Service Restoration Method Research Articles

A robust microgrid formation method for risk-resistant service restoration considering subsequent contingency

Service restoration (SR) is essential for resilience enhancement of distribution systems (DSs) when natural disasters occur. However, a restored DS may face potential outage risks caused by subsequent contingency during the prolonged event. To improve the risk-resistance of SR strategies, this paper proposes a microgrid-based SR method to continuously supply critical loads when considering subsequent contingency. The microgrids are proactively formed to mitigate the influence of subsequent contingencies, which can effectively reduce the load shedding and wind power curtailment during the SR process. To deal with uncertainties of subsequent contingency and wind power, a two-stage robust model is established. Moreover, an improved uncertainty set is established to further reduce the conservatism of robust optimization. Specially, the line interruption probability is considered when constructing the Shannon entropy-based uncertainty set of subsequent contingencies, and the spatial–temporal correlations of wind power is considered when formulating the budget-constrained uncertainty set. Then, the robust model is solved by column-and-constraint generation algorithm efficiently. Numerical results demonstrate that the proposed SR method can effectively enhance the risk-resistance of SR strategy and reduce the conservatism while ensuring the robustness of solutions.

Collaborative Service Restoration with Network Reconfiguration for Resilience Enhancement in Integrated Electric and Heating Systems

Coordinated fault recovery is essential for the resilience enhancement of integrated electric and heating systems (IEHS) following natural catastrophes as the linkage of the power distribution system (PDS) and district heating system becomes tighter. DHS reconfiguration is a viable method for service restoration because it could adjust the energy between energy sources and achieve uninterrupted energy supplies. In this paper, a collaborative service restoration model considering DHS reconfiguration is proposed to achieve better recovery after natural disasters. DHS reconfiguration could guarantee interrupted power supply in non-fault regions by shifting electric loads between power sources and accomplish optimal service restoration by adjusting the power output of combined heat and power units. Numerous case studies are undertaken to demonstrate the performance of coordinated reconfiguration on resilience enhancement and to confirm the efficacy of the proposed paradigm.

Service Restoration Using Deep Reinforcement Learning and Dynamic Microgrid Formation in Distribution Networks

A resilient power distribution network can reduce length and impact of power outages, maintain continuous services, and improve reliability. One effective way to enhance the system's resilience is to form microgrids during outages. In this paper, a novel dynamic microgrid formation-based service restoration method using deep reinforcement learning is proposed, and it is treated as a Markov decision process (MDP) while taking operational and structural limitations of microgrids into account. The deep Q-network is employed to obtain optimal control strategies for microgrid formation. We have introduced a new way for the agent to choose actions when building a microgrid using the deep Q-learning method, which ensures that the microgrid has a feasible radial structure. The proposed service restoration method enables real-time computing to facilitate online formation of dynamic microgrids and adapts to changing conditions. The influence of optimal switch placement on service restoration using proposed method is also investigated. The effectiveness of proposed service restoration method is validated by case studies using the modified IEEE 33-node test system and a real 404-node distribution system operated by Saskatoon Light and Power in Saskatoon, Canada.

Incorporating public feedback in service restoration for electric distribution networks

AbstractPower outages in urban area carry heavy social and economic costs. Although social cost, especially public sentiment, is concerned by engineers and managers, it has been only qualitatively investigated without a rigorous model in the state‐of‐the‐art research and practice of service restoration (SR) for a long time. To fill this gap, this paper investigates a hybrid model which takes public sentiment into consideration by quantifying public sentiment triggered by power outage. Furthermore, conventional SR method focused on the optimization model with ideal conditions, which leaves a large room for improvement in complex environment. To improve the robustness of the model, the authors propose a reinforcement learning framework to analyze emergency management process without prior rules. At each time step, the optimal decision can be made automatically by a learned model. The numerical simulations with modified IEEE 33‐bus and IEEE 123‐bus systems demonstrate the effectiveness of the proposed method.

Robust service restoration of distribution systems towards coordinated parallel network reconstruction and cold load pick-up

This paper proposed a robust service restoration method coordinating parallel network reconstruction and load restoration decisions considering cold load pick-up (CLPU) effects. Firstly, a novel parallel network reconstruction model is proposed, which can simultaneously guarantee the radiality of each subsystem and the feasibility of the restoration sequence. The load behaviors during restoration are modeled considering CLPU effects over multiple steps. A two-stage service restoration model is developed to address the uncertainties arising from renewable DGs and loads. The first stage is to minimize total load losses by optimizing the operational decisions of DGs, switches and loads ahead of restoration, while the second state aims to optimize power flow after the decisions are implemented. The model is further formulated as a two-stage robust optimization model with infeasibility penalties in the second stage, which can be solved by the column-and-constraint generation algorithm. Numerical simulation results on a 33-bus distribution system demonstrate the high efficiency and robustness of the proposed method.

Comparative results of applying the FDLISR method for rural and urban distribution networks with distributed generations

High penetration of distributed generations (DGs) into a distribution network (DN) could not only enhance the power-system reliability indexes but also reduce power losses. The use of DGs is effective for service restoration plans of the DN to minimize outage customers and power loss. However, the presence of DGs could lead to the protection miscoordination of overcurrent relays/reclosers due to fault isolation and service restoration processes. In light of this assumption, a fault detection, location, isolation, and service restoration (FDLISR) method is proposed to apply to both rural and urban distribution networks with DGs such that the comparative results of this FDLISR method are analyzed and discussed to demonstrate which type of the distribution network has a high risk of protection miscoordination of overcurrent relays/reclosers from service restoration scenarios. The faulted-section isolation and the distribution-network service restoration can be performed by solving a two-constraints-based objective function and seven appropriate performance indices (PI). By using the E-Terra Dispatching Operator Training Simulator (E-DOTS), simulation results of the FDLISR method applied for both the 22 kV rural-type and urban-type distribution networks are presented and compared to demonstrate its effectiveness by showing the possibility of protection miscoordination and the consideration of power losses for each service restoration plan. Consequently, the computation time of the FDLISR method proposed for the DN is within one-and-half minutes. Moreover, two performance indices of power loss and protection miscoordination, LOSS-PI and PRO-PI respectively, can highly impact the priority rank of possible service restoration plans for the rural DN while only the PRO-PI affects multiple service-restoration plans for the urban DG-contained DN.

Hybrid Stochastic-Robust Service Restoration for Wind Power Penetrated Distribution Systems Considering Subsequent Random Contingencies

Service restoration (SR) is essential to recovering distribution systems with outages when extreme weather conditions occur. However, the occurrence of long-duration extreme weather events may cause a restored system to become susceptible to subsequent random contingencies. To address this challenge, this paper proposes a microgrid-based SR approach for recovering critical loads in wind power penetrated distribution systems incorporating the impacts from subsequent random contingencies. A hybrid stochastic-robust optimization model is developed considering both the subsequent random contingencies that may occur and the uncertainty of wind power generation. The expected load shedding is minimized by locating mobile emergency generators, dynamically adjusting microgrid (MG) topologies, and dispatching power sources. To ensure the tractability of the optimization, the original problem is reformulated and decomposed into a master problem and several robust optimization subproblems based on the column-and- constraint generation approach. In addition, the master problem, which is in the format of stochastic optimization is solved by an improved progressive hedging algorithm to accelerate optimization. The proposed method is validated on the modified IEEE distribution systems and the simulation results show the effectiveness of the proposed SR method in reducing security violation risk and reducing the computational complexity.

Distribution System Service Restoration Using Electric Vehicles

Nowadays the utilization of Electric Vehicles (EVs) has greatly increased. They are attaining greater attention due to their impacts on the grid at the distribution level. However, due to the increased need for electricity, EVs are also used to serve the load in the instance of electrical failure in the distribution systems. This paper presents a new approach to a service restoration method for a low-voltage distribution network at the time of a power outage using existing EVs available in a parking place. The objective function formulated here was a constrained linear optimization model. It aimed to develop priority-based scheduling of the residential user appliances while meeting all the operational constraints if the EV’s power was in a deficit at the hour of the outage. Weight factors were assigned to various residential appliances to decide their priority while scheduling. To substantiate the proposed methodology, a day load profile of a 20 kVA distribution transformer feeding eight residential users is considered. This was tested during an hour-long power outage scenario in the MATLAB and LINGO platforms, with four EVs available during the outage period. This method restored the maximum power to the residential appliances.

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.

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.

MILP-Based Service Restoration Method Utilizing Both Existing Infrastructure and DERs in Active Distribution Networks

This study proposes a service restoration method that utilizes the distributed energy resources (DERs) as the control measures as well as the existing infrastructure of the distribution system. The existing infrastructure includes switching operations, load shedding, and tap operations of the on-load tap changers (OLTCs). In addition, the DERs include reactive power control of the distributed generation (DG), charge/discharge of the energy storage system (ESS), and intentional islanding operation using the black-start function of the ESS. For this purpose, the following model is applied to the optimization problem based on mixed-integer linear programming. First, a linear constraint considering the voltage drop due to the impedance of the main transformer is used for practical application of an OLTC. Next, a linear constraint is formulated for grid code-based DG reactive power control. In addition, constraints for the ESS are defined. Here, the radiality constraints are formulated to distinguish the grid-connected and islanding operation modes based on the location and state of the ESS. Finally, simulations of the application of the control measures conducted on a 150-node test system, designed based on the data of the actual system in Korea, are conducted and the results are analyzed to verify the proposed method.

Dynamic Service Restoration for Integrated Energy Systems Under Seismic Stress

Seismic events and following aftershocks can cause devastating impacts on overground and underground energy system infrastructures. This paper designs a novel approach to restore the system functionality in response to seismic stress by using coordinated dynamic system reconfiguration and operation. First, seismic impact on system infrastructures is evluated, considering substations, pipes and distribution lines. Load curtailment is assumed to be caused by the overloading of system branches, buckling towers, reversed gas flows, and isolated sub-circuits. Smart system operation and reconfiguration are coordinatedly applied by using combined soft open points (SOP) and energy storage units, aiming to restore system service by minimizing load curtailment and system recovering time. Service index is quantified considering both load curtailment and recovering time. A representative integrated electricity and gas system is employed to demonstrate the effectiveness of the proposed method. Results illustrate that the adverse impact of seismic on systems can be effectively reduced by the proposed dynamic service restoration method. The model provides system operators with a powerful tool to restore the functionality of distribution systems under seismic events.

Resilience-Motivated Distribution System Restoration Considering Electricity-Water-Gas Interdependency

A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is to provide electricity, water, and natural gas supplies to critical customers in the desired ratio according to their needs after an extreme event. The operational constraints of electricity, water, and natural gas networks are considered. The characteristics of electricity-driven coupling components, including water pumps and gas compressors, are also modeled. Relaxation techniques are applied to nonconvex constraints posed by physical laws of those networks. Consequently, the restoration problem is formulated as a mixed-integer second-order cone program, which can readily be solved by the off-the-shelf solvers. The proposed method is validated by numerical simulations on electricity-water-gas integrated systems, developed based on benchmark models of the subsystems. The results indicate that considering the interdependency refines the allocation of limited generation resources and demonstrate the exactness of the proposed convex relaxation.

Intending Island Service Restoration Method With Topology-Powered Directional Traversal Considering the Uncertainty of Distributed Generations

The intending island service restoration method is one of the core technologies of self-healing control for smart distribution systems, which aims to maximize the restoration of the out-of-service loads in the out-of-service area without faults quickly. For this reason, a topology-weighted directional traversal intending island recovery method considering the uncertainty of distributed generation sources is proposed. First, divide the network level of the power-loss feeder and calculate the interval power flow of the feeder before the fault, and obtain the power flow direction when the active and reactive power of the faulted branch is the smallest so as to determine whether the distributed generation supply in the non-faulty power-loss area can restore all load power supply. If not, to determine the island recovery plan, continue to compare the distributed generation supply and the load capacity at all levels, and give priority to recovering loads with higher importance levels and smaller network levels. The traversal of the topological authority and direction effectively reduces the island recovery time and can make full use of the distributed generation output to maximize the recovery of the non-faulty power-loss area. Taking the PG&E 69-node system as an example and using the BFGS trust region algorithm to calculate the island power flow without unbalanced nodes for verification, the results show that this method consumes less time and can restore more load power supply than the existing island recovery method, which verifies the method’s effectiveness and reasonability.

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.

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.

A Comprehensive Resilience-Oriented FLISR Method for Distribution Systems

Under extreme events, distribution systems may suffer blackouts and numerous restoration tasks will compete for limited crews and facilities. To enhance the resilience of distribution systems, multiple resources should be fully coordinated, coupling relationships between fault location, fault isolation, and service restoration should be considered, and flexible strategies should be formed for different situations faced by distribution systems. To this end, a comprehensive resilience-oriented fault location, fault isolation, and service restoration (RO-FLISR) method is proposed in this article, where, the fault location, fault isolation, and service restoration under extreme events are comprehensively considered at the same time for the first time, and multiple resources are coordinated. Three basic modules are formulated in the RO-FLISR method, i.e., the possible faulted zone determination (PFZD) module which could determine all the possible faulted zones when there are multiple faults and DGs, the line patrolling (LP) module which supports the optimal scheme for line paroling based on crews, and the fault isolation and service restoration (FISR) module in which the operation of MSs and RCSs are coordinated for comprehensive fault isolation and service restoration. The modules are formulated as MILP models. Effectiveness of the proposed method is verified with the IEEE 123-bus system.

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.

Unified data‐driven stochastic and robust service restoration method using non‐parametric estimation in distribution networks with soft open points

The service restoration is a vital measurement to enhance the resilience of the electrical distribution network. With the integration of distributed generations (DGs) in the distribution network, the uncertainties are introduced, which have a considerable impact on service restoration. Meanwhile, the soft open points (SOPs) are electronic devices installed in the tie-line of the distribution networks. They have the ability to control the branch power flow, which can be utilised to assist the service restoration. This study proposes a data-driven stochastic and robust service restoration method to handle the uncertainties of DGs outputs, which is modelled as a two-stage conic optimisation programming problem. The non-parametric estimation method is introduced to estimate the probability density function of forecasted error based on historical data. Furthermore, the nonlinear constraints of the SOPs are reformulated into conic forms so that the proposed model could be solved efficiently. Numerical tests are carried out on the IEEE 33-bus and 123-bus distribution networks to show the effectiveness of the proposed method, and the results show the superiority of the proposed method.