Microgrids expansion problems with battery energy storage (BES) have gained a great attention in recent years. To ensure reliable, resilient, and cost effective operation of microgrids, the installed BES must be optimally sized. However, critical factors that have a great impact on the accuracy and practicality of the BES sizing results are normally overlooked. These factors include the wide range of characteristics for different technologies, the distributed deployment, the impact of depth of discharge and the number of charging/discharging cycles on the BES degradation, and the coordination of microgrid operation modes. Thus, this paper proposes a comprehensive BES sizing model for microgrid applications which takes these critical factors into account when solving the microgrid expansion problem and accordingly returns the optimal BES size, technology, number, and maximum depth of discharge. The microgrid expansion problem is formulated using mixed integer linear programming. The nonlinear relationship between BES depth of discharge and lifecycle is linearized using piece wise linearization technique and implemented to model BES degradation. The conducted numerical simulation shows that the proposed model is able to determine the optimal BES size, technology, number, and maximum depth of discharge and further enhances the accuracy and practicality of the BES sizing solution.

The increasing penetrations of renewable energy and electric vehicles bring more uncertainties and challenges to the existing power grid. The coordinated networked microgrids (MGs) contain renewable distributed generations (DGs) and nonrenewable DGs, which will be an important component in the future. We formulate an optimization problem based on a transactive energy (TE) framework for the energy schedule of upstream network and networked MGs to minimize the operation cost. The energy management between MGs and upstream network is operated by the distribution system operator (DSO), which is different from the direct control signal and fixed pricing mechanism in the traditional power system. We develop a distributionally robust optimization algorithm with ambiguity set based on Wasserstein distance (DROW) to solve the optimization problem with the uncertainties from real-time electricity price, renewable energy, loads, and electric vehicles. We carry out case studies about the energy schedule of the modified IEEE 33-bus and IEEE 118-bus power system with networked MGs. Numerical results indicate that the TE framework is conducive to schedule the energy of upstream network and networked MGs efficiently with the dynamic pricing scheme and the proposed DROW algorithm can seek a robust energy schedule of DSO and networked MGs with uncertainties.

This paper presents a two-phase mathematical framework for efficient power network damage assessment using unmanned aerial vehicle (UAV). In the first phase, a two-stage stochastic integer programming optimization model is presented for damage assessment in which the first stage determines the optimal UAV locations anticipating an arrival of an extreme weather event, and the second stage is to adjust the UAV locations, if necessary, when the arrival time of the predicted extreme weather becomes closer with updated information. UAV paths to scan the power network are generated in the second phase to minimize operating costs and final damage assessment completion time of the UAVs. Computational techniques are developed to help reduce the solution time. Numerical experiments show that the proposed stochastic model outperforms the deterministic counterpart in terms of the total UAV pre-positioning setup cost. Additionally, sensitivity analysis discovered the relations among damage assessment time, UAV pre-positioning setup cost, and operating cost.

This paper develops an effective model for microgrid optimal scheduling with dynamic network reconfiguration. Network reconfiguration can effectively alter local power flow and thus provide an opportunity to reduce microgrid distribution network losses during grid-connected operation (supporting microgrid economic objectives) and to reduce potential load curtailments during the islanded operation (supporting microgrid reliability objectives). The proposed optimal scheduling model is decomposed into a grid-connected operation master problem and an islanded operation subproblem. A novel and highly-accurate dynamic linear power flow model, with the ability of line switching, is developed and included in both problems. The optimal schedule determined in the master problem is assessed to meet the microgrid islanding feasibility in the subproblem. If infeasible, the decision variables are amended using the islanding cuts, which will accordingly revise the network reconfiguration, as well as the schedule of dispatchable units, energy storage, and adjustable loads. The simulation results on a test microgrid demonstrate the effectiveness and satisfying performance of the proposed model.

An optimal scheduling model for a microgrid participating in the electricity distribution market in interaction with a distribution market operator (DMO) is proposed in this paper. The DMO administers the established electricity market in the distribution level, sets electricity prices, determines the amount of the power exchange among market participants, and interacts with the independent system operator. Considering a predetermined main grid power transfer to the microgrid, the microgrid scheduling problem will aim at balancing the power supply and demand while taking financial objectives into account. Numerical simulations exhibit the application and the effectiveness of the proposed market-based microgrid scheduling model and further investigate merits over a price-based scheme.