Optimal Reconfiguration Problem Research Articles

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

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.

Short-circuit constrained distribution network reconfiguration considering closed-loop operation

This paper presents a novel scenario-based stochastic mixed-integer second-order cone programming model to solve the problem of optimal reconfiguration of distribution systems with renewable energy sources considering short-circuit constraints. The proposed formulation minimizes technical losses by modifying the statuses of sectionalizing and tie switches, allowing the operation of distribution networks with radial and closed-loop topologies. Since the formation of loops could impact fault current levels, short-circuit constraints are considered in the problem formulation. Numerical experiments are carried out using an 84-node system and the results demonstrate the effectiveness of the proposed formulation to reduce technical losses notably when a closed-loop operation is allowed. Additionally, it is verified that short-circuit constraints prevent the adoption of network configurations with high short-circuit values.

A Dynamic Reconfiguration Method based on a Deterministic Optimization Approach in Active Distribution Systems

This article proposes a method for dynamic distribution network reconfiguration (DDNR) with distributed generation for loss reduction. Advantages and conceptual principles of a deterministic optimization method are exposed, which allows the minimization of technical power losses in distribution systems based on a linear approximation of the loads. In this work is proposed the application of this approach for the modelling of distributed generation sources, obtaining a mathematical formulation that exploits mixed-integer nonlinear programming in solving the optimal reconfiguration problem. The DDNR method is implemented in an automatic tool integrating OpenDSS and GAMS software. IEEE69-Bus test system is considered to demonstrate the effectiveness of the technique. Dynamic results are obtained by implementing the optimal reconfiguration strategy, making the dynamic adjustments of the mathematical modelling in the face of variations in distributed generation and demand profiles. The results reveal schemes that range from one to twenty-four daily control actions.

A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders

This paper deals with the optimal reconfiguration problem of DC distribution networks by proposing a new mixed-integer nonlinear programming (MINLP) formulation. This MINLP model focuses on minimising the power losses in the distribution lines by reformulating the classical power balance equations through a branch-to-node incidence matrix. The general algebraic modelling system (GAMS) is chosen as a solution tool, showing in tutorial form the implementation of the proposed MINLP model in a 6-nodes test feeder with 10 candidate lines. The validation of the MINLP formulation is performed in two classical 10-nodes DC test feeders. These are typically used for power flow and optimal power flow analyses. Numerical results demonstrate that power losses are reduced by about 16% when the optimal reconfiguration plan is found. The numerical validations are made in the GAMS software licensed by Universidad Tecnológica de Bolívar.

A backtracking evolutionary algorithm for power systems

This paper presents a backtracking variable scaling hybrid differential evolution, called backtracking VSHDE, for solving the optimal network reconfiguration problems for power loss reduction in distribution systems. The concepts of the backtracking, variable scaling factor, migrating, accelerated, and boundary control mechanism are embedded in the original differential evolution (DE) to form the backtracking VSHDE. The concepts of the backtracking and boundary control mechanism can increase the population diversity. And, according to the convergence property of the population, the scaling factor is adjusted based on the 1/5 success rule of the evolution strategies (ESs). A larger population size must be used in the evolutionary algorithms (EAs) to maintain the population diversity. To overcome this drawback, two operations, acceleration operation and migrating operation, are embedded into the proposed method. The feeder reconfiguration of distribution systems is modelled as an optimization problem which aims at achieving the minimum loss subject to voltage and current constraints. So, the proper system topology that reduces the power loss according to a load pattern is an important issue. Mathematically, the network reconfiguration system is a nonlinear programming problem with integer variables. One three-feeder network reconfiguration system from the literature is researched by the proposed backtracking VSHDE method and simulated annealing (SA). Numerical results show that the perfrmance of the proposed method outperformed the SA method.

On multi-objective optimal reconfiguration of MV networks in presence of different grounding

The present work faces the traditional multi-objective optimal reconfiguration problem of a distribution grid including the safety issue in the objective functions. Actually, in many medium voltage networks still transformers with ungrounded neutral and with resonant grounded neutral coexist in the same area. This may be sometimes cause of problems during a single-line-to-ground fault if the ground electrodes of one or more cabins, initially designed for satisfying the safety conditions in a resonant grounded neutral network, after the reconfiguration are in a grounded neutral one or vice versa. In the paper a safety objective function is defined and the Non dominated Sorting Genetic Algorithm II used for solving the optimization problem formulated considering the minimization of the energy losses, the maximization of the load balancing among the transformers, and the minimization of the safety function. The safety function is defined both in the case of independent grounding systems and in the case of grounding systems interconnected by the metal shields of the cables. Also the effects of a Global Grounding System on the multi-objective reconfiguration problem so formulated are discussed. Finally a case study is analyzed presenting also a comparison between the Non dominated Sorting Genetic Algorithm II and a traditional Fuzzy Evolution Strategy algorithm.

A modified KH algorithm to solve the optimal reconfiguration problem in the presence of distributed generations

This paper aims to propose an effective intelligent optimization method to solve the multi-objective distribution feeder reconfiguration (DFR) problem considering distributed generations (DGs). In this regard, we introduce a novel population based algorithm based on krill herd (KH) algorithm to solve the multi-objective distribution feeder reconfiguration problem considering DG units. In order to improve the search ability of the algorithm, a new modification process is proposed too. This modification enhances the overall outcome of the KH algorithm in both search and convergence area. During the search process of the proposed modified KH (MKH) algorithm, the achieved non-dominated solutions are stored in an external repository. Owing to distinctive objective functions, a fuzzy clustering technique is applied to control the size of the repository within the restrictions. The objective functions considered in this paper are power losses, voltage deviation of buses and total cost of the active power produced by DG units and distribution companies. In order to evaluate the feasibility and effectiveness of the method, the proposed approach is tested on a distribution test system.

Analysis of the radial operation of distribution systems considering operation with minimal losses

Abstract Electric power distribution systems, and particularly those with overhead circuits, operate radially but as the topology of the systems is meshed, therefore a set of circuits needs to be disconnected. In this context the problem of optimal reconfiguration of a distribution system is formulated with the goal of finding a radial topology for the operation of the system. This paper utilizes experimental tests and preliminary theoretical analysis to show that radial topology is one of the worst topologies to use if the goal is to minimize power losses in a power distribution system. For this reason, it is important to initiate a theoretical and practical discussion on whether it is worthwhile to operate a distribution system in a radial form. This topic is becoming increasingly important within the modern operation of electrical systems, which requires them to operate as efficiently as possible, utilizing all available resources to improve and optimize the operation of electric power systems. Experimental tests demonstrate the importance of this issue.

Analytic Solution to Optimal Reconfigurations of Satellite Formation Flying in Circular Orbit under $J_2$ Perturbation

This paper presents an analytic solution to the optimal reconfiguration problem of satellite formation flying in J2 orbital perturbation. Continuous and variable low-thrust accelerations are represented by the Fourier series, and initial and final boundary conditions are used to establish the constraints on the thrust functions. The thrust functions are implemented by optimal Fourier coefficients that minimize the cost during the maneuver. The analytic solution composed of these Fourier coefficients are simply represented in a closed form, and no approximation is needed. Numerical simulations are conducted to visualize and compare the results obtained in this paper with those of previous papers with no perturbations. The analytic solution developed in this paper is more accurate in that the general behavior of the optimal control history and reconfiguration trajectories are easily calculated even in the presence of the J2 potential disturbance. The analytic solution is useful for designing a reconfiguration controller for satellite formation flying under J2 orbital perturbation.

Optimal Spacecraft Formation Reconfiguration with Collision Avoidance Using Particle Swarm Optimization

This paper presents an energy-optimal trajectory planning method for spacecraft formation reconfiguration in deep space environment using continuous low-thrust propulsion system. First, we employ the Legendre pseudospectral method (LPM) to transform the optimal reconfiguration problem to a parameter optimization nonlinear programming (NLP) problem. Then, to avoid the computational complexity for calculating the gradient information caused by traditional optimization methods, we use particle swarm optimization (PSO) algorithm to solve the NLP problem. Meanwhile, in order to avoid the collision between any pair of Legendre-Gauss-Lobatto (LGL) points, we insert some test points in the region where collision may happen most likely. What’s more, the collision avoidance constraints are also checked at these test points. Finally, numerical simulation shows that the energy-optimal trajectories for spacecraft reconfiguration could be generated by the method we proposed in a relative short time, so that it could be adopted on-board for practical spacecraft formation problems. DOI: http://dx.doi.org/10.5755/j01.itc.41.2.854

Analytical solution to optimal relocation of satellite formation flying in arbitrary elliptic orbits

The current paper presents and examines a general analytical solution to the optimal reconfiguration problem of satellite formation flying in an arbitrary elliptic orbit. The proposed approach does not use any simplifying assumptions regarding the eccentricity of the reference orbit. For the fuel optimal reconfiguration problem, continuous and variable low-thrust accelerations can be represented by the Fourier series and summed into closed-form solutions. Initial and final boundary conditions are used to establish the constraints on the thrust functions. The analytical solution can be implicated by the Fourier coefficients that minimize propellant usage during the maneuver. This solution is found that compares favorably with numerical simulations. Also, this analytical solution is very useful for designing a reconfiguration controller for satellite formation flying in a general elliptic orbit.

Approximate Analytical Solutions to Optimal Reconfiguration Problems in Perturbed Satellite Relative Motion

This paper presents new analytical solutions to the optimal reconfiguration problems of formation flying using a power-limited low-thrust propulsion that includes perturbations associated with differential gravity effects, the eccentricity effects of a chief orbit, and J 2 effects. The perturbation approach and the calculus of variations are applied to optimal reconfiguration problems to obtain an approximate analytical solution. Some nonlinear relative dynamics are also presented to account for the nonlinear terms generated by each perturbation. For the sake of validity, numerical simulations are performed for satellite reconfiguration cases in which the distance between the satellites is large and the chief orbit is eccentric. The improved capacity of the new analytical solutions to ensure reasonable fuel usage is confirmed, and the magnitudes of the errors caused by each perturbation are compared and analyzed. It is found that the errors caused by unperturbed analytical solutions based on only the Hill―Clohessy― Wilshire equations are reduced by the new analytical solutions by one-half to one over 500, depending on which perturbation is considered.

Considering safety issues in minimum losses reconfiguration for MV distribution networks

AbstractThis paper offers a new perspective over the traditional problem of the multiobjective optimal reconfiguration of electrical distribution systems in regular working state. The issue is indeed here formulated including also safety issues. Indeed, dimensioning the earth electrodes of their own secondary substations, distribution companies take into account the probable future configurations of the network due to transformations of overhead lines into cable lines or realization of new lines. On the contrary, they do not consider that, during normal working conditions, the structure of the network can be modified for long periods as a consequence of reconfiguration manoeuvres, with differences between the design current of the earthing systems and the fault current in certain substations significant. As a consequence, often distribution companies limit the implementation of the optimal reconfiguration layouts because they are unable to suitably evaluate the safety issue. In the paper, the problem is formulated including a further objective in order to account for the safety. A suitable constrained multiobjective formulation of the reconfiguration problem is therefore used aiming at: the minimal power losses operation, the verification of safety at distribution substations, the load balancing among the HV/MV transformers while keeping the voltage profile regular. The application carried out uses an NSGA‐II algorithm whose performance is compared to that of a fuzzy logic‐based multiobjective evolutionary algorithm. In the considered automated network, the remote control of tie‐switches is possible and their layout is the optimization variable. After a brief description of the optimal reconfiguration problem for automated distribution networks, the most recent papers on the topic are reported and commented. Then the problem formulation and the solution algorithm are described in detail. Finally, test results on a large MV distribution network are reported and discussed. Copyright © 2008 John Wiley & Sons, Ltd.

A new formulation of the optimal compensation and reconfiguration problem including minimum load nodes unavailability for automated distribution networks

This paper deals with a new formulation of the optimal operation of electrical distribution networks problem in regular working state. In the new deregulated energy market providing reliable and economical service to customers is a primary task. The multiobjective formulation of the reconfiguration and compensation problem used in this paper considers as a primary object also the minimisation of the load nodes unavailability (UA) expressed in probabilistic terms. Therefore, the objectives to be attained through the optimisation strategy are: minimal power losses operation, minimum UA of the load nodes, load balancing among the HV/MV transformers, and voltage profile regularisation. The application carried out uses an evolutionary algorithm and a particular normalisation technique for the multiple objectives formulation. In the considered automated network, the remote control of capacitor banks and tie‐switches is possible and their layout is the optimisation variable. After a brief description of the optimal reconfiguration and compensation problem for automated distribution networks, the most recent papers on the topic are reported and commented. Then the problem formulation and the solution algorithm are described in detail. Finally, the test results on a large MV distribution network are reported and discussed.

Minimum Losses Reconfiguration of MV Distribution Networks through Local Control of Tie Switches

This paper deals with the problem of optimal reconfiguration of radial distribution networks for minimum loss operation. The proposed control strategy of the open-closed status of the tie switches is distributed, since every MV/LV node is provided with local controllers having some measured entities as input. It also does not prevent the system from the future implementation of centralized control; instead, it may represent the first step towards a complete automation of the distribution system. The proposed strategy is organized in hierarchic levels, the highest of which may be in the future a central control. After introducing the general problem of network reconfiguration, a review of the state of art on the subject is reported, even though the solution methodologies are usually related to the centralized formulation of the reconfiguration problem. The proposed local control strategy is outlined, and a detailed description of its different parts is reported with special attention to all the measures for a better performance of the system. Results of a number of simulation runs are reported in order to test the behavior of the proposed local control system in different possible operating conditions.

Optimal reconfiguration strategy for a degradable multimodule computing system

A new quantitative approach to the problem of reconfiguring a degradable multimodule system is presented. The approach is concerned with both assigning some modules for computation and arranging others for reliability. Conventionally, a fault-tolerant system performs reconfiguration only upon a subsystem failure. Since there exists an inherent trade-off between the computation capacity and fault tolerance of a multimodule computing system, the conventional approach is a passive action and does not yield a configuration that provides an optimal compromise for the trade-off. By using the expected total reward as the optimal criterion, the need and existence of an active reconfiguration strategy, in which the system reconfigures itself on the basis of not only the occurrence of a failure but also the progression of the mission , are shown. Following the problem formulation, some important properties of an optimal reconfiguration strategy, which specify (i) the times at which the system should undergo reconfiguration and (ii) the configurations to which the system should change, are investigated. Then, the optimal reconfiguration problem is converted to integer nonlinear knapsack and fractional programming problems. The algorithms for solving these problems and a demonstrative example are given. Extensions of the optimal reconfiguration problem are also discussed.