Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm
TL;DR: A new evolutionary method called Teaching–Learning Based Optimization (TLBO) algorithm has been presented, which is modified and used in this paper to find the best sites to connect DG systems in a distribution network, choosing among a large number of potential combinations.
About: This article is published in International Journal of Electrical Power & Energy Systems.The article was published on 2013-09-01. It has received 282 citations till now.
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TL;DR: In order to get the final optimal solution in the real-world multi-objective optimization problems, trade-off methods including a priori methods, interactive methods, Pareto-dominated methods and new dominance methods are utilized.
377 citations
TL;DR: This study identifies future research opportunities in relation to challenges for optimal ESS placement planning, development and implementation issues, optimisation techniques, social impacts, and energy security.
Abstract: The deployment of energy storage systems (ESSs) is a significant avenue for maximising the energy efficiency of a distribution network, and overall network performance can be enhanced by their optimal placement, sizing, and operation. An optimally sized and placed ESS can facilitate peak energy demand fulfilment, enhance the benefits from the integration of renewables and distributed energy sources, aid power quality management, and reduce distribution network expansion costs. This paper provides an overview of optimal ESS placement, sizing, and operation. It considers a range of grid scenarios, targeted performance objectives, applied strategies, ESS types, and advantages and limitations of the proposed systems and approaches. While batteries are widely used as ESSs in various applications, the detailed comparative analysis of ESS technical characteristics suggests that flywheel energy storage (FES) also warrants consideration in some distribution network scenarios. This research provides recommendations for related requirements or procedures, appropriate ESS selection, smart ESS charging and discharging, ESS sizing, placement and operation, and power quality issues. Furthermore, this study identifies future research opportunities in relation to challenges for optimal ESS placement planning, development and implementation issues, optimisation techniques, social impacts, and energy security.
373 citations
TL;DR: In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO-ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) on distribution systems.
303 citations
TL;DR: Fireworks Algorithm is used to simultaneously reconfigure and allocate optimal DG units in a distribution network using a new swarm intelligence based optimization algorithm conceptualized using the fireworks explosion process of searching for a best location of sparks.
281 citations
TL;DR: In this paper, a review of the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers is presented, and an attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal size and siting of DG in distribution network.
Abstract: To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although well-matched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more meta-heuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers. An attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal sizing and siting of DG in distribution networks. The present study can contribute meaningful knowledge and assist as a reference for investigators and utility engineers on issues to be considered for optimal sizing and siting of DG units in distribution systems.
266 citations
Cites background or methods from "Optimal distributed generation loca..."
...Modified teaching learning based optimization (MTLBO) and EP Reduce the system losses [38]...
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...The algorithm has several advantages over other algorithm in terms of convergence speed, accuracy and stability implemented by [38]....
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...Modified Teaching Learning Based Optimization (MTLBO) algorithm Reliable, accurate and robust and outstanding performance for global optimization Expensive, suffers from premature convergence, slow convergence rate for multi-objective problems [38]...
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References
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TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.
Abstract: A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.
3,357 citations
TL;DR: An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions.
1,359 citations
TL;DR: In this article, an analytical expression to calculate the optimal size and an effective methodology to identify the corresponding optimum location for DG placement for minimizing the total power losses in primary distribution systems is proposed.
1,060 citations
TL;DR: In this article, the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system has been investigated to obtain the maximum potential benefits.
Abstract: Power system deregulation and the shortage of transmission capacities have led to increased interest in distributed generation (DG) sources. Proper location of DGs in power systems is important for obtaining their maximum potential benefits. This paper presents analytical methods to determine the optimal location to place a DG in radial as well as networked systems to minimize the power loss of the system. Simulation results are given to verify the proposed analytical approaches.
1,042 citations
TL;DR: A novel hybrid Genetic Algorithm (GA) / Particle Swarm Optimization (PSO) for solving the problem of optimal location and sizing of DG on distributed systems is presented to minimize network power loss and better voltage regulation in radial distribution systems.
920 citations