Optimal Sizing Of Distributed Generation Research Articles

Impact Optimal DG Placement Against Harmonic Distribution on Reconfiguration Distribution Network on Microgrid System

Increases in harmonic distortion in Radial Distribution System (RDS) will result in additional shorter insulation lifetime, higher temperature and also malfunction. These are undesirable as they cause power losses and also affect the voltage profile. In this paper the harmonic analysis is carried effect and performance placement Distributed Generation (DG) after reconfiguration distribution network in active radial distribution system or microgrid concept. The optimal size of DG is determining using K-Means Clustering method and Load Curtailment method to designing distribution network reconfiguration after restoration. The load flow analysis and harmonic flow analysis is carried out with direct approach using BIBC and BCBV matrices. The proposed method is carried out on IEEE 33 bus system having the harmonic sources injected as current sources using MATLAB and ETAP software. The result show DG placement on 4 bus points using K-Means Clustering can improve voltage levels and reduce harmonic dispersion. After restoration and reconfiguration of the system, the harmonic spread is strongly influenced by the current flowing on the bus and the location of the source harmonics on the system.

Integrating the PV-Diesel Hybrid System for Reliability Improvement in Distribution System

In Myanmar, as the main power generation is hydro power generation. the utility cannot supply sufficient power to customers during the dry season. Besides interruptions occur frequently due to aging system and lack of prospered protection. Therefore, reliability is an urgent issue in Myanmar. As a result of unbalance between generation and load, the distribution system is getting poor voltage profile, instability and high power losses in high load condition. According to network characteristics, the failure of a component always leads to consequence interruption in a radial distribution system. Therefore, it is a must consideration to mitigate these challenges to enhance the system reliability. There are many techniques to solve the reliability problems such as reclosers, switching devices (manual and automated switches), system reconfiguration, feeder re-conducting and integration of distributed generation (DG). In this paper, system reliability assessment is evaluated in detail with the integration of the distributed generation such as PV-Diesel Hybrid System. The location of DG is chosen according to the expected energy not supply (EENS) and the voltage drop in proposed system. Next, the optimal sizing of DG is chosen depends on the penetration level of generator. Reliability indices can be evaluated depending on the failure rate(λ), repair time(r) and annual outage time(U) in Electrical Transient and Analysis Program (ETAP) software. The case study of this thesis is carried out in 33/11 kV network which is connected Kyatminton Substation, Kyaukse, Middle Myanmar.

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.

Embedded adaptive mutation evolutionary programming for distributed generation management

<span>Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.</span>

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.

Determining Optimal Size and Placement of Renewable DG Considering Variation of Load

An advance algorithm to decide optimal size and place of renewable DG by applying adaptive particle swarm optimization (A-PSO) technique is reported in this work. A multiobjective function has been proposed to improve voltage profile, maximize economic benefit and reduction in active power losses. This work includes renewable energy sources based distributed generation (DG) technique like solar and biomass DGs. The time variations characteristics for solar DG and load have been considered in the system. Due to the problem of easily get trapped into local optima, particle Swarm optimization (PSO) may not be able to solve complex power system problems. Hence, APSO with variable weight function has been used in this work. The implementation of proposed technique has been presented for practical 94-bus portuguese radial distribution system. Simultaneous placement of both renewable DGs provides maximum benefits. The comparison between proposed and existing techniques have been presented which confirms that the suggested technique gives the superior result

Integration of Distributed Generations in Radial Distribution Network using ABC Algorithm

This paper keeps aim to integrate distributed generations(DGs) in radial distribution networks (RDNs) using ABC algorithm. The appropriate nodes of RDN is found out by the well-established loss sensitivity index. The optimal size of DGs are found out by using the ABC algorithm. The optimal integration of DGs not only reduces the loss but also increases the voltage profile and enhances the voltage stability index and hence improves the stability of the system. Here 33-node and 69-node RDNs are considered to implement the proposed method. The outcomes obtained by ABC algorithm have also been compared with that Firefly, PSO and GA algorithms

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.

Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches

This report addresses the problem of optimal location and sizing of constant power sources (distributed generators (DGs)) in direct current (DC) networks for improving network performance in terms of voltage profiles and energy efficiency. An exact mixed-integer nonlinear programming (MINLP) method is proposed to represent this problem, considering the minimization of total power losses as the objective function. Furthermore, the power balance per node, voltage regulation limits, DG capabilities, and maximum penetration of the DG are considered as constraints. To solve the MINLP model, a convex relaxation is proposed using a Taylor series expansion, in conjunction with the transformation of the binary variables into continuous variables. The solution of the relaxed convex model is constructed using a sequential quadratic programming approach to minimize the linearization error using the Taylor series method. The solution of the relaxed convex model is used as the input for a heuristic random hyperplane method that facilitates the recovery of binary variables that solve the original MINLP model. Two DC distribution feeders, one having 21 and the other having 69 nodes, were used as test systems. Simulation results were obtained using the MATLAB/quadprog package and contrasted with the large-scale nonlinear solvers available for General algebraic modeling system (GAMS) software metaheuristic optimization approaches to demonstrate the robustness and effectiveness of our proposed methodology.

Effect of Inertia Weight of PSO on Optimal Placement of DG

Integrating Distributed Generation (DG) at appropriate location in distribution system can reduce its real power losses and can also increase the voltage regulation. Optimal allocation of DG has two parts, i) location identification of DG, ii) capacity determination of DG. This article uses loss sensitivity methods for fixing of optimal place of DG and then Particle Swarm Optimization (PSO) technique is implemented for determination of optimal size of DG at that location. During optimization, effect of inertia weight of PSO on the optimal placement of DG is demonstrated in this paper. For detailed study, IEEE 33-bus system is considered and impact of integration DG in the system is also shown.

Effective network reconfiguration with distributed generation allocation in radial distribution networks using water cycle algorithm

This paper presents a novel application of watercycle algorithm (WCA) for optimal distribution network reconfiguration (DNR) and distributed generation (DG) allocation in radial distribution network (RDN). WCA is proposed to simultaneously have the optimal topology of RDN and get the optimal DG size and placement. The objective is to minimize power losses and the voltage profile index. in addition, study the variation in both voltage magnitude and voltage stability profiles. Loss sensitivity factor index is utilized to crop the candidate buses for DG allocation aiming to narrow the search space for DG sizing. Moreover, the graph theory is presented to get the fundamental loops that used to reduce the search space for DNR by limiting the infeasible configurations during the optimization process, where the connection matrix of the radial network is used to check the radial constraint of each configuration. The proposed method has been applied on two different RDN with different scenarios. The obtained results verify the effectiveness of the proposed algorithm in comparison with previous works in literature.

Cost\u2013benefit analysis for optimal DG placement in distribution systems by using elephant herding optimization algorithm

Distributed generations (DGs) are small generating plants which are connected to consumers in distribution systems to improve the voltage profile, stability improvement, reduction in power losses and economic benefits. The above benefits can be achieved by optimal placement of DGs. In this paper, a novel nature-inspired algorithm called elephant herding optimization algorithm is used to determine the optimal distributed generation size. It has been developed based on herding behaviour of elephant groups in nature. The proposed algorithm is tested on IEEE 15-, 33- and 69-bus test systems. The proposed algorithm with type III DG unit operating at 0.9 pf gives better results when compared with other methods in the literature.

Multi-objective optimal sizing of distributed generation by application of Taguchi desirability function analysis

Distributed Generation (DG) is modular power generating technology. DG has beneficial impact on tail end voltages, line losses and operating cost. The sizing and location of DG in distribution system is a vital undertaking. In this paper Taguchi Desirability Function Analysis (TDFA) is presented. The remarkable highlight of TDFA is the skillful handling of simultaneous optimization of nearly thousand objectives and its ability to generate multiple optimal solutions. It facilitates the effortless addition of each objective. TDFA is used for finding the optimal size of single as well as multiple DG. Size estimation is carried out for multiple objectives of minimizing the line losses, improving the voltage profile and improving voltage stability index (VSI). The objectives may be minimized, maximized or assigned target values simultaneously. In the scope of this paper more than nine objectives have been optimized simultaneously. TDFA has been implemented to determine optimal size of DG for different load conditions. The proposed approach is tested and verified on IEEE 33-bus and IEEE 85- bus radial distribution system (RDS).

Optimal Allocation of Distributed Generation With Optimal Sizing of Fault Current Limiter to Reduce the Impact on Distribution Networks Using NSGA-II

Nowadays, the presence of distributed generation (DG) units in the distribution network is increasing due to their advantages. As well as these advantages, there are some problems, such as increasing the level of short circuits in buses and disturbing the protection operation of the network. The main objective of this paper is optimal using of fault current limiters (FCLs) in series with DG units to reduce the negative impacts of the DG units. The method of simultaneously determining the optimal location and size of DGs and the optimal size of FCLs is proposed in this paper. By appropriate allocation of DG units in the distribution network, a considerable reduction in the size of fault current limiters can be achieved. The locations of DG units are considered to be discrete variables and the sizes of FCLs and DGs to be continuous variables, and a nondominated sorting genetic algorithm (NSGA-II) is used to reduce losses and optimize the sizes of fault current limiters. The proposed method is implemented on two test networks and the results demonstrate the efficiency of the method.

Multi-objective whale optimization based minimization of loss, maximization of voltage stability considering cost of DG for optimal sizing and placement of DG

Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.

Multiple DGs for Reducing Total Power Losses in Radial Distribution Systems Using Hybrid WOA-SSA Algorithm

Distributed generators (DGs) are currently extensively used to reduce power losses and voltage deviations in distribution networks. The optimal location and size of DGs achieve the best results. This study presents a novel hybridization of new metaheuristic optimizations in the last two years, namely, salp swarm algorithm (SSA) and whale optimization algorithm (WOA), for optimal placement and size of multi-DG units in radial distribution systems to minimize total real power losses (kW) and solve voltage deviation. This hybrid algorithm is implemented on IEEE 13- and 123-node radial distribution test systems. The OpenDSS engine is used to solve the power flow to find the power system parameters, such power losses, and the voltage profile through the MATLAB coding interface. Results describe the effectiveness of the proposed hybrid WOA-SSA algorithm compared with those of the IEEE standard case (without DG), repeated load flow method, and WOA and SSA algorithms applied independently. The analysis results via the proposed algorithm are more effective for reducing total active power losses and enhancing the voltage profile for various distribution networks and multi-DG units.

Multi-Objective Optimal Location and Sizing of Hybrid Photovoltaic System in Distribution Systems Using Crow Search Algorithm

The optimal size and location of Distributed generation (DG) in a distribution system can minimize the power loss and improve the voltage profile of the distribution system. But the random placement of DG will not solve the problem of power loss and voltage profile. Therefore optimization algorithms are used to find the optimal size and location of DG to minimize the power loss and to improve the voltage profile. This research paper introduces a new metaheuristic algorithm which is the Crow search algorithm (CSA) to calculate the optimal size and location of multiple solar photovoltaic (PV) units for reducing the power loss and improving the voltage profile. This paper also discusses the complete mathematical modeling of probabilistic PV generation, time-varying load modeling, loss calculation, voltage stability, and optimal PV size calculation. The method proposed in this paper is tested on IEEE 30 and 57 bus test systems and compared with other existing methods like Genetic algorithm (GA), Particle swarm optimization (PSO) and Hybrid GA-PSO method. CSA has only 2 settings parameters while GA and PSO have 6 and 3 settings parameters. Because of this, computation time and complexity of the algorithm are less in comparison to other methods and it gives a better result. Therefore by using the CSA method, the optimal size and location of the DG can be found which gives less power loss and improved voltage profile, less computational time and easy to implement compared to other existing methods proposed here.

Two States for Optimal Position and Capacity of Distributed Generators Considering Network Reconfiguration for Power Loss Minimization Based on Runner Root Algorithm

Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for a long-term purpose while the latter is an operational problem which is used for a short-term purpose. In this situation, the optimal value of the position and capacity of DGs is a value which must be not affected by changing the operational configuration due to easy changes in the status of switches compared with changes in the installed location of DG. This paper demonstrates a methodology for choosing the position and size of DGs on the DN that takes into account re-switching the status of switches on distribution of the DN to reduce power losses. The proposed method is based on the runner root algorithm (RRA) which separates the problem into two states. In State-I, RRA is used to optimize the position and size of DGs on closed-loop distribution networks which is a mesh shape topology and power is delivered through more than one line. In State-II, RRA is used to reconfigure the DN after placing the DGs to find the open-loop distribution network which is a tree shape topology and power is only delivered through one line. The calculation results in DN systems with 33 nodes and 69 nodes, showing that the proposed method is capable of solving the problem of the optimal position and size of DGs considering distribution network reconfiguration.