scholarly journals Photovoltaic Generation Integration with Radial Feeders Using GA and GIS

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Elias Mandefro Getie ◽  
Belachew Bantyirga Gessesse ◽  
Tewodros Gera Workneh
Keyword(s):  
Power System ◽  
Power Loss ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Generation System ◽  
Photovoltaic Generation ◽  
Gis Methods ◽  
Pv Generation ◽  
Transmission And Distribution

The electric power generated from different electricity sources are not used efficiently by end users in the world. This is due to the loss of power supplied in the case of electricity transmission and distribution to residential, commercial, and industrial loads. Even if the loss of power in the power system cannot be avoided 100%, it should be reduced to the minimum optimal value. The loss of power in the radial feeders can be minimized using an optimally allocated photovoltaic (PV) generation system by considering the information of geography, solar irradiance of the site, and space availability, which should not have shadow from large buildings and trees. The PV generation system eliminates the problem of power demand by enhancing the capacity of the power network as well as by reducing the depletion and consumption of fossil fuel resources. To reduce power loss and improve system loading capacity for demand response, the integration and finding the optimal place of photovoltaic generation take high concern from power system operators and technicians. The optimal allocation of PV has been done using the Genetic Algorithm (GA) for optimization of a multiobjective function with different constraints. The main objective of this paper is to minimize the power loss of the radial distribution networks by maintaining the phase voltage of the load in balance and improving the drop in voltage along the phase. So, GA is used to determine the best location and capacity of PV generation that can reduce the loss of power in the system. The IEEE-33 bus system is used to test the proposed method. Generally, using the GA and GIS methods results in a high accuracy for optimal placement of PV generation in the IEEE-33 bus radial feeder and enables to reduce the loss of power during transmission and distribution by maintaining the power quality for consumers.

scholarly journals Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdullahi Bala Kunya ◽  
Gaddafi Sani Shehu ◽  
Usman Muhammad Hassan ◽  
Abdurrahman Umar Lawal
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Combinatorial Optimization Problems ◽  
Power Loss Reduction ◽  
Real Coded Genetic Algorithm

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations.  To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.  

scholarly journals Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1648
Author(s):  
Marinko Barukčić ◽  
Toni Varga ◽  
Vedrana Jerković Jerković Štil ◽  
Tin Benšić
Keyword(s):  
Power System ◽  
Power Management ◽  
Computational Intelligence ◽  
Input Data ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Distributed Generations ◽  
Data Resolution ◽  
The Impact

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

scholarly journals Electric Power System Operation Mechanism with Energy Routers Based on QoS Index under Blockchain Architecture

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 418 ◽  
Author(s):  
Gangjun Gong ◽  
Zhening Zhang ◽  
Xinyu Zhang ◽  
Nawaraj Kumar Mahato ◽  
Lin Liu ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Electric Power ◽  
Distribution Networks ◽  
Electric Power System ◽  
Control Flow ◽  
Utilization Efficiency ◽  
Stable Operation ◽  
Bidirectional Power Flow ◽  
Transmission And Distribution

With the integration of highly permeable renewable energy to the grid at different levels (transmission, distribution and grid-connected), the volatility on both sides (source side and load side) leading to bidirectional power flow in the power grid complicates the control mechanism. In order to ensure the real-time power balance, energy exchange, higher energy utilization efficiency and stability maintenance in the electric power system, this paper proposes an integrated application of blockchain technology on energy routers at transmission and distribution networks with increased renewable energy penetration. This paper focuses on the safe and stable operation of a highly penetrated renewable energy grid-connected power system and its operation. It also demonstrates a blockchain-based negotiation model with weakly centralized scenarios for “source-network-load” collaborative scheduling operations; secondly, the QoS (quality of service) index of energy flow control and energy router node doubly-fed stability control model were designed. Further, it also introduces the MOPSO (multi-objective particle swarm optimization) algorithm for power output optimization of multienergy power generation; Thirdly, based on the blockchain underlying architecture and load prediction value constraints, this paper puts forward the optimization mechanism and control flow of autonomous energy coordination of b2u (bottom-up) between router nodes of transmission and distribution network based on blockchain.

Optimal Daily Configuration of a Distribution Network Based on Photovoltaic Generation and System Loading Using Imperialist Competitive Algorithm and Selection Approach

2015 ◽  
Vol 785 ◽  
pp. 541-545 ◽  
Author(s):  
K.G. Ing ◽  
Hazlie Mokhlis ◽  
Hazlee Azil Illias ◽  
Jasrul Jamani Jamian ◽  
Muhammad Mohsin Aman
Keyword(s):  
Distribution System ◽  
Power Loss ◽  
Distribution Network ◽  
Imperialist Competitive Algorithm ◽  
Test System ◽  
Photovoltaic Generation ◽  
Competitive Algorithm ◽  
Selective Approach ◽  
Selection Approach ◽  
Pv Generation

This paper presents a new method to determine the best configuration for a distribution system for a day considering Photovoltaic (PV) generation and load profile. In the first part, the hourly optimal configuration for a day is obtained by using Imperialist Competitive Algorithm (ICA) and in second part; a selective approach based on minimum total daily power loss is used to select the optimal daily configuration. The proposed method is validated on IEEE 33 bus test system.

Application of Cascaded Multilevel Inverter for High Voltage Grid-Connected PV System

2015 ◽  
Vol 1092-1093 ◽  
pp. 17-21
Author(s):  
Yi Dan Li ◽  
Du Ting Wang ◽  
Hong Ming Kang ◽  
Hong Fang Ru
Keyword(s):  
Multilevel Inverter ◽  
Operating Voltage ◽  
Generation System ◽  
Pv System ◽  
Photovoltaic Generation ◽  
In Series ◽  
Simulation Results ◽  
High Level ◽  
Pv Generation ◽  
Cascaded Multilevel Inverter

The cascaded multilevel inverter may be the best topology to satisfy continuously increasing capacity and scale of grid-connected photovoltaic generation system due to its modularized circuit layout and sufficiently high operating voltage without devices in series. The operating principle of the cascaded multilevel inverter is presented. The control strategy of carrier phase shift PWM is proposed and discussed in detail. A grid-connected photovoltaic (PV) generation system based on a seven level cascaded inverter is provided. Simulation model of a seven level cascaded inverter with phase shifted PWM is built in Simulink environment and simulation results verify that the cascaded multilevel inverter can output high level voltage without devices in series, reduce harmonics, and output high quality waveforms.

scholarly journals Optimal placement and sizing of the storage supporting transmission and distribution networks

2016 ◽  
Vol 94 ◽  
pp. 651-659 ◽  
Author(s):  
Mahdi Motalleb ◽  
Ehsan Reihani ◽  
Reza Ghorbani
Keyword(s):  
Distribution Networks ◽  
Optimal Placement ◽  
Transmission And Distribution

scholarly journals Multi-Objective Grey Wolf Optimization for Optimal Allocation of Distributed Generators in Distribution Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 664-670
Keyword(s):  
Radial Distribution ◽  
Power Loss ◽  
Distribution Network ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Real Power ◽  
Multi Objective ◽  
Distributed Generators

The power loss in the radial distribution network is appreciable as compared to transmission network. To reduce the power loss in distribution network which is radial in nature, the solution methodology adopted in this paper is optimal placement of distributed generators (DG). The optimization incorporated is Multi-objective Grey Wolf Optimization (MOGWO). The optimization is accomplished for three different cases. In each case two objective functions are simultaneously optimized to obtain non-dominated solutions using Multi-objective Grey Wolf Optimization. Case (1): To minimize the real power loss and maximize the savings obtained due to DG installation. Case (2): To minimize real power loss and maximum voltage deviation in the network. Case (3): To minimize real power loss and rating of DG installed. MOGWO method maintains an archive which contains pareto-optimal solutions. The archive mimics the behaviour of grey wolves. MOGWO method is verified on radial distribution networks. The effectiveness of the optimization method is proven by comparing the results with other optimization methods available in the literature.

scholarly journals Voltage Control and Power Loss Reduction in Distribution Networks using Distributed Generation

2019 ◽  
Vol 8 (12) ◽  
pp. 2863-2867
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Voltage Profile ◽  
Vector Method ◽  
Index Vector ◽  
Power Loss Reduction ◽  
Whale Optimization

Distributed generation (DG) units can provide many benefits when they are incorporated along the distribution network/system. These benefits are more if DG units are connected at suitable nodes with appropriate rating otherwise, they may cause to increased power loss and poor voltage profile. In this work, optimal allocation (both location and size) problem is solved by considering power loss minimization as an objective function. An analytical method “index vector method (IVM)” is applied to find DG location. A new optimization algorithm “Whale Optimization Algorithm (WOA)” is employed to determine the DG rating. Two popularly known test systems “IEEE 33 & IEEE 69”bus systems are used to evaluate the efficacy of IVM and WOA.

scholarly journals Optimal Placement and Optimal Combination of DTC, STATCOM and Line Reconfiguration to Minimize the Power Loss in Distribution Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 11631-11636 ◽  
Keyword(s):  
Power Loss ◽  
Renewable Energy Sources ◽  
Distribution Networks ◽  
Optimal Combination ◽  
Optimal Placement ◽  
Loss Reduction ◽  
Power Loss Reduction ◽  
Network Operation ◽  
Time Variables ◽  
Simulation Package

Due to deregulation, exponential growth in the electricity demand, integration of renewable energy sources, lack of analytical computing facility and expansion of network increases the complexity with poor operation of the network. Existing analytical computing facility is failed to give efficient and accurate results for secure operation of the distribution network. Many researchers are working to give potential solution to improve the performance of network operation considering the real time variables. In this paper minimization of power loss is chosen as objective function. Considering the network parameters the optimal placements with different combination of DTC, STATCOM and line reconfiguration are tested on IEEE-15 bus system using MiPower simulation package. The obtained result shows more than 50% power loss reduction, which leads to efficient and stress free operation of the distribution networks.

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