Chaotic butterfly optimization algorithm applied to Multi objective Economic and Emission dispatch in modern power system

2020 ◽  
Vol 13 ◽  
Author(s):  
Arun Kumar Sahoo ◽  
Tapas Kumar Panigrahi ◽  
Soumya Ranjan Das ◽  
Aurobinda Behera
Keyword(s):  
Optimization Algorithm ◽  
Thermal Power ◽  
Real Life ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Chaotic Mapping ◽  
Prohibited Operating Zones ◽  
Test Systems ◽  
Emission Dispatch ◽  
Practical Constraints

Aims : To optimize the economic and emission dispatch of the thermal power plant. Background: Considering both the economic and environmental aspects, a combined approach had made to attain a solution is known as the combined economic and emission dispatch problem. The CEED problem is a nonlinear bi-objective problem with conflicting behaviour with all the practical constraints. Objective: A new optimization method is improvised by applying the chaotic mapping to the butterfly optimization algorithm. This method is applied to the Combined Economic and Emission Dispatch (CEED) problem for optimizing consumed fuel cost and produced environment pollutant. Methods: Improved Chaotic Butterfly algorithm is applied to the optimization problem to optimize combined economic and emission dispatch. Result : The proposed technique is tested for four different test systems with various practical constraints like valve point loading, ramp rate limit and prohibited operating zones. The obtained results from the chaotic butterfly optimization algorithm (CBOA) are compared with other optimization techniques provide an optimum solution for CEED problem. Conclusion : Considering the environmental impact the novel metaheuristic swarm intelligence technique is applied with conflict of interest. Different test systems, with different practical operational constraints like valve-point loading, prohibited operating zones and ramp rate limits and emission dispatch have been analyzed to validate the implementation of the proposed algorithm in real life CEED problem situations.

scholarly journals Solving non-convex economic dispatch with prohibited zones using artificial fish swarm optimization

2018 ◽  
Vol 7 (2.18) ◽  
pp. 1
Author(s):  
Jun Seog Ko ◽  
Surender Reddy Salkuti ◽  
Chan Mook Jung
Keyword(s):  
Power Plants ◽  
Search Algorithm ◽  
Thermal Power ◽  
Economic Dispatch ◽  
Ramp Rate ◽  
Swarm Optimization ◽  
Prohibited Operating Zones ◽  
Novel Approach ◽  
Artificial Fish Swarm ◽  
Practical Constraints

In this paper, a novel approach is proposed to solve the non-convex and discontinuous economic dispatch (ED) problem of power system with thermal power plants. All the practical constraints (loss constraint, generators ramp rate constraints and network constraints) are considered for solving the ED problem. Here, the proposed ED problem is solved by considering the generators with valve point loading (VPL) effects and prohibited operating zones (POZs) effects. In this paper, to solve this practical ED problem, an evolutionary based Artificial Fish Swarm Optimization Algorithm (AFSOA) is utilized. The AFSOA is a global search algorithm based on the characteristics of fish swarm and its autonomous model. The detailed algorithm with its flow chart is presented in this paper. To show the effectiveness of the proposed ED approach, 3 test systems (3, 6 and 20 generating unit systems) are considered. The obtained results are compared with other algorithms reported in the literature.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals Economic Dispatch Solution Using Moth-Flame Optimization Algorithm

2018 ◽  
Vol 214 ◽  
pp. 03007 ◽  
Author(s):  
Mohd Herwan Sulaiman ◽  
Zuriani Mustaffa ◽  
Muhammad Ikram Mohd Rashid ◽  
Hamdan Daniyal
Keyword(s):  
Optimization Technique ◽  
Economic Dispatch ◽  
Minimum Cost ◽  
Ramp Rate ◽  
Total Cost ◽  
Prohibited Operating Zones ◽  
Operating Limits ◽  
Moth Flame Optimization Algorithm ◽  
Ramp Rate Limits ◽  
Practical Constraints

This paper proposes an application of a recent nature inspired optimization technique namely Moth-Flame Optimization (MFO) algorithm in solving the Economic Dispatch (ED) problem. In this paper, the practical constraints will be included in determining the minimum cost of power generation such as ramp rate limits, prohibited operating zones and generators operating limits. To show the effectiveness of proposed algorithm, two case systems are used: 6-units and 15-units systems and then the performance of MFO is compared with other techniques from literature. The results show that MFO is able to obtain less total cost than those other algorithms.

A Probabilistic Multi-Objective Approach for Power Flow Optimization in Hybrid Wind-Based Power Systems Using Grasshopper Optimization Algorithm

2020 ◽  
Vol 11 (4) ◽  
pp. 61-86
Author(s):  
Barun Mandal ◽  
Provas Kumar Roy
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Thermal Power ◽  
Optimal Power ◽  
Valve Point Effect ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Practical Constraints

This article introduces a grasshopper optimization algorithm (GOA) to efficiently prove its superiority for solving different objectives of optimal power flow (OPF) based on a mixture thermal power plant that incorporates uncertain wind energy (WE) sources. Many practical constraints of generators, valve point effect, multiple fuels, and the various scenarios incorporating several configurations of WEs are considered for both singles along with multi-objectives for the OPF issue. Within the article, the considered method is verified on two common bus experiment systems, i.e. IEEE 30-bus as well as the IEEE 57-bus. Here, the fuel amount minimization and emission minimization are studied as the primary purposes of a GOA-based OPF problem. However, the proposed algorithm yields a reasonable conclusion about the better performance of the wind turbine. Computational results express the effectiveness of the proposed GOA approach for the secure and financially viable of the power system under various uncertainties. The comparison is tabulated with the existing algorithms to provide superior results.

scholarly journals Dynamic Economic/Environmental Dispatch Problem Considering Prohibited Operating Zones

2019 ◽  
Vol 9 (5) ◽  
pp. 4586-4590 ◽  
Author(s):  
A. Torchani ◽  
A. Boudjemline ◽  
H. Gasmi ◽  
Y. Bouazzi ◽  
T. Guesmi
Keyword(s):  
Inequality Constraints ◽  
Ramp Rate ◽  
Convex Optimization Problem ◽  
Prohibited Operating Zones ◽  
Assignment Method ◽  
Time Period ◽  
Equality And Inequality Constraints ◽  
Loading Effects ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Along with economic dispatch, emission dispatch has become a key problem under market conditions. Thus, the combination of the above problems in one problem called economic emission dispatch (EED) problem became inevitable. However, due to the dynamic nature of today’s network loads, it is required to schedule the thermal unit outputs in real-time according to the variation of power demands during a certain time period. Within this context, this paper presents an elitist technique, the second version of the non-dominated sorting genetic algorithm (NSAGII) for solving the dynamic economic emission dispatch (DEED) problem. Several equality and inequality constraints, such as valve point loading effects, ramp rate limits and prohibited operating zones (POZ), are taken into account. Therefore, the DEED problem is considered as a non-convex optimization problem with multiple local minima with higher-order non-linearities and discontinuities. A fuzzy-based membership function value assignment method is suggested to provide the best compromise solution from the Pareto front. The effectiveness of the proposed approach is verified on the standard power system with ten thermal units.

scholarly journals Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

2021 ◽  
Vol 7 (4) ◽  
pp. 64
Author(s):  
Tanguy Ophoff ◽  
Cédric Gullentops ◽  
Kristof Van Beeck ◽  
Toon Goedemé
Keyword(s):  
Computational Complexity ◽  
Object Detection ◽  
Network Optimization ◽  
Real Life ◽  
Optimization Techniques ◽  
Training Data ◽  
Single Shot ◽  
Standard Object ◽  
Number Of Classes

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).

AN EFFICIENT APPROACH FOR THE THREE-DIMENSIONAL CONTAINER PACKING PROBLEM WITH PRACTICAL CONSTRAINTS

2004 ◽  
Vol 21 (03) ◽  
pp. 279-295 ◽  
Author(s):  
ZHIHONG JIN ◽  
KATSUHISA OHNO ◽  
JIALI DU
Keyword(s):  
Three Dimensional ◽  
Real Life ◽  
Packing Problem ◽  
Utilization Rate ◽  
Benchmark Problems ◽  
Rectangular Container ◽  
Efficient Packing ◽  
High Degree ◽  
Practical Constraints ◽  
Inherent Stability

This paper deals with the three-dimensional container packing problem (3DCPP), which is to pack a number of items orthogonally onto a rectangular container so that the utilization rate of the container space or the total value of loaded items is maximized. Besides the above objectives, some other practical constraints, such as loading stability, the rotation of items around the height axis, and the fixed loading (unloading) orders, must be considered for the real-life 3DCPP. In this paper, a sub-volume based simulated annealing meta-heuristic algorithm is proposed, which aims at generating flexible and efficient packing patterns and providing a high degree of inherent stability at the same time. Computational experiments on benchmark problems show its efficiency.

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