Siting Of Distributed Generation Research Articles

Application of multi-objective optimization algorithm for siting and sizing of distributed generations in distribution networks

Multi-objective optimization for siting and sizing of Distributed Generations (DGs) is difficult because of the highly non-linear interactions of a large number of variables. Furthermore, effective optimization algorithms are often highly problem-dependent and need broad tuning, which limits their applicability to the real world. To address this issue, in this study, Multi-Objective Differential Evolution (MODE) algorithms have been proposed for siting and sizing of DGs. The site and size of DGs play a vital role in the minimization of real power losses and enhancement of voltage profile in distribution systems. This study intends to attain the technical, economic, and environmental benefits of DGs. Hence, the Objective Functions such as minimization of power losses, voltage deviation, energy cost, emissions while generating power, and enhancement of the Voltage Stability Index have been considered. The simulations of two different multi-objective operational cases have been carried out on IEEE 33 bus system, IEEE 69 bus system, and Tamil Nadu Generation and Distribution Corporation Limited, as a real part of 62 bus Indian Utility System. The simulation results of MODE have shown its superior performance and have ensured the economic and environmental benefits of integrating DGs.

Constrained Composite Differential Evolution Search for Optimal Site and Size of Distributed Generation Along with Reconfiguration in Radial Distribution Network

This paper proposed a novel method to find the Optimal Feeder Reconfiguration (OFR) of radial distribution network along with optimal site and size of Distributed Generation (DG) with an objective of power loss reduction. OFR and DG allocation problems are highly non-linear and complex optimization problems, complexity of problem is further increased with the addition of distribution operation constraints. In the last two decades, Evolutionary Algorithms (EAs) have been implemented successfully to find the OFR and DG allocation considering different objective functions. However, search space adopted by EAs are unconstrained, therefore numerous methodologies are adapted to discard infeasible solutions. The penalty approach is most widely used in the literature, it requires appropriate selection of penalty parameters using a tedious trial and error method. In this paper, a new EA Constrained Composite Differential Evolution (C2oDE) is proposed find optimal capacity and site of DG along with reconfiguration problem. In the proposed algorithms three trial vectors are generated to strike balance among exploration and exploitation. Furthermore, to find the feasible solutions in the decision space two most widely used constraint handling techniques that include feasibility rule and ɛ-constrained method are added to select the trail vector and individuals respectively for the next generation. Proposed C2oDE method has been validated by considering IEEE 33-bus, 69-bus and 119-bus distribution networks at five different cases. Simulation results obtained shows better performance of proposed method against the most recent literature to find both OFR and DG allocation

Optimal reconfiguration and DG integration in distribution networks considering switching actions costs using tabu search algorithm

The penetration of distributed generation (DG) units has been steadily increasing in distribution networks (DNs). However, oversizing and improper locating or operating of them can increase the losses or deteriorate the voltage profile. In this regard, distribution network reconfiguration (DNR) can be envisioned as a solution to maximize the DG penetration while improving the voltage profile and minimizing the losses. So, a study on DNR with the presence of DGs is necessary, in which the switching action costs are taken into consideration, since they can impose an extra cost on the daily operations. This study proposes a solution to solve the siting, sizing, and operating of DGs and DNR problems simultaneously considering switching action costs, losses costs, and reactive power generation of DGs. To consider the reactive power limits, their capability curve (P–Q curve) is included in the model. DNR is a combinatorial optimization problem, while the entire problem is modeled as a mixed-integer nonlinear programming problem. To solve that, tabu search algorithm (TSA) as one of the most efficient global solver for combinatorial problems is used, and its results are validated by particle swarm optimization (PSO) algorithm. To enlighten the effectiveness of the proposed approach, 5 scenarios are defined on IEEE 33-bus and IEEE 69-bus test systems. The results are also compared to previous studies. It is shown that, thanks to embedding the reactive power generation and switching costs at the same time, maximum loss reductions can be realized, while the results are more realistic and reliable.

Siting and Sizing of Distributed Generation and Shunt Capacitor Banks in Radial Distribution System Using Constriction Factor Particle Swarm Optimization

This article proffers an advanced solution to allocate the distributed generation (DG) and shunt capacitor (SC) banks optimally in radial distribution system (RDS) to alleviate the power losses, revamp the voltage profile, inflate the voltage stability index, shrink the total voltage deviation, and acquire more energy savings. In the framework of finding the optimal site and size of DGs and SC banks, Particle swarm optimization (PSO) technique is modified with constriction factor and the same is aimed to apply in IEEE 33-bus system and practical Brazil 136-bus RDS. Different case studies are carried out for multiobjective problems while considering equality and inequality constraints. A comparative analysis with PSO and modified PSO is presented to project the precedence of the adopted algorithm. In addition, the obtained results are also compared to the recently surfaced few state-of-the-art algorithms to present the optimizing capability of the implemented algorithm.

A new index‐based method for optimal DG placement in distribution networks

AbstractThe expansion of distributed generations (DG) in the distribution networks brings visible technical and economic benefits to the grid. On the other hand, the place and size for DGs in a grid are very important, and optimal selection can decrease voltage profile volatility, system losses, and increase reliability indices especially. The issue of reliability in the distribution system is one of the important parameters in selecting the optimal location of DG. In other words, by considering this parameter, DG location will change in the distribution system and by installing it at this point; the reliability of the network has improved. Therefore, this paper proposes an analytical index to determine the optimal size and siting of DGs in a distribution network. The proposed index has consisted of loss sensitivity factor, voltage stability index, and reliability based factors. To evaluate the efficiency of the proposed method in the previous section, the simulation procedure has been implemented on the IEEE 33 and 69 bus distribution systems. The effectiveness of the suggested index is clarified by using different test systems, and the results have been compared with other proposed methods. The results indicated that the proposed simple index could be acceptable in finding the optimal location and size of DG better than complicated optimization methods.

Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm

The integration of network reconfiguration and distributed generation (DG) can enhance the performances of overall networks. Thus, proper sizing and siting of DG need to be determined, otherwise it will cause degradation in system performance. However, determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space. This search space mostly contains non-radial network configurations. Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution. To reduce the searching complexity, this paper considers the discretized network reconfiguration via dataset approach. Water cycle algorithm (WCA) is used to obtain the near optimal solution of network reconfiguration, and sizing and sitting of DG. In addition, the power factor of DG is also optimized to reduce the power loss. The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor. The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima. The proposed method outperforms other technique such as harmony search algorithm (HSA), fireworks algorithm (FWA), Cuckoo search algorithm (CSA) and uniform voltage distribution based constructive algorithm (UVDA) and improves the solution quality of IEEE 33-bus network and 69-bus network by 29.20% and 27.88%, respectively.

A Comprehensive Study of Simultaneous Placement of DG and DSTATCOM for Multi-Objective Optimization using Jaya Algorithm

Power system is undergoing a paradigm shift and new technologies are being adopted on a continuous basis to improve the operational efficiency. To cope with the increasing load demand several techniques have been manifested and amidst these Distributed Generators (DG) have outshined every other technique and brought a noteworthy change in the modern power system. The location and size of the DGs have a remarkable affect on power loss. In order to serve the forecasted peak load, DGs present themselves as one of the best alternative solutions to procrastinate the transmission and distribution expansion. This research work proposes an optimization model using parameter independent Jaya algorithm to determine optimal DG size and sites that would minimize the active and reactive power looses along with enhanced systems voltage profile ensuring. Performance of type-1 and type-3 DGs along with DSTATCOM is studied to achieve the objective.

Optimal Sizing and Sitting of Distributed Generation for Power Quality Improvement of Distribution Network

The Point of Common Coupling (PCC) where suppliers’ responsibility and customers demand meet is of great concern due to increase degree of voltage variation assessment; valuable indicator of system conditions (voltage profile). Unstable condition of the power system outside operational or statutory limit, an adverse effect of nonlinear loads usually generate harmonics as well as fundamental frequency voltage variations and increase rate of power losses. These loads need to be compensated for. The major concerns of utility operations is to mitigate adverse effect of this system conditions. This research work focuses on optimal siting and sizing of Distributed Generation (DG) in a 43 bus distribution system. Power losses coupled with voltage deviation, considering objective function that compute present percentage losses in 11kV Dikko feeder, Abuja Electricity Distribution Company (AEDC), Suleja Distribution Network, Nigeria. We identified buses with poor voltage profile without DG installation and determined optimal sizing and siting of DGs where losses can be mitigated and power quality improved. ETAP version 12.6 2014 was used for load flow analysis to establish a decisive based case. The total load of the system considered was (3490 + j2700) kVA. Active and Reactive power losses in the system before DG installation were 246.300 kW and 289.903 kVAR respectively. DGs installation in the case study, has a considerable effects on loss reduction in the network. It is observed that 8.10% and 7.20% active and reactive power loss reduction was achieved while bus voltage improved by 0.4%. Genetic Algorithm Optimization techniques programmed in MATLAB 2015 software was used for optimal placement and sizing of the DG in the system.

Optimal Sizing and Sitting of Distributed Generation for Power Quality Improvement of Distribution Network

The Point of Common Coupling (PCC) where suppliers’ responsibility and customers demand meet is of great concern due to increase degree of voltage variation assessment; valuable indicator of system conditions (voltage profile). Unstable condition of the power system outside operational or statutory limit, an adverse effect of nonlinear loads usually generate harmonics as well as fundamental frequency voltage variations and increase rate of power losses. These loads need to be compensated for. The major concerns of utility operations is to mitigate adverse effect of this system conditions. This research work focuses on optimal siting and sizing of Distributed Generation (DG) in a 43 bus distribution system. Power losses coupled with voltage deviation, considering objective function that compute present percentage losses in 11kV Dikko feeder, Abuja Electricity Distribution Company (AEDC), Suleja Distribution Network, Nigeria. We identified buses with poor voltage profile without DG installation and determined optimal sizing and siting of DGs where losses can be mitigated and power quality improved. ETAP version 12.6 2014 was used for load flow analysis to establish a decisive based case. The total load of the system considered was (3490 + j2700) kVA. Active and Reactive power losses in the system before DG installation were 246.300 kW and 289.903 kVAR respectively. DGs installation in the case study, has a considerable effects on loss reduction in the network. It is observed that 8.10% and 7.20% active and reactive power loss reduction was achieved while bus voltage improved by 0.4%. Genetic Algorithm Optimization techniques programmed in MATLAB 2015 software was used for optimal placement and sizing of the DG in the system.

Integration of distributed generation in electrical grid: Optimal placement and sizing under different load conditions

This paper proposes a novel approach for the placement and sizing of distributed generation (DG) ensuring voltage stability and reducing total power losses in electrical grid. The main concern of the proposed method is to guarantee voltage stability along with load fluctuations, while selecting optimal DG sites and power capacities. First, vulnerable buses are determined using Voltage Stability Margin Index (VSMI). Optimum size of DG unit is computed using Matlab curve-fitting approximation. For each load level, optimal DG size and its corresponding site are recomputed. General expressions for DG power capacity are established. For different load scenarios, the determined DG locations and size are always valid. The proposed method is implemented on the 33-bus and the 69-bus distribution systems using PSAT MATLAB toolbox and results are compared to other techniques published in the literature. The obtained results prove the effectiveness, the robustness and the good performances of the proposed method.

Optimal allocation of distributed generation using an analytical method with consideration of technical and economic parameters

In order to benefit maximally from positive features of Distributed Generation (DG) supplies, the finding of location (siting), and determination of optimal capacity of these supplies are inevitable in power distribution networks so that the researches indicate that if this important process is not considered in installation of DG supplies, not only the given positive goals and features are not fulfilled in installation of DG supplies, but also it is probably led to the worse status in installation of power supplies than the past as well. As a result, determination of optimal size and position of power distributed generation supplies is assumed as an important point. An analytical technique has been proposed in this essay for siting and sizing of DG supplies. Technical and economic aspects have been simultaneously considered in the aforesaid method and it is aimed at maximization of profit due to installation of these supplies. Initially, all parameters have been expressed as a function of DG power and then the optimal siting and sizing of DG is determined by employing the suggested method. The studied supplies are of wind and solar types. Simulations are implemented in MATLAB software and the studied systems include IEEE standard 33 and 69- bus networks.

The application of hydrogen and photovoltaic for reactive power optimization

Hydrogen and photovoltaic (PV) are two typical new energies, which are important to sustainable development. Introducing hydrogen or PV into smart grid as distributed generation (DG) becomes a promising approach. These kinds of power generations will help the grid gather more energy and introduce new chances of grid management. In this paper, we will introduce an application of hydrogen and PV in reactive power control. PV is used for hydrogen harvest, and PV is variable and dependent on weather conditions compared with a conventional generator that produces a stable output. Photovoltaic hydrogen fuel cell (PV-H2-FC) is introduced as DG, which connects to the grid. Adding hydrogen-based DG would help improve the quality of supply power. A genetic algorithm for DG site selection supporting DG cost optimization is proposed. Reactive power optimization (RPO) is an important function in planning for the future and daily operations of the smart grid system. Implementation of reactive power optimization based on the historical solution matching is also proposed, it considers the PV-H2-FC features and grid historical data, which uses Cosine distance for similarity measurement. The proposed RPO algorithm has a great advantage in calculation speed compared with traditional algorithms. The historical load data with the highest similarity are extracted, and its historical RPO scheme is applied to simulate the current RPO scheme. Results show that this method could help to find out an RPO solution effectively. The proposed solution would provide processing purposes for power company information data and further explore the supporting role of information resources in grid operations, which has broad social benefits.

Energy Loss Minimization by Optimal Siting and Sizing of DG with Network Reconfiguration in Distribution Networks

The main aim of the distribution system is delivery the power to the consumers. Because of, aging of electrical infrastructure, old control mechanism, increased power demand causing exploitation of the present electrical networks leads to low voltage profile, more active and reactive power loss with various power quality related issues causing poor network operation. In this method maximization of voltage profile with energy loss minimization is carried using network reconfiguration along with optimal siting of the distributed generation (DG). The proposed methodology is carried out on five bus system. The obtained results are impressive interms of voltage stability and power loss reduction.

Heuristic optimisation‐based sizing and siting of DGs for enhancing resiliency of autonomous microgrid networks

A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.

Optimal siting and sizing of distributed renewable energy in an active distribution network

The distributed generation (DG) plays an important role in the context of the environmental problems and sustainable development throughout the world. This paper proposes a DG siting and sizing model in an active distribution network (ADN). The objective is to minimize the total cost, including investment, operation and maintenance costs. The proposed model is transferred to a Mixed Integer Second-Order Cone Programming (MISOCP) model based on a distribution network forward backward-sweep power flow and constraint relaxation. The CVX platform and GUROBI solver are used for the solution. The scenario analysis is used for the uncertainties of load and DG. Different numbers of operational scenarios are considered in order to analyze the effect of a non-network solution to the final planning result and total investment. The planning results with and without consideration of active managements, and the planning results with and without taking environmental profits into consideration, are compared and analyzed. The proposed methodology is verified with a modified IEEE 33 example.

Simultaneous distribution system reconfiguration and DG sizing algorithm without load flow solution

This study presents a loss formula-based simultaneous reconfiguration and distributed generation (DG) sizing algorithm for active power loss minimisation in distribution networks. A new heuristic technique based on the exact loss formula for reconfiguration is proposed. The exact loss formula-based analytical approach is used for DG sizing and siting. The proposed algorithm is tested on 33-, 69-, 84- and 136-bus distribution systems. The simulation results demonstrate the effectiveness of the proposed approach to obtain the best solution. The impact of the initial voltage selection on this approach is analysed. The results are suggesting that it requires no load flow solution and it is the unique feature of this approach. The proposed approach can be used as a primary analysis tool for active distribution network real-time operation and planning. The proposed method further extended to the multi-objective approach using the weighted sum technique.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

Switching strategy for DG optimal allocation during repairing fault periods on loop distribution networks

Summary It is expected that the use of distributed generation (DG) will increase sharply in distribution systems, especially with the realization of smart grids. In spite of the benefits of DG in distribution systems, high penetration level of DGs could cause some challenges for the network. Therefore, the optimal sizing and siting of DGs should be identified carefully. In addition, different operating conditions have to be taken into account in this process. The DGs are usually installed depending on optimal siting and sizing techniques considering normal operating periods. During repairing fault periods, different locations and capacities of DGs may be preferable. In this paper, 2 scenarios of switching strategies are presented for attaining the optimal sizing and siting of DGs during repairing fault period starting from the optimal allocation obtained for normal operating period. Genetic algorithm is used for solving the optimization problem that is implemented for an actual loop distribution system in Egypt.

Optimal multiple microgrids based forming of greenfield distribution network under uncertainty

Multiple microgrid (MMG) operation concept of community microgrid (MG) provides enhanced resiliency and reliability through self-healing, enabling a high penetration of distributed generations (DGs), power interchange between MGs and real-time communication. In this study, the methodology of optimal design of low-voltage greenfield distribution grids based on multiple MGs in the presence of uncertainties is presented. The electrical and geographical borders of MGs community are determined using some important criteria such as power balance and spatial distribution of load. Within MGs, both of dispatchable and non-dispatchable DGs are considered. The external grid is assumed as a backup for each MG in abnormal conditions. The proposed method determines the optimal borders of MGs, optimal size and site of DGs for each autonomous MG simultaneously. The imperialist competitive algorithm is used to optimise the total cost of optimal MG clustering problem. The method is implemented to a vacant area with residential and commercial customers. The MGs optimal service area, DGs location, size and type within each MG and LV feeder's route are indicated and compared both for deterministic and probabilistic planning cases.

A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique

In this paper, a new optimization method is proposed to solve the optimal sizing and siting problem of distributed generation (DG) in a distribution system (DS). The optimization problem is solved using a new ant lion optimizer (ALO) with considering different objectives. These objectives are reduction of purchased energy cost from upstream network (due to DGs’ power generation), reliability improvement, reduction of DGs’ application cost, reduction of DS losses and reduction of buses’ voltage deviation. This problem is solved as a multi-objective optimization (MOO) in addition of a single-objective optimization (SOO). The proposed DGs optimal sizing and siting is implemented on 33 and 69-bus IEEE networks. The obtained results show that the ALO has the best performance in extracting the solution of DGs optimal sizing and siting problem in comparison to particle swarm optimization (PSO) and genetic algorithm (GA).