Novel analytical method for the placement and sizing of distributed generation unit on distribution networks with and without considering P and PQV buses

Chakraborty

2019

Summary Recent research indicates that future power networks will witness a major enhancement in renewable energy–based distributed generation (DG). The impact of DG and distribution management system (DMS) action can be directly and easily implemented in the distribution networks for the improvement of its voltage security states. In the first phase of this paper, the voltage security state of the distribution network is identified using combined Kohonen's self‐organizing feature map (SOFM) and learning vector quantization (LVQ) algorithm. Two indicators namely voltage stability index (VSI) and distribution system stability indicator (DSSI) are used in this paper for verification of classification result. To ensure voltage security, it is essential to improve the voltage profile of the system. In the next phase, genetic algorithm (GA) and spider monkey optimization (SMO) techniques have been applied to find the optimal location and size of DG for voltage security state improvement of the reconfigured distribution system. Accurate allocation of DG can help in demand side management (DSM) for providing better service to the consumers in real time in smart grid scenario. This methodology has been tested on IEEE 33 bus, IEEE 69 bus, and Indian 85 bus practical radial distribution system. Result shows that the proposed methodology can successfully and appropriately identify the voltage security states of power system by combined SOFM and LVQ algorithms, and utilization of suitable amount of DGs at best locations as obtained from SMO algorithm can improve the operating states of distribution network in terms of voltage security.

Section: Improvement Of System Operating State By Optimal Allocatiomentioning

confidence: 99%

“…Analytical approach proposed in Tah and Das 34 In Tah and Das, 34 a DG unit is placed at bus k, and it is shown that maximum loss reduction will occur in the branches from the source bus to the bus k where DG is connected. The optimal size of DG at unity power factor is given as 34…”

Section: Standard Techniques Of Dg Placementmentioning

confidence: 99%

Spider monkey optimization technique–based allocation of distributed generation for demand side management

Chakraborty

2019

“…The negative of this approach is that feeders are loading with uniformly distributed loads, centrally distributed loads, or increasingly distributed loads which is not the practical case. With the same objective as that of the Wang and Nehrir, different authors have proposed different approaches such as power stability index (PSI), sensitivity of line loss, sensitivity of combined power loss, voltage stability index (VSI), sensitivity of branch loss, active power loss sensitivity factor (LSF), and AE that minimises the losses of active and reactive component of line/branch currents to solve the OADG problem. Gopiya Naik SN et al has suggested the AE that have been derived based on change in active and reactive components of branch currents caused by DG to address the OADG problem with the aim of minimisation of both real and reactive power losses of the system.…”

Section: Introductionmentioning

confidence: 99%

“…35 Further, the AE methods are of two-stage optimisation problems that can be classified into two types. In the first type of AEs [3][4][5][6][7][8][9][10][11] : First stage-optimal location(s) is determined based on the AEs; Second stage-optimal DG size is determined using a repeated search approach. On the other hand, in the second type of AEs [12][13][14][15][16][17][18][19] : First stage-DG size at all the buses is determined based on AE; Second stage-the repeated search is applied to find both optimal location and size of DG that optimises the objective function.…”

mentioning

confidence: 99%

“…From the literature pertaining to the AEs of type I, [3][4][5][6][7][8][9][10][11] it has been identified that the OADG problem is solved only for minimising the real power loss except the authors. 11 As per the authors' knowledge, AEs of type I [3][4][5][6][7][8][9][10][11] methods are not explored to solve the multi-objective DG placement and sizing problem of power distribution networks. In this paper, a Branch Loss Bus Injection Index (BLBII) method is proposed to find the optimal access point for the accommodation of a DG unit.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A solution to multi‐objective optimal accommodation of distributed generation problem of power distribution networks: An analytical approach

Babu

Sydulu

2019

Summary Distributed generation (DG) technologies are considered an integral part of power distribution networks. The desired performance of power distribution networks can be obtained through the optimal accommodation of DG units. This paper proposes an analytical method called Branch Loss Bus Injection Index (BLBII) to solve the multi‐objective optimal accommodation of distributed generation (MO‐OADG) problem of power distribution networks. Four objectives were considered in the optimisation model: energy loss minimisation, overall bus voltage deviation minimisation, overall voltage stability margin maximisation, and energy not served (ENS) minimisation. A multiple criteria decision‐making method such as the analytical hierarchy process is employed to optimise the weight coefficients of the individual objectives as the optimisation problem is solved based on the weighted‐sum method. The proposed method is demonstrated on benchmark radial power distribution networks of IEEE 33‐bus and INDIAN 85‐bus. The applicability of the proposed BLBII has been validated, and results are presented.

Hybrid Harris Hawks optimizer for integration of renewable energy sources considering stochastic behavior of energy sources

Rizwan

Hong

Muhammad

et al. 2021

Summary Renewable energy sources powered distributed generation (RES‐DG) is getting more indispensable to encounter the considerable increase in demand for electric energy owing to its techno‐economic benefits and eco‐friendly nature. An economic solution to this demand can only be obtained with the optimal placement and sizing of RES‐DGs. The optimal siting and sizing of RES‐DG, such as Photovoltaic (PV) and Wind Turbine (WT) is still a hot topic due to the uncertainties in solar irradiance (SI) and wind speed (WS). The main objective of this research paper is to develop a RES‐DG siting and sizing strategy for the discrete, nonlinear siting and sizing pattern of RES‐DGs using a novel hybrid Harris' Hawk optimizer (HHHO), considering the stochastic nature of SI and WS. The Weibull and Beta probability density functions (PDFs) are utilized for modeling the stochastic nature of WS and SI, respectively. The optimization of the multiobjective function comprises active power loss reduction, enhancement in voltage profile, and improvement in voltage stability index (VSI). Different scenarios of single and multiple RES‐DGs and capacitor banks (CB) are examined to validate the efficiency of the proposed novel HHHO based RES‐DGs siting and sizing strategy. The results show a considerable reduction in power loss, enhancement in the system voltage profile, and improvement in VSI. Evaluation of results by comparing withstate‐of‐art hybrid algorithms shows that the proposed solution using HHHO algorithm is globally optimum.