cooperative operation strategy was proposed for multi-agent energy systems integrating wind, solar, and buildings, utilizing chance-constrained programming (CCP). The system was modeled with detailed electric and thermal characteristics to enhance flexibility. A Nash bargaining model was used to maximize profits, addressing coalition income and power payment, with the adaptive alternating direction method of multipliers ensuring agent privacy. The CCP method managed uncertainties from wind, solar generation, and outdoor temperatures. Simulations validated that the strategy preserves privacy and boosts profitability for both the system and individual agents. In [39], a stochastic bi-level optimal allocation strategy was introduced to reduce energy storage costs for urban development, integrating BESS with intelligent buildings (IBs). Detailed thermal dynamic models were created for IBs with air conditioning systems and the BESS station. A bi-level model addressed electricity price uncertainties: the upper-level reduces BESS station planning costs, while the lower-level minimizes IB operating costs. This nonlinear model was simplified to a single-level mixed-integer linear programming problem using Karush-Kuhn-Tucker (KKT) conditions. The results showed it met temperature comfort needs and benefited both BESS stations and IBs.