Distribution Network Reconfiguration Problem Research Articles

Multi-objective optimal reconfiguration of distribution networks using a novel meta-heuristic algorithm

Reconfiguration strategies are used to reduce power losses and increase the reliability of the distribution systems. Since the optimal reconfiguration problem is a multi-objective optimization problem with non-convex functions and constraints, meta-heuristic algorithms are the most suitable choice for the problem-solving approach. One of the new meta-heuristic algorithms that exhibits excellent performance in solving multi-objective problems is the wild mice colony (WMC) algorithm, which is implemented based on aggressive and mating strategies of wild mice. In this paper, the distribution network reconfiguration problem is solved to reduce power losses, improve reliability, and increase the voltage profile of network buses using the WMC algorithm. In addition, the obtained results are compared with conventional multi-objective algorithms. The optimal reconfiguration problem is applied to the IEEE 33-bus and 69-bus test systems. The comparative study confirms the superior performance of the proposed algorithm in terms of convergence speed, execution time, and the final solution.

Distribution Network Reconfiguration in Effective Presence of DGs, EVCSs and DRP: A Novel Multi-Objective Approach

By determining the ideal combination of switch on/off status, distribution network reconfiguration (DNR), a critical operational problem in distribution systems, has been employed to increase system efficiency. Additionally, benefits such as improved voltage profiles and loss reduction through reactive power compensation are provided by placing capacitors in the distribution network optimally. Thus, this study formulates multi-objective DNR with optimal allocation of distributed generators (DGs), electric vehicles charging stations (EVCSs) and capacitors. Additionally, the demand response programme (DRP) is employed to enhance the distribution system’s operation performance. Losses and voltage variation, which are significant objectives for conventional distribution systems, are typically the focus of the DNR problem. Modern distribution systems’ security concerns caused by DGs that could jeopardize power system security have been all but ignored in terms of the issue of power system operation. As a result, the primary objective of this study is to address the DNR issue in order to increase reliability. This study specifies the energy not provided (ENS) in order to do this. This work uses a modified shuffled frog leaping algorithm (MSFLA), which benefits from a novel mutation technique, to reduce processing time and improve solution quality, in particular to avoid becoming stuck in local optima. To evaluate the efficiency and adaptability of the suggested method, it is applied to three distribution systems, comprising 33, 69, and 86-node test networks. After that, the outcomes are contrasted with those of various approaches. Power loss and ENS are reduced by around 45% and 5.5%, respectively, by executing the suggested strategy on a 33-node test system compared to the initial value before the DNR.

Active and Reactive Power loss Minimization Along with Voltage profile Improvement for Distribution Reconfiguration

Optimal distribution network reconfiguration (DNR), distributed generations location and sizing (DGs-LS), tap changer adjustment (TCA), and capacitors bank location and sizing (CAs-SL) are different methodologies used to reduce loss and enhance the voltage profile of distribution systems. DNR is the process of changing the network topography by changing both sectionalized and tie switch states. The optimal location looks to find the optimal setting of the DG and CA within the distribution network. Optimal size seeks to find the optimal output generation of both DG and CA. The TCA looks to find the optimal position for TC. These methods are challenging optimization problems and resort to meta-heuristic techniques to find a globally optimal solution. This paper presents a new methodology with which to simultaneously solve the problem of DNR, DGs-LS, TCA, and CAs-SL in distribution networks. This work aims to minimize active and reactive power losses, including voltage profile improvement using a multi-objective decision approach. The firefly algorithm (FA) and analytic hierarchy process (AHP) are used to optimize the fitness function and determine the function weight factors through the use of MATLAB software. Several scenarios were considered on the IEEE 69-bus network. In terms of active power and reactive losses, reductions in the test system of 96.16% and 92.7%, respectively, were achieved, evidencing the positive impact of the proposed methodology on distribution networks.

A Novel Solution Method for the Distribution Network Reconfiguration Problem based on an Objective Function and considering the Cost of Electricity Transmission

A Novel Solution Method for the Distribution Network Reconfiguration Problem based on an Objective Function and considering the Cost of Electricity Transmission

Designing electricity distribution networks: The impact of demand coincidence

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84% in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e.g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Loop-Based Encoding and Decoding Algorithms for Distribution Network Reconfiguration

The distribution network reconfiguration (DNR) improves the operational efficiency of the distribution network (DN) through changing the open/closed status of switches. However, since numerous switches are complexly connected, topology analysis to determine whether it is radial requires a lot of time and effort. This paper proposes an encoding and decoding algorithm based on the loop, the fundamental structure of the DN, for effectively finding the global optimal configuration in any DN. The encoding algorithm consisted of reduction, simplification, and loop search systematically organizes the DN, and significantly reduces the search space for the DNR problem. The decoding algorithm consisted of BSS selection criteria and data modification provides the methods for strategically selecting the switches that satisfy the radial structure. Additionally, the independent operations of multiple sources can be applied to proposed algorithms easily through the virtual branch. The validity and performance of the proposed algorithms are verified through the DNR case study using a genetic algorithm (GA) in the IEEE 33, 69, and 118 bus DNs. In addition, a two-stage GA algorithm is implemented to improve the convergence speed and global optimization search rate. Finally, the performance of the proposed algorithm is compared to other methods.

Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization

This paper describes a computational procedure to establish the optimal distribution of network reconfiguration by means of a novel gray wolf optimization (GWO) algorithm. The procedure aimed to diminish the system’s power loss and produce a better voltage profile while fulfilling the operating constraints described by different operating conditions. Under practical restrictions, the distribution network reconfiguration (DNR) problem is classified as multimodal and highly nonlinear. Constraint breaches were appropriately handled to produce stable convergence characteristics, and high-quality solutions were obtained in a shorter execution time. The 33-bus and 69-bus systems were used to obtain the optimal reconfiguration by incorporating the method developed in this work. The simulation results obtained were collated and compared with the outcomes of other well-known optimization techniques, confirming the efficacy of the GWO algorithm in solving the DNR problem.

Distribution Network Reconfiguration Using Iterative Branch Exchange and Clustering Technique

The distribution network reconfiguration problem (DNRP) refers to the challenge of searching for a given power distribution network configuration with better operating conditions, such as minimized energy losses and improved voltage levels. To accomplish that, this paper revisits the branch exchange heuristics and presents a method in which it is coupled with other techniques such as evolutionary metaheuristics and cluster analysis. The methodology is applied to four benchmark networks, the 33-, 70-, 84-, and 136-bus networks, and the results are compared with those available in the literature, using the criteria of the number of power flow executions. The methodology minimized the four systems starting from the initial configuration of the network. The main contributions of this work are the use of clustering techniques to reduce the search space of the DNRP; the consideration of voltage regulation banks and voltage-dependent loads in the feeder, requiring the addition of a constraint to the mono-objective model to guarantee the transferred load will be supplied at the best voltage magnitude level, and the application of the methodology in real distribution networks to solve a set of 81 real DNRPs from CEMIG-D (the distribution branch of the Energy Company of Minas Gerais). Four out of those are presented as case studies to demonstrate the applicability of the approach, which efficiently found configurations with lower power and energy losses with few PF runs.

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles. Most existing DNR methods rely on accurate network parameters and lack scalability and optimality. This study uses model-free reinforcement learning algorithms for training agents to take the best DNR actions in a given distribution system. Five reinforcement algorithms are applied to the DNR problem in 33- and 136-node test systems and their performances are compared: deep Q-learning, dueling deep Q-learning, deep Q-learning with prioritized experience replay, soft actor-critic, and proximal policy optimization. In addition, a new deep Q-learning-based action sampling method is developed to reduce the size of the action space and optimize the loss reduction in the system. Finally, the developed algorithms are compared against the existing methods in literature.

Economic scheduling of mobile energy storage in distribution networks based on equivalent reconfiguration method

Compared with traditional stationary energy storage system(SESS), mobile energy storage system(MESS) has power transfer ability in both spatial and temporal dimensions. Thus, it can provide greater flexibility in power system auxiliary services. To simplify and clarify the optimal scheduling problem, a novel “virtual switch” indicator is defined to describe the scheduling of MESS and an equivalent reconfiguration method is proposed by analogy with distribution network reconfiguration. Based on the method proposed, the dynamic economic scheduling of MESS in distribution networks(DNs) can be formulated as a scenario-based stochastic optimization problem, where the uncertainties of traffic flow and renewable energy output are considered by generating scenarios using Monte Carlo simulation. The objective function is to maximize the profit of distribution network operator. The time and cost of MESS scheduling are transformed into those of virtual switch switching in distribution network reconfiguration. Therefore, the operation scheduling problem of a MESS is equivalent to a distribution network reconfiguration problem with a SESS and several virtual switches. In this way, a grid-traffic network coupling problem is simplified to a pure distribution network problem, which provides a novel way to solve the MESS scheduling problems. Finally, simulation results on the IEEE 33-bus distribution system and simple traffic network verify the validity and feasibility of the method proposed.

Reliability-Oriented Reconfiguration of Power Distribution Systems Considering Load and RES Production Scenarios

The ever-increasing integration of non-dispatchable distributed generation, i.e., renewable energy sources (RES), arises new challenges in the field of power system's reliability. Distribution network reconfiguration (DNR) is a cost-effective approach for the distribution system operator (DSO) that wishes to enhance system's reliability without infrastructure upgrades. This paper introduces a novel path-based mixed-integer second-order cone programming model to optimally solve the reliability-oriented DNR problem. The DSO's objectives that are optimized are: a) improvement of distribution system's reliability indices and b) minimization of power losses. The proposed model is enriched with a scenario-based stochastic programming formulation that considers multiple levels of load and RES production. The standard 33-nodes distribution system and a real-world 83-nodes distribution system are employed to prove the efficiency and applicability of the model. Firstly, the multi-objective nature of the reliability-oriented DNR problem is investigated by conducting a sensitivity analysis, which reveals a trade-off region between reliability indices and power losses. Moreover, the obtained results show different global optimal solutions when the variability of load and RES production is considered. This highlights the importance of considering a scenario-based approach for load and RES production when solving the reliability-oriented DNR problem.

Power Loss Minimization and Voltage Profile Improvement by System Reconfiguration, DG Sizing, and Placement

A number of algorithms that aim to reduce power system losses and improve voltage profiles by optimizing distributed generator (DG) location and size have already been proposed, but they are still subject to several limitations. Hence, new algorithms can be developed or existing ones can be improved so that this important issue can be addressed more appropriately and effectively. This study proposes a reconfiguration methodology based on a hybrid optimization algorithm, consisting of a combination of the genetic algorithm (GA) and the improved particle swam optimization (IPSO) algorithm for minimizing active power loss and maintaining the voltage magnitude at about 1 p.u. The buses at which DGs should be injected were identified based on optimal real power loss and reactive power limit. When applying the proposed optimization algorithm for DGs allocation in power system, the search space or number of iterations was reduced, increasing its convergence rate. The proposed reconfiguration methodology was test in an IEEE-30 bus electrical network system with DGs allocations and the simulations were conducted using MATLAB software compared to other optimization algorithms, such as GA, PSO, and IPSO, the combination of GA and IPSO or Hybrid GA & IPSO (HGAIPSO) method has a smaller number of iterations and is more effective in optimization problems. The effectiveness of the proposed HGAIPSO has been tested on IEEE-30 bus network system with DGs allocations, and the obtained test results have been compared to those from other methods (i.e., GA, PSO, and IPSO). The simulation results show that the proposed HGAIPSO can be an efficient and promising optimization algorithm for distribution network reconfiguration problems. The IEEE-30 bus test system with DGs integrated at various location revealed reductions in overall real power loss of 40.7040%, 36.2403%, and 42.9406% for type 1, type 2, and type 3 DGs allocation, respectively. The highest bus voltage profile goes to 1.01 pu in the IEEE-30 bus.

Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization

The particle swarm optimization (PSO) algorithm is widely used to solve a variety of complicated engineering problems. However, PSO may suffer from an effective balance between local and global search ability in the solution search process. This study proposes a new acceleration coefficient for the PSO algorithm to overcome this issue. The proposed coefficient is implemented on the distribution network reconfiguration (DNR) problem to reduce power loss. The lowest power loss is obtained while problem constraints (maintain radial structure, voltage limits, and power flow balance) are satisfied with the proposed method. The validity of the proposed acceleration coefficient-based binary particle swarm optimization (BPSO) in handling the DNR problem is examined through simulation studies on IEEE 33-bus, P&G 69-bus, and 84-bus Taiwan Power Company (TPC) practical distribution networks. Furthermore, the DNR problem is evaluated regarding energy cost and environmental issues. Finally, the average computational times of the different acceleration coefficient-based PSO methods are compared. The solution speed of the proposed acceleration coefficient-based method is faster than the other methods in the DNR problem.

Distribution network reconfiguration for minimizing impact of wind power curtailment on the network losses: A two‐stage stochastic optimization algorithm

AbstractNowadays, with the increasing penetration rate of wind turbine generators (WTGs) in power distribution networks, possible network‐related problems should be considered. Distribution network reconfiguration (DNR) can reduce the effect of wind power curtailment by transferring loads to the other feeders and compensating for wind forecast error. In this paper, a two‐stage policy is presented to deal with the network configuration and wind power uncertainty. In the first stage, the scheduled output of WTGs is determined, and the wind power uncertainty is modeled using a scenario‐based approach. In the second stage, a modified firework (MFW) algorithm is considered for the network reconfiguration to minimize the network losses. In the MFW algorithm, a new local search method is implemented to enhance the performance of the FW algorithm by increasing the direction of the explosion. The impact of wind power curtailment in the network operation is minimized using the proposed policy that changes the network configuration. The proposed methodology is tested on a 70‐bus IEEE distribution test system. The simulation results demonstrate the efficiency of the proposed algorithm in solving the DNR problem in the presence of WTGs, considering the uncertainty of wind speed.

A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

Dynamic reconfiguration of distribution network based on dynamic optimal period division and multi-group flight slime mould algorithm

This paper suggests a multi-period dynamic reconfiguration method for the distribution network. The reconfiguration period is preliminarily divided based on the load monotonicity and amplitude change with taking the length of the actual load as the reconfiguration time section. A multi-objective dynamic reconfiguration is modeled based on the time-varying load distribution network considering network active power loss, static voltage stability, and load balance. The optimal period partition function is dynamically adjusted by the time-division method under the constraint of switching actions. The paper also proposes a multi-group flight slime mould algorithm (MFSMA) by applying a multi-group optimization strategy and adaptive Levy flight for the dynamic distribution network reconfiguration problem. Through experiments on IEEE-33 and IEEE-118 bus distribution networks, the experimental results show that the MFSMA for the multi-period dynamic reconfiguration effectively simulates the actual reconfiguration of the distribution network and provides a new scheme for the economic, stable, and safe operation of the distribution network.

Distribution Network Reconfiguration Method based on Adaptive Multi Population Fruit Fly

Aiming at the nonlinear multi-objective optimization problem of distribution network reconfiguration, the weight of three objective functions, namely network loss, load balance and voltage offset, is determined by the random weight method, which is transformed into a single objective function with the same dimension, the same attribute and the same order of magnitude. The optimal reconfiguration scheme is obtained by using the adaptive multi-population fruit fly optimization algorithm (AMFOA). The node-branch matrix is used for binary coding to avoid the generation of infeasible solutions, and the dynamic step adjustment strategy is introduced to enhance the local search ability of FOA and achieve the effect of balancing global and local search, Cooperative subpopulation strategy and Chebyshev chaotic mapping are used to improve the global search ability and increase the optimization speed. Through the simulation and analysis of a typical IEEE 33-node system with DG, the effectiveness and practicability of the strategy are verified.

Multi-Objective Network Reconfiguration and Allocation of Capacitor Units in Radial Distribution System Using an Enhanced Artificial Bee Colony Optimization

Distribution network reconfiguration (DNR) can be defined as an optimization problem in the power system that is done through changing the switching status to reduce loss and operating cost. Moreover, optimal capacitor allocation in the distribution network provides benefits such as loss reduction and improved voltage profile by reactive power compensation. Thus, this study provides a multi-objective framework for DNR and capacitor allocation problem in the presence of distributed generation (DG) sources. In the common DNR studies, the requirement of reliability is not usually fulfilled, usually loss and voltage deviation are often chosen as the objective functions. In this paper, the multi-objective problem is considered as a combination of energy not supplied (ENS) and emission produced by DG units and grid, moreover network loss is defined as the other objective function. The simultaneous presence of DG units and capacitors has made the problem more complicated, and there will be a demand for a precise approach to solve the multi-objective optimization problem. Hence, an enhanced artificial bee colony optimization (EABCO) method is proposed to tackle the complexity of the optimization problem. In order to shown the efficacy of the proposed approach, it is tested on a 33 and 86- node systems, the obtained results of EABCO method are compared with common evolutionary, hybrid and state-of–the-art algorithms. By implementing the proposed approach in two small-scale test systems, approximately 74% and 15% reduction in ENS are achieved compared to the initial value before distribution feeder reconfiguration.

Distribution Network Reconfiguration Considering Voltage and Current Unbalance Indexes and Variable Demand Solved through a Selective Bio-Inspired Metaheuristic

Operation of distribution networks involves a series of criteria that should be met, aiming for the correct and optimal behavior of such systems. Some of the major drawbacks found when studying these networks is the real losses related to them. To overcome this problem, distribution network reconfiguration (DNR) is an efficient tool due to the low costs involved in its implementation. The majority of studies regarding this subject treat the problem by considering networks only as three-phase balanced, modeled as single-phase grids with fixed power demand, which is far from representing the characteristics of real networks (e.g., unbalanced loads, variable power and unbalance indexes). Due to the combinatorial nature of the problem, metaheuristic techniques are powerful tools for the inclusion of such characteristics. In this sense, this paper proposes a study of DNR considering balanced and unbalanced systems with variable power demand. An analysis of the direct influence of voltage unbalance index (VUI) and current unbalance index (CUI) is carried out for unbalanced cases. To solve the DNR problem, a selective bio-inspired metaheuristic based on micro bats’ behavior named the selective bat algorithm (SBAT) is used together with the EPRI-OpenDSS software (California, US, EPRI). Tests are initially conducted on balanced systems, aiming to validate the technique proposed for both demands and state their differences, and then they are conducted on unbalanced systems to study the influence of VUI and CUI in the DNR solution.

Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm

This paper proposes a solution for the distribution network reconfiguration (DNR) problem with distributed generation (DG) based on the parallel slime mould algorithm (PSMA). Firstly, we construct the four optimization objectives of active power loss, voltage stability index, load balance degree, and switching operation times are integrated by the analytic hierarchy process (AHP) and the DNR problem models with different DG types. Next, we propose PSMA based on the grouping communication strategy and inertia weight; we verify the performance of PSMA by testing a CEC2014 test suite. Finally, we apply the PSMA for the six types of DG of the IEEE-33 bus distribution network as the case study; the outcomes are compared with the other four algorithms. Experimental results show that the PSMA can solve the DNR problem with DG more accurately and quickly than the other three algorithms. At the same time, the results show that the type and access location of DG will affect the DNR problem in different degrees.