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Accurate wind power forecast (WPF) is critical for ensuring secure and economic operation of a power system, and combination forecasting approaches for WPF have been proved effective on attaining accurate forecasting results. However, the weights in a combination forecasting model are usually predetermined based on the global performance on the training set, limiting the adaptability of the model to different scenarios. To further enhance the accuracy and effectiveness of the combination forecasting approaches, this article proposes an adaptive weighted combination forecasting approach based on the deep deterministic policy gradient (DDPG) so as to consider the local behavior accompanied by the change of external environment. Three sub-models are first selected considering the equal-likelihood and dispersion indices to construct a combination model. Then, the DDPG agent is trained to act as a weight generator by interacting with the environment and to maximize the reward. Thus, the DDPG agent can perceive the environmental changes online and dynamically weight the sub-models to attain accurate forecasting results. Case studies demonstrate that the forecasting accuracy of the proposed approach is better than that of all sub-models and several benchmark combination forecasting approaches.

This article presents a dynamic restoration strategy for active distribution networks (ADNs) by coordinating repair crew dispatch and frequency-constrained cold load pickup. To incorporate the stochastic repair time, the repair crew dispatch is formulated as “event-driven” with the implementation of model predictive control (MPC). The stochastic repair time is estimated, convexified, and updated dynamically with each MPC execution. The finish of a repair task triggers the subsequent cold load pickup model, where the frequency dynamics are computed and linearly constrained with the help of a uniform frequency response model for low-inertia systems. Next, a co-optimization framework of the two models is developed to coordinate the repair crew dispatch and cold load pickup under a unified time scale. Numerical results on a modified IEEE 33-node test feeder and a real-world 136-node distribution system have verified the effectiveness of the proposed model.

Forming microgrids (MG) in distribution systems (DSs) is an operational measure proposed to enhance power system flexibility. The computational performance of the applied optimization techniques varies greatly according to the constraints ensuring network radiality. Formulations of radiality constraints differ from each other by model complexity and the search space of the relaxed linear programming (LP) problem. In this paper, widely used formulations of radiality constraints are first revisited and flexibly enhanced to address the complex operating environment of modern DSs. To incorporate various radiality constraints and provide an optimization basis for comparison, a flexible MG formation problem is proposed with the ability to automatically allocate distributed generators (DGs) into multiple MGs. Combined formulations with parent-child (PC) constraints are then developed to shrink the LP feasible region. Moreover, to accelerate the branching process by applying the branch-and-cut (B&C) algorithm, an efficient formulation of radiality constraints is proposed by adding extra constraints that exclude fundamental loops into the single-commodity flow formulation. To further accelerate the branching process, a dynamic constraint generation algorithm is developed without substantially increasing the complexity of the LP problem. DSs of various complexities and sizes are employed to verify the computational efficiency of the proposed formulations. The paper concludes by suggesting the most efficient formulations of radiality constraints according to the scale of the targeted DS.

When power systems encounter outages and large-scale blackouts, system restoration is critical and should be carried out with dedicated schemes. In this past, most studies divided the power system restoration into three stages (i.e., black-start zone partitioning, network reconfiguration, and load restoration) and deal with them separately. After that, few studies considering the three stages together were emerging while the support of multiple flexible resources, i.e., renewable energy source (RES), electric vehicle system (EVS) and energy storage system (ESS), were not considered comprehensively. Therefore, a bi-level coordinated power system restoration (BiCPSR) model is proposed in this work considering the support of multiple flexible resources. In the upper level, two network topology indices that describe the “reachability” and “shortest reachable distance” of buses in power systems, and the restoration characteristics of generators and loads are utilized for optimizing the start-up sequence of generators and network reconfiguration. In the lower level, the uncertainties of RES and EVS are considered by various scenarios and the support of multiple flexible resources is utilized cooperatively for accelerating the restoration process and maximizing the restorable load. Case studies on the revised IEEE 39-bus, WECC 179-bus and the actual Zhejiang power systems are performed to illustrate the basic features of the proposed model and its availability in bulk power systems. The comparisons between the proposed model and other models are also performed to illustrate the strengths of the proposed model.

As the agent and risk taker of demand side resources, inaccurate recognition of user characteristics will lead to large trial-and-error cost of load aggregator (LA) at the early stage of market-oriented demand response (DR). In this work, the typical invitation-based DR program implemented in China's provincial DR trading pilot is first introduced, and a bidding and scheduling strategy optimization model is built as a simulation tool for LA's decision-making in the DR transactions. Then, a two-stage user characteristic recognition method is proposed for LA to improve its market strategies based on a labelled demand side resources database named demand resource pool (DRP). In the first stage of the method, the DRP data completion algorithm based on Bias- singular value decomposition matrix factorization is proposed to preliminarily recognize the potential demand side resources with unknown characteristic labels. In the second stage, the Bayesian inference-based modification algorithm is proposed to realize a refined recognition of user characteristic labels according to LA's empirical conclusions and the new samples of each round of the DR transactions. Simulations based on the data from the emerging DR pilot in Zhejiang province, China illustrate that the proposed method has lower error in user characteristic recognition, which is conducive to LA to bear lower loss risk and enhance its income at the early stage of DR trading.