Feeder Reconfiguration Problem Research Articles

Mixed Integer Optimization of the Flow Pattern for Stochastic Feeder Reconfiguration

Mixed-integer conic programming provides an approach to solving the Optimal Feeder Reconfiguration (OFR) problem with guarantees on the quality of the solution. Integrating renewable generation into the distribution network and its associated variability renders stochastic OFR, which simultaneously considers several snapshots of the network, a more viable approach. While the established mixed-integer conic program for deterministic OFR can be extended to the stochastic case, the resulting optimization problem that still uses the bus injection relaxation is challenging to solve. This paper proposes a new mixed integer conic optimization of the flow pattern for solving the stochastic OFR. The new optimization framework exploits the perspective reformulation to obtain a tighter relaxation for stochastic OFR, which improves computational performance, and a feasibility pump heuristic to give a feasible solution. The mixed integer optimization of the flow pattern can also provide an initial solution to the mixed integer conic program employing the bus injection relaxation, giving rise to a hierarchical solution approach. Numerical results on stochastic OFR show that the hierarchical approach provides much-improved system performance compared to solutions considering a single snapshot. In addition, the proposed feasibility pump heuristic gives rise to network configurations close to global optimality in several test instances.

A three-stage multi-objective optimization framework for day-ahead interaction between microgrids in active distribution networks considering flexible loads and energy storage systems

This paper provides a three-stage framework in which the microgrid strategy for participation in the electricity market is determined by considering economic, technical, and environmental indicators. In the first stage, microgrids do their day-ahead scheduling and submit offers/bids to the independent system operator (ISO). In the second stage, ISO determines the hourly market clearing price (MCP) according to the microgrids' offers/bids and finally, in the third stage, ISO solves the distribution feeder reconfiguration (DFR) problem subject to the power flow constraints. The conditional value at risk (CVaR) method is utilized to deal with uncertainties of demand and renewable energy resources (RER), and the final problem is formulated as a mixed-integer quadratic programming (MIQCP) problem. The proposed model is implemented on a 69-bus distribution system and the results show that the simultaneous presence of electrical energy storage (EES) systems and demand response (DR) program reduces the marginal prices of microgrids and consequently leads to a 19.3% reduction in MCP. The results also illustrate that the reconfigurable topology increases the ISO flexibility and thus leads to an 8.22% reduction in MCP.

Energy management of multi-microgrids based on game theory approach in the presence of demand response programs, energy storage systems and renewable energy resources

In the deregulated power market, decentralized scheduling of microgrids is a complex problem with many technical and economic barriers. Achieving a comprehensive model that satisfies all technical, security and economic constraints and prevents market power is one of the concerns of distribution system operators. Therefore, this paper presents a two-stage framework in which DFR is implemented to satisfy technical and security constraints and microgrids day-ahead scheduling is done by a game-theoretic approach to avoid market power. The proposed model is implemented on a modified 69-bus distribution network by implementing a shiftable-load demand response (DR) program. The problem is formulated as a two-stage model: In the first stage, the distribution feeder reconfiguration (DFR) problem is solved to reduce losses, while in the second stage, the Disco and microgrids day-ahead scheduling problem is solved by a multi-objective optimization method based on the game theory with regard to the obtained topology. The optimization problem is also modeled as a mixed-integer quadratic programming (MIQCP) problem, solved using the CPLEX solver in GAMS software. The results show the high impact of the optimal topology on the decrease in loss, voltage deviation, and operation costs. In addition, the operation results indicate that the multi-objective optimization based on the game-theoretic method not only reduced market power, but also reduced operating costs by 23.7%.

A stochastic multi-objective optimization framework for distribution feeder reconfiguration in the presence of renewable energy sources and energy storages

In this paper, a multi-objective optimization framework is proposed to solve the distribution feeder reconfiguration (DFR) problem and its operation considering the demand response (DR) program, renewable energy sources (RES's), and electrical energy storages (EES's). The proposed model is implemented on 33-bus and 118-bus radial distribution systems while the uncertainties of RES's output power, load demand and electricity price are taken into account. The Monte Carlo simulation approach is used to generate scenarios while the backward scenario reduction approach is used to reduce the number of scenarios. The studied problem is modeled using the Epsilon-constrained method as a two objective problem and it is solved in the form of five case studies using the GUROBI solver in GAMS software. Our analysis of the results shows that reducing losses and increasing system reliability increases the production of local generation units, thereby increasing the operating costs. In addition, simulation results demonstrate that considering the dynamic topology reduced losses by 9.73% and increased reliability by 4.7%. The results also show that using the DR program reduces LMP by about 20% during peak hour.

Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty

Abstract Due to the spread and dispersed of load points and also growing penetration of wind farms to distribution systems, feeder reconfiguration strategy has been encountered with a high degree of uncertainty in such networks. In this way, efficient stochastic framework analysis based on cloud theory is proposed to handle the feeder reconfiguration problem considering uncertainty in load demands and wind turbine power. The cloud model, as a linguistic approach, comprises a correlation between randomness and fuzziness and provides good information to study the uncertain parameter effects on system performance through qualitative cloud models. According to qualitative-quantitative bidirectional transmission characteristic of the cloud theory through backward-forward cloud generator algorithm, a stochastic multi-objective feeder reconfiguration problem is formulated and solved utilizing powerful non-dominated sorting group search optimization algorithm. After obtaining Pareto fronts, the best compromise solution is determined by using the fuzzy decision-making technique. To demonstrate the applicability of the proposed method and to compare obtained results with the other literature, deterministic and stochastic analysis is implemented on the IEEE 33-bus and 69-bus radial distribution systems. The superiority and satisfying performance of the proposed algorithm can be inferred from the quality of simulation solutions.

Optimal participation of demand response aggregators in reconfigurable distribution system considering photovoltaic and storage units

Feeder reconfiguration is an important operational process in power distribution grids, which is implemented to enhance the system’s performance by managing the switches. Given variations of the electricity price and load pattern in smart distribution networks, the operational problems of the distribution system are largely time-dependent and very complex. To deal with these temporal dependencies, it is important to extend the problem across different time intervals. Toward this end, a multi-objective optimization model is presented in this study for dynamic feeder reconfiguration problem in the distribution system over multiple time intervals considering distributed generators, energy storage systems, and solar photovoltaic units. The demand response program including interruptible/curtailable service is proposed to enable energy consumers to rethink their energy consumption patterns based on incentive and punitive policies. The common objectives considered in the feeder reconfiguration problem are power loss and voltage deviation which are important objectives for traditional distribution systems, but less attention has been paid to distribution network voltage security. This study considers operational cost, energy loss and voltage stability index as objective functions that can meet operational and voltage security expectations. Dynamic feeder reconfiguration problem is a complex integer nonlinear program problem and hence it is difficult to solve which requires the use of appropriate optimization algorithms to converge to global optima or find close to global optima. In this paper, a modified honey bee matting optimization algorithm based on the new mating mechanism is presented to solve the multi-objective dynamic feeder reconfiguration problem. The proposed approach uses an eliminating zone concept to finds a set of non-dominated solutions during the search process. Furthermore, a fuzzy decision-maker is adopted to select the best compromise solution among the non-dominated solutions. The suggested approach is tested on the IEEE 33-node and 136-node test systems and its superiorities are shown through comparison with other evolutionary algorithms.

Enhanced feeder reconfiguration in primary distribution networks using backtracking search technique

ABSTRACT The Backtracking Search (BS) technique is a new evolutionary optimiser which simply prepared to make use of the previous populations. It has been employed to solve the feeder reconfiguration problem to minimise the energy losses to face the different loading conditions on two distribution networks. The first system is 11-kV realistic Egyptian distribution ring which is called El asal-Magles El madinah primary distribution ring in Gharbia governorate. The second system is a 12.66-kV radial distribution system of 32 buses and 37 distribution segments. It has been handled using Electrical Transient Analyzer Program (ETAP) to analyse the impacts of disconnecting the distribution segments and varying the normal open points on the active and reactive power losses. The different loading conditions are based on real measurements of the feeders and transformers loading on both the medium voltage side and the low voltage side at the rush hours. Significant technical and economical merits are satisfied for the studied distribution network. Both the BS application and ETAP analysis demonstrate the great technical and economical merits of optimally disconnecting the distribution segments to minimise the related costs of the energy losses, improve the voltage profile, and avoid the overloading in branches.

Secure and Reliable Distribution Feeder Reconfiguration in the Presence of Static VAR Compensator

This paper presents a multi-objective approach for solving distribution feeder reconfiguration (DFR) problem aiming at optimizing active power of distributed generations (DGs) and locating static VAR compensators (SVCs) in distribution network. To have a positive effect, DGs and SVCs must suitably integrate and coordinate with the distribution system feeder design. Besides the power loss reduction as a main purpose, expansion of distribution systems forces the system operators to obtain an acceptable reliability and the voltage stability level in distribution system. Furthermore, increasing DG penetration in distribution systems pushes system operators to take transient stability of DGs into account. Considering multiple objective functions beside different operational constraints of distribution systems makes the proposed DFR problem a complex optimization problem that needs to be solved by a proper solution methodology. In this connection, teacher learning-based algorithm, a well-known evolutionary algorithm, is employed to solve the proposed problem. The proposed optimization algorithm is coded in DPL environment of DIgSILENT® Power Factory with a detailed dynamic modeling of all electric devices. The effectiveness of the proposed approach is demonstrated using a typical 84-bus test system.

Path-Based Distribution Feeder Reconfiguration for Optimization of Losses and Reliability

This paper presents a path-based modeling framework for the distribution feeder reconfiguration (DFR) problem. The framework maps the decision variables— on/off status indicators for the paths—into linear expressions for the network flows and reliability indices. These linear expressions are suitably deployed for DFR optimization, where reliability can feature either as an objective or as a constraint. Additionally, the usual objective of loss minimization can also be incorporated, leading to a multi-objective DFR optimization framework. In its most general form, the proposed framework is a mixed-integer quadratic programming formulation, and can be solved using existing solvers. The paper makes three other important contributions: first, a probabilistic approach is adopted to validate the approximation of load point failure rates as the sum of failure rates of upstream components; second, a graph algorithm is proposed for identifying topologies with best reliability; and third, simulation studies with representative loading patterns are used to identify the appropriate choice of loading conditions for optimizing overall active power losses. Application of the proposed framework to standard test systems demonstrates its ability to optimize network losses or reliability or both. Numerical results also show the effect of topology and loading conditions on the performance of optimized topologies.

Smart brute-force approach for distribution feeder reconfiguration problem

The massive introduction of generation at the distribution level changes the electric network operation paradigm. According to the rules that govern the relationship between the distribution system operator and the transmission system operator, it becomes interesting to reconfigure the distribution system to control the power consumption and injection at each substation and to determine the switch configuration that optimizes this behaviour at substations. This paper proposes a method to solve this distribution feeder reconfiguration (DFR) problem. First, an offline computation, based on a graph-oriented approach, finds all valid radial configurations and their similarities, then, according to a load setting, a sub approximation of the operating cost is computed and used with electrical constraints detection to reduce the number of necessary load flow computations in a brute-force approach to choose the optimal configuration. This method is exemplified on a test case including 4 substations, 124 nodes, 115 uncontrolled lines and 23 switch controlled lines.

Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems

Distribution feeder reconfiguration is an important operation problem in distribution system which has been used to improve the efficiency of distribution systems by obtaining the best combination of on/off status of the switches. It is a mixed integer non-linear program problem and hence hard to solve which necessitate employing proper optimization algorithms to converge to global optima or find near global optima. Smart grid implementation has made loads and electricity prices more volatile and as a result makes operational power system problems to be much more time dependent and more complicated rather than before. To cope with these time dependencies, it is crucial to extend the problems on different time intervals. To this end the dynamic distribution feeder reconfiguration, extension of the problem over multiple time intervals, with various objective functions including operation cost, power loss and energy not supplied is developed and investigated in this study. Time varying electricity prices and different load levels juxtapose with the effect of distributed generations are taken into account in order to generalize the proposed approach. Inherent complexities of distribution feeder reconfiguration problem have made proposing solution techniques an ongoing research topic. A new optimization algorithm is proposed to solve the proposed problem.

Network Reconfiguration of Primary Distribution System Using GWO Algorithm

This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.

Optimal Use of Existing Distribution Feeders to Accommodate Transportation Electrification

An analytical method that can be used to study the optimal topology and maximum capacity of the existing distribution network to accommodate electric vehicle (EV) charging demands is presented in this paper. In order to facilitate a large number of EV integrations, the feeder reconfiguration problem is formulated as a discrete nonlinear optimization problem that finds optimal feeders' tie switch locations and their on/off schedule to minimize operation costs and comply with the system operation constraints. A novel stochastic dynamic programming technique is adopted to solve the problem that includes various uncertainties associated with feeder baseline and EV charging loads. Simulation results obtained from an 84-bus feeder test system have indicated that for a case with 20% EV penetration to a 70% capacity utilization feeder area, a 10.78% reduction of operation cost and a 14.4% decrease in maximum feeder utilization can be obtained by distribution feeder reconfiguration.

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.

Power system reconfiguration and loss minimization for a distribution systems using “Catfish PSO” algorithm

One of the very important ways to save electrical energy in the distribution system is network reconfiguration for loss reduction. Distribution networks are built as interconnected mesh networks; however, they are arranged to be radial in operation. The distribution feeder reconfiguration is to find a radial operating structure that optimizes network performance while satisfying operating constraints. The change in network configuration is performed by opening sectionalizing (normally closed) and closing tie (normally opened) switches of the network. These switches are changed in such a way that the radial structure of networks is maintained, all of the loads are energized, power loss is reduced, power quality is enhanced, and system security is increased. Distribution feeder reconfiguration is a complex nonlinear combinatorial problem since the status of the switches is non-differentiable. This paper proposes a new evolutionary algorithm (EA) for solving the distribution feeder reconfiguration (DFR) problem for a 33-bus and a 16-bus sample network, which effectively ensures the loss minimization.

Optimal Feeder Reconfiguration of Distribution System with Distributed Generation Units using HC-ACO

The objective of optimal feeder reconfiguration of radial distribution system problem is to obtain the best set of branches to be opened, one each from each loop, such that the resulting radial distribution system has the desired performance. This paper presents a feeder reconfiguration problem in the presence of distributed generators to minimize the system power loss while satisfying operating constraints using Hyper Cube-Ant Colony Optimization (HC-ACO) algorithm. Loss Sensitivity analysis is used to identify optimal location for installation of DG units. Simulations are conducted on 33 - bus radial distribution system at four different cases to verify the efficacy of the proposed method with other recent published approaches reported in the literature. The result shows that the method proposed is fast and effective. Keyword: Distribution system, radial distribution system, Distribution feeder reconfiguration, Distributed generator, Ant Colony Optimization (ACO) algorithm, Real power loss. Nomenclature P i Real power flowing out of bus i Q i Reactive power flowing out of bus i P j Real power flowing out of bus j Q j Reactive power flowing out of bus j Li P Real power load connected at bus i Q Li Reactive power load connected at bus i PLj Real power load connected at bus j Q Lj Reactive power load connected at bus j ij R Resistance of line section between i and j ij X Reactance of line section between i and j , Lj eff P Effective real power load connected at bus j , L je ff Q Effective reactive power load connected at bus j

A novel multi-objective self-adaptive modifiedθ-firefly algorithm for optimal operation management of stochastic DFR strategy

Summary This paper suggests a new self-adaptive modification method using firefly algorithm (FA) to investigate the multi-objective probabilistic distribution feeder reconfiguration problem. In this regard, the idea of phase angle vector is employed to replace the traditional Cartesian framework in the FA and thus called θ-FA. Also, a new modification method based on an adaptive mechanism is suggested that will allow each firefly to choose the appropriate modification technique during the optimization suitably. As regards the objective functions, the main focus of this paper is to assess the effect of the reconfiguration on the reliability indices including active power losses, voltage deviation, and system average interruption frequency index. In order to handle the uncertainty effects, a sufficient framework based on 2m + 1 point estimate method is proposed too. The satisfying performance of the proposed method is checked using IEEE 32-bus radial distribution system. Copyright © 2014 John Wiley & Sons, Ltd.

Enhanced gravitational search algorithm for multi‐objective distribution feeder reconfiguration considering reliability, loss and operational cost

Power loss reduction can be considered as one of the main purposes for distribution system operators. Reconfiguration is an operation process used for this optimisation by means of changing the status of switches in a distribution network. Recently, all system operators tried their best in order to obtain well-balanced distribution systems to decrease the operation cost, improve reliability and reduce power loss. This study presents an efficient method for solving the multi-objective reconfiguration of radial distribution systems with regard to distributed generators. The conventional distribution feeder reconfiguration (DFR) problem cannot meet the reliability requirements, because it only considers loss and voltage deviation as objective functions. The proposed approach considers reliability, operation cost and loss simultaneously. By adding the reliability objective to the DFR problem, this problem becomes more complicated than before and it needs to be solved with an accurate algorithm. Therefore this study utilises an Enhanced Gravitational Search Algorithm called EGSA which profits from a special mutation strategy in order to reduce the processing time and improve the quality of solutions, particularly to avoid being trapped in local optima. The proposed approach has been applied to two distribution test systems including IEEE 33 and 70-node test systems.

Distribution System Network Reconfiguration by using Artificial Bee Colony Algorithm.

This paper presents a new methodology using artificial bee's colony algorithm (ABC) in the radial distribution systems for the placement and size of Distributed Generators (DG). The ABC algorithm is a new population based meta heuristic approach exalted by intelligent foraging behavior of honey bee swarm. This paper presents a feeder reconfiguration problem with Distributed Generation to the distribution system. In the recent years the application of Distributed Generation (DG) has found increasing attention. Optimal reconfiguration needs selection of the best set of branches to be opened so that the resulting radial distribution system has improved voltage profile system and minimized power loss. The developed methodology is demonstrated by a radial distribution system with distributed generation. The study results show that the optimal on/off patterns of the switches can be identified which give the minimum power loss while respecting all the constraints.

Feeder Reconfiguration in Distribution Networks Based on Convex Relaxation of OPF

The feeder reconfiguration problem chooses the on/off status of the switches in a distribution network in order to minimize a certain cost such as power loss. It is a mixed-integer nonlinear program and, hence, hard to solve. In this paper, we propose a heuristic algorithm that is based on the recently developed convex relaxation of the ac optimal power flow problem. The algorithm is computationally efficient and scales linearly with the number of redundant lines. It requires neither parameter tuning nor initialization for different networks. It successfully computes an optimal configuration on all four networks we have tested. Moreover, we have proved that the algorithm solves the feeder reconfiguration problem optimally under certain conditions for the case where only a single redundant line needs to be opened. We also propose a more computationally efficient algorithm and show that it incurs a loss in optimality of less than 3% on the four test networks.