Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy.

With high renewable penetration, independent user-side storage suffers from low utilization and high investment. Shared energy storage (SES) addresses this by aggregating multi-user demand. This paper proposes a bi-level optimal configuration method for user-side SES considering the conflicting interests between the investor and users. The upper level maximizes the investor’s annual revenue by optimizing SES capacity and power, while the lower level minimizes users’ comprehensive energy cost via charge/discharge scheduling and load shifting. The bi-level model is transformed into a single-level mixed-integer linear programming (MILP) problem using strong duality theory, and the Shapley value method ensures fair cost allocation based on marginal contributions. Case studies on a community with three typical user groups show that, under the considered time-of-use price and cost parameter settings, the proposed method outperforms independent storage in the studied scenarios, reducing total user costs by 12.8% and increasing investor revenue by 1.9%; with a 30% load shifting capability, investor revenue rises by 13.2% and user costs drop by 0.78%. The Shapley value-based allocation guarantees fairness while quantifying the actual costs of multi-category users jointly using SES.

To support low-carbon smart buildings, this study proposes a multi-objective capacity optimization method for PV-energy storage systems considering the flexibility potential of orderly electric vehicle (EV) charging loads. First, an orderly EV charging model based on price-guided charging quantifies the flexibility potential of EV charging loads. Then, a bi-level multi-objective capacity configuration model is proposed. In the upper level, the building operator minimizes both economic and carbon emission costs, jointly optimizing PV-energy storage capacity configuration and EV charging prices. In the lower level, EV users optimize their charging schedules to minimize their charging costs in response to the charging prices. To solve this bi-level multi-objective problem, the two objectives are normalized and weighted into a single-objective function, and then a heuristic solution method based on a genetic algorithm is developed. Case study results show that the proposed mode reduces building economic cost and electricity-related carbon emissions, demonstrating the benefits of incorporating carbon emissions into demand-side optimization under the “dual-carbon” targets.