Power system restoration has attracted more attention and made great progress recently. Research progress of the power system restoration from 2006 to 2016 is reviewed in this paper, including black-start, network reconfiguration and load restoration. Some emerging methods and key techniques are also discussed in the context of the integration of variable renewable energy and development of the smart grid. There is a long way to go to achieve automatic self-healing in bulk power systems because of its extreme complexity. However, rapidly developing artificial intelligence technology will eventually enable the step-by-step dynamic decision-making based on the situation awareness of supervisory control and data acquisition systems (SCADA) and wide area measurement systems (WAMS) in the near future.

/pdf/power-system-restoration-a-literature-review-from-2006-to-2dgwjznf88.pdf

Power system restoration: a literature review from 2006 to 2016

There is a global consensus on making full use of renewable energy because of human concern for energy security and environmental deterioration. Power generation with large-scale renewable energy such as solar and wind energy has become the trend of modern power systems, and the resulting impacts on power systems are more and more prominent. Therefore, there is great concern from academic and engineering areas of all countries. In China, as the fastest growing country in the photovoltaic (PV) industry in the world, these problems are particularly outstanding. This paper extensively reviews the present research and application status of large-scale PV (LSPV) power generation in China, discusses the modelling and simulation techniques of LSPV, explores impacts of LSPV integration on dynamic and static characteristics of power systems, and describes key technologies about LSPV power generation delivery and consumption from the perspective of power system planning, simulation, dispatching and control. Additionally, this paper also refines academic and engineering problems, and recommendations are proposed for future research and development of LSPV power generation in China.

A review on China׳s large-scale PV integration: Progress, challenges and recommendations

This paper presents a methodology for the optimal location, selection, and operation of battery energy storage systems (BESSs) and renewable distributed generators (DGs) in medium–low voltage distribution systems. A mixed-integer non-linear programming model is presented to formulate the problem, and a planning-operation decomposition methodology is proposed to solve it. The proposed methodology is separated into two problems (planning and operation problems). The planning problem is related to the location and selection of these devices, and the operation problem is responsible for finding the optimal BESS operating scheme. For solving the planning problem is used a simulated annealing algorithm with a defined neighborhood structure that uses a sensitivity analysis based on the Zbus matrix. A novel decomposition method, which guarantees near-global-optimal solutions with low computational effort, is proposed for solving the operation problem. The effectiveness and accuracy of the proposed decomposition method is validated and tested on an 11-node test system from the specialized literature, and the robustness of the proposed method is assessed and tested on a modified version of an IEEE 135-node test system. The proposed planning-operation decomposition methodology is tested on a real medium–low voltage distribution system of 230 nodes. To verify the efficiency of the proposed methodology, four cases are compared: (I) without BESS and DGs, (II) with DGs, (III) with BESS, and (IV) with BESS and DGs. The numerical results demonstrate the effectiveness and robustness of the proposed methodology.

Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks

Agent-based restoration approach for reliability with load balancing on smart grids

In this paper, firstly the defects of current algorithms for optimal power filter planning are analyzed, and then a new model, taking both filter costs and network loss as objective functions is proposed. In this model, reactive power compensation is also taken into account. Fast nondominated sorting genetic algorithm (NSGA-II), a new multi-objective genetic algorithm, is used to solve the model. The simulation results show that the obtained Pareto-optimal solutions have much better spread of solutions, better convergence and robustness, which provide decision-makers with a wide choice of filter optimization plan. The comparison with the classical single objective optimal filter planning demonstrated the proposed model solved by improved NSGA-II can obtain better optimum solutions. In addition, three widely used methods of transformer harmonic loss are analyzed in this paper. And through a case a more accurate method under varying range of harmonic distortion for voltage is used in the optimization model.

Multi-objective optimal power filter planning in distribution network based on fast nondominated sorting genetic algorithms

A multi-objective optimization model is proposed for power system reconstruction.Objective functions of the model are generating units' starting-up sequence,system-partitioning strategy and time requirements for system reconstruction.Fast and elitist non-dominated sorting genetic algorithm(NSGA-Ⅱ) is applied in order to avoid artificially balanced solutions.Priority-based genetic encoding and decoding are designed for NSGA-Ⅱ.With non-dominated sorting and crowding-distance calculation the means of dummy fitness are used to find optimal solutions in different objectives.The system-partitioning and time requirements for unit start-up are considered in power system reconstruction algorithms.An elitist checking strategy is adopted to check Pareto-optimal solutions.Simulation results on IEEE 30-bus system show that the proposed algorithms are able to find better spread of solutions,better convergence and lower computational complexity compared to other power system reconstruction methods based on genetic algorithms.The effectiveness of the proposed algorithms is further validated by the numerical results on the power system of Shandong Province,China.

Multi-objective Optimization of Power System Reconstruction Based on NSGA-II

Extreme meteorological events,such as icing weather,may influence the operation of power system,and even destroy power transmission equipment.Power system intelligent dispatching,optimal decision making and security defence system all need to predict meteorological events,and to quantitatively assess their impacts.The characteristics of ice disaster invading power grid were analyzed,and the framework for power system numerical simulation with extreme snow-icing weather was presented.The relevant simulation program was developed.Based on analytic hierarchy process(AHP),a comprehensive assessment method for assessing the influence of meteorological events on power system operation was proposed according to the stability,security,and economical attributes of power system.The proposed method was applied to the comprehensive assessment of the typical stages of ice disaster invading power system.Numerical results show that it can be used to quantify the extent and tendency of the impacts of extreme snow-icing weather event on power system.

Comprehensive Assessment of Extreme Ice Disaster Affecting Power System Operation

Nowadays, more and more distributed photovoltaic (PV) and battery energy storage system (BESS) are integrated into distribution networks, which brings a series of challenges to secure, stable, and economic operation of distribution networks. As BESS plays an important role in distribution networks operation, optimizing the operation requires consideration of life-time characteristic and parameter optimization of BESS. This paper proposes a day-ahead two-level operating model for distribution networks with PV and BESS. Dynamic active-reactive optimal power flow (DAROPF) is adopted to cover multiple time periods. The influences of BESS parameters are analyzed considering the uncertainty of solar resource. The solution provides an optimal operating strategy which ensures the flexibility. A case study is used to illustrate the strategy and the results show that the operation can be improved and lifetime of BESS can be increased using the proposed strategy.

Optimal operating strategy for distribution networks with PV and BESS considering flexible energy storage

Some parameters in the traditional particle swarm optimization (PSO) algorithm need to be predefined by test, so the algorithm's practicability is restricted. For this reason, the authors propose an adaptive particle swarm optimization (APSO) algorithm and apply it to reactive power optimization of power system. This algorithm can adjust parameters automatically in the optimization process to find the global optimal solution. The reactive power optimization results of an actual 151-bus power system with 71 control variables show that the proposed algorithm is more efficient than traditional PSO in searching global optimal solution.

Adaptive Particle Swarm Optimization and Its Application in Reactive Power Optimization

Power system skeleton restoration is of multi objectives,multi variables,multi constraints and uncertainties,difficult to establish accurate mathematical modelsIt needs to comprehensively consider various constraints,such as transient overvoltage,sustained power frequency overvoltage,self-excitation,frequency fluctuation,generating unit and load characteristics,restoration duration,available transfer capability of transmission line,equipment failure probability,etcBased on the data warehouse technology,a group intelligent decision support system model is constructedIt consists of three parts,ie,data platform,intelligent control sub-system and group decision support sub-systemA three-stage skeleton restoration strategy is proposed to control the restoration processAccording to the evaluation value of each benefit-oriented attribute,the multi-attribute utility theory is employed to dynamically determine the next restoration targetSimulation results on a real power system demonstrate that this method can not only generate feasible cases for case-based reasoning,but also provide online supports in actual process of restoration

Liu Yutian

Papers

Multi-objective Optimization of Power System Reconstruction Based on NSGA-II

Comprehensive Assessment of Extreme Ice Disaster Affecting Power System Operation

Optimal operating strategy for distribution networks with PV and BESS considering flexible energy storage

Adaptive Particle Swarm Optimization and Its Application in Reactive Power Optimization

A Group Intelligent Decision Support System for Power System Skeleton Restoration Based on Data Warehouse