Wanxing Sheng

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.

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.

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.

A parallel algorithm of optimal power flow on Hadoop platform

Application of smart grid leads to significant increase in scale and data of the power systems, bringing new challenges to the calculation of optimal power flow. However, the existing parallel algorithms, such as MPI-based solutions, suffer from high computational complexity. In this paper, we propose a parallel algorithm of optimal power flow based on Map-Reduce framework. More concretely, the node reordering in our algorithm can greatly accelerate solution speed of the linear equations meanwhile fit well with Map-Reduce programming specifications. Moreover, we determine the appropriate formats for input, intermediate and output data sets and partition the algorithm into separate map/reduce tasks. This facilitates our algorithm to be executed in parallel on a large number of computing nodes. The proposed algorithm is verified on a Hadoop cluster. The experimental results demonstrate that the effectiveness of the propose algorithm.

Optimal placement and sizing of distributed generation in distribution power system based on multi-objective harmony search algorithm

This study deals with the optimal placement and sizing issue in distribution power system with distributed generation (DG). This issue is a sophisticated multi-objective optimization problem with constraints of investment and power system operation demands. In this paper, minimum power loss, minimum voltage deviation and maximal voltage stability margin are taken into account for the optimization of DG planning. To solve the comprehensive multi-objective optimization problem, a meta-heuristic searching algorithm harmony search (HS) is improved with the fast non-dominated sorting approach, which forms the novel intelligent optimization algorithm called multi-objective harmony search (MOHS). Simulation on IEEE 33-bus test system and comparisons with some other multi-objective evolutionary algorithms yield promising results for optimal placement and sizing of DG based on MOHS.

Solving coordinated voltage optimization in distribution network based on a robust sequential quadratic programming algorithm

This paper investigates the optimal operation problem for distribution networks with the integration of distributed generation (DG). By considering the objectives of minimal line loss, minimal voltage deviation and maximum DG active power output, the proposed operational optimization formulation is a multi-object optimization problem. Through normalization of each objective function, the multi-objective optimization is transformed to single objective optimization. To solve such a non-convex problem, the trust region robust sequential quadratic programming (TRR-SQP) method is proposed which iteratively approximates by a quadratic programming with the trust region guidance. Numerical tests on IEEE 33-bus, and multiple actual systems show the applicability, and comparisons with the primal-dual interior point method and sequential linear programming method are provided.

On parallelizing analysis of power systems in cloud environment

Application of smart grid produces massive data. Unfortunately, the traditional analysis of power systems can not process such a huge volume of data in an efficient manner. Distributed computing like Map-Reduce framework in cloud environment can provide a promising solution to this problem by processing the data in parallel on a large amount of computing nodes. In this paper, we parallelize power flow calculation based on Map-Reduce programming framework and evaluate the efficiency. The classic Newton-Raphson method commonly used in power flow calculation is totally parallelized. To solve sparse linear equations in Map-Reduce framework, the factorization tree is carefully partitioned and in turn, all the variables in subtrees which are independent with each other can be calculated in parallel. Moreover, to map the proposed algorithm into Map-Reduce jobs, we also define the appropriate formats for the input, intermediate and output data. Experimental results demonstrate that the effectiveness of the proposed scheme.

Decentralized voltage optimization and coordinated method in smart distribution grid

In order to meet requirements of distributed generator (DG) integration with distribution system and real-time simulation, a novel voltage optimization algorithm which is used to solve the active power output of DG units in distributed environment is presented in this paper. Firstly, an automatic partitioning method based on hierarchical agglomerative clustering (HAC) is proposed to decompose the distribution network. Next, according to the decoupling model, the distributed sequential quadratic programming for distributed generation (DSQP-DG) algorithm is adopted to solve the optimal outputs of DG units in a parallel way through alternating of internal and external iterations. The IEEE 33-bus and PG&E 69-bus system with DG units are considered and evaluated. Compared with auxiliary problem principle (APP) algorithm, the proposed algorithm shows that the DSQP-DG algorithm has better performance in distributed parallel voltage optimization of distribution system.

Optimal allocation of distributed generation in distribution system considering time sequence data and low-carbon economy

With the consideration of time sequence characteristics of load and distributed generation (DG), a novel method is presented for optimal sitting and sizing of DG in distribution system. Multiple-objective functions have been formed with the consideration of minimum investment and operational cost of DG, minimum voltage deviation and maximal voltage stability margin. To solve the multiple-objective optimization problem, an Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) has been proposed. Several experiments have been made on the modified PG&E 69-bus and actual 292-bus test systems. The result and comparisons indicate the proposed method for optimal placement and sizing of DG units is feasible and effective.

Unbalanced three-phase distribution state estimation using cooperative particle swarm optimization

Unbalanced three-phase state estimation for advanced distribution management system (DMS) in a smart distribution grid is presented in this paper. With the consideration of renewable distributed generation and variable loads, the distribution state estimation (DSE) is investigated. To solve the state estimation optimization problem, the cooperative particle swarm optimization (Co-PSO) algorithm is proposed based on the particle swarm optimization algorithm. The proposed method can estimate variable loads and renewable distributed generation (DG) values efficiently. Experiment is made on the IEEE 123-bus radial distribution case with variable loads and DG units. Test results and the comparisons with other evolutionary optimization algorithms such as original PSO, improved PSO algorithms, genetic algorithm (GA) demonstrate that the proposed Co-PSO is effective for the DSE problems.