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The operational flexibility of electric power systems (EPS) is restricted by combined heat and power (CHP) units that act to maintain sufficient heating supply. By exploiting the pipeline heat storage property in central heating systems (CHS), combined heat and power dispatch (CHPD) can significantly increase the operational flexibility and reduce wind power curtailment. In this paper, pipeline heat storage is modeled in the CHPD model under the constant mass flow heating dispatch mode, and the CHPD model can be formulized as a quadratic programming problem. Since the EPS and CHS are independently operated by the EPS and CHS operators, a decentralized solution to the CHPD model is proposed. During each iteration of the decentralized procedure, an optimal cut or a feasible cut is generated by the CHS operator and sent to the EPS operator. A robust model considering the wind power uncertainty is also studied with the proposed decentralized solution. This decentralized solution has a high efficiency and a light communication burden. Numerical tests on practical systems demonstrate the feasibility of the proposed decentralized method and the economic benefits brought by reducing wind curtailment.

The dynamic dispatch (DD) of battery energy storage systems (BESSs) in microgrids integrated with volatile energy resources is essentially a multiperiod stochastic optimization problem (MSOP). Because the life span of a BESS is significantly affected by its charging and discharging behaviors, its lifecycle degradation costs should be incorporated into the DD model of BESSs, which makes it non-convex. In general, this MSOP is intractable. To solve this problem, we propose a reinforcement learning (RL) solution augmented with Monte-Carlo tree search (MCTS) and domain knowledge expressed as dispatching rules. In this solution, the Q-learning with function approximation is employed as the basic learning architecture that allows multistep bootstrapping and continuous policy learning. To improve the computation efficiency of randomized multistep simulations, we employed the MCTS to estimate the expected maximum action values. Moreover, we embedded a few dispatching rules in RL as probabilistic logics to reduce infeasible action explorations, which can improve the quality of the data-driven solution. Numerical test results show the proposed algorithm outperforms other baseline RL algorithms in all cases tested.

Distributed generators are integrated into microgrids or geographically distributed subsystems in active distribution networks and may belong to various entities. Centralized solutions cannot meet future requirements such as high resilience and privacy protection. Therefore, fully distributed economic dispatch (ED) methods are needed and most existing algorithms exhibit linear or sublinear convergence. This paper introduces an efficient fully distributed dynamic ED solution for minimizing photovoltaic power curtailment, based on a parallel primal-dual interior-point algorithm with a quasi-Newton technique. In this method, each area optimizes its own problem with limited information exchanged with its neighbors, and no central coordinator is needed. Based on a peer-to-peer communication paradigm, this method can achieve a global optimum while the privacy of each area is obliquely protected. Numerical tests demonstrated that the algorithm outperforms gradient-based methods such as ADMM and maintains stability under a partial communication failure.

The integration of volatile distributed energy resources (DERs) brings new challenges for the active distribution network maintenance scheduling (DN-MS). Conventionally, the DN-MS is formulated as a deterministic optimization model without considering the uncertainties of DERs. In this paper, the DN-MS is formulated as a multistage stochastic optimization problem, which is cast as a stochastic mixed-integer nonlinear programming model. It aims to reduce the total maintenance cost constrained by the reliability indices. To capture the operational characteristics of active distribution networks, the uncertainties of DERs and post-outage operation strategies of switching devices are incorporated into the model. In general, this type of model is intractable and mainly solved by heuristic search methods with low efficiency. Recently, Monte-Carlo tree search (MCTS) is emerging as a scalable and promising reinforcement learning approach. We propose a stochastic MCTS solution to this problem. In the tree search procedure, a sample average approximation technique is developed to estimate multistage maintenance costs considering uncertainties. To speed up the MCTS, the complicated constraints of the original problem are transformed to penalty or heuristics functions. This approach can asymptotically approximate the optimum with promising computation efficiency. Numerical test results demonstrate the superiority of the proposed method over benchmark methods.

Fast decoupled state estimation (FDSE) is proposed for distribution networks, with fast convergence and high efficiency. Conventionally, branch current magnitude measurements cannot be incorporated into FDSE models; however, in this paper, branch ampere measurements are reformulated as active and reactive branch loss measurements and directly formulated in the proposed FDSE model. Using the complex per unit normalization technique and special chosen state variables, the performance of this FDSE can be guaranteed when it is applied to distribution networks. Numerical tests on seven different distribution networks show that this method outperforms Newton type solutions and is a promising method for practical application.

Analytical methods for evaluating the reliability of simple and radial distribution networks have been well established. Since these analytical methods cannot consider post-fault load transfer between feeders, the reliability indices are significantly underestimated for mesh-constructed distribution networks. To accommodate various application scenarios, Monte-Carlo simulations are widely used for complex distribution networks and heavy computation burden is involved. In this paper, we propose a novel linear programming model which includes precisely assessing reliability and considers post-fault network reconfiguration strategies involving operational constraints. Moreover, this model also can formulate the influences of demand variations, uncertainty of distributed generations and protection failures on the reliability indices. Numerical simulations show that the proposed model yields the same results as the simulation-based algorithm. Specifically, the system average interruption duration indices are reduced when considering post-fault network reconfiguration strategies in all tested systems. Moreover, the proposed model is suitable for inclusion in reliability-constrained operational and planning optimization models for power distribution systems.