scholarly journals Multi-objective optimal placement of distributed generations for dynamic loads

Author(s):  
Shah Mohazzem Hossain ◽  
Abdul Hasib Chowdhury

<span lang="EN-US">Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High </span><em><span lang="EN-US">R</span></em><span lang="EN-US">/</span><em><span lang="EN-US">X</span></em><span lang="EN-US"> ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.</span>

Electrician ◽  
2019 ◽  
Vol 13 (3) ◽  
pp. 61-68
Author(s):  
Christopher Theophilus Prayogo ◽  
Osea Zebua ◽  
Khairudin Hasan

Intisari — Jarak yang jauh antara sisi penyuplai energi listrik dan sisi konsumen (beban) pada jaringandistribusi menimbulkan permasalahan seperti meningkatnya rugi-rugi daya di sepanjang saluran dan jatuhtegangan. Pemasangan kapasitor adalah salah satu solusi untuk meminimalkan rugi-rugi daya sekaligusmemperbaiki profil tegangan. Tujuan dari penelitian ini adalah mencari nilai kapasitas optimal daribeberapa bank kapasitor yang dipasang pada jaringan distribusi untuk meminimisasi rugi-rugi daya aktifmenggunakan metode Grey Wolf Optimizer (GWO). Lokasi penempatan bank kapasitor ditentukan denganmenggunakan metode faktor sensitivitas rugi-rugi atau Loss Sensitivity Factor (LSF). Studi kasus yangdigunakan adalah jaringan distribusi 20 kV Penyulang Wortel, di Gardu Induk Menggala, ProvinsiLampung. Simulasi penentuan lokasi penempatan dan optimasi kapasitas bank kapasitor dilakukan denganmenggunakan perangkat lunak MATLAB. Hasil simulasi menunjukkan bahwa lokasi optimal penempatanempat bank kapasitor menggunakan metode LSF adalah pada bus 42, 51, 58 dan 60 dan kapasitas optimalbank kapasitor pada bus-bus tersebut yang diperoleh dengan menggunakan metode GWO masing-masingadalah sebesar 0,15 MVAR, 0,45 MVAR, 0,15 MVAR, dan 0,15 MVAR. Rugi-rugi daya aktif yang diperolehsetelah pemasangan bank kapasitor adalah sebesar 0,1041 MW atau berkurang sebesar 23% dari nilai rugirugi daya aktif sebelum pemasangan bank kapasitor yakni 0,1352 MW. Nilai tegangan minimum yangdiperoleh setelah pemasangan bank kapasitor adalah 0,944 pu dan memperbaiki profil tegangan dari nilaitegangan minimum sebelum pemasangan bank kapasitor yakni sebesar 0,916 pu.Kata-kata kunci - optimasi kapasitas, capacitor bank, Grey Wolf Optimizer, rugi-rugi daya aktif, faktorsensitivitas rugi-rugi.Abstract — Long distance between the electricity supply side and the consumer side (load) on the distributionnetwork can cause problems such as increasing power losses along the line and voltage drop. Installingcapacitors is one solution to minimize power losses while improving the voltage profile. The aim of this researchis to find the optimal capacity value of several capacitor banks installed in the distribution network to minimizeactive power losses using the Grey Wolf Optimizer (GWO) method. The location of the capacitor bank placementis determined by using the Loss Sensitivity Factor (LSF) method. The case study used is a 20 kV distributionnetwork of Wortel Feeders, in Menggala substation, Lampung Province. Simulation of determining theplacement location and optimization of capacitor banks capacity is performed using MATLAB software. Thesimulation results show that the location of four capacitor banks using the LSF method is on buses 42, 51, 58and 60 and the optimal capacitor bank capacity on those buses obtained using the GWO method are 0.15 MVAR,0.45 MVAR, 0.15 MVAR, and 0.15 MVAR, respectively. The active power losses obtained after the installation ofcapacitor bank are equal to 0.1041 MW or reduced by 23% from the value of active power losses before theinstallation of capacitor bank which is 0.1352 MW. The minimum voltage value obtained after the installation ofcapacitor bank is 0.94 pu and improves the voltage profile of the minimum voltage value before the installationof capacitor bank which is equal to 0.916 pu.Keywords— capacity optimization, capacitor bank, Grey Wolf Optimizer, active power losses, Loss SensitivityFactor.


Author(s):  
Souhil Mouassa ◽  
Tarek Bouktir

Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems. Design/methodology/approach A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed. Findings Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions. Originality/value The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.


2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Claudio Goncalves ◽  
J. Paulo A. Vieira ◽  
Dione J. A. Vieira ◽  
M. Emilia L. Tostes ◽  
Bernard C. Bernardes ◽  
...  

This paper proposes an analytical methodology to allocate and size active power photovoltaic generation (PVG) units with embedded DC/AC inverter (PVGI) to be integrated as concentrated or dispersed generation in isolated medium voltage electrical grids. The methodology considers multiple objectives: improving the electrical grid voltage profile; reducing active power losses and the diesel generation participation. To validate the proposed methodology, the IEEE 33 and 69 buses networks and an isolated real electrical system were simulated. The results obtained demonstrated that the proposed methodology is effective in providing a solution with improvement in voltage profile, active power losses reduction, diesel generation participation reduction.


2021 ◽  
Vol 13 (18) ◽  
pp. 10224
Author(s):  
Sasan Azad ◽  
Mohammad Mehdi Amiri ◽  
Morteza Nazari Heris ◽  
Ali Mosallanejad ◽  
Mohammad Taghi Ameli

Considering the strong influence of distributed generation (DG) in electric distribution systems and its impact on network voltage losses and stability, a new challenge has appeared for such systems. In this study, a novel analytical algorithm is proposed to distinguish the optimal location and size of DGs in radial distribution networks based on a new combined index (CI) to reduce active power losses and improve system voltage profiles. To obtain the CI, active power losses and voltage stability indexes were used in the proposed approach. The CI index with sensitivity analysis was effective in decreasing power losses and improving voltage stability. Optimal DG size was determined based on a search algorithm to reduce active power losses. The considered scheme was examined through IEEE 12-bus and 33-bus radial distribution test systems (RDTS), and the obtained results were compared and validated in comparison with other available methods. The results and analysis verified the effectiveness of the proposed algorithm in reducing power losses and improving the distribution system voltage profiles by determining the appropriate location and optimal DG size. In IEEE 12 and 33 bus networks, the minimum voltage increased from 0.9434 p.u and 0.9039 p.u to 0.9907 p.u and 0.9402 p.u, respectively. Additionally, the annual cost of energy losses decreased by 78.23% and 64.37%, respectively.


Author(s):  
Wan Iqmal Faezy Wan Zalnidzam ◽  
Hasmaini Mohamad ◽  
Nur Ashida Salim ◽  
Hazlie Mokhlis ◽  
Zuhaila Mat Yasin

The increasing penetration of electric vehicle (EV) at distribution system is expected in the near future leading to rising demand for power consumption. Large scale uncoordinated charging demand of EVs will eventually threatens the safety operation of the distribution network. Therefore, a charging strategy is needed to reduce the impact of charging. This paper proposes an optimal centralized charging schedule coordination of EV to minimize active power losses while maintaining the voltage profile at the demand side. The performance of the schedule algorithm developed using particle swarm optimization (PSO) technique is evaluated at the IEEE-33 Bus radial distribution system in a set time frame of charging period. Coordinated and uncoordinated charging schedule is then compared in terms of active power losses and voltage profile at different level of EV penetration considering 24 hours of load demand profile. Results show that the proposed coordinated charging schedule is able to achieve minimum total active power losses compared to the uncoordinated charging.


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