Network reconfiguration is an effective strategy for different purposes of distribution systems (DSs), e.g., resilience enhancement. In particular, DS automation, distributed generation integration and microgrid (MG) technology development, etc., are empowering much more flexible reconfiguration and operation of the system, e.g., DSs or MGs with flexible boundaries. However, the formulation of DS reconfiguration-related optimization problems to include those new flexibilities is non-trivial, especially for the issue of topology, which has to be radial. That is, the existing methods of formulating the radiality constraints can cause under-utilization of DS flexibilities. Thus, in this work, we propose a new method for radiality constraints formulation that fully enables the topological and some other related flexibilities of DSs, so that the reconfiguration-related optimization problems can have extended feasibility and enhanced optimality. Graph-theoretic supports are provided to certify its theoretical validity. As integer variables are involved, we also analyze the issues of tightness and compactness. The proposed radiality constraints are specifically applied to post-disturbance MG formation, which is involved in many resilience-oriented DS service restoration and infrastructure recovery problems. The resulting new MG formation model, which allows more flexible merge and/or separation of the sub-grids, etc., establishes superiority over the models in the literature. Demonstrative case studies are conducted on two test systems.

The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an OPF problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed MPC-based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulation on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.

This paper addresses the optimal allocation of battery energy storage systems (BESS) in radial distribution systems for bus voltage regulation and energy cost reduction. A hierarchical planning model is proposed to obtain the optimal configuration (number, locations, and sizes) of BESS. The three interacting levels in the proposed model are: (1) determination of the optimal BESS number; (2) use of a heuristic strategy based on voltage sensitivity analysis to select the most effective locations for a given BESS number in the network; and (3) sizing the BESS capacity to realize optimal operation objectives. The final BESS configuration is selected based on minimization of total life cycle cost, which takes into account the investment cost, residual value of BESS, and system operation cost. The voltage regulation objectives are modeled as constraints. The proposed method reduces computational burden significantly and provides a comprehensive sensitivity analysis and locate the most effective buses to mitigate voltage violation. The effectiveness of this optimal allocation method was demonstrated for a modified IEEE 15-bus and IEEE 69-bus distribution network.

Volatile voltage profiles in distribution systems caused by the fluctuating nature of renewable distributed generation (DG) are attracting growing concern. In this paper we develop a new formulation of network reconfiguration to mitigate voltage volatility. This formulation provides new insights into the voltage regulation problem with high renewable penetration that is commonly addressed by power electronic controllers. From the linear DistFlow equations, we first propose a novel index that measures the voltage volatility of each bus in the system. This index is a function of distribution network parameters that characterizes the role of network structure in voltage volatility. Then, we formulate a new reconfiguration model that minimizes the network loss and restricts the voltage volatility indices with the coordination of switched capacitor banks. A Benders decomposition-based approach is designed to solve the problem using mixed-integer quadratic programming. The simulations on the IEEE 69-bus system show that the reconfiguration scheme is able to: 1) minimize network loss when DG outputs are as predicted; and 2) significantly reduce the risk of voltage violations when DG outputs deviate from the prediction. The proposed formulation unleashes the distinctive power of network reconfiguration in reducing voltage volatility level, by which the cost of power electronic voltage controllers can be saved.

We present a novel control strategy of charging and discharging batteries in a distribution system to optimize the energy transaction cost. With an increased proportion of renewable energy in a distribution system, the real demand curve may significantly deviate from the forecast curve, which can lead to an increased challenge for an energy distribution company in making an effective purchase plan. The proposed strategy aims at tracking the total forecast demand curve, and can mitigate risk and encourage demand-side bidding. In this paper, short-term load forecasting, wind power forecasting, and solar power forecasting are performed. To optimize profit, the optimal operation of energy storage systems in a distribution system was developed and solved in a two-level framework considering forecast uncertainties in day-ahead operation and mitigating the net demand gap in real-time operation. To quantify the risk mitigation and profits, the purchase strategies for uncertain and certain demand that occurs on the next day were compared. The promising results show that optimal operation of a battery energy storage system can reduce the energy cost and the transaction risk for an energy distribution company.

In this letter, an optimal operation model for a multi-agent system (MAS) based microgid is proposed. The objective is to save the overall energy cost represented as a sum of locally observable convex functions. An asynchronous and distributed alternating directions of multipliers method is developed for the MAS-based microgrid to solve the optimization problem with local timer and reach a convergence state. The proposed ap-proach is shown to be highly effective through a simulation case study, indicating high potential for applications.