In recent years, the global power industry has experienced rapid development, with significant advancements in the source, network, load sectors, and energy storage technologies. The secure, reliable, and economical operation of power systems is a critical challenge. Due to the stochastic nature of intermittent renewable energy generation and the coupled time-series characteristics of energy storage systems, it is essential to simulate uncertain variables accurately and develop optimization algorithms that can effectively tackle multi-objective problems in economic dispatch models for microgrids. This paper proposes a pelican algorithm enhanced by multi-strategy improvements for optimal generation scheduling. We establish eight scenarios with and without pumped storage across four typical seasons—spring, summer, autumn, and winter—and conduct simulation analyses on a real-world case. The objective is to minimize the total system cost. The improved pelican optimization algorithm (IPOA) is compared with other leading algorithms, demonstrating the validity of our model and the superiority of IPOA in reducing costs and managing complex constraints in optimization.

With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly important for high-quality development of the power industry. Analyzing the intrinsic connections among these three types of markets can facilitate their coordinated development. In this study, we selected monthly data on European Union (EU) carbon emission futures, French electricity trading prices, and the price of Guarantees of Origin (GO) in France from March 2019 to March 2024 and utilized the Bayesian time-varying stochastic volatility vector autoregression model (TVP-SV-VAR) with time-varying parameters to effectively capture the dynamic changes among the three markets and to analyze the relationships and characteristics of the EM, CEM, and RECM across different historical contexts. Simulation results showed that the influences of the EM and CEM on the RECM were relatively low, with more pronounced short-term effects and relatively stable medium- and long-term effects. In contrast, the influences of the CEM and RECM on the EM were significant, with more pronounced short-term effects and stable medium- and long-term effects. The influences of the EM and RECM on the CEM were significant in the short term.

With the rapid development of renewable energy technologies, numerous distributed energy resources (DERs) have been integrated into power systems. How to fully exploit renewable energy while maintaining the stable operation of power systems remains an urgent challenge. Furthermore, the diversity of DERs’ ownership requires scheduling approaches that account for the distinct interests and characteristics of multiple stakeholders. To address these challenges, this study introduces a two-stage operational optimization framework for the virtual power plant (VPP), which is grounded in a Stackelberg game model. This strategy innovatively combines two conventional control methods: the day-ahead stage employs direct control for global pre-scheduling, leveraging its cost optimization capability; the intraday stage utilizes dynamic pricing to guide prosumers, tapping into DERs’ flexibility while accommodating their individual energy usage preferences. The Stackelberg game is resolved through a tiered solution methodology employing particle swarm optimization (PSO). To enhance solution efficiency, a Kriging surrogate model is introduced to replace the prosumers’ models, significantly reducing the computational burden of the PSO. Case studies demonstrate that the proposed strategy can balance operating costs and energy usage preferences, and the proposed solution approach can significantly enhance solution efficiency.