Multi-Objective Evolutionary Algorithm with Node-Depth Encoding and Strength Pareto for Service Restoration in Large-Scale Distribution Systems

A practical and efficient method for service restoration in distribution systems was developed and demonstrated on tests performed on the real and large-scale distribution system of Londrina city (Brazil). The method combines Multi-objective Evolutionary Algorithms with the tree encoding named Node-Depth Encoding and an Alarming Heuristic in order to find adequate service restoration plans for distribution systems with size from 3,860 buses and 632 switches to 30,880 buses and 5,166 switches, requiring running time less than 37 seconds for all the test cases. Moreover, the method requires no network simplification (as modeling a set of loads in a unique load point or using a relatively small set of switches instead of all switches) in order to generate adequate service restoration plans for those systems. This paper proposes a simple yet effective improvement by incorporating an exhaustive search as a first stage of that method, which guarantees the analysis of all possible initial service restoration plans (i.e. those requiring the minimal number of switching operations that can reconnect all the healthy out-of-service areas). Simulations results have shown the effectiveness of including the exhaustive search in that method.

Service restoration in contingency situations is one of the most critical problems in the context of distribution system operation. It is a problem with multiple objectives and multiple constraints whose solution must be gotten as soon as possible. Besides, due to the existence of customers with higher priority of supply (e.g., big industries and hospitals), it is necessary to prioritize service restoration to these loads. However, it is important to highlight that these priority customers have different priority levels, that also must be considered during the determination of a feasible service restoration plan. In this paper, a methodology based on multi-objective evolutionary algorithm is proposed for solving service restoration problem in large-scale distribution system taking into account the existence of three levels of priority customers. Simulations results have shown the proposed methodology is able to find suitable service restoration plans for large-scale real distribution systems (from 3,860 to 30,880 buses) with relatively soft computing without requiring any network simplification.

Distribution system (DS) service restoration is a very complex multi-objective and multi-constraint optimization problem, which requires high-quality Pareto fronts for helping the DS operators' work. This paper proposes a multi-objective evolutionary algorithm (MOEA) that combines the advantages of MOEAs in Tables with the properties of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to recover Pareto fronts of the service problem. Its main differentials are its ability of recovering high-quality Pareto fronts, even in large-scale DSs, and of prioritizing switching operations in Remotely Controlled Switches (RCSs), independently of amount of RCSs. In opposite of Manually Controlled Switches (MCSs), RCS can be faster and cheapest operated from operating center. Several tests were carried out to validate and to compare the proposed MOEA to three MOEAs published in literature. Four metrics were used for assessing the quality of Pareto fronts generated and a Welch's t hypothesis test enabled the comparison of MOEAs' performance. Test results indicate the proposed MOEA outperforms those three MOEAs published in literature.

This paper proposes an enhanced differential evolution (EDE) approach to solve the problem of service restoration in distribution systems. Service restoration of distribution systems is to restore power to the blacked out but unfaulted area. Basically, it is a combinatorial optimization problem that often has a great number of candidate solutions to be evaluated by the system operators. To tackle the problem of service restoration, an EDE method which combines variable scaling DE and ant colony algorithm is employed to solve the multi-objective optimization problem. The proposed method was tested on a typical distribution system of the Taiwan Power Company (TPC) and compared with the heuristic-based fuzzy algorithm and classical DE method. Results show that the proposed method was superior to that of the existing methods in terms of convergence time and obtained restoration plans.

This paper proposes a new method for optimizing the network reconfigurations for service restoration in distribution systems. The service restoration may be expressed in a complicated combinatorial optimization problem. In this paper, meta-heuristics are considered to solve the problem efficiently. The proposed method makes use of parallel tabu search (PTS) that considers a couple of strategies in tabu search (TS). One strategy is to decompose the neighborhood into subneighborhoods to reduce computational effort. The other is to introduce several tabu lengths into TS to make solution candidates more diverse. The proposed method is applied to the 69-node distribution systems. A comparison is made between the conventional and the proposed methods in terms of computational effort and solution accuracy.

The structure of power system is changing around the world. With the increase in size and complexity of the distribution network, it is important to automate the system for proper monitoring and control. Service restoration is one of the techniques used by utilities to supply the power to healthy part of the network which gets isolated after occurrence of fault. The restoration plans have changed over the years to cope up with this changing nature of power system. A vast change is observed in methods, strategies used for service restoration. In this paper, review of different methods which are proposed to solve service restoration problem is presented which includes knowledge based systems, expert systems, heuristic methods, artificial intelligence techniques with search algorithm, hybrid methods like combining fuzzy logic method with Genetic Algorithm (GA), expert system and coloured petri nets etc., and different guidelines formed.

Distributed generator-based distribution system service restoration strategy and model-free control methods

By changing the status of switches on distribution feeders, service to de‐energized customers can be restored after an occurrence of a fault. In this paper, multi‐agent system (MAS) is applied for the power distribution system service restoration task using local information by utilizing the characteristics of information sharing. Each software agent is running autonomously and is able to exchange information within limited distance. Every agent is capable of performing service restoration such that service restoration can be performed in parallel when a multiple fault scenario occurs. As a result, the outage period can be reduced. BDI (believe–desire–intention) agent architecture is implemented in this paper for group and zone service restoration for power distribution systems. Zone/switch relation table is built and used by agents in order to identify adequate switching actions during the service restoration planning stage. The proposed MAS is implemented on JADE (Java Agent Development) platform. The simulation results show that the proposed BDI‐based service restoration agent system is capable of providing adequate restoration switching plan in several scenarios. Copyright © 2010 John Wiley & Sons, Ltd.

This paper proposes a fast restoration strategy of distribution systems using an enhanced differential evolution (EDE) approach. Service restoration of distribution systems is an emergent task that must be performed rapidly by the system operators. Basically, it is a complicated combinatorial optimization problem, often having many candidate solutions to be evaluated by the operators. To improve the efficiency of restoration and reduce the burden on the operators, this paper proposes an EDE method combining variable scaling differential evolution (VSDE) algorithm and ant system (AS) to solve the combinatorial optimization problem. To verify the effectiveness of the proposed method, a typical distribution system of the Taiwan Power Company (TPC) was tested and compared with the existing methods. The results show the proposed method was superior to the existing methods in terms of convergence time and the obtained restoration plan.

Reasonable repair crew dispatch can accelerate the service restoration in distribution systems after some components are damaged. It is worthy noting that as traditional distribution systems are involving into cyber-physical distribution systems composed of physical distribution networks and cyber networks, damaged components in these two networks should be coordinately repaired to achieve maximal restoration performance. However, repair crews in physical distribution networks are scheduled without considering the impact of the corresponding cyber networks in existing repair crew dispatch strategies. In this paper, a coordinated model is proposed to co-optimize the repair crew schedule of the two networks. Besides the cyber-physical interdependence between the two networks, backup power systems, topology reconfiguration and intentional distribution generator (DG) island are also modelled to improve the repair performance. The proposed model is formulated as a mixed-integer linear programming model, and its effectiveness is verified on the modified IEEE 33-node and 123-node test systems. Numerical results highlight the necessity of co-optimizing repair crew dispatch in both physical distribution networks and cyber networks.

This work intends to foresee an optimum restoration plan for power distribution network with numerous DGs through system reconfiguration. The destinations, for example, minimizing the out of service area, diminishing the quantity of switching operations, lessening the aggregate system losses alongside imperatives, for example, radial system structure, voltage and current points of confinement are considered while taking care of the restoration issue. Rowlett wheel choice procedure and elitism is utilized as a part of Genetic Algorithm which gave better enhancement results. An effective calculation is created which predicts appropriate restoration arrangement for conveyance framework arranged or spontaneous blackouts. Genetic algorithm is utilized to settle multi-goal and multi obliged enhancement issue.

A multiagent design for self-healing in electric power distribution systems

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering radial network constraints and power source limits. Up to now, the problem has been dealt with using conventional methods such as branch and bounds method, expert system, neural networks, and fuzzy reasoning. Recently, Genetic Algorithms (GA) are noticed as one of the efficient methods for solving large combinator-ial optimization problems. The method can perform parallel search and can search optimal solution easier. This paper presents an application of GA to service restoration in distribution systems. The feasibility of the proposed method is demonstrated on a typical distribution system model. The result shows that the method can solve the problem efficiently and this tendency becomes dominant by increasing of problem dimensions.

This paper proposes a new tabu search based method for service restoration in distribution systems. The formulation of the service restoration may be expressed as a combinatorial optimization problem for determining a new route with switches to recover the service in an outage area. It aims at minimizing an outage area and equalizing the power source margins in the recovered areas as well as computational efficiency. In this paper, a new method is proposed to integrate parallel tabu search (PTS) with ordinal optimization (OO) in distribution system service restoration. PTS has better performance than TS in terms of solution quality and computational time. OO contributes to the reduction of computational effort in selecting solution candidates in the neighborhood of TS. In other words, it plays an important role to improve computational as a complementary tool efficiently. The proposed method is successfully applied to a 138-node system.

This paper proposes a new multiagent control system (MACS) for service restoration in distribution systems with integrated distributed generation (DG) units. First, the MACS detects and locates faults, then decides the optimal reconfiguration of the network for restoring de-energized loads, and finally regulates the nodes voltages. Unintentional islanding operation of DG units is avoided and different postfault response modes of DG unit are addressed in the MACS design. The MACS has a hybrid centralizeddecentralized structure where agents are arranged in two layers with different responsibilities and communication capabilities. Agents at load buses in the first layer can only communicate directly with its next neighbor load agents on the same feeder and to its feeder agent, whereas the higher level agents in the second layer can communicate directly with each other. This MACS structure reduces the possibilities of control system failures for a moderate communication network infrastructure. Full dynamic simulation model for evaluating the MACS is implemented.