Under partial-shading conditions, landscape-oriented photovoltaic (PV) modules can exhibit dual- or multi-peak power–voltage (P–V) characteristics, which may trap conventional maximum power point tracking (MPPT) algorithms at local optima. A 60-cell full-cell PV module model incorporating bypass diodes was established, and global maximum power point tracking (GMPPT) simulations were conducted under partial-shading conditions. The model agreed well with previously obtained experimental data, with a mean absolute percentage error of 3.48% between the simulated and measured powers. An improved particle swarm optimization–incremental conductance strategy (PSO-INC) was developed by incorporating a transition stage, an irradiance-change-triggered global-search restart mechanism, and a search-direction-dependent adaptive step size. This strategy combines the global exploration capability of particle swarm optimization (PSO) with the local refinement capability of incremental conductance (INC). Thirty independent simulations with prescribed random seeds were performed for each scenario. Relative to the experimentally measured global maximum power point (GMPP), PSO-INC achieved a mean tracking efficiency of 99.20% and a mean relative error of 0.80% across the six static-shading configurations. Its mean settling time was 44.81% shorter than that of PSO. Under 25% shading, the mean tracked power was 256.23 W, exceeding the reported outputs of INC and jump-exploration incremental conductance (JEINC) by 41.95 W (19.58%) and 40.10 W (18.55%), respectively. Following irradiance transitions from 1000 to 500 W/m² and from 1000 to 0 W/m², the mean settling time was reduced by 53.03% and 53.05%, respectively, relative to PSO. These results demonstrate that PSO-INC improves GMPPT accuracy, settling performance, and steady-state behavior under partial-shading conditions and rapidly changing irradiance.

This study proposes a mixed-integer linear programming(MILP) framework for the joint capacity sizing and hourly dispatch of a grid-connected industrial park electricity–hydrogen system with fixed daily hydrogen demand. Unlike conventional formulations that prescribe an hourly hydrogen-load profile, the proposed model adopts a daily cumulative delivery constraint, allowing the electrolyzer to flexibly schedule hydrogen production according to renewable generation, electricity prices, and storage states. Three configurations are compared under consistent technical and economic assumptions: external hydrogen procurement (EHP), local renewable hydrogen production(LRH), and a bidirectional electricity–hydrogen system (BEHS) with hydrogen-to-power reconversion. The model jointly optimizes photovoltaic generation, wind turbines, battery energy storage, electrolyzers, hydrogen storage tanks, and fuel cells over a full 8760-hour horizon. Under the baseline conditions, the optimized BEHS achieves a total annualized cost of 5.865 million CNY/year and a renewable energy utilization rate of 99.49%, while satisfying both electricity and hydrogen demands without shortages. Compared with EHP, LRH reduces the total annualized cost by approximately 8.5%, whereas introducing the fuel cell provides an additional reduction of approximately 0.56%. The results indicate that local renewable hydrogen production is the main source of economic benefit, while hydrogen-to-power reconversion primarily provides supplementary flexibility. Sensitivity analyses further show that daily hydrogen demand determines the scale of the local hydrogen-production system, grid purchase capacity strongly affects the value of bidirectional coupling, and higher carbon price promotes renewable generation and battery deployment while reducing grid electricity use and indirect emissions.

Existing parameter extraction methods for photovoltaic modules are mostly developed for specific module types or structural configurations, making it difficult to achieve unified modeling across different materials, structures, and operating conditions. To address this issue, this study proposes a unified five-parameter identification method based on the single-diode model of photovoltaic cells and combines it with a genetic algorithm. The photocurrent, reverse saturation current, series resistance, shunt resistance, and diode ideality factor are selected as the identification parameters, and the parameter extraction problem is formulated as a global optimization problem. On this basis, a cell-unit-based parameter correction model considering the effects of irradiance and temperature is established, and unified modeling of photovoltaic modules with different architectures is achieved according to their internal electrical connections, enabling the prediction of their electrical characteristics. The proposed method is validated using experimental data from a half-cell mono-crystalline silicon module under different shading conditions, as well as mono-crystalline silicon, multi-crystalline silicon, and thin-film modules under varying irradiance and temperature conditions. In addition, the RTC France solar cell and Photowatt-PWP201 module benchmark datasets are employed to further assess the reliability of the parameter identification procedure. The results demonstrate that the proposed method can effectively reproduce the electrical characteristics of the investigated photovoltaic modules, with maximum relative errors of 2.48%, 2.17%, and 4.32% for open-circuit voltage, short-circuit current, and maximum power, respectively. The benchmark validation further demonstrates that the proposed method achieves fitting accuracy comparable to that of other representative optimization algorithms while exhibiting good repeatability and convergence performance. These results collectively demonstrate the feasibility of the proposed cell-unit-based five-parameter modeling framework for photovoltaic modules with different materials and structures under the investigated operating conditions, providing a simple and feasible approach for unified parameter identification, performance characterization, and engineering modeling.