Distribution Feeder Reconfiguration Research Articles

A two-stage risk-based framework for dynamic configuration of a renewable-based distribution system considering demand response programs and hydrogen storage systems

Distribution feeder reconfiguration (DFR) in distribution systems can enhance operating conditions by reducing losses and improving voltage indices. This paper introduces a dual-stage risk-based framework that concurrently tackles day-ahead reconfiguration and microgrid scheduling within the distribution network. To control the negative aspects of uncertainties, the proposed framework integrates energy storage systems (ESS/HSS) and demand response programs (DRPs) at the network level, enhancing adaptability. The initial stage of the proposed model employs the AC power flow model, utilizing loss and voltage deviation functions as objective benchmarks for network reconfiguration. The scheduling is meticulously executed per interval, deriving optimized structures under diverse scenarios with the aid of a case reduction technique (CRT) to streamline solutions. The ultimate solution employs the grey wolf optimization (GWO) algorithm and CPLEX solver in the first and second stages respectively. Outcomes from applying this approach to the adjusted 118-bus network manifest improved operational conditions and voltage quality through reconfiguration. Impressively, the integration of ESS/HSS and DRPs yields a substantial 22.34% reduction in total operating costs, a conclusion substantiated by the numerical findings. Furthermore, by leveraging the CRT, a remarkable 56.17% reduction in problem-solution time is achieved.

Distribution Network Reconfiguration Using Genetic Algorithm for Loss Reduction: A Case Study of Katunje Feeder, Bhaktapur

The power distribution system has difficulties with regard to power loss and unacceptable voltage drops as a result of the rapidly expanding power system network, rising electrical energy consumption, and longer distances of power distribution. A typical strategy to address the issues with the distribution system is to perform distribution system reconfiguration. The study focuses on distribution feeder reconfiguration of the Katunje Feeder of Bhaktapur, Nepal where optimization problem is formulated to minimize the system active power loss and investment cost of the system. Genetic algorithm is employed in a co-simulation framework to solve the optimization problem where states of 26 different tie switches are to be altered to achieve the desired optimum results. Two cases are formulated: in case I active power loss is assigned more weight than investment cost whereas, in case II equal weights are assigned for active power loss and investment cost. The results showed the reduction in active power loss and investment cost for both the cases. Case I resulted in more active power loss reduction compared to case II, and case II resulted in more investment cost reduction compared to case I. From this, decision makers can obtain insights in adopting one of the cases for distribution feeder reconfiguration based on technical consideration (active power loss reduction) or economic consideration (investment cost reduction).

Enhancing resilience by outage management strategy based on semidefinite programming relaxation method in unbalanced microgrids

During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model.

Enhancing Electric Vehicle Reliability and Integration with Renewable Energy: A Multi-Faceted Review

Electric vehicles (EVs) are gaining attention due to their zero carbon emissions, but concerns about their reliability, especially critical components, persist. Previous research has primarily focused on EV drive motor reliability, neglecting the motor controller. To address this gap, this study assesses the reliability of the entire motor system in electric vans, including both drive motor and motor controller components. It predicts failure rates for these components, highlighting vulnerabilities and shortcomings in existing research, which can inform future design and maintenance. In addition, the integration of EVs and renewable energy resources has garnered attention, but concerns about component reliability have arisen. A novel approach called " Innovative Incentive-Driven Fuzzy Fault Tree Analysis " (IIFFTA) is introduced for power systems incorporating EVs and renewable energy, addressing vague and imprecise events and data deficiencies in conventional fault tree analysis. IIFFTA considers different component failure rates and probability values of fault occurrences, offering a more effective risk assessment method. Lastly, plug-in electric vehicles (PEVs) impact distribution systems, and this paper explores distribution feeder reconfiguration (DFR) as a reliability-enhancing strategy for coordinating vehicle-to-grid (V2G) services from PEV fleets within a stochastic framework. The study accounts for uncertainties related to network demand, energy prices, wind power generation, and PEV fleet behavior, employing a self-adaptive evolutionary algorithm (SOS) to address the stochastic optimization problem.

Multi-Timescale Coordinated Control With Optimal Network Reconfiguration Using Battery Storage System in Smart Distribution Grids

This paper focuses on the global optimality of the relaxed solutions of a multi-timescale co-optimization problem. The proposed co-optimization framework involves the multi-timescale co-optimization of distribution feeder reconfiguration with the optimal dispatch of traditional voltage regulating devices and utility-scale distributed energy resources. The on-load tap changer (OLTC) is scheduled on an hourly basis while the potential of fast-acting battery energy storage system and photovoltaic inverters is exploited by dispatching them on a 20-min basis. The optimal switching plan is computed on a daily basis. The proposed multi-timescale co-optimization model is formulated as a mixed-integer second-order cone program to achieve global optimum. The objective is to reduce power losses and improve load balancing among feeders. The proposed co-optimization framework satisfies the grid security constraints by employing the accurate DistFlow branch equations and exact linearization of the OLTC model. To ensure radiality, the limitation of widely used spanning tree constraints is addressed by combining them with single-commodity flow constraints. The simulation results demonstrate the feasibility (hence global optimality) of the relaxed solutions computed by the co-optimization framework.

A stochastic model for microgrids planning considering smart prosumers, electric vehicles and energy storages

This paper introduces a three-stage stochastic optimization (TSSO) structure for energy management of a distribution system consisting of four microgrids, where distribution system planning is performed considering the requested schedule of microgrids and SHs. SHs do their planning with the goal of cost minimization at the first stage. Then, microgrid planning is performed taking into account the SH requested program at the second stage. Ultimately, main grid planning is performed taking into account the requested program of microgrids at stage 3. In order to deal with uncertainties caused by renewable power generations (RPGs), load demand and electric vehicle (EV) users' behavior, a scenario-based (SB) stochastic technique is used and the problem is modeled in the linear programming (LP) format. The model is solved through the GUROBI solver in the GAMS environment and the results reveal that energy storage (ES) devices, demand response program (DRP) and vehicle-to-grid (V2G) services lead to the improvement of the voltage profile, the relief of the congestion in the vital lines and a 20.37 % reduction of operating cost. In addition, the outcomes showed that the main grid operator can adopt a more optimal schedule for microgrids through distribution feeder reconfiguration (DFR) and thereby reduce their operating cost.

Comparison of Grid Reactive Voltage Regulation with Reconfiguration Network for Electric Vehicle Penetration

Renewable energy sources and EV growth brings new challenges for grid stabilization. Smart grid techniques are required to reconfigure and compensate for load fluctuation and stabilize power losses and voltage fluctuation. Numerical tools are available to equip the smart grid to deal with such challenges. Distribution Feeder reconfiguration and reactive voltage injection to the disturbed grid are some of the techniques employed for the purpose. However, either reconfiguration or injection alone is used commonly for this purpose. In this study, both techniques are applied to EV penetration as load and compared. A balanced IEEE 33 Radial network is used in this study and selected branches with high power losses are targeted for the reactive voltage injection and Minimum Spanning tree techniques (MST). EV charging loads are usually modelled with time base distribution which requires times base power flow analysis for reactive power injection. A comparison between coordinated, reconfiguration, and reactive voltage injection shows differences in power losses, voltage distortion, and cost saving. The analysis is carried out with an integer linear programming technique for coordinated charging, a minimum spanning tree for network reconfiguration, and genetic optimization for reactive power injection. Besides, all power flow analyses are carried out with the Backward/Forward sweep method. The information would help lowering power losses, grid stabilization, and charging station infrastructure planning.

A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units

Nowadays, decentralized microgrids (DC-MGs) have become a popular topic due to the effectiveness and the less complexity. In fact, DC-MGs resist to share their internal information with the distribution system operator (DSO) to protect their privacy and compete in the electricity market. Further, lack of information sharing among MGs in normal operation conditions leads to form a competitive market. However, in emergency operation conditions, it results numerous challenges in managing network outages. Therefore, this paper presents a hierarchical model consisting of three stages to enhance the resilience of DC-MGs. In all stages, the network outage management is performed considering the reported data of MGs. In the first stage, proactive actions are performed with the aim of increasing the network readiness against the upcoming windstorm. In the second stage, generation scheduling, allocation of mobile units and distribution feeder reconfiguration (DFR) are operated by DSO to minimize operating costs. In the final stage, the repair crew is allocated to minimize the energy not served (ENS). Uncertainties of load demand, wind speed and solar radiation are considered, and the effectiveness of the proposed model is investigated by integrating to the 118-bus distribution network. Finally, the results of the simulation indicate that DFR and proactive actions decrease the ENS by 19,124 kWh and 4101 kWh, respectively. Further, the sharing of information among MGs leads to a 48.16% growth in the supply service level to critical loads, and consequently a 3.47% increase in the resilience index.

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.

A multi-objective bi-level optimization framework for dynamic maintenance planning of active distribution networks in the presence of energy storage systems

Improving the reliability of microgrids as well as satisfying technical and economic constraints are very important challenges for distribution system operators (DSOs), which should be considered in both the long-term and short-term horizons. Therefore, this paper presents a dynamic model for multi-microgrids maintenance planning over a 10-year period, taking into account daily operating conditions. The proposed model is designed as a bi-level problem, in the first level of which the microgrids perform their maintenance planning and report its result to the DSO. Then, in the second level, DSO performs day-ahead scheduling of the entire network with the goals of adhering to microgrids schedule and minimizing system average interruption frequency index (SAIFI). Note that the objective functions of both levels are formulated in the form of two-objective problems through the Epsilon-constraint method. Finally, the proposed model is implemented on a 69-bus distribution network and results demonstrate that maintenance services, despite the increase in planning costs, will lead to a significant reduction in operating costs and will be cost-effective in the long-term horizon. The results also indicate that distribution feeder reconfiguration (DFR), despite a 19.62% increase in maintenance costs, leads to a 15.23% reduction in operating costs. Finally, the results illustrate that coordinated operation of storage systems and demand response (DR) programs reduces operating costs by 5.67%.

A bi-level optimization framework for resilience enhancement of electricity and natural gas networks with participation of energy hubs

This study develops a comprehensive model for enhancing the resilience of integrated electricity and natural gas networks in the presence of industrial, commercial and residential hubs. Distribution system hardening is used as a long-term plan to improve system resilience, while a bi-level operation model is proposed to enhance real-time resilience. In the proposed model, in the upper level, the hubs perform their optimal scheduling and send it to distribution system operator (DSO). Then, in the lower level, the DSO performs network scheduling with regard to hubs’ schedules, and determines limitations and power/gas shortages during emergency conditions. It should be noted that DSO performs network scheduling with the possibility of distribution feeder reconfiguration (DFR) and deploying mobile distributed generations (MDGs) and mobile energy storage systems (MESSs). Finally, the limitations are sent to the upper level and the hubs perform their scheduling according to the new condition. The proposed model is implemented on a 69-bus electricity network integrated with a 14-node gas network, and the results show that the proposed model not only reduces the deviation of the final schedule of hubs from their requested schedule but also significantly reduces the amount of forced load shedding (FLS) in the network.

Multi-Objective Distribution Feeder Reconfiguration Considering Reliability in the Presence of Distributed Generators

Improving the efficiency of power systems is a major concern as it provides various benefits including deferment of large investments in substations and generation. In this regard, the distribution feeder reconfiguration (DFR) can help reduce system losses by changing the status of switches. In solving the DFR problem, power loss and voltage deviation of buses are commonly considered objective functions, but less attention has been paid to the reliability indexes as objective functions. In this study, DFR is solved as a multi-objective problem using the proposed reliability index along with power loss and switching number in the presence of distributed generators. A powerful hybrid algorithm based on the combination of particle swarm optimization and modified shuffled frog leaping is proposed to solve the DFR problem. To evaluate the efficiency of the proposed hybrid algorithm, two 33-bus and 70-bus systems are tested.

A stochastic tri-layer optimization framework for day-ahead scheduling of microgrids using cooperative game theory approach in the presence of electric vehicles

This study provides a tri-layer optimization framework in which the microgrid strategy for day-ahead market participation is determined by considering the uncertainties of load, RESs and EVs. Scenario-based method is utilized to deal with uncertainties. Besides, distribution feeder reconfiguration (DFR) and an incentive-based demand response (DR) program are used to enhance scheduling flexibility. In the first layer of the proposed model, the buy/sell bids of the microgrids are determined and sent to the pool market. In the second layer, the market clearing price (MCP) is determined according to the buy/sell bids of microgrids, and in the third layer, the microgrids are scheduled by a cooperative game theory approach. The proposed model is implemented on a 118-bus distribution system consisting of 4 microgrids and the results show that the dynamic topology improves the scheduling flexibility and thus reduces the total operating cost by 9.2%. The simulation results also show that EVs participation in scheduling leads to a reduction in the MCP during peak hours. Finally, the results illustrate that considering energy storage systems (EESs) and DR program leads to a reduction in the MCP and thus a 9.33% reduction in the total operating cost.

A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources

This paper presents a new framework for the scheduling of microgrids and distribution feeder reconfiguration (DFR), taking into consideration the uncertainties due to the load demand, market price, and renewable power generation. The model is implemented on the modified IEEE 118-bus test system, including microgrids and smart homes. The problem has been formulated as a two-stage model, which at the first stage, the day-ahead self-scheduling of each microgrid is carried out as a two-objective optimization problem. The two objectives include the minimization of the total operating cost and maximization of the consumer's comfort index. Then, the solution, obtained from the first stage is delivered to the distribution system operator (DSO). Then, at the second stage, the DSO determines the optimal configuration of the system with the aim of minimizing operating costs of the main grid and the penalty of deviating from microgrid scheduling. Note that the penalty is due to the difference in power exchange requested by the microgrids from the power exchange finalized by the DSO. The presented two-stage optimization problem is modeled in a mixed-integer linear programing (MILP) framework with four case studies, and solved in GAMS by using the GURUBI solver. The simulation results show that in the cases the DSO is able to reconfigure the system, the deviation from the optimal scheduling of microgrids would be considerably lower than the cases with fixed system configuration. • Proposing a two-stage optimization framework for scheduling of microgrids and DFR. • Considering smart homes and traditional loads in the model. • Two-objective modeling of microgrids' scheduling problem by the fuzzy method. • Solving the DFR problem with regard to the optimal scheduling of microgrids. • Investigating the impact of different objective functions on smart homes' scheduling.

Multi-Objective Network Reconfiguration and Allocation of Capacitor Units in Radial Distribution System Using an Enhanced Artificial Bee Colony Optimization

Distribution network reconfiguration (DNR) can be defined as an optimization problem in the power system that is done through changing the switching status to reduce loss and operating cost. Moreover, optimal capacitor allocation in the distribution network provides benefits such as loss reduction and improved voltage profile by reactive power compensation. Thus, this study provides a multi-objective framework for DNR and capacitor allocation problem in the presence of distributed generation (DG) sources. In the common DNR studies, the requirement of reliability is not usually fulfilled, usually loss and voltage deviation are often chosen as the objective functions. In this paper, the multi-objective problem is considered as a combination of energy not supplied (ENS) and emission produced by DG units and grid, moreover network loss is defined as the other objective function. The simultaneous presence of DG units and capacitors has made the problem more complicated, and there will be a demand for a precise approach to solve the multi-objective optimization problem. Hence, an enhanced artificial bee colony optimization (EABCO) method is proposed to tackle the complexity of the optimization problem. In order to shown the efficacy of the proposed approach, it is tested on a 33 and 86- node systems, the obtained results of EABCO method are compared with common evolutionary, hybrid and state-of–the-art algorithms. By implementing the proposed approach in two small-scale test systems, approximately 74% and 15% reduction in ENS are achieved compared to the initial value before distribution feeder reconfiguration.

Microgrids energy management in automated distribution networks by considering consumers’ comfort index

Modern distribution networks' scheduling faces many more challenges than traditional networks. Because on the one hand, it must satisfy the technical and security constraints of the distribution system, and on the other hand, it must observe the optimal scheduling of microgrids. Hence, in this paper, a bi-level mixed-integer linear programming (MILP) model is presented in which the scheduling of the distribution network is done by considering the optimal scheduling of microgrids and smart homes. In the first level, each microgrid performs its optimal scheduling in a decentralized manner and delivers it to the distribution system operator (DSO). Then, in the second level, DSO plans the main grid according to the optimal schedule of microgrids and by implementing distribution feeder reconfiguration (DFR). Finally, the problem is solved in the form of four case studies by the CPLEX solver in GAMS software and the results show that the proposed model not only reduces the total operating cost by 27.05% but also significantly increases the consumers’ comfort index. The results also illustrate that DFR implementation and participation in the DR program reduce total operating costs by 19.11% and 14.02%, respectively.

Multi-Objective Feeder Reconfiguration Using Discrete Particle Swarm Optimization

Electric power distribution systems have been heavily engaged in evolutionary changes toward effective usage of distribution networks for dependability, quality, and improvement of services delivered to customers throughout the years. This was accomplished via a procedure known as reconfiguration. Several strategies have been offered by various authors for successful distribution feeder reconfiguration with a novel optimization method. As a result, this work developed a Discrete Particle Swarm Optimization (DPSO) method to address the issue of distribution system feeder reconfiguration during both steady-state and dynamic power system operations. In a dynamic state, the power demand and generation required are continually changing over time, and the DPSO algorithm finds a new set of solutions to fulfill the power demand. Many network topologies are investigated for the dynamic operation. The feeder reconfiguration single-objective optimization problem was transformed into a multi-objective optimization problem by taking into account both real power loss reduction and distribution system load balancing. The suggested technique was verified using various IEEE 16, 33, and 69 bus standard test distribution systems to determine the efficiency of the developed DPSO algorithm. The simulation findings reveal that DPSO outperforms other optimization algorithms in terms of actual power loss reduction and load balancing, while solving multi-objective distribution system feeder reconfiguration.

A sustainable framework for resilience enhancement of integrated energy systems in the presence of energy storage systems and fast-acting flexible loads

This paper provides a bi-level framework for resilience enhancement of electricity-gas-heating networks integrated with energy hubs by considering fast-acting flexible loads, electric vehicles (EVs), power-to-gas (P2G) technologies, and gas storage (GS) systems. In this study, a scenario-based method is utilized to model the uncertainties of load, renewable energy sources (RES), and customers’ participation in demand response (DR) programs. In the upper-level, the hubs scheduling problem is solved by the decentralized method and in the lower-level, DSO plans the electricity-gas-heating networks. Note that DSO plans the system with the possibility of distribution feeder reconfiguration (DFR). Besides, two different DR programs are considered to enhance the resiliency of the system. Finally, the proposed model is formulated as a mixed-integer non-linear programming (MINLP) problem and solved by DECOPT solver in GAMS software. The simulation results show that DFR, fast-acting DR, direct load control (DLC) program, electrical energy storage (EES) systems and EV participation in load supply led to an increase of 36.32%, 3.56%, 2.35%, 1.55% and 0.29% of the resilience index during line outage condition, respectively. The results also illustrate that the presence of P2G technologies and GS systems have reduced forced load shedding (FLS) by 30.66% during gas pressure drop condition.

A heap-based algorithm with deeper exploitative feature for optimal allocations of distributed generations with feeder reconfiguration in power distribution networks

The optimal combination of power distribution feeder reconfiguration (PDFR) with distributed generators (DGs) is one of the most attractive combinatorial optimization issues. This paper proposes an improved Heap-based algorithm to enhance the performance of a recently published technique called Heap-based optimizer (HO). HO’s performance is enhanced with a Deeper Exploitative Improvement (DEI) by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The effectiveness of the proposed HODEI in comparison with the conventional HO algorithm is checked on 35 benchmark optimizing functions which have either unimodal and multimodal characteristics. The proposed HODEI and the conventional HO are employed for simultaneous DG allocations and PDFR using two IEEE standard distribution networks of 33 and 69-bus at various loading conditions. Additionally, a large-scale 137-bus distribution network is utilized to assess the validity and efficiency of the proposed HODEI with several independent variables. The proposed HODEI is contrasted with several recent algorithms of harmony search, genetic, equilibrium, marine predators, fireworks, and firefly optimizers. The simulation results demonstrates that the proposed HODEI always gives better performance compared to the conventional HO algorithm. The proposed HODEI shows significant improvement for the voltages at all buses. Furthermore, the proposed HODEI outperforms the compared algorithms in acquiring the minimum best, mean, worst and standard deviations of the considered fitness.

Optimal Intelligent Reconfiguration of Distribution Network in the Presence of Distributed Generation and Storage System

In the present paper, the distribution feeder reconfiguration in the presence of distributed generation resources (DGR) and energy storage systems (ESS) is solved in the dynamic form. Since studies on the reconfiguration problem have ignored the grid security and reliability, the non-distributed energy index along with the energy loss and voltage stability indices has been assumed as the objective functions of the given problem. To achieve the mentioned benefits, there are several practical plans in the distribution network. One of these applications is the network rearrangement plan, which is the simplest and least expensive way to add equipment to the network. Besides, by adding the DGRs to the distribution grid, the radial mode of the grid and the one-sided passage of power are eliminated, and the ordinary and simple grid is replaced with a complex grid. In this paper, an improved particle clustering algorithm is used to solve the distribution network rearrangement problem with the presence of distributed generation sources. The PQ model and the PV model are both considered, and for this purpose, a model based on the compensation technique is used to model the PV busbars. The proposed developed model has particularly improved the local and global search of this algorithm. The reconfiguration problem is discussed and investigated considering different scenarios in a standard 33-bus grid as a well-known power system in different scenarios in the presence and absence of the DGRs. Then, the obtained results are compared.