Xianhao Li

The increasing penetration of wind and photovoltaic power intensifies power fluctuations and raises the requirement for reserve capacity allocation in hydro-wind-solar (HWS) systems. To address this issue, this study proposes a two-stage optimization framework for coordinated reserve configuration. In the first stage, the entropy weight method is used to evaluate heterogeneous reserve resources according to unit capacity cost, response time, and carbon emission intensity, thereby determining their response priority and obtaining an initial reserve allocation. In the second stage, alternative preference coefficient ratios for economy, rapidity, and low-carbon performance are assessed, and the resulting allocation proportions are fed back to the first stage to form a closed-loop optimization process. To solve the model, an improved Osprey Optimization Algorithm incorporating a Lens Imaging Opposition-Based Learning mechanism is adopted. A case study based on the Wudongde regional grid shows that the 2:1:2 preference-ratio scenario provides the best overall trade-off among the tested cases, with a reserve cost of 18,640.38 CNY (Chinese Yuan), carbon emissions of 8718.30 kg CO₂, and a response time of 4336.7 s. Compared with representative benchmark models, the proposed method achieves lower carbon emissions and faster response while maintaining competitive economic performance. The results demonstrate that the proposed framework can improve reserve allocation quality and operational adaptability in HWS systems with high renewable penetration.

The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%.