An increased number of microgrids (MGs) will exist in future active distribution systems (ADSs); however, these MGs belong to multiple operators and cannot simply be controlled as a public grid in the electricity market environment. For multimicrogrids (MMGs) which have multiple coexisting beneficiaries, it will be unfeasible to realize a cooperative control among them. Hence, we propose a game theoretic non-cooperative distributed coordination control (NCDCC) scheme to address multi-operator energy trading and facilitate a powerful control structure for MMGs. The proposed NCDCC coordinates the individual benefits of each MG and achieves a global objective, based on differential game theory (DGT). Furthermore, it simplifies the implementation of many ADS control functions, including global coordination, local autonomy, and self-healing, by reconsidering an ADS as MMGs and every MG as an agent. Moreover, the NCDCC reduces the control dimension and number of agents by obviating the requirements for a central controller and complex communication topologies. We investigate the effectiveness, adaptability, and fairness of this scheme by means of several representative cases studies.

For an active distribution system (ADS) that integrates high levels of distributed generators (DGs), the control dimension of such a wide and dynamic set of resources would become overwhelming. This study proposes a novel pinning group consensus (PGC)-based distributed coordination control to simplify ADS control by using the virtual clusters building block concept. A given ADS is rethought as coupled small virtual clusters, including virtual microgrid clusters and virtual power plant clusters; accordingly, the ADS can be coordinated by controlling a small number of selected pinning agents instead of a huge number of DGs. The predefining of the PGC values for these pinning agents, which comprehensively considers both clusters features and DG capacities, is the most distinguishing work for the proposed control scheme and can lead to effective global coordination in a distributed manner. Simulation cases under normal/disturbed/emergency conditions verify the effectiveness and advantages of the proposed scheme.

Accurate load forecasting can scientifically guide the optimal operation and scheduling of urban power grids. This study introduces an enhanced power load forecasting algorithm, integrating slime mould algorithm (SMA) and long short‐time memory (LSTM) to effectively address the hyperparameter challenges associated with LSTM, while also applying variational modal decomposition (VMD) to load forecasting. In the data processing stage, the Bisecting Kmeans algorithm (Bi‐Kmeans) is used to identify the outliers of the measured load data, then the random forest (RF) is used to correct them, which determines reasonable load data. In the data analysis stage, the processed load data undergoes VMD, yielding components with distinct central frequencies, and the components of different frequencies are determined according to their energy values. In the prediction stage, an optimized LSTM using SMA is proposed to predict different frequency components separately, and the prediction results of multiple components are inversely reconfigured to obtain the load prediction results. Case studies demonstrate that the proposed algorithm outperforms other power load forecasting methods in prediction accuracy.