Ma et al. established a robust environmental economic scheduling model based on robust optimization, aiming at the multi-microgrid scheduling problem while considering its economy and environment, the
The two-stage optimization schedule strategy for microgrid group leasing SES is shown in Fig. 3. The stage 1 is SES optimization strategy. Firstly, The charging and discharging demand of the
This study converts the microgrid optimization scheduling issue into reinforcement learning tasks by identifying the observation space, state space, and reward functions within the microgrid model.
Currently, research on the optimization and scheduling of port microgrids often focuses on individual microgrids, involving the planning of output power for various generation
This study proposes an innovative multi-objective optimization scheduling model for micro grids, which integrates real-time meteorological data and load forecasting technology to achieve
In order to improve the problem of energy distribution shortage in smart micro-grid, Garcia reduced load demand based on demand response constraints, optimized resource scheduling and increased
In today''s energy and climate landscape, microgrid technology has emerged as a promising solution to enhance power reliability and grid integration capacity, leading to its widespread
Optimal Scheduling of Microgrid Based on Improved Particle Swarm Optimization Abstract: Microgrids have attracted more and more attention due to their low cost, low voltage, and low
In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed.
The proposed optimal scheduling method that considers the coordination of long and short-term storage, and its corresponding solution algorithm, can effectively complete the optimization scheduling of the microgrid.
A multi-time scale optimal scheduling framework is proposed for microgrid scheduling to deal with the uncertainty of source and load. A two-stage distributionally robust model is constructed to improve the robustness of the day-ahead scheduling plan.
As the last step of the entire microgrid multi-time scheduling optimization, the real-time adjustment stage takes 15 min as the control time domain and 5 min as the index value.
Additionally, the microgrid system used in this paper contains renewable energy, which will interfere with the scheduling because of its randomness. Therefore, to avoid the interference of uncertain factors, and obtain objective weights, we used a method of weight determination based on the two-person zero-sum game.
In order to improve the problem of energy distribution shortage in smart micro-grid, Garcia reduced load demand based on demand response constraints, optimized resource scheduling and increased energy consumption of micro-grid under the premise of ensuring the safe operation of grid 12.
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