Transportation electrification is one of the main research areas for the past decade. Electric vehicles (EVs) are taking over the market share of conventional internal combustion engine vehicles. The increasing popularity of EVs results in higher number of charging stations, which have significant effects on the electricity grid. Different charging strat2egies, as well as grid integration methods, are being developed to minimize the adverse effects of EV charging and to strengthen the benefits of EV grid integration. In this paper, a comprehensive review of the current situation of the EV market, standards, charging infrastructure, and the impact of EV charging on the grid is presented. The paper introduces the current EV status, and provides a comprehensive review on important international EV charging and grid interconnection standards. Different infrastructure configurations in terms of control and communication architectures for EV charging are studied and evaluated. The electric power market is studied by considering the participation roles of EV aggregators and individual EV owners, and different optimization and game based algorithms for EV grid integration management are reviewed. The paper specially presents an evaluation on how the future EV development, such as connected vehicles, autonomous driving, and shared mobility, would affect EV grid integration as well as the development of the power grid moves toward future energy Internet and how EVs would affect and benefit the development of the future energy Internet. Finally, the challenges and suggestions for the future development of the EV charging and grid integration infrastructure are evaluated and summarized.

Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review

The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.

https://www.mdpi.com/1996-1073/13/8/1879/pdf

Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm

Voltage and reactive power control via inverter-based distributed generators (DGs) is necessary for distribution networks to mitigate voltage violations. This article presents an accelerated alternating direction method of multiplier (ADMM)-based reactive power optimization algorithm in a fully distributed manner. In the proposed method, each area optimizes its own subproblem with limited boundary information communicating with neighbors to protect information privacy. Based on the distributed quasi-Newton technique, the second-order curvature of the dual function is estimated and applied to improve the convergence property of the ADMM. Simulation results have verified the effectiveness of the proposed method compared with other gradient-based methods, such as ADMM and dual decomposition. The proposed acceleration strategy can also be extended to other gradient-based distributed optimization in power systems.

Accelerated ADMM-Based Fully Distributed Inverter-Based Volt/Var Control Strategy for Active Distribution Networks

This paper presents a fully distributed algorithm for the stochastic Volt/VAr control (VVC) problem in active distribution networks. Exact convexification of the VVC problem is achieved through the use of second-order cones on the continuous relaxation of the original optimization structure. The global optimum solution of the relaxed problem is obtained through the application of the alternating direction method of multipliers (ADMM). In order to minimize the effect of rounding off on the final solution, an adaptive threshold discretization technique is used. A two-stage control strategy is adopted where the discrete controllers, like load tap changers and switched capacitors, are dispatched at the beginning of the optimization interval and the continuous controllers, like distributed generation (DG) inverters, are adjusted in real time according to an optimized decision rule. The superiority of the proposed algorithm is demonstrated through numerical simulations on the UKGDS-95 and the IEEE-123 bus systems.

Distributed Solution of Stochastic Volt/VAr Control in Radial Networks

Increasing penetration of direct current (dc) based distributed energy resources and dc loads in the conventional alternating current (ac) network necessitate the deployment of ac/dc hybrid distribution networks (HDNs). In view with the development of advanced energy management policy for ac/dc HDNs, unlike previous literatures, this article proposes a risk constrained energy efficient management algorithm by merging load shifting (LS) and conservation voltage reduction (CVR) techniques. The optimization framework aims to simultaneously minimize both true and conditional risk or conditional value at risk values of the expected energy cost under uncertain solar power generation, load demand, and upper grid energy price. In contrast with the available stochastic optimization process, in this article, two-point estimation strategy is employed in place of Monte Carlo simulation for scenario generation from the probability density functions of the uncertain parameters to reduce computational exertion. The proposed centralized optimization framework is initially developed as mixed integer nonconvex programming but to avoid computation complexity, the nonlinear components are replaced by their linear counterparts. Later, a new solution process named successive mixed integer linear programming (s-MILP) is proposed to obtain the optimal decisions for deployment of LS and CVR through smart inverters and volt-VAR controlling devices. Efficacy of the proposed technique is demonstrated on modified IEEE 33 bus ac/dc HDN and the most energy efficient operation is found by merging LS and CVR. Simulation outcomes prove fast and near optimal convergence of the s-MILP compared to conventional second-order conic programming relaxed mixed integer convex programming and piecewise linearization based MILP. Further, to assess the impact of network size on the solution time and optimality, the proposed advanced distribution network management systems strategy is employed on 132 bus ac/dc HDN.

Risk Constrained Energy Efficient Optimal Operation of a Converter Governed AC/DC Hybrid Distribution Network With Distributed Energy Resources and Volt-VAR Controlling Devices

With the increasing penetration of distributed photovoltaics (PVs), the operation and control of distribution networks (DNs), especially voltage control, have become more complicated. To deal with the voltage violation problem caused by large-scale PV access, this paper presents a fully distributed optimization method that combines the alternative direction multiplier method (ADMM) with the branch and bound method (BBM) for regional DNs. The total cost of active power losses and PV generation losses is minimized by making full use of the voltage regulation resources, e.g., reactive power compensators, step voltage regulators (SVR), and PV inverters, and the ADMM is employed to realize the intra-regional optimization and inter-regional coordination. To overcome the non-convex problem that is introduced by the SVR, the constraints of real-value tap positions are reformulated as linear inequality constraints of boundary voltages and added to the original problem, then the integer optimal solutions of SVR tap positions are obtained by BBM. The effectiveness of the proposed method is verified via numerical simulations on a practical 32-bus DN in China and a modified IEEE123-bus system.

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A Fully Distributed Voltage Optimization Method for Distribution Networks Considering Integer Constraints of Step Voltage Regulators

This paper investigates the optimal configuration and control of electric vehicle aggregators equipped with smart meters in the regional power distribution network. The mixed optimization of configuration and control is originally formulated as a mixed integer nonlinear programming (MINLP) problem with the stochastic operation model of EV aggregations. The controllable charging load is calculated by using a queuing model with non-homogeneous Poission process and integrated into the reliability constrained economic dispatch of the power system. Benders decomposition technique is adopted to lessen the dimensional and computational complexities introduced by the charging control of massive EVs. The total operating cost is minimized in the master problem, and charging plan for each individual EV is made by EVA in the sub-problems. In the case of any subproblem violations in the feasibility check of the optimal solution, Benders cuts are produced and added to the master problem. The proposed method is demonstrated in a 123-bus test network and EVA optimization results are compared with the direct control of EV aggregations for validating the effectiveness of the proposed method in the electrification of automobiles.

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Integrated Configuration and Optimization of Electric Vehicle Aggregators for Charging Facilities in Power Networks With Renewables

This paper presents a methodology for evaluating the techo-economic feasibility of mixed AC and DC distribution network to replace the existing AC distribution network with large-scale integration of photovoltaic energy sources. Economic dispatch of mixed AC/DC system is formulated in the presence of power electronic transformer (PET) for the flexible control of AC and DC power flows while maintaining the reliability constraints in terms of bus voltage, branch current carrying capacity, and the efficient operation of power electronic converters. Furthermore, the configuration of the PET and power losses of converters with a changing operation efficiency is incorporated into the optimal dispatch problem to assess the energy savings provided by the AC/DC power network. Two objectives for minimizing the operating cost and total power losses are also considered in the formulated economic dispatch. To obtain the Pareto-optimal solutions of this reliability constrained multi-objective optimization problem, the non-dominated sorting genetic algorithm-II (NSGA-II) associated with the interior-point method are applied in the proposed two-stage optimal algorithm to solve the optimization for PET configuration and operation. The method has been tested on the IEEE 33-bus radial distribution system with excessive PV installation and updated PETs. In comparison with the optimal dispatch of existing AC power distribution system, the joint optimization of PET AC/DC ports and PV inverters improves the flexibility of power flow control for both AC and DC networks and thus minimizing the PV curtailment and total operating cost.

/pdf/techno-economic-evaluation-of-mixed-ac-and-dc-power-y18213rqj9.pdf

Techno-Economic Evaluation of Mixed AC and DC Power Distribution Network for Integrating Large-Scale Photovoltaic Power Generation

The present invention discloses a day-ahead optimal scheduling method for incremental power distribution network with high permeability distributed photovoltaic. The method comprises following steps:constructing an incremental distribution network day-ahead optimization model according to a incremental distribution network model, the optimization model includes a running cost objective function and constraint conditions: using the constructed incremental distribution network day-ahead optimization model and taking minimization of the objective function as a target to optimize the incrementaldistribution network; the day-ahead optimal scheduling method for incremental distribution network with high permeability distributed photovoltaic has following advantages: comprehensively consideringtechnologies of a photovoltaic converter RPC, a power distribution station SVC, an OLTC tap action and a user side ESS charge and discharge control methods, considering superiority of the distributedphotovoltaic in the incremental distribution network and user side energy storage complementary power supply, establishing a quadratic constrained quadratic QCQP optimization model based on a branchpower flow model BFM. The control method is more comprehensive and can obtain a scheduling plan with a smaller day-ahead running cost and achieve the desired voltage regulation effect.

Day-ahead optimal scheduling method for incremental distribution network with high permeability distributed photovoltaic

The invention discloses a distribution network regional voltage distributed coordinated control method and system. The method comprises a step of determining the installation positions of line voltageregulators in a power transmission line when there is no light, a load is maximum and a voltage loss rate is smaller than a set value, a step of dividing the power transmission line into multiple regions by using the installation positions of the line voltage regulators as boundaries, a step of establish a regional optimization model with the minimalization of the active loss of the power transmission line and the total cost of loss of photovoltaic power generation in the regions as an optimization target, a step of performing distributed coordination optimization between the regions by usingan alternating direction multiplier method and determining the optimal objective function of each regional optimization model and the boundary voltage of each region after distributed coordination optimization, and a step of determining the gear positions of the line voltage regulators by using a branch bound method, and regulating the voltage of each region of a distribution network. According to the invention, the problems of the time and space mismatch of a power supply with a high-permeability photovoltaic distribution network and the load and voltage out-of-limit can be solved.

Shuang Gao

Papers

A Fully Distributed Voltage Optimization Method for Distribution Networks Considering Integer Constraints of Step Voltage Regulators

Integrated Configuration and Optimization of Electric Vehicle Aggregators for Charging Facilities in Power Networks With Renewables

Techno-Economic Evaluation of Mixed AC and DC Power Distribution Network for Integrating Large-Scale Photovoltaic Power Generation

Day-ahead optimal scheduling method for incremental distribution network with high permeability distributed photovoltaic

Distribution network regional voltage distributed coordinated control method and system