Distribution System Reconfiguration Research Articles

Simultaneous feeder reconfiguration and distributed generation planning in the presence of voltage-dependent and variable loads

In distribution systems, where distribution losses and the output of distributed generators (DGs) are significantly impacted by load power, an effective approach for reducing power loss is required, which the hybrid operation of DG units and grid reconfiguration can serve as a best alternative. Load power exhibits variability, altering alongside the voltage fluctuations which occur over time. Furthermore, the correlation between power demand and voltage relies on the type of load. Nonetheless, the incorporation of these important concerns into research on reconfiguration and distributed generation planning is rare. Only a limited number of papers have taken into account the voltage dependence and the type of time-varying loads in their respective models. Nevertheless, they proposed models with significant nonlinearity, requiring computation through nonlinear solvers or metaheuristic algorithms. Meanwhile, these models require the use of intensive linearization techniques to facilitate their implementation through linear solvers. High computational time is demanded by nonlinear solvers, while metaheuristic algorithms cannot guarantee the attainment of optimal solutions. Hence, the accurate modeling of load behavior holds importance in the active distribution system reconfiguration. In this paper, a proficient reconfiguration model is presented, which is both straightforward for implementation in conventional optimization tools and adept at identifying appropriate solutions for the reconfiguration and DG planning problem.

An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance

An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance

A techno-economic scheduling of distribution system including multi-microgrids considering system reconfiguration and bus selector switches

The Microgrid (MG) as an alternative energy system, would reduce power loss, enhance energy consumption efficiency, and improve the system reliability for a sustainable city. Energy scheduling of distribution systems (DSs) including multi-microgrids (MMGs), would result in a multi-objective problem from DS operators’ point of view for a techno-economic operation. In this regard, a multi-objective model for operation scheduling of DS and connected MGs, is proposed in this paper, which also considers the system management solutions such as reconfiguration and determination of the energy trading points for MGs connection. The proposed model is applied to a modified IEEE 33-bus distribution system including 4 MGs. Simulation results and analysis based on the fuzzy satisfying method indicate that the simultaneous consideration of distribution system reconfiguration and Bus Selector Switches (BSSs) for MGs can improve the social welfare function by 4.58 % and reduces the energy losses by 15.82 %.

Distribution Systems Reconfiguration Considering Dependency of Loads on Grid Voltage and Temperature

Distribution Systems Reconfiguration Considering Dependency of Loads on Grid Voltage and Temperature

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.

Analysis of large scale distribution network using Whale Optimization Algorithm

In this study, we use a loop matrix to describe the reorganisation of the RDN's formulation. Calculation time is increased when an optimum reorganisation is determined analytically. More network buses means more time to calculate. Therefore, a technique of optimisation is required to determine the best reorganisation of the radial distribution system. The optimum reorganisation aims to reduce network losses to a minimum and the voltage profile is enhanced. Loop matrices are used to describe the re-formulation of the radial distribution system. The analytical technique of identifying optimum reconfiguration involves additional calculation time. The computational complexity grows as the system's bus count rises. Therefore, a search for the best possible radial distribution system reconfiguration necessitates an optimization technique. The ideal reconfiguration focuses mostly on reducing the system's overall loss. The genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA) are the optimization methods used in this paper. Two test systems consisting of 119 buses, and 135 buses are used to evaluate the effectiveness of various optimization strategies. The outcomes are then compared with one another.

Evaluation of Voltage Fluctuations Effect on Reconfiguration of Distribution Systems

Distribution system operators seriously restrict bus voltage deviations within a small range from the nominal voltage to ensure security aspects. The distribution network reconfiguration technique is often performed to improve these voltage levels, if not to reduce power losses. However, distribution systems feed various kinds of consumers, and their power demand dependency on bus voltages is different based on their load type. Modifying the network configuration changes the system's nodal voltage levels, and then the load power is also impacted after the reconfiguration. Only a few research works in the field of distribution system reconfiguration have included load power dependency to nodal voltages in their proposed models. However, these studies have not discussed whether adding voltage-depend loads to the reconfiguration problem is beneficial or only increases the computational efforts without substantial gains on the optimal solution. With the intention to fill this gap, this article develops a comprehensive analysis of how the change of load according to the voltage affects reconfiguration solutions. This investigation aims to help distribution system researchers and readers understand this phenomenon and highlight the benefits and drawbacks of taking it into account. Also, a robust reconfiguration model for considering load types and nodal voltage is proposed. The model is validated in different network models and sizes, where the obtained results are compared with other models presented in the literature. The results analysis indicates that voltage fluctuations impact the distribution network configuration through voltage effect on power losses and the system nodal loads. Also, load voltage-dependent models are more flexible than constant demand based-formulations in finding feasible solutions in more restricted voltage tolerances.

Optimal reconfiguration of distribution systems considering reliability: Introducing long-term memory component AEO algorithm

This article introduces a modified version of the Artificial Ecosystem Optimization (AEO) algorithm, called Long-term Memory Component AEO (LMAEO), for optimizing the reconfiguration of radial distribution networks. The LMAEO algorithm incorporates a long-term memory component, enabling individuals in the population to make decisions based on past experiences. This integration of long-term memory allows the algorithm to explore a wider range of potential solutions during the optimization process, potentially leading to improved performance and better exploration of the solution space. To verify the effectiveness and superiority of the LMAEO technique, it is compared with the conventional AEO algorithm and other well-known algorithms using seven benchmark functions. The proposed LMAEO algorithm successfully addresses the reconfiguration of distribution systems considering reliability for the modified 12-bus, 33-bus and 69-bus IEEE test systems. Leveraging the strengths of AEO and the long-term memory component, the LMAEO algorithm achieves efficient solutions for this problem. To assess the performance of the proposed LMAEO, a comparison is made with the original AEO algorithm. The results demonstrate that the LMAEO technique surpasses the AEO optimizer in terms of optimal reconfiguration of distribution systems jointly considering reliability, system losses and voltage deviations.

A Mixed-Integer Programming Model for Reconfiguration of Active Distribution Systems Considering Voltage Dependency and Type of Loads and Renewable Sources

A Mixed-Integer Programming Model for Reconfiguration of Active Distribution Systems Considering Voltage Dependency and Type of Loads and Renewable Sources

Constrained-multiobjective Evolutionary Algorithm for Distribution System Reconfiguration under Severe Constraints

Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the integration of renewable energy sources, which introduce uncertainties and further tighten the constraints. This paper proposes a constrained multiobjective optimization evolutionary algorithm (CMOEA) based on a dual-stage approach with a dual-population genetic operator. The proposed CMOEA is applied to a case study of a distribution system reconfiguration problem with severe constraints, uncertainties, and high computational demands, and it shows good performance in finding infeasible solutions with small constraint violations and feasible solutions with small objectives.

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).

Restoration of the service and loss minimization in electrical distribution systems

<p>Restoration of service during faults to healthy zones and the minimization of true or real power losses are the main aspects of the satisfactory operation of an electrical distribution system (EDS). These objectives can be achieved with artificially intelligent optimisation techniques through electrical distribution system reconfiguration. The restoration of service is an important part of ensuring a stable power supply. If there is a fault with the EDS, the damaged zone is separated, and the healthy system is restored without breaching limitations. In the distribution system, ohmic losses are 25-35%. It is essential to reconfigure the system toreduce these losses. In this paper, a hybrid artificial rabbits’optimisation and improved mayfly optimisation (ARO-IMO) technique is adopted for restoration of serviceand loss minimization. The method being suggested will be carried out on an IEEE 4-feeder network for restoration of service and on IEEE 33 and69 radial distribution networks (RDN) for diminution of losses.</p>

Distribution grid reconfiguration sensitive to transmission cost allocation

AbstractThe reconfiguration of electrical distribution systems has been addressed in numerous scientific studies. Thus, several techniques and models are considered, especially to reduce electrical losses and improve the voltage profile. However, it is clear that even with the increase in research, which has been accentuated since the last decade, the locational pricing and the transmission costs allocation is little explored. In this regard, the proposed method is a distribution system reconfiguration (DSR) optimization problem focused on minimizing the use of the transmission system. In order to validate the meta‐heuristic employed for the DSR, the IEEE 33 and 69 node test systems are used as a proof of concept. In addition, case studies with four substations, three different transmission cost allocation techniques, and transmission system capacity scenarios are analyzed. Numerical results showed an important transmission expansion deferral with consequent decrease in overall costs compared to the classic DSR. The transmission network use of system (TNUoS) charges can play a crucial role in reducing the total cost of the electrical system, promoting distributors to change their feeder patterns so that a larger share of loads access substations with lower charges.

Economic, Technical, and Environmental Benefits of Occupants’ Thermal Comfort-Based Heat Loads Participation in Demand Response Program

The electrification of heating and transportation systems is one of the objectives of developed countries to minimize CO2 emissions. This objective pushes distribution systems’ (DBs) operators to incorporate numerous high-power loads into low-voltage networks that were not designed for such loads. DBs’ reconfiguration and the loads’ flexible characteristics are two remedies exploited to overcome this issue. Heat pumps (HPs), as the most prevalent loads, can be managed by demand response programs (DRPs) to postpone the costly reconfiguration of distribution systems. HPs’ DRP participation affects indoor air temperature. If this is not accomplished reasonably, the occupants’ thermal comfort (OTC) will be compromised, and it will be impossible to convince them to continue contributing to DRPs. Based on the ASHRAE55 standard and an experimental building electrothermal model, this article presents a novel framework for determining the HPs’ DRPs participation. This framework ensures the OTC and optimizes the HPs’ DRP participation. The modified IEEE 33-bus network is employed as the test system to evaluate the proposed method. The simulation results confirm the usefulness of the proposed strategy to improve the technical and economic aspects of the network.

Purchase cost based reconfiguration of distribution system under varying loads and renewable generation

Optimal reconfiguration of distribution system is complex process and thus demands high computing power and time. In a competitive market for a distribution system with multiple feeders owned by multiple agencies, the cost of purchasing power for each of those feeds are often different from one another. In such case, reconfiguration of distribution system, exclusively on the basis of loss minimization may not be appropriate. Furthermore, increasing solar PV generation (SPVG) integration is posing new type of operational challenges due to its sudden and high variability. Hence, the conventional approaches of hourly stepwise load curve discretization may not be cost-effective for feeder reconfiguration exercise. In view of these twin objectives, a novel heuristic strategy is proposed for minimizing costs using a two-stage process that takes into account sensitivity. The primary objective of the proposed approach is to reduce the computing load associated with the exercise, while also incorporating the variability of loads and renewable energy sources. This has been achieved without significantly increasing the computational workload, by employing a reconfiguration strategy that best aligns with the specific hour under consideration, which is further divided into four subintervals of fifteen minutes each. Moreover, despite the high variability, the frequency of reconfiguration is not increased in order to extend the lifetime of the gear switches. To show the efficacy of the proposed approach, simulation results for two distinct test systems have been obtained.

Distribution system reconfiguration based on MILP considering voltage stability

Reconfiguration of distribution network with consideration of voltage stability index (VSI) in the presence of distributed generation (DG) is a time consuming and complicated problem, which needs considering various technical constraints. Hence, in this paper a novel formulation based on the mixed integer linear programming (MILP) is proposed. Several linearization methods have been used to present a linear VSI. Network reconfiguration based on MILP not only increases accuracy and pace of solving, but also guaranties the optimal solution. Minimizing real power loss and improving VSI are the objectives of this problem. In addition, some constraints like branches currents, nodal voltage limits, radiality of distribution network, and penetration level of DGs are determined. In order to evaluate the performance of algorithm, the proposed method is tested on 33-bus and 69-bus radial network. All scenarios are simulated linearly and non-linearly in GAMS.

Impact of Distributed Generation on the Effectiveness of Electric Distribution System Reconfiguration

Distribution system reconfiguration (DSR) is an essential activity in the operation of distribution utilities, usually carried out to lower active power losses and improve reliability metrics. The insertion of distributed generation (DG) units in electric power distribution systems (EPDS) causes the rearrangement of power flows through the conductors and changes the real power losses and voltage profile; therefore, up to a certain point, the insertion of certain quantities of DG may potentially delay or change the reconfiguration strategy of EPDS. This article presents an analysis of the impact of DG, for different locations of the units and different levels of active power supplied by them, on real power losses and on the effectiveness of DSR. The article presents tests with different distribution systems with varying sizes and topologies, showing that the allocation of DG units in buses far from the substation provided the best cost–benefit results. The DSR impact changes depending on the installment location and the generation level of the DG units, corroborating that DSR must be considered and performed using certain criteria, to maximize its efficiency.

Distribution System Reconfiguration Considering Constraints for Asset Planning

Distribution System Reconfiguration Considering Constraints for Asset Planning

Enhancing Voltage, Reliability, and Demand Balance via Combined Model of Distributed Generation Allocation and Distribution System Reconfiguration

The present study attempts to deal with two problems: optimal allocation of distribution generations (DGs) and reconfiguration of a distribution system to satisfy several multiobjectives including smoothing the voltage profile, minimizing the cost of reliability, and balancing the amount of load on the end-user side. These problems are merged into one single optimization problem with the aforementioned objective functions. The problem type is mixed-integer nonlinear programming (MINLP) and is subject to various limitations imposed by, for example, the operating status of DGs, demand equilibrium, bus voltages, and power passing on the distribution lines. Moreover, the paper adopts particle swarm optimization to find a solution to the MINLP problem under study. The test system was implemented in the MATLAB environment to validate the capability of the suggested approach.

Optimal tuning of Reconfiguration of Radial Distribution System using Quasi-Oppositional Moth Flame Optimization

Optimal tuning of Reconfiguration of Radial Distribution System using Quasi-Oppositional Moth Flame Optimization