Optimal Allocation of CHP-Based Distributed Generation on Urban Energy Distribution Networks

2014 ◽  
Vol 5 (1) ◽  
pp. 246-253 ◽  
Author(s):  
Xianjun Zhang ◽  
George G. Karady ◽  
Samuel T. Ariaratnam
Keyword(s):  
Energy Distribution ◽  
Distributed Generation ◽  
Optimal Allocation ◽  
Distribution Networks ◽  
Urban Energy

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 Allocation of Combined Renewable Distributed Generation and Capacitor Units for Interconnection Cost Reduction

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Saad Ouali ◽  
Abdeljebbar Cherkaoui
Keyword(s):  
Distributed Generation ◽  
Network Topology ◽  
Distribution System ◽  
Optimal Allocation ◽  
Reactive Power ◽  
Distribution Networks ◽  
Cost Evaluation ◽  
Renewable Generation ◽  
Voltage Distribution ◽  
Medium Voltage

In this paper, a new methodology for the optimal investment in distributed generation is presented, based on an optimal allocation of combined DG and capacitor units to alleviate network voltage constraints and reduce the interconnection cost of renewable generation integration in public medium voltage distribution networks. An analytical optimization method is developed, with the inclusion of practical considerations that are typically neglected in developed works: network topology reconfiguration and the geographical data of the generation land-use and network infrastructure. Powerful results concluded from a sensitivity analysis study of the most impacted parts of the network by the variation of active and reactive power injection under network topology reconfiguration are used as a basis for capacitor units placement. A case study, with two meshed IEEE 15-bus feeders and a new DG to connect, geographical dispersed, are used to simulate the performance of the proposed approach. A cost evaluation of the obtained results proves the effectiveness of the proposed approach to reduce the required charges for connecting new renewable generation units in medium voltage distribution system.

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 Optimal Allocation of Intermittent Distributed Generation under Active Management

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2608 ◽  
Author(s):  
Zhong Shi ◽  
Zhijie Wang ◽  
Yue Jin ◽  
Nengling Tai ◽  
Xiuchen Jiang ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Optimal Allocation ◽  
Programming Model ◽  
Distribution Network ◽  
Distribution Networks ◽  
Active Management ◽  
Management Mode ◽  
Distributed Power ◽  
Primal Dual ◽  
Upper Level

In recent years, distributed generation (DG) has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. At present, most distributed generators follow the principle of “installation is forgetting” after they are connected to a distribution network. This principle limits the popularization and benefit of distributed generation to a great extent. In order to solve these problems, this paper presents a two-tier model for optimal allocation of distributed power sources in active distribution networks (ADN). The objective of upper level planning is to minimize the annual comprehensive cost of distribution networks, and the objective of lower level planning is to minimize the active power cut-off of distributed generation through active management mode. Taking into account the time series characteristics of load and distributed power output, the improved K-means clustering method is used to cluster wind power and the photovoltaic output in different scenarios to get the daily curves in typical scenarios, and a bilevel programming model of distributed generation based on multiscenario analysis is established under active management mode. The upper level programming model is solved by Quantum genetic algorithm (QGA), and the lower level programming model is solved by the primal dual interior point method (PDIPM). The rationality of the model and the effectiveness of the algorithm are verified by simulation and analysis of a 33-bus distribution network.

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