In order to solve the voltage violation problem caused by high penetration of photovoltaic (PV) units in distribution power networks, this paper proposes a double-layer voltage control strategy based on the distribution network partition. By optimizing the active and reactive power outputs of PV units, the minimization objective of PV active power curtailment and network active power loss can be achieved. In the basis of community detection algorithm, a novel cluster performance index based on the electrical distance and regional voltage regulation capability is presented to partition a distribution network into several clusters. The proposed double-layer voltage control strategy combines the cluster autonomous optimization and distributed inter-cluster coordination optimization in different time scales. The cluster autonomous optimization can eliminate intra-cluster voltage violation rapidly by alternately updating the intra-cluster optimal solution and the virtual slack bus voltage. The distributed inter-cluster coordination optimization with a long-time scale is based on the alternating direction method of multipliers (ADMM) and realizes global optimal control through finite boundary data exchange among adjacent clusters. The effectiveness and feasibility of proposed method have been demonstrated via simulation tests on a real 10.5kV feeder in China and the IEEE 123-bus system.

With large-scale distributed photovoltaics (PVs) being integrated into distribution networks (DNs), coordinated optimal power flow (OPF) of high voltage (HV) and medium voltage (MV) DNs should be investigated. This paper presents a hierarchical distributed OPF method for HV and MV DNs with on-load tap changers, reactive power compensators, feeder switches and distributed PVs. A hierarchical master-slave control architecture is applied to implement coordinated OPF of two-layer DNs. Since there is no efficient distributed algorithm to solve such OPF models with integer subproblems, a novel distributed algorithm is proposed in this paper to efficiently solve the hierarchical coordinated OPF model. In the proposed algorithm, the coordinated OPF model is solved in a branch-and-bound framework, where in each branch node generalized Benders decomposition (GBD) algorithm is applied to decompose the coordinated OPF model into a master problem and relaxed subproblems and solves them iteratively to get optimal solution. The GBD optimal and feasible cutting planes generated in a branch node are proved to be valid for its descendants. Moreover, three acceleration methods are presented to improve the computational efficiency of the proposed algorithm. Finally, the effectiveness and accuracy of the proposed method are verified via simulation tests in Jinzhai DNs of China.

Existing Non-Intrusive Load Monitoring (NILM) methods primarily rely on data-driven approaches for feature extraction and matching, overlooking electricity usage scenario information and prior knowledge within datasets. This limitation leads to constrained disaggregation performance when appliance load signatures are similar. This letter introduces Large Language Models (LLMs) to the NILM domain for the first time, proposing a novel load disaggregation framework based on electricity usage scenario understanding, thereby shifting NILM paradigm from feature matching to scenario cognition. First, we propose a scenario-oriented comprehensive prompt construction method, designing structured prompts that integrate background information, task instruction, and aggregate load temporal features to accurately describe electricity usage scenarios. Second, we develop an LLM-based load disaggregation method that leverages pre-trained LLMs’ reasoning capabilities to comprehend electricity usage scenarios described in prompts, and transfers LLMs to load disaggregation tasks through fine-tuning. Finally, we incorporate appliance state information to refine LLM disaggregation results based on device state distributions. Experimental results on the UK-DALE dataset demonstrate that proposed method reduces MAE and RMSE by 9.26% and 5.09% respectively compared to state-of-the-art methods, validating the effectiveness of LLM-based scenario cognition in NILM.

In the stochastic optimization control of power systems, renewable energy has strong uncertainty and underlying correlations at spatial and temporal scales, which need to produce a large number of discrete scenarios to characterize. However, it will bring a huge solving burden to the stochastic optimization, thus requiring necessary scenario reduction operation. Moreover, the inability to accurately measure and mitigate correlation distortion poses a major challenge for uncertainty quantification, potentially compromising the fidelity of stochastic optimization. In this paper, a novel adaptive dynamic scenario clustering algorithm (ADSCA) is proposed for the problem that the spatiotemporal correlations cannot be well preserved. The concept of spatiotemporal correlation loss is firstly presented to measure the deviation degree of linear and tail dependence after reduction. Based on the constructed clustering model aiming to minimize spatiotemporal correlation loss, the optimal number of preserved scenarios does not need to prespecified, and can be dynamically adjusted through iterative splitting and merging operations, ultimately obtaining the most representative scenarios while improving reduction efficiency. Numerical results show that ADSCA has outstanding advantages in preserving the statistical and correlation features, while ensuring the representativeness of scenarios. Application studies to probabilistic optimal power flow are also conducted to verify the effectiveness of ADSCA in improving the power system reliability and economic benefits.

High penetration of distributed photovoltaics (PVs) has caused severe voltage violation problem in distribution networks. In view of the actual situations, this paper proposes a coordinated optimization method for distributed PVs clusters considering inadequate measurement data. Based on pivotal measurement data, each cluster firstly uses network simplification method to obtain a simplified network with complete measurement information. And then each cluster implements coordinated optimization with adjacent clusters to minimize its network losses, PVs generation losses and voltage violation amount of downstream cluster. The coordinated optimization includes two stages, i.e. evaluating voltage regulation capability of intra-cluster reactive power resources and implementing cluster autonomous optimization control. These two stages can use the reactive power resources of upstream clusters to solve the voltage violation of downstream clusters, which avoids unnecessary generation losses of distributed PVs. The effectiveness and accuracy of the proposed method are demonstrated via simulation tests on a real 10kV feeder in China and modified IEEE 123-bus system. Compared with distributed optimization method, the proposed coordinated optimization requires much less calculation time and communication data between clusters.