517 research outputs found

    Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

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    This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios

    Optimal distributed generation planning in active distribution networks considering integration of energy storage

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    A two-stage optimization method is proposed for optimal distributed generation (DG) planning considering the integration of energy storage in this paper. The first stage determines the installation locations and the initial capacity of DGs using the well-known loss sensitivity factor (LSF) approach, and the second stage identifies the optimal installation capacities of DGs to maximize the investment benefits and system voltage stability and to minimize line losses. In the second stage, the multi-objective ant lion optimizer (MOALO) is first applied to obtain the Pareto-optimal solutions, and then the 'best' compromise solution is identified by calculating the priority memberships of each solution via grey relation projection (GRP) method, while finally, in order to address the uncertain outputs of DGs, energy storage devices are installed whose maximum outputs are determined with the use of chance-constrained programming. The test results on the PG&E 69-bus distribution system demonstrate that the proposed method is superior to other current state-of-the-art approaches, and that the integration of energy storage makes the DGs operate at their pre-designed rated capacities with the probability of at least 60% which is novel.Comment: Accepted by Applied Energ

    Critical Review of Different Methods for Siting and Sizing Distributed-generators

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    Due to several benefits attached to distributed generators such as reduction in line losses, improved voltage profile, reliable system etc., the study on how to optimally site and size distributed generators has been on the increase for more than two decades. This has propelled several researchers to explore various scientific and engineering powerful simulation tools, valid and reliable scientific methods like analytical, meta-heuristic and hybrid methods to optimally place and size distributed generator(s) for optimal benefits. This study gives a critical review of different methods used in siting and sizing distributed generators alongside their results, test systems and gaps in literature

    Integration of Distributed Generations in Smart Distribution Networks Using Multi-Criteria Based Sustainable Planning Approach

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    Energy planning has become more complicated in the 21st century of sustainable development due to the inclusion of numerous standards such as techno-economic, and environmental considerations. This paper proposes multi-criteria sustainable planning (MCSP) based optimization approach for identifying DGs’ optimal allocations and rating powers. The main objectives of this paper are the reduction of the network’s total power loss, voltage profile improvement, energy loss saving maximization, and curtailing environmental emissions and water consumption to achieve Sustainable Development Goals (SDGs 3, 6, 7, 13, and 15) by taking the constraints into consideration. Different alternatives are evaluated across four aspects of performance indices; technical, cost-economic, environmental, and social (TEES). In terms of TEES performance evaluations, various multi-criteria decision-making (MCDM) approaches are used to determine the optimal trade-off among the available solutions. These methods are gaining wide acceptance due to their flexibility while considering all criteria and objectives concurrently. Annual energy loss saving is increased by 97.13%, voltage profile is improved to 0.9943 (p.u), and emissions are reduced by 82.45% using the proposed technique. The numerical results of the proposed MCSP approach are compared to previously published works to validate and may be used by researchers and energy planners as a planning tool for ADN schemes

    Optimal generator placement in a distributed network

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    Since the industrial and residential huge demand for electricity, as well as higher requirements for the electricity reliability and power quality. Meanwhile, the world has been in the energy crisis, the power shortage, and a large area often experienced blackouts accident. All exposed the deficiencies of ‘centralized power’. Thus, the approaches of reducing active power loss in power systems have become increasingly important. Among them, the method of the optimal location and size of distribution generators in a distributed network is one of the most prospective approaches in the future. And it can use into practice to achieve the purpose of low-carbonate, less cost and flexible power generation power systems because install DGs at the optimal location and with optimal size in distributed network can dramatically reduce active power loss in power systems. There are several models and methods have been suggested for solving the optimal DG placement problem. This paper presents models and methods applied to solve the optimal DG placement problem, and especially propose a new approach that based on an exact solution method using the enumerative method to reduce the number of the combination by request of constraints. In this case, the proposed method firstly will be applied to the simple 6 buses system, and then applied to 14 buses real size distribution network model which is based on the 126 buses real size distribution system. It can be evaluated for active power loss reduction features by DGs installation and choose optimal location and size of distribution generation in distributed network considering actual use

    Analysis of the Effect of Distributed Generation on Loss Reduction in Electrical Distribution Network

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    Distribution network is said to be the most visual part of the electric production and the most observed by the utilities for investment, maintenance and operation. The system have been operated under stressed conditions due to limited structure and increasing day to day requirement of power consumption, which have a significant economic and social impact on the system. Due to the system high resistance to impendence ratio, large amount of power loss occur in the network. This loss is the most severity factors affecting the power quality delivered to the end users and depend on power network expansion and load complexity. Among the support methods available for power loss minimization in distribution network, strategic allocation of Distributed Generation (DG) in distribution system is widely considered a viable option. DGs are electrical sources connected to the power network located to consumer’s side but very small when compared with the centralized power plant. They can be in form of wind, mini-hydro, photovoltaic and fuel-based system such as fuel cells and micro-turbines. Therefore, in this study, different approaches for power loss minimization in electrical distribution system with the incorporation of DG by various researchers were reviewed. These approaches have become powerful tools to overcome the problem of power loss minimization in distribution system. Keywords: Distribution System, Power Loss. Distributed Generation, Power Consumption, Photovoltaic System, Centralized Power Plant. DOI: 10.7176/JETP/11-6-02 Publication date: November 30th 202

    Improvement of active distribution systems with high penetration capacities of shunt reactive compensators and distributed generators using Bald Eagle Search

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    This work proposes an intelligent allocation of distributed generation (DG) units and shunt reactive compensators (SRC) with high penetration capacities into distribution systems for power loss mitigation using the Bald Eagle Search (BES) optimization algorithm. The intelligent allocation causes a reduction in voltage variations and enhances the voltage stability of the systems. The SRC units include shunt capacitors (SC), Static Var Compensators (SVC), and Distribution Static Compensators (DSTATCOM), which are determined according to their capacities. The optimization study includes the 33-bus and the 118-bus distribution systems as medium to large systems. Performance parameters, including the reactive power loss, Total Voltage Deviation (TVD), and Stability Index (SI), besides the power loss, are recorded for each optimization case study. When the BES algorithm optimizes 1, 2, and 3 DG units operating at optimal power factor (OPF) into the 33-bus systems, percentage reductions of power loss reach 67.84%, 86.49%, and 94.44%, respectively. Reductions of 28.26%, 34.47%, 35.24%, and 35.44% are achieved in power loss while optimizing 1, 3, 5, and 7 SRC units. With a combination of DG/SRC units, the power loss reductions achieve 72.30%, 93.89%, and 97.49%, optimizing 1, 3, and 5 pairs of them. Similar reductions are achieved for the rest of the performance parameters. With high penetration of compensators into the 118-bus system, the percentage reductions of power loss are 29.14%, 73.27%, 83.72%, 90.14%, and 93.41% for optimal allocations of 1, 3, 5, 7, and 9 DG units operating at OPF. The reduction reaches 11.15%, 39.08% with 1 and 21 devices when optimizing the SRC. When DG SRC units are optimized together, power loss turns out to be 32.83%, 73.31%, 83.32%, 88.52%, and 91.29% with 1, 3, 5, 7, and 9 pairs of them. The approach leads to an enhanced voltage profile near an acceptable range of bus voltages, reduces the voltage fluctuation substantially, and enhances the system stability. The study also ensures the BES algorithm’s capability to solve these nonlinear optimization problems with high decision-variable numbers

    Optimal Siting of Distributed Generators in a Distribution Network using Artificial Immune System

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    Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function
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