Particle swarm optimisation based DG allocation in primary distribution networks for voltage profile improvement and loss reduction

As there are many potential benefits by integrating the distributed generation (DG) units in a distribution network over conventional system, DG plays a vital role in a power system network. Renewable energy based DG units are located close to the consumers or load centers in order to improve voltage profile, reduce the network power losses and improves substation capacity release etc. Thus, while allocating DG units care has to be taken in order to maximize the benefits. In this thesis, by installing DG, an optimal way of managing real and reactive power and improving the nodal voltages in primary distribution network explained. Optimal location of DG is identified by using voltage stability index (VSI). The optimal rating of DG is computed by using Particle Swarm Optimization (PSO) technique to ensure reduction in power losses and to attain better voltage regulation. To demonstrate the efficiency of proposed techniques a clear and detailed analysis of performance has been carried out on IEEE-33 & 69 bus system

Optimal Planning of Type-1DGs in EV Incorporated Radial Distribution Network

A vital task for effective operation of distribution system is reducing power losses and to save energy. One of the most effective methods to reduce losses is distributed generation (DG). The proper planning of EVCSs is a significant problem for distribution system operators as the installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) increases on a larger scale. Power losses and generation-demand mismatch rise with EV load adoption in the electrical grid. As a result, there will be impact on voltage levels and the voltage stability margin deteriorates. It is critical to integrate EVCSs at appropriate locations to reduce the negative effects of increasing EV load penetration on Radial Distribution Systems (RDS). The integration of EVs into distribution systems undergoes charging and discharging modes of operation for power exchange with the grid, resulting in energy management. Inadequate EVCS planning has a negative influence on the distribution system, causing voltage variation and an increase in power losses. DG units are integrated with EVCSs to reduce this. The DGs help to keep the voltage profile within limits which reduces power flows and losses and thus results in improved power quality and reliability. Therefore, the DGs and EVCS should be properly allocated and sized to avoid issues such as protection, voltage rise, and reverse power flow. This research demonstrates an approach for minimizing losses in an EV-integrated radial distribution system by optimizing the location and sizing of DGs. This study proposes the sizing and location of renewable (wind, solar) DG units (type-1 DG) incorporated in radial distribution network. This methodology decreases power losses while simultaneously improving network voltages. The accuracy of the proposed method is elaborated in four scenarios. The proposed methodology is implemented in IEEE 33 bus and 69 bus systems using the Particle Swarm Optimization technique (PSO). The results show that the suggested optimization technique increases system efficiency and performance by optimizing the planning and operation of both DGs and EVs

Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems

The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption

Spring search algorithm for simultaneous placement of distributed generation and capacitors

Purpose. In this paper, for simultaneous placement of distributed generation (DG) and capacitors, a new approach based on Spring Search Algorithm (SSA), is presented. This method is contained two stages using two sensitive index Sv and Ss. Sv and Ss are calculated according to nominal voltageand network losses. In the first stage, candidate buses are determined for installation DG and capacitors according to Sv and Ss. Then in the second stage, placement and sizing of distributed generation and capacitors are specified using SSA. The spring search algorithm is among the optimization algorithms developed by the idea of laws of nature and the search factors are a set of objects. The proposed algorithm is tested on 33-bus and 69-bus radial distribution networks. The test results indicate good performance of the proposed methodЦель. В статье для одновременного размещения распределенной генерации и конденсаторов представлен новый подход, основанный на "пружинном" алгоритме поиска (Spring Search Algorithm, SSA). Данный метод состоит из двух этапов с использованием двух показателей чувствительности Sv и Ss. Показатели чувствительности Sv и Ss рассчитываются в соответствии с номинальным напряжением и потерями в сети. На первом этапе определяются шины-кандидаты для установки распределенной генерации и конденсаторов согласно Sv и Ss. Затем, на втором этапе размещение и калибровка распределенной генерации и конденсаторов выполняются с использованием алгоритма SSA. "Пружинный" алгоритм поиска входит в число алгоритмов оптимизации, разработанных на основе идей законов природы, а факторы поиска представляют собой набор объектов. Предлагаемый алгоритм тестируется на радиальных распределительных сетях с 33 и 69 шинами. Результаты тестирования показывают хорошую эффективность предложенного метода

Swarm Intelligence Based Multi-phase OPF For Peak Power Loss Reduction In A Smart Grid

Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. During the formulation of the proposed algorithm, multi-phase power distribution system is considered which has unbalanced loadings, voltage control and reactive power compensation devices. The proposed algorithm is built upon a co-simulation framework that optimizes the objective by adapting a constriction factor Particle Swarm optimization. The proposed multi-phase OPF technique is validated using IEEE 8500-node benchmark distribution system.Comment: IEEE PES GM 2014, Washington DC, US

Reducing Voltage Volatility with Step Voltage Regulators: A Life-Cycle Cost Analysis of Korean Solar Photovoltaic Distributed Generation

To meet the United Nation’s sustainable development energy goal, the Korean Ministry of Commerce announced they would increase renewable energy generation to 5.3% by 2029. These energy sources are often produced in small-scale power plants located close to the end users, known as distributed generation (DG). The use of DG is an excellent way to reduce greenhouse gases but has also been found to reduce power quality and safety reliability through an increase in voltage volatility. This paper performs a life-cycle cost analysis on the use of step voltage regulators (SVR) to reduce said volatility, simulating the impact they have on existing Korean solar photovoltaic (PV) DG. From the data collected on a Korean Electrical Power Corporation 30 km/8.2 megawatts (MW) feeder system, SVRs were found to increase earnings by one million USD. SVR volatile voltage mitigation increased expected earnings by increasing the estimated allowable PV power generation by 2.7 MW. While this study is based on Korean PV power generation, its findings are applicable to any DG sources worldwide.11Nsciescopu

Optimasi Kapasitas dan Penempatan Distributed Generation Menggunakan Differential Evolution Untuk Meminimalkan Rugi Daya

Distributed Generation atau biasa disebut dengan DG adalah pembangkit skala kecil dan menengah yang dikoneksikan langsung pada jaringan distribusi atau dekat dengan pusat beban. Pemasangan DG bertujuan untuk mengurangi rugi daya dan memperbaiki tegangan. Penggunaan DG memiliki beberapa kelebihan antara lain dari segi ekonomi karena lebih menghemat penggunaan energi, ramah lingkungan, dan dari segi teknik meningkatkan stabilitas dan keandalan sistem tenaga listrik. Dalam tugas akhir ini menampilkan sebuah metodelogi untuk menentukan optimal penempatan dan penentuan kapasitas dari DG menggunakan metode Differential Evolution. Hasil analisis pemodelan optimasi kapasitas dan peletakan DG ini menggunakan software MATLAB diaplikasikan pada sistem distribusi IEEE 33 bus. Pada analisa aliran daya sebelum pemasangan DG masih didapatkan beberapa bus kritis dibawah 0.95 p.u dengan nilai tegangan terendahnya adalah 0.913 p.u, nilai rugi daya aktifnya yaitu 217.8946 kW. Setelah dilakukan pemasangan DG sebanyak 4 unit dapat meningkatkan profil tegangan antara 0.95-1.05 p.u dan dapat meminimalisasikan rugi daya aktif menjadi 48.80 kW. Sehingga dari hasil tersebut dengan menggunakan metode Differential Evolution untuk optimasi peletakan dan kapasitas DG diperoleh penghematan rugi daya sebesar 169.09 kW. ====================================================================================================== Distributed Generation or called DG is small scale generators to be connected close to distribution networks or near load center. Installing of DG has decision for minimize power losses and improving voltage profile. Using DG has some advantages are from economical advantages is saving of fuel, reduction of noise pollution, and improve stability and reliability power of generation. In this paper present a method for determine optimal placement and sizing of distributed generation using differential evolution method. Analysis result of optimal placement and sizing DG is performed with MATLAB tested on IEEE 33 bus radial distribution networks. At power flow analyze before applying DG still obtained some critical bus under 0.95 p.u with lowest voltage value is 0.913 p.u, power losses active value is 217.8946 kW. After applying of 4 DG units can improve voltage profile between 0.95-1.05 p.u and minimalize active power losses to be 48.80 kW. In thus, from the result using differential evolution method for optimal placement and sizing DG obtained saving losses is 169.09 kW