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With the rapid development of renewable energy generation in recent years, microgrid technology has increasingly emerged as an effective means to facilitate the integration of renewable energy. To efficiently achieve optimal scheduling for microgrid cluster (MGC) systems while guaranteeing the safe and stable operation of a power grid, this study,...
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Citations
... At present, advanced energy management systems and optimisation algorithms have improved the scheduling efficiency of microgrids [5] and achieved more accurate load forecasting and resource allocation. Microgrids can adjust the power consumption mode according to real-time power demand and price signals, as well as optimise economic benefits [6]. Reference [7] developed an optimal microgrid operation model aimed at minimizing operational costs to achieve efficient microgrid management. ...
With the proposed “double carbon” target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park.
Microgrid development is strongly influenced by improved energy storage system (ESS), which holds a vital key to stabilizing the grid. A hybrid energy storage system combines various storage technologies to further synchronize frequency, reducing fluctuations and intermittency problems created by renewable energy sources. The main focus of this research is the advantages and obstacles of incorporating next-generation long-duration energy storage in clustered microgrids with flexibly generated electricity from renewable energy sources like solar, wind, and energy-from-waste units. The key challenges of the research area in focus are the management and control of power, quality of power, economic viability, reliability, and environmental issues such as carbon traces. A thorough review of popular optimization methods and tools is performed, discussing ways to tackle these issues in accordance with industry standards and operational settings. This study offers important insights into enhancing the efficiency, scalability, and sustainability of microgrid-integrated storage systems, facilitating the worldwide shift towards decentralized energy systems.
To address the collaborative optimization of environmental protection and economic efficiency in isolated microgrids, an Improved Sparrow Search Algorithm (ISSA) is proposed for economic optimization and scheduling. First, a mathematical model is established with economic cost as the performance indicator. This model incorporates engineering constraints such as power balance and equipment output limits, formulating a multi-objective scheduling optimization problem. Second, the population is initialized using an improved Tent chaotic sequence. The basic sparrow search algorithm is enhanced by introducing a golden-ratio-based sinusoidal strategy into the discoverer phase, enabling thorough local exploration while balancing global and local search capabilities. To avoid premature convergence, a particle swarm velocity update strategy is integrated into the participant position update, improving adaptability to complex optimization problems. Additionally, a dynamic selection adaptive t-distribution mutation operator is introduced to perturb individual positions, enhancing the algorithm's ability to escape local optima. Finally, the ISSA is applied to isolated microgrid dispatching, significantly improving operational economic efficiency while maintaining high clean energy utilization rates. Simulation results validate the rationality and scientific rigor of the proposed strategy.
More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operation model including a photovoltaic array, wind turbine, micro gas turbine, diesel generator, energy storage, and grid connection is constructed, considering the demand response and the uncertainty of wind and solar power. The modeling demand response is determined via a price–demand elasticity matrix, whereas the uncertainty of wind and solar power is established using Monte Carlo sampling and a K-means clustering algorithm. Secondly, a multi-objective function that includes operational and environmental treatment costs is constructed. To optimize the objective function, an Improved Dung Beetle Optimization algorithm (IDBO) is proposed. A tent mapping, non-dominated sorting, and reverse elite learning strategy is proposed to improve the Dung Beetle Optimization algorithm (DBO); therefore, the IDBO is developed. Finally, the proposed model and algorithm are validated through some simulation experiments. A benchmark function test proves that IDBO has a fast convergence speed and high accuracy. The microgrid system scheduled by IDBO has the lowest total cost, and its ability to achieve peak load shifting and improve the utilization of renewable energy is proved through tests involving different scenarios. The results show that compared with traditional optimal scheduling models and algorithms, this approach is more reliable and cost-effective.
