Service Restoration Problem Research Articles

A novel variable neighborhood descent algorithm for service restoration in radial electrical distribution networks

Abstract This paper presents a variable neighborhood descent algorithm for the service restoration problem (SRP) in electrical distribution systems. The restoration problem appears when a permanent fault occurs in the system. The fault must be localized and isolated from the rest of the system. As a consequence of the isolation, the downstream area is de-energized and requires restoration. The variable neighborhood descent algorithm features its ability to solve the SRP for radial distribution systems and introduces a strategy that helps to deal with the system’s radiality. Tests are performed using a 53-node test system. The results show that the proposed algorithm can efficiently solve the SRP in a distribution system, and as a deterministic algorithm, the results can be used to help nondeterministic algorithms.

Service restoration in distribution systems considering priority customers and microgrids

Service restoration (SR) consists of automatically generating and executing a plan to restore the service in healthy zones using the least number of maneuvers after detecting and isolating a permanent fault in the distribution system zone. This component is essential to self-healing functionality in smart grids and allows customers to reconnect quickly to the distribution grid after a power outage. Distributed generation (DG) supports the distribution network when there is insufficient capacity to restore all zones out of service or supply the loads locally through microgrids. The power supply must be restored to the highest priority customers in case of partial restoration. Also, most research works use simplified or linearized models to propose restoration algorithms. This paper proposes a complete AC formulation for the service restoration problem in distribution systems considering network reconfiguration (NR), the integration of distributed generation (DG), and priority customers (PCs) into the solution. The optimization problem is solved by a centralized algorithm based on combining the Differential Evolution (DE) and Continuous Population-Based Incremental Learning (PBILc) metaheuristics techniques. Simulation results are presented for three case studies in which the IEEE 33-bus distribution system is tested for different fault scenarios. The numerical results show the robustness and efficiency of the proposed algorithm.

Perspective Reformulations of the Two-Level Service Restoration Problem

Perspective Reformulations of the Two-Level Service Restoration Problem

Chance constrained robust hydrogen storage management to service restoration in microgrids considering the methanation process model

After a natural disaster, electrical grids might go into a self-healing mode to restore service and reconnect any customers that lost power. In the self-heal system, the fast operation and restoring the critical loads (CLs) during a fault by available resources can increase the flexibility and resilience of the network. In addition to renewable energy sources and electric vehicles (EVs) storage devices, the hydrogen storage system and the process of converting hydrogen into electric energy by micro-gas turbine (MGT) have been modeled in order to assess the impact of this storage system in electrical loads restoration in critical conditions. Self-healing functionality is enhanced by using alternative and novel energy sources. In such a situation, how to interact with the stochastic nature of network components will be challenging. This paper formulates the optimal service restoration (SR) problem using chance-constrained Adaptive Distributionally Robust Optimization (ADRO) to address the ambiguities introduced by the uncertain parameters. A tri-level robust optimization model is developed such that the optimal switching in the network until clearing the fault is determined at the upper level, and the maximization of the SR based on the load demand values is determined at the lower level. The proposed method is demonstrated by simulation results on the modified Civanlar test system. In an equal operating condition and considering β = 3, it can be seen that the initial hydrogen State of the Tank (SoT) by 50 % can improve the load restoration by 7.5 % compared to the uncharged hydrogen tank. By increasing the initial SoT to the nominal value (10 Kg), the load restoration can increase to 13.2 %.

Optimal Service Restoration Scheme for Radial Distribution Network Using Teaching Learning Based Optimization

In the event of a fault isolation process, all loads located downstream from the faulted point become out of service, and as a consequence, the power interruption affects a greater portion of the radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing the network topology configuration via optimal tie-switch and section switch combinations. However, when the network topology configuration is performed, it results in increased load currents. As a result, some Protective Devices (PDs) can operate undesirably and some network branches may become unprotected. Therefore, it is essential to consider protection constraints in the SR problem to maintain service continuity during power interruptions. The proposed method aims at optimal SR with minimum out-of-service loads, minimum power loss, and improved voltage profiles and at the same time ensures PDs operate correctly during the normal and overloading conditions. The proposed method was carried out on the Debre Markos distribution networks, using the Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Differential Evolutionary (DEV) algorithms. The proposed SR was carried out considering and without considering protection constraints. The obtained SR topology was not feasible for SR without considering protection constraints, since some PDs fail to operate properly in normal loading conditions. After executing the proposed SR algorithms by considering protection constraints for a single fault case, the power loss reductions in TLBO, DEV, and PSO were 64.9073%, 45.9073%, and 55.358 %, respectively. The minimum voltage profiles obtained in each proposed TLBO, DEV, and PSO algorithm were 0.96%, 0.95%, and 0.96%, respectively. In each algorithm, except for the branch under fault, all healthy out-of-service branches were restored. When the protection constraints were considered in an optimal SR, load current did not exceed the rating of the fuses. The results show the importance of considering protection constraints during SR to prevent dysfunction of the PDs in the network. Comparative analyses were carried out on each algorithm and TLBO algorithms performed better than PSO and DEV for search functions.

Service restoration framework for distribution networks incorporating switching crew routing

Here, an optimization framework is proposed for the service restoration (SR) problem in distribution networks based on mixed-integer second-order cone programming (MISOCP). The objective function is to minimize (i) curtailed loads, (ii) the number of switching operations, (iii) crew dispatching cost, and (iv) operational cost. A novel approach is developed for the optimal switching crew routing (SCR) to perform the manual switching operations in a short time. Self-healing actions including (i) network reconfiguration, (ii) load shedding, (iii) adjusting the output power of the dispatchable distributed generations (DGs), and (iv) the optimal tap setting of the voltage regulation devices are adopted in the SR strategy. Both the voltage dependency and the uncertainty nature of loads are modelled. Besides, the switch failure scenario is considered in the proposed model to deal with real operating conditions. Hence, the developed optimization framework offers the most robust SR solution. The simulation studies are performed on an actual 87-bus system in MATLAB/Yalmip environment by adopting Gurobi solver. The obtained results verify that the proposed model enhances quality of the solution in terms of SR process time compared to the other existing models.

A Hierarchical Restoration Mechanism for Distribution Networks Considering Multiple Faults

Service restoration of distribution networks in contingency situations is one of the highly investigated and challenging problems. In the conventional service restoration method, utilities reconfigure the topological structure of the distribution networks to supply the consumer load demands. However, the advancements in renewable distributed generations define a new dimension for developing service restoration methodologies. This paper proposes a hierarchical service restoration mechanism for distribution networks in the presence of distributed generations and multiple faults. The service restoration problem is modeled as a complicated and hierarchical program. The objectives are to achieve the maximization of loads restored with minimization of switch operations while simultaneously satisfying grid operational constraints and ensuring a radial operation configuration. We present the service restoration mechanism, which includes the dynamic topology analysis, matching isolated islands with renewable distributed generations, network reconfiguration, and network optimization. A new code scheme that avoids feasible solutions is applied to generate candidate solutions to reduce the computational burden. We evaluate the proposed mechanism on the IEEE 33 and 69 systems and report on the collected results under multitype fault cases. The results demonstrate the importance of the available renewable distributed generations in the proposed mechanism. Moreover, simulation results verify that the proposed mechanism can obtain reasonable service restoration plans to achieve the maximization of loads restored and minimization of switching operations under different faults.

A New Method for Solving Service Restoration Problem in Distribution Networks Considering Expected Switching Time

A New Method for Solving Service Restoration Problem in Distribution Networks Considering Expected Switching Time

Service Restoration in Distribution Networks Using Available Data Including Uncertainty

The growth in penetration of photovoltaic generation systems (PVs) makes it difficult to reconfigure electrical power distribution networks, including service restorative operations. This is because distribution network operators cannot monitor or control the PVs, and this brings an additional uncertainty in available data that the operators must rely on. The authors focus on the service restorative operations and propose a problem framework and its solution method that finds the optimal restoration configuration under extensive PV installation. The traditional service restoration problems require accurate data on load sections; however, it is not practical in the distribution networks where PVs are extensively installed. Based on this background, the robust optimization and the two-stage stochastic programming are applied in the proposed formulation to address the PV-originated uncertainty using only readily available data. Through numerical simulations and discussion on their results, the validity of the proposal is verified.

Self‐healing strategy to enhance microgrid resilience during faults occurrence

The interruption in the power system has a substantial social and economic effect, especially during natural disasters and faults causing a massive outage. Therefore, the appropriate and fast diagnosis of the fault locations and service restoration should be conducted quickly to restore power to as many undamaged sections as possible. In this context, self-healing service restoration can restore power systems and enhance distribution networks’ resilience. This paper proposes a novel self-healing topology to maintain the power system's balance while prioritizing the critical loads in a micro-grid system. The micro-grid system is operated in two different modes, which are the normal mode and self-healing mode. In the normal mode, the energy management system (EMS) solves the economic power dispatch function to minimize the cost while satisfying the load demand. The self-healing mode is triggered once a fault occurred in any of the distributed energy resources (DERs). This mode aims to maximize the undamaged DERs power generation to meet the total prioritized critical loads. The metaheuristic binary teaching-learning-based optimization (BTLBO) technique is utilized to obtain the service restoration problem's optimal switching action sequence and avoid tuning the parameters to handle the problem of premature convergence. The micro-grid model is based on the IEEE 37 bus, where several disturbance scenarios have been simulated to evaluate the proposed self-healing topology's performance. The results show the proposed technique outperforms traditional self-healing methods.

Multi-Objective User Preference Enabling Method for Service Restoration in Distribution Networks with High Renewable Energy Penetration

Service restoration (SR) in distribution networks by nature is a multi-objective optimization problem. To solve the multi-objective SR and to ensure sufficient available delivery capability for delivering renewable energy, a user preference enabling (UPE) method is proposed in this paper. The proposed method is user-oriented, allowing a user’s preferred solution over a range of each objective function value to be achieved. The proposed UPE method can adopt each user’s specific preference over each objective function, allowing the user to directly place a preference for each objective function without compromising their independence from each other. If the user preference problem has no solution, the proposed method will identify the non-existence of a solution and suggest a remedy scheme to recover a solution. To achieve its computational efficiency, the proposed UPE method contains a discrete optimization stage and a relaxation and correction stage to solve the multi-objective SR problem, which has a large number of binary variables. A user-preferred solution that truly meets the user's needs for each objective is thus fast obtained. The effectiveness and flexibility of the proposed UPE-based method is evaluated on large distribution networks with multiple scenarios.

Service Restoring Reconfiguration for Distribution Networks Considering Uncertainty in Available Information

The recent growth in the penetration of photovoltaic generation systems (PVs) has brought new difficulties in the operating and planning of electric power distribution networks. This is because operators of the distribution networks normally cannot monitor or control the output of the PVs, which introduces additional uncertainty into the available information that operations must rely on. This paper focuses on the service restoration of the distribution networks, and the authors propose a problem framework and its solution method that finds the optimal restoration configuration under extensive PV installation. The service restoration problems have been formulated as combinatorial optimization problems. They do, however, require accurate information on load sections, which is impractical in distribution networks with extensively installed PVs. A combined framework of robust optimization and two-stage stochastic programming adopted in the proposed problem formulation enables us to deal with the PV-originated uncertainty using readily available information only. In addition, this problem framework can be treated by a traditional solution method with slight extensions. The validity of the authors’ proposal is verified through numerical simulations on a real-scale distribution network model and a discussion of their results.

Service restoration in modern distribution systems addressing grid-connected and islanded operations

In modern Distribution Systems (DSs), multiple decentralized generators can be connected to the network and, in some scenarios, can operate isolated from the substation. The island operation affects the Service Restoration (SR) process and may benefit the Distribution Companies (DICOs). However, most of the studies involving SR take into account only the system’s static behavior, ignoring the transient characteristics. While these studies have been validated for decades on classic passive DSs, they cannot be extended for SR with load insertion into islands. This paper studies the dynamic aspects of the SR process, verifying the protective devices’ performance due to the island’s formation and operation, and loads reconnection through Distributed Generators (DGs). Based on the test results for a 405-bus DS, the paper points out the possibility of infeasible SR plans due to disregarding the transient behavior of DGs. The paper also proposes a novel set of constraints that aggregates practical dynamic and static aspects for the SR problem. Finally, a computationally efficient Multi-Objective Evolutionary Algorithm (MOEA) based on subpopulation tables is employed to solve the SR problem considering the newly proposed constraints.

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

The rapid growth of distributed generator (DG) capacities has introduced additional controllable assets to improve the performance of distribution systems in terms of service restoration. Renewable DGs are of particular interest to utility companies, but the stochastic nature of intermittent renewable DGs could have a negative impact on the electric grid if they are not properly handled. In this study, we investigate distribution system service restoration using DGs as the primary power source, and we develop an effective approach to handle the uncertainty of renewable DGs under extreme conditions. The distribution system service restoration problem can be described as a mixed-integer second-order cone programming model by modifying the radial topology constraints and power flow equations. The uncertainty of renewable DGs will be modeled using a chance-constrained approach. Furthermore, the forecast errors and noises in real-time operation are solved using a novel model-free control algorithm that can automatically track the trajectory of real-time DG output. The proposed service restoration strategy and model-free control algorithm are validated using an IEEE 123-bus test system.

A two‐stage service restoration method for electric power distribution systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. Although the emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, enhance the self-healing capability and allow faster recovery, they still pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. Herein, a novel two-stage restoration framework is proposed to generate a restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and the CLPU models. It is demonstrated that on using a multi-feeder test case, the proposed framework is effective in utilizing all available resources to quickly restore the service and generate an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.

Service restoration in multi-modal optical transport networks with reinforcement learning.

Optical transport networks (OTNs), while increasingly popular, can be affected in ways that are challenging to restore efficiently. This study investigates the problem of service restoration under OTNs with an optical channel data unit (ODU)-k switching capability (OTN-OSC) environment. An advantage actor-critic-based service restoration (A2CSR) algorithm is presented with the objective of increasing the service restoration rate. In our experimental setup, A2CSR uses the advanced image recognition model MobileNetV2 and an advantage actor-critic reinforcement learning algorithm. Simulation results show that the proposed A2CSR algorithm can achieve better blocking probability and resource utilisation than the benchmark algorithm (first fit (FF)), and the restoration time is within the acceptable range.

Dynamic power service restoration method for distribution network with distributed generation penetration

In this paper, a dynamic power service restoration method for the distribution network with DG is proposed. The mathematical model of the power service restoration problem is analyzed and established. Based on the mathematical model, a two-stage dynamic power service restoration strategy is developed. In the proposed method, the initial power service restoration scheme can be obtained by solving the optimal model in the first stage. Then, the power service restoration scheme can be adjusted and updated according to the actual DG output power periodically by the rolling strategy in the second stage. The correctness and effectiveness of the proposed method are verified by a MATLAB simulation based case study. The computing speed of the proposed method is fast, which makes it have good practical application values and prospects.

Integrating Protection Constraints to a MEAN-Based Method for Service Restoration in Radial Distribution Systems

The distribution system service restoration (SR) process implies to change the network topology configuration via a real-time maneuver plan, which in turn results in increased load current and changed short-circuit levels in some adjacent feeders. As a consequence, some protective devices (PD) can operate undesirably. In addition, some network branches may itself become totally unprotected. Therefore, it is essential to consider phase and neutral overcurrent protection in the SR problem, in order to maintain service continuity during emergency situations. A very efficiently approach, named MEAN (Multi-Objective Evolutionary Algorithm with NDE), for solving the SR problem is based on Multi-objective Evolutionary Algorithm (MOEA) that combines subpopulation tables with the Node-Depth Encoding (NDE) technique. This paper proposes to incorporate the phase and neutral overcurrent protection constraints into the MEAN approach for service restoration in radial distribution systems. The approach proposed in this paper, named MEAN-PC (MEAN with Protection Constraints), uses additional subpopulation tables to store the solutions that consider protection constraints. The objective is to show that the overcurrent protection constraints on service restoration problems can be considered in an effective way, correctly indicating feasible solutions of maneuver plan. The proposed overcurrent protection constraints in service restoration are tested and verified on the 135-bus test distribution system.

A Binary Water Cycle Algorithm for Service Restoration Problem in Power Distribution Systems Considering Distributed Generation

The traditional service restoration methods are experiencing a significant challenge because the performance of such methods is often dominated by very large processing time. Therefore, it is important to develop a fast and an accurate method to solve a complex non-linear optimization problem representing the service restoration problem in a distribution system. In this paper, due to a binary nature of the service restoration problem, the Binary Water Cycle Algorithm (BWCA) is proposed to reduce out-of-service loads and system losses considering operational constraints, the radial topology of the distribution systems, and the influence of the presence of distributed generations (DGs) on the service restoration problem. The proposed method is a modified form of the Water Cycle Algorithm (WCA) that is mainly used for discrete and continuous optimization problems. The effectiveness of the proposed method is validated by comparing with other well-known meta-heuristic algorithms using a modified IEEE 33-node radial distribution system. The simulation results demonstrate the robustness of this method to provide a precise decision with short convergence time for solving service restoration problems.

Distributed Self-Healing Scheme for Unbalanced Electrical Distribution Systems Based on Alternating Direction Method of Multipliers

With the advent of Smart Grids and advanced communication technologies, the self-healing scheme has become a desirable function of the operation and planning of electrical distribution systems (EDSs). In the presence of a permanent fault, an optimized self-healing scheme minimizes the unsupplied demand while maintaining the faulted section of the network isolated. The service restoration of the self-healing scheme is a combinatorial optimization problem whose computational complexity grows exponentially with the number of binary variables. To resolve this issue, a distributed optimal service restoration strategy is developed based on the alternating direction method of multipliers (ADMM). The service restoration problem is formulated as a mixed-integer second-order cone programming (MISOCP) problem. The decision variables of the problem are the status of the remote-controlled switches, load zones and load shedding at each controllable demand. Operational constraints, such as current and voltage magnitude constraints, distributed generation (DG) capacity constraints and radial topology constraints, are respected in the optimization problem. Through the ADMM, the optimization problem is distributed among the zones of the EDS, without requiring a central controller. Two test systems, an unbalanced 44-node system and the IEEE 123-node system, were used to conduct case studies. Results show that the proposed method can provide optimal service restoration solutions in reasonable time without a central controller.