Advances in Electrical Engineering

Volume 2017, Article ID 5764054, 6 pages

https://doi.org/10.1155/2017/5764054

## Control Strategy for Power Loss Reduction considering Load Variation with Large Penetration of Distributed Generation

State Grid Jilin Province Electric Power Research Institute, 4433 Renmin Street, Changchun, Jilin Province, China

Correspondence should be addressed to Chang Liu; moc.621@7290zgnahcuil

Received 17 August 2016; Accepted 8 December 2016; Published 15 February 2017

Academic Editor: Mamun B. Ibne Reaz

Copyright © 2017 Chang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

With the increase of penetration of distribution in distribution systems, the problems of power loss increase and short-circuit capacity beyond the rated capacity of the circuit breaker will become more serious. In this paper, a methodology (modified BPSO) is presented for network reconfiguration which is based on the hybrid approach of Tabu search and BPSO algorithms to prevent the local convergence and to decrease the calculation time using double fitness to consider the constraints. Moreover, an average load simulated method (ALS method) considering load variation is proposed such that the average load value is used instead of the actual load for calculation. Finally, from a case study, the results of simulation certify that the approaches will decrease drastically the losses and improve the voltage profiles obviously; at the same time, the short-circuit capacity is also decreased into smaller shut-off capacity of the circuit breaker. The power losses will not be increased too much even if the short-circuit capacity constraint is considered; voltage profiles are better with the constraint of short-circuit capacity considered. The ALS method is simple and the calculation time is fast.

#### 1. Introduction

With the large amount of penetration of DGs, reverse power may occur and the quantity of reverse power flow will be also different with the load variation. This will make the power losses change more, whereas it is necessary to consider the load variation during the losses optimization calculation. It has been also discussed that network reconfiguration methods described in previous papers [1–3] are effective in power distribution systems.

Regarding network reconfiguration for loss minimization, one of the first papers published in this field was presented by Merlin and Back [4], who developed a heuristic approach. This solution scheme starts with a total meshed system in which all the switching elements are closed. They are then opened one by one until all the closed circuits are eliminated, and a radial system is obtained. Taking into account load variation, Chen and Cho [5] showed hourly reconfiguration benefits based on short- and long-term loss reduction. An optimal power flow model for minimal losses is applied by Shin [6]. This paper presents only results and conclusions about hourly reconfiguration for online power operation in an energy control center. Tzeng et al. [7] presented an algorithm for minimal loss reconfiguration, based on the dynamic programming approach considering the load variation.

One conclusion in common of these previous works is that they focus on the improvement of network reconfiguration algorithm, not the load model characteristics during network reconfiguration. For loss minimization calculation for a time based on network reconfiguration, if the method of network configuration changed at unit time is used, it may make the switches change frequently; although the results of losses minimization are the best, for example, for daily load, it will perform 24 operation modes in a day. To avoid this case, in the paper, the method to divide the fluctuant load into several stages is presented. The network configuration is the same at any time in the same stage, and the configuration is built based on the sum of losses minimization in each stage and short-circuit capacity reduction by the network reconfiguration method.

Average load simulated method (ALS method) is proposed for calculation during network reconfiguration considering load variation. It will be illustrated in the next sections in detail.

#### 2. Approach for Loss Minimization and Short-Circuit Capacity Reduction

##### 2.1. Problem Formulation

Mathematically, the problem can be formulated as follows:

*Objective*where* I*_{ij} is the current of branch* j* at* i* o’clock,* r*_{j} is the resistance of branch* j, N*_{b} is the number of branches in the system,* N*_{s} is the number of divided stages,* X* is the switch status array, and is the switch status of branch* j* at* i* o’clock; if branch is energized, ; else, .* T*_{sk} and* T*_{ek} are the start and end time point in stage .

*Constraints* Power flow equations: Voltage limit: Switch status: Branches capacity limit: Short-circuit capacity limit: Radial distribution system:where* N* is the number of nodes, and are the maximum and minimum voltage limit of node* j* at* i* o’clock, is the maximum of branch current, DT_{i} is the status of time point *, * is the maximum of SCC, is the row rank of branch-to-node incidence matrix , and is the number of energized branches.

##### 2.2. Reconfiguration Algorithm for Power Loss for Minimum and SCC Reduction Based on MBPSO

In the paper, Modified Binary Particle Swarm Optimization (BPSO) is used for switches optimal combination. BPSO is an optimization method of discrete problem based on PSO and was proposed by Kennedy and Eberhart in 1997 [8]. BPSO has fewer parameters and is able to reach convergence fast; moreover, the result is not affected by the initial state value. The demerit of BPSO is that it easily leads to local convergence. Tabu search algorithm is able to prevent the fitness from local convergence [9, 10]. To avoid local convergence, Tabu search algorithm and BPSO are hybrid to be used to find the in this paper. In detail, if the value of is invariable and the number of times for this is over the setting threshold value, this will be considered as the initial state and, at the same time, Tabu search algorithm will begin. Furthermore, during the searching process, once the fitness of is better than the initial value (), the Tabu search process will be terminated. The above measures are performed and the proposed method is called the modified BPSO (MBPSO).

The constraints must be dealt with especially because the initial BPSO does not take them into account. The equation constraints will be satisfied when the network is configured and power flow is calculated. In this paper, to dispose the inequation constraints, double fitness will be formed; namely, one is the loss minimized fitness and the other is the constraint fitness which is defined according to [11]. Furthermore, the constraint fitness will take priority. It is certain that it converges quickly into the solution zone.

##### 2.3. Average Load Simulated Method

Average load simulated method (ALS method) is a method where the average load value is used instead of the actual load for calculation. In detail, the daily load is divided into several stages according to different loads (such as light, normal, and heavy load stage), and then the average load ratio of every stage is calculated; afterwards, network reconfiguration is done, and the optimal switches combination is got by the MBPSO algorithm. However, the fitness of switch status used to update and of the particle is the losses, which is calculated by the average value of all loads in a stage. Therefore, the calculation time of network reconfiguration in a stage can be decreased. Finally, the hourly loss under optimal network configuration is calculated once more by using the actual load.

##### 2.4. Algorithm of the Proposed Method

The algorithm of ALS method for loss minimization considering SCC reduction and load variation can be described in detail as follows:(1)Input the data and initial parameters, for example, the impedance of branches, active and reactive power of loads, the number () of divided stages, and number of particles.(2)Iteration of optimal divided time begins; confirm the divided stages number of each particle.(3)Get the optimal network in stage using the average load as described above and the flowchart is shown in Figure 1.(4); then go to step 3.(5)Continue until termination criterion () is satisfied.(6)Obtain the optimal network in each stage and then calculate the hourly loss using the actual load.(7)Put out the , which includes the loss minimum in a day, the optimal switch combination in each time stage, and the optimal divided time mode in a day.The flowchart of finding the optimal network by network reconfiguration is based on MBPSO in a stage. Figure 1 is the flowchart of finding the optimal network by network reconfiguration based on modified BPSO and average load in a stage. The whole flowchart of ALS method is shown in Figure 2.