This paper proposes a comprehensive two-stage method for siting and sizing stand-alone electric-vehicle charging stations on highway networks. In the first stage, locations, where individual vehicles require charging services, are obtained from Monte Carlo simulation provided with the traffic demand and battery data; an integer programming model is proposed to determine the optimal sites of charging stations from potential candidates, ensuring that every vehicle is able to visit at least one charging station without depleting the battery; afterward, the spatial and temporal distribution of charging demand at individual selected sites can be simulated. In the second stage, a datadriven distributionally robust optimization model is developed to optimize the capacities of renewable generations and energy storage units in each charging station. The uncertain generation and demand are described by a family of inexact distributions around an empirical distribution, and their distance in the sense of Kullback-Leibler divergence is controlled by an adjustable scalar. Two reformulations of the robust model are suggested based on risk theory. The first one relies on Value-at-Risk (VaR) and gives rise to a mixed-integer linear program (MILP), which is more accurate; the second one offers a conservative approximation based on Conditional VaR and comes down to a linear program, which is more tractable. Numerical study on a test system demonstrates the effectiveness of the proposed methods.

Electric vehicles (EVs) has tiny environmental impact and will constitute a major mean of urban transportation in the future. Shared EV is quickly becoming a new business model under the sharing economy initiatives, providing easy access for commuters who possess no private cars. In this paper, we consider a carsharing company that owns EVs and some parking lots. Passengers can hire an EV at one parking lot and drive it to another one and pay for the service at a certain price determined by the company. A dedicated EV mobility model is proposed to capture the spatial transportation of energy. Price elasticity is described by a linear demand-price function. The company schedules the charging of unoccupied EVs in each parking lot, aiming at maximizing its profit. Parking lots possess relatively large capacity and have to participate in a distribution power market; energy consumption is paid at the locational marginal price. The decision-making problem of the company is formulated as a bilevel program. The lower level simulates the distribution market clearing, and the upper level represents pricing and charging scheduling problem faced by the company. Starting from a global polyhedral approximation of the power flow model, we develop an equivalent mixed-integer program based on primal-dual optimality condition and integer algebra technique. Case studies demonstrate that the proposed business model can reshape the load profile by shaving the peaking and filling the valley without harming the profit of the company.

Renewable generation has grown rapidly these years due to its advantages in low environmental impacts. Stand-alone microgrid with renewable generation and energy storage is a promising option in remote areas beyond the reach of an existing power grid. This paper proposes a multiobjective optimization model to co-optimize the sizes of renewable generation and energy storage in stand-alone microgrids, which minimizes the load shedding risk and the total investment cost. The load shedding degree is obtained by an optimal operation problem and measured by shortfall risk, considering the uncertainties of demand and renewable resources. To deal with the inaccurate empirical distribution, distributionally robust shortfall risk is further established under a Wasserstein-metric-based ambiguity set, and an equivalent distributionally robust formulation is obtained. Then a conservative approximation is developed, and the resulting problem appears to be a bi-objective linear program. The Pareto frontier is proven to be piecewise linear, and the analytical expression of the Pareto frontier is derived. Case studies demonstrate the effectiveness and performance of the proposed method.

Multiple pulsed loads arise in all-electric ship power systems, which consume enormous amount of energy in a short time. Moreover, pulsed load requirements may change rapidly, and a fast regulation method is indispensable for the optimal energy management. To this end, the paper formulates an optimal coordination problem and develops an online algorithm for a shipboard power system, where the periodic operation of pulsed loads is utilized. The problem is divided into two parts: i) the energy allocation for each pulsed load is optimized to maximize total utility; ii) the joint charging period is determined to supply pulsed loads in a shorter cycle. In the solution method, the Fourier series are used to represent the operation period of pulsed loads and accelerate the algorithm. Case studies on a shipboard power system with 8 pulsed loads show that the proposed algorithm is effective, and applicable for online use.

Robust optimization is an essential tool for addressing the uncertainties in power systems. Most existing algorithms, such as Benders decomposition and column‐and‐constraint generation (C&CG), focus on robust optimization with decision‐independent uncertainty (DIU). However, increasingly common decision‐dependent uncertainties (DDUs) in power systems are frequently overlooked. When DDUs are considered, traditional algorithms for robust optimization with DIUs become inapplicable. This is because the previously selected worst‐case scenarios may fall outside the uncertainty set when the first‐stage decision changes, causing traditional algorithms to fail to converge. This study provides a general solution algorithm for robust optimization with DDU, which is called dual C&CG. Its convergence and optimality are proven theoretically. To demonstrate the effectiveness of the dual C&CG algorithm, we used the do‐not‐exceed limit (DNEL) problem as an example. The results show that the proposed algorithm can not only solve the simple DNEL model studied in the literature but also provide a more practical DNEL model considering the correlations among renewable generators.