Xiaoli Meng

Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II

An improved nondominated sorting genetic algorithm-II (INSGA-II) has been proposed for optimal planning of multiple distributed generation (DG) units in this paper. First, multiobjective functions that take minimum line loss, minimum voltage deviation, and maximal voltage stability margin into consideration have been formed. Then, using the proposed INSGA-II algorithm to solve the multiobjective planning problem has been described in detail. The improved sorting strategy and the novel truncation strategy based on hierarchical agglomerative clustering are utilized to keep the diversity of population. In order to strengthen the global optimal searching capability, the mutation and recombination strategies in differential evolution are introduced to replace the original one. In addition, a tradeoff method based on fuzzy set theory is used to obtain the best compromise solution from the Pareto-optimal set. Finally, several experiments have been made on the IEEE 33-bus test case and multiple actual test cases with the consideration of multiple DG units. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been proved.

Study on technology system of self-healing control in smart distribution grid

Smart distribution grid is an important part of smart grid, which connects the main network and user-oriented supply. As an “immune system”, self-healing is the most important feature of smart grid. Major problem of self-healing control is the ‘uninterrupted power supply problem’, that is, real-time monitoring of network operation, predicting the state power grid, timely detection, rapid diagnosis and elimination of hidden faults, without human intervention or only a few cases. First, the paper describes major problems, which are solved by self-healing control in smart distribution grid, and their functions. Then, it analysis the structure and technology components of self-healing control in smart distribution grid, including the base layer, support layer and application layer. The base layer is composed of the power grid and its equipments, which is the base for smart grid and self-healing control. The support layer is composed of the data and communication. High-speed, bi-directional, real-time and integrated communications system is the basis of achieving power transmission and the use of high efficiency, reliability and security, and the basis for intelligent distribution network and the key steps of self-prevention and self-recovery in distribution grid. The application layer is composed of Monitoring, assessment, pre-warning/analysis, decision making, control and restoration. Six modules are interconnected and mutual restraint. The application layer is important means of self-prevention and self-recovery in distribution grid. Through the research and analysis on the relationship and the technical composition of six modules in the application layer, the paper divides running states of smart grid distribution grid having self-healing capabilities into five states, which are normal state, warning state, critical state, emergency state and recovery state, and defines the characteristics and the relationship of each state. Through investigating and applying self-healing control in smart distribution grid, smart distribution grid can timely detect the happening or imminent failure and implement appropriate corrective action, so that it does not affect the normal supply or minimize their effects. Power supply reliability is improved observably and outage time is reduced significantly. Especially in extreme weather conditions, the distribution grid will give full play to its self-prevention and self-recovery capability, give priority to protecting peoples life and provide electricity for the people furthest.

A Trust Region SQP Method for Coordinated Voltage Control in Smart Distribution Grid

This paper investigates the coordinated voltage control problem for smart distribution grid with the integration of distributed generation (DG). By actively participating in voltage control together with under-load tap changer and shunt capacitors, DG can operate more effectively in the distribution network. The objective of the proposed control method is to minimize the active power loss in the distribution system and to decrease the number of switching device operations while maintaining the grid voltage within the allowable range. Nondispatchable and dispatchable DG are both considered in the control method. To solve the mixed integer nonlinear programming problem, the trust region sequential quadratic programming method is integrated with the branch and bound approach to iteratively approximate the optimization with trust region guidance. Numerical tests on a standard 10-kV distribution system, a real 10-kV distribution system in the Sichuan province of China, and the IEEE 13-bus demonstrate the applicability of the proposed coordinated voltage control method.

An Improved Strength Pareto Evolutionary Algorithm 2 with application to the optimization of distributed generations

This paper presents an Improved Strength Pareto Evolutionary Algorithm 2 (ISPEA2), which introduces a penalty factor in objective function constraints, uses adaptive crossover and a mutation operator in the evolutionary process, and combines simulated annealing iterative process over SPEA2. The testing result of ISPEA2 by authoritative testing functions meets the requirement of Petro-optimum fronts. The case study result shows that the proposed algorithm provides a rapid convergence in obtaining Pareto-optimal solutions during the calculation process of evolution. Based on the fuzzy set theory, ISPEA2 is able to solve the multi-objective problems in the IEEE 33-bus system, and its validity and practicality are demonstrated by the utilization on DGs economic dispatch and optimal operation in the field of power industry.

Probabilistic Modelling and Simulation of Stochastic Load for Power System Studies

The electric load forecasting results are essential for the simulation process of power system to predict the uncertainty of load fluctuation. At present, the load profile is affected by economic factors, weather conditions and customers behaviours. The load data are required by power flow and risk assessment of the grid to accurately simulate the operation behaviour of electric distribution systems. This paper explores the probabilistic distribution of time-series load in the distribution network level of power systems and develops a probabilistic distribution model based on uniform and normal distribution with the historical load data observed from a distribution-level feeder from a residential community. A probabilistic load model is developed to expresses the random nature of electric load data. This paper describes the procedures for the probabilistic load model development and confirms its fitness for the real-time simulation and power flow computation in power system studies.

A Big Data Online Cleaning Algorithm Based on Dynamic Outlier Detection

To effectively clean the large-scale, mixed and inaccurate monitoring or collective data, reduce the cost of data cache and ensure the consistent deviation detection on timing data of each cycle, a big data online cleaning algorithm based on dynamic outlier detection has been proposed. The data cleaning method is improved by local outliner detection upon density, sampling cluster uniformly dilution Euclidean distance matrix retaining some corrections into next cycle of cleaning, which avoids a sampling causing overall cleaning deviation and reduces amount of calculation within data cleaning stable time, enhancing the speed greatly. Finally, the distributed solutions on online cleaning algorithm based on Hadoop platform.

Improved Multiobjective Harmony Search Algorithm with Application to Placement and Sizing of Distributed Generation

To solve the comprehensive multiobjective optimization problem, this study proposes an improved metaheuristic searching algorithm with combination of harmony search and the fast nondominated sorting approach. This is a kind of the novel intelligent optimization algorithm for multiobjective harmony search (MOHS). The detailed description and the algorithm formulating are discussed. Taking the optimal placement and sizing issue of distributed generation (DG) in distributed power system as one example, the solving procedure of the proposed method is given. Simulation result on modified IEEE 33-bus test system and comparison with NSGA-II algorithm has proved that the proposed MOHS can get promising results for engineering application.

Research and application of maintenance schedule optimization based on intelligent algorithm

According to the features of power system, a new mathematical model for distribution maintenance scheduling is proposed, which combines load path transfer and maintenance schedule time optimization problem. Network reconfiguration algorithm and genetic algorithm are used to solve load path transfer problem and maintenance schedule time optimization problem respectively. Due to the large number variables of the network reconfiguration, encoding with round line and feasible solution operator are adopted to decrease the searching space in the paper. The searching space is significantly reduced and the solution speed is raised. The proposed model and method are applied to IEEE33 test system, the result show that proposed method can obtain the most optimal maintenance schedule and the load transfer line with minimal network loss. The method observably reduces the power off lose which maintenance brings.

Distribution network planning considering distributed generations based on genetic algorithm

Network-connected distributed generations is the development trend of future distribution network. It is important to investigate the position and determine the capacity of distributed generations (DG) in the existing distribution network. Considering the balance of power flow and the quality of electric energy supply, a DG mathematical model of position and capacity determining is built. The planning objective is to maximize the size of DG and minimize line losses of distribution system. The case studies have been carried on a 33-node distribution network through genetic algorithm. The simulation results show the proposed DG planning model and genetic algorithm are correct and feasible.

A system identification method for smart distribution grid

With the development of the smart distribution grid, risk assessment, early risk warning, fault diagnosis and self-healing control have put forward a requirement of getting the precise impedance parameters of a distribution line. In order to solve the problem, a method for online virtual metrology of distribution line impedance is proposed. The paper builds the voltage drop linear equivalent computiong model by using probability theory. Then based on the collected three-phase currents and voltages data at the head and end of the line, it uses mathematical method, such as regression analysis method and the average value of solving equations method, to analyze and calculate the coefficients of the voltage drop linear equivalent calculative model. The coefficient of terminal current corresponding is the impedance of the line. The method can be used to calculate the impedance of a running distribution line. The calculation result and the actual impedance were compared and analyzed. The analysis result shows that it is feasible and effective and has the advantages of a fast calculation speed and a good real-time performance.