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

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.

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.

In the current power and energy scenario, distributed generation (DG) has generated a lot of interest across the globe due to the growing concerns about gradual depletion of fossil fuels, steep load growth, environmental pollution and global warming caused by greenhouse gas emissions. Renewable DGs such as wind generators and solar photovoltaic are well-recognized now-a-days as sources of clean energy. Voltage dips have always been a serious problem in electricity networks accounting for the disruption, poor power quality and affecting the cost and productivity of power utilities and the consumers. Therefore, with more DG penetration into the network, utilizing the DGs for improving power quality through voltage dip mitigation has become an important area of research in itself. In this context, this paper presents a novel technique for voltage dip mitigation with Distributed Generation (DG) using a simple feed forward Artificial Neural Network (ANN) to mitigate the effects of the power quality problems arising out of voltage dips in a distribution network. The scheme is simulated in DIgSILENT Power Factory 14.0 software and the tests are carried out on IEEE 9-bus test system. A three-phase short circuit fault on a line is simulated as the disturbance causing the voltage dip in the system. The model is trained, tested and validated in Matlab using mean square error and regression analysis.

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.

The network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objectives functions. For large networks, no exact algorithm has found adequate restoration plans in real-time, on the other hand, Multi-objective Evolutionary Algorithms (MOEA) using the Node-depth enconding (MEAN) is able to efficiently generate adequate restorations plans for relatively large distribution systems. An MOEA for the restoration problem should provide restoration plans that satisfy the constraints and reduce the number of switching operations in situations of one fault. For diversity of real-world networks, those goals are met by improving the capacity of the MEAN to explore both the search and objective spaces. This paper proposes a new method called MEA2N with Strength Pareto table (MEA2N-STR) properly designed to restore a feeder fault in networks with significant different bus sizes: 3 860 and 15 440. The metrics R 2, R 3, Hypervolume and e-indicators were used to measure the quality of the obtained fronts.

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.

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.

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.

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 service restoration model considering a variety of power resources when the power outages of the superior substation is caused by extreme events. The model can dispatch energy storage system (ESSs), mobile emergency generator (MEGs) and renewable distributed generators (DGs), and can change the topology of distribution system in real time by utilizing soft open points (SOPs) and dynamic reconfiguration, which improves the network flexibility of the distribution system and maximizes the use of existing resources for service restoration. After being linearized, the model is a mixed integer linear programming (MILP), which can be efficiently solved by commercial solvers such as CPLEX and Gurobi. The modified IEEE 37 bus distribution system is tested, and the results demonstrate the effectiveness of the proposed model.

Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks

Local distribution resources, such as distributed generations (DGs), energy storage systems, and some renewable energy resources, can be interconnected for service restoration in distribution systems and operate as an island after extreme events. High penetration of renewable energy resources makes it difficult to make decisions on restoration strategies because the power injections become uncertain. This paper proposes a chance-constrained program with relaxed second-order cone constraints (SOCC) to formulate the critical load restoration problem. The joint probability density function of power outputs of PVs and WTs is represented by a Gaussian mixture model (GMM). Using the sample average approximation method, an approximate analytical form of the chance constraints is proposed, which transforms the probabilistic program to a deterministic mixed-integer second-order cone program (MISOCP). The MISOCP can be solved by off-the-shelf optimization solvers. The effectiveness of the proposed method is validated by the simulation of the IEEE 33-node test system.

An essential aspect that contributes to the resilience of radial electrical distribution systems (EDS) is the deployment of efficient and fast service restoration methods. In the presence of a permanent fault, the service restoration minimizes the amount of de-energized consumers by reducing the area that needs to be disconnected to isolate the faulty section of the network. To do so, the service restoration algorithm can make use of automated switching devices and dispatchable energy resources allocated throughout the network to transfer loads among feeders, considering the system's operational constraints. Depending on the size of the system and the complexity of the telecommunication infrastructure, the application of centralized restoration systems may neither be practical nor flexible. Thus, in this paper, a distributed service restoration method is proposed, using the alternating direction method of multipliers (ADMM) algorithm to solve the optimization problem. As such, the original service restoration model is distributed among the nodes and branches of the system that exchange limited information with neighboring nodes and branches. Radiality constraint is also distributed by the proposed ADMM algorithm. A 44-node test system was used to conduct case studies. Results demonstrate that the proposed method efficiently solves the service restoration in active EDS without using a centralized Intelligence.

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.

Distributed energy storage planning in soft open point based active distribution networks incorporating network reconfiguration and DG reactive power capability