The growing penetration of renewable energy resources (RESs) increases uncertainties in active distribution networks (ADNs), leading the networks to suffer low inertia, bidirectional power flows, and rapid power changes. The frequency overshoots, overvoltage, and unbalance power sharing are prone to happen in ADNs. To address those issues, this paper proposes a novel unified modeling and a hierarchical control strategy for different distributed generators (DGs). First, to unify the control states of different DGs and separate control states of different control layers, a unified modeling method for grid-following and grid-forming DGs is proposed. Then, based on the unified model, a two-layer control strategy is designed. The robust controllers are locally configured to achieve rapid recovery control, avoiding the impact of communication delays. The collaborative power controller is centralized designed to prevent power circulation caused by the interaction of local controllers of DGs. Compared with other hierarchical control strategies, our strategy can make further use of the residual capacities of different DGs and can more rapidly suppress the unpredictable changes of RESs. The effectiveness of the proposed strategy is validated by several cases in MATLAB/Simulink.
Accurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax algorithm. Firstly, four categories including 24 fault features of the distribution system are determined through data investigation and preprocessing. Considering the frequency of distribution system faults, and then their consequences, the risk classification method of the distribution system is presented. Secondly, the K-maxmin clustering algorithm is introduced to improve the random sampling process, and then an improved RelieF feature extraction method is proposed to determine the optimal feature subset with the strongest correlation and minimum redundancy. Finally, the loss function of Softmax is improved to cope with the influence of sample imbalance on the prediction accuracy. The optimal feature subset and Softmax classifier are applied to forewarn the fault risk in the distribution system. The 191-feeder power distribution system in south China is employed to demonstrate the effectiveness of the proposed method.
The load characteristic of typical household electrical equipment is elaborately analyzed. Considering the electric vehicles’ (EVs’) charging behavior and air conditioning’s thermodynamic property, an electricity price-based demand response (DR) model and an incentive-based DR model for two kinds of typical high-power electrical equipment are proposed to obtain the load curve considering two different kinds of DR mechanisms. Afterwards, a load shedding strategy is introduced to improve the traditional reliability evaluation method for distribution networks, with the capacity constraints of tie lines taken into account. Subsequently, a reliability calculation method of distribution networks considering the shortage of power supply capacity and outages is presented. Finally, the Monte Carlo method is employed to calculate the reliability index of distribution networks with different load levels, and the impacts of different DR strategies on the reliability of distribution networks are analyzed. The results show that both DR strategies can improve the distribution system reliability.
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