Electric vehicle (EV) is a promising technology for reducing environmental impacts of road transport. In this paper, a framework for optimal design of battery charging/swap stations in distribution systems based on life cycle cost (LCC) is presented. The battery charging/swap station models are developed to compare the impacts of rapid-charging stations and battery swap stations. Meanwhile, in order to meet the requirements of increased power provided during the charging period, the distribution network should be reinforced. In order to control this reinforcement cost, stations should be placed at appropriate places and be scaled correctly. For optimal cost-benefit analysis and safety operation, the LCC criterion is used to assess the project and a modified differential evolution algorithm is adopted to solve the problem. The proposed method has been verified on the modified IEEE 15-bus and 43-bus radial distribution systems. The results show that battery swap station is more suitable for public transportation in distribution systems.

An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method.

Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO's profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated.

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.

Economic load dispatch (ELD) is an important topic in the operation of power plants which can help to build up effective generating management plans. The ELD problem has nonsmooth cost function with equality and inequality constraints which make it difficult to be effectively solved. Different heuristic optimization methods have been proposed to solve this problem in previous study. In this paper, quantum-inspired particle swarm optimization (QPSO) is proposed, which has stronger search ability and quicker convergence speed, not only because of the introduction of quantum computing theory, but also due to two special implementations: self-adaptive probability selection and chaotic sequences mutation. The proposed approach is tested with five standard benchmark functions and three power system cases consisting of 3, 13, and 40 thermal units. Comparisons with similar approaches including the evolutionary programming (EP), genetic algorithm (GA), immune algorithm (IA), and other versions of particle swarm optimization (PSO) are given. The promising results illustrate the efficiency of the proposed method and show that it could be used as a reliable tool for solving ELD problems.

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.