Dynamic economic emission dispatch of thermal-wind-solar system with constriction factor-based particle swarm optimization algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soudamini Behera ◽  
Sasmita Behera ◽  
Ajit Kumar Barisal ◽  
Pratikhya Sahu
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Dynamic Load ◽  
Thermal Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Content Type ◽  
Emission Dispatch ◽  
Operation Cost

Purpose Dynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA). Design/methodology/approach Different model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically. Findings The efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established. Research limitations/implications Cost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution. Practical implications The proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible. Social implications As the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact. Originality/value In this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.

Economic emission dispatch using an advanced particle swarm optimization technique

2019 ◽  
Vol 16 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Hamid Rezaie ◽  
Mehrdad Abedi ◽  
Saeed Rastegar ◽  
Hassan Rastegar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Content Type ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Purpose This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems. Design/methodology/approach The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators. Findings The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem. Originality/value The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

Comparative analysis of lunar capture braking method based on particle swarm optimization

2019 ◽  
Vol 91 (4) ◽  
pp. 558-566
Author(s):  
Chengchao Bai ◽  
Jifeng Guo ◽  
Wenyuan Zhang ◽  
Tianhang Liu ◽  
Linli Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Comparative Analysis ◽  
Fuel Consumption ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Lunar Exploration ◽  
Swarm Optimization ◽  
Capture Process ◽  
Content Type ◽  
Advantages And Disadvantages

Purpose The purpose of this paper is to verify the feasibility of lunar capture braking through three methods based on particle swarm optimization (PSO) and compare the advantages and disadvantages of the three strategies by analyzing the results of the simulation. Design/methodology/approach The paper proposes three methods to verify capture braking based on PSO. The constraints of the method are the final lunar orbit eccentricity and the height of the final orbit around the Moon. At the same time, fuel consumption is used as a performance indicator. Then, the PSO algorithm is used to optimize the track of the capture process and simulate the entire capture braking process. Findings The three proposed braking strategies under the framework of PSO algorithm are very effective for solving the problem of lunar capture braking. The simulation results show that the orbit in the opposite direction of the trajectory has the most serious attenuation at perilune, and it should consume the least amount of fuel in theoretical analysis. The methods based on the fixed thrust direction braking and thrust uniform rotation braking can better ensure the final perilune control accuracy and fuel consumption. As for practice, the fixed thrust direction braking method is better realized among the three strategies. Research limitations/implications The process of lunar capture is a complicated process, involving effective coordination between multiple subsystems. In this article, the main focus is on the correctness of the algorithm, and a simplified dynamic model is adopted. At the same time, because the capture time is short, the lunar curvature can be omitted. Furthermore, to better compare the pros and cons of different braking modes, some influence factors and perturbative forces are not considered, such as the Earth’s flatness, light pressure and system noise and errors. Practical implications This paper presents three braking strategies that can satisfy all the constraints well and optimize the fuel consumption to make the lunar capture more effective. The results of comparative analysis demonstrate that the three strategies have their own superiority, and the fixed thrust direction braking is beneficial to engineering realization and has certain engineering practicability, which can also provide reference for lunar exploration orbit design. Originality/value The proposed capture braking strategies based on PSO enable effective capture of the lunar module. During the lunar exploration, the capture braking phase determines whether the mission will be successful or not, and it is essential to control fuel consumption on the premise of accuracy. The three methods in this paper can be used to provide a study reference for the optimization of lunar capture braking.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

scholarly journals An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4480
Author(s):  
Qun Niu ◽  
Han Wang ◽  
Ziyuan Sun ◽  
Zhile Yang
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Thermal Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Solar Thermal Power ◽  
Bare Bone

Solar energy has many advantages, such as being abundant, clean and environmentally friendly. Solar power generation has been widely deployed worldwide as an important form of renewable energy. The solar thermal power generation is one of a few popular forms to utilize solar energy, yet its modelling is a complicated problem. In this paper, an improved bare bone multi-objective particle swarm optimization algorithm (IBBMOPSO) is proposed based on the bare bone multi-objective particle swarm optimization algorithm (BBMOPSO). The algorithm is first tested on a set of benchmark problems, confirming its efficacy and the convergency speed. Then, it is applied to optimize two typical solar power generation systems including the solar Stirling power generation and the solar Brayton power generation; the results show that the proposed algorithm outperforms other algorithms for multi-objective optimization problems.

scholarly journals Combined Economic and Emission Dispatch Problem of Wind-Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Shanhe Jiang ◽  
Chaolong Zhang ◽  
Wenjin Wu ◽  
Shijun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm Optimization Algorithm ◽  
Thermal Power ◽  
Particle Swarm ◽  
Compromise Solution ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Thermal Power System ◽  
Emission Dispatch

In this paper, a novel hybrid optimization approach, namely, gravitational particle swarm optimization algorithm (GPSOA), is introduced based on particle swarm optimization (PSO) and gravitational search algorithm (GSA) to solve combined economic and emission dispatch (CEED) problem considering wind power availability for the wind-thermal power system. The proposed algorithm shows an interesting hybrid strategy and perfectly integrates the collective behaviors of PSO with the Newtonian gravitation laws of GSA. GPSOA updates particle’s velocity caused by the dependent random cooperation of GSA gravitational acceleration and PSO velocity. To describe the stochastic characteristics of wind speed and output power, Weibull-based probability density function (PDF) is utilized. The CEED model employed consists of the fuel cost objective and emission-level target produced by conventional thermal generators and the operational cost generated by wind turbines. The effectiveness of the suggested GPSOA is tested on the conventional thermal generator system and the modified wind-thermal power system. Results of GPSOA-based CEED problems by means of the optimal fuel cost, emission value, and best compromise solution are compared with the original PSO, GSA, and other state-of-the-art optimization approaches to reveal that the introduced GPSOA exhibits competitive performance improvements in finding lower fuel cost and emission cost and best compromise solution.

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

A novel particle swarm optimization based robust H-infinity control for rotorcrafts

2014 ◽  
Vol 31 (4) ◽  
pp. 726-741 ◽  
Author(s):  
Jiyang Dai ◽  
Jin Ying ◽  
Chang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Mutation ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
H Infinity ◽  
Content Type ◽  
Modified Particle Swarm Optimization ◽  
H Infinity Control

Purpose – The purpose of this paper is to present a novel optimization approach to design a robust H-infinity controller. Design/methodology/approach – To use a modified particle swarm optimization (PSO) algorithm and to search for the optimal parameters of the weighting functions under the circumstance of the given structures of three weighting matrices in the H-infinity mixed sensitivity design. Findings – This constrained multi-objective optimization is a non-convex, non-smooth problem which is solved by a modified PSO algorithm. An adaptive mutation-based PSO (AMBPSO) algorithm is proposed to improve the search accuracy and convergence of the standard PSO algorithm. In the AMBPSO algorithm, the inertia weights are modified as a function with the gradient descent and the velocities and positions of the particles. Originality/value – The AMBPSO algorithm can efficiently solve such an optimization problem that a satisfactory robust H-infinity control performance can be obtained.

scholarly journals Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 226-231
Author(s):  
Vijayakumar T ◽  
Vinothkanna R
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Load Dispatch ◽  
Electric Power Generation ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

Reduction of emission and energy conservation plays a major role in the current power system for realizing sustainable socio-economic development. The application prospects and practical significance of economic load dispatch issue in the electric power market is remarkable. The various generating sets must be assigned with load capacity in a reasonable manner for reducing the cost of electric power generation. This problem may be overcome by the proposed modified particle swarm optimization (PSO) algorithm. The practical issue is converted and modelled into its corresponding mathematical counterpart by establishing certain constraints. Further, a novel interdependence strategy along with a modified PSO algorithm is implemented for balancing the local search capability and global optimization. Multiple swarms are introduced in the modified PSO algorithm. Certain standard test functions are executed for specific analysis. Finally, the proposed modified PSO algorithm can optimize the economic load dispatch problem while saving the energy resources to a larger extent. The algorithm evaluation can be performed using real-time examples for verifying the efficiency. When compared to existing schemes like artificial bee colony (ABC), genetic algorithms (GAs), and conventional PSO algorithms, the proposed scheme offers lowest electric power generation cost and overcomes the load dispatch issue according to the simulation results.

An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory

Kybernetes ◽  
2016 ◽  
Vol 45 (2) ◽  
pp. 210-222 ◽  
Author(s):  
Qichang Duan ◽  
Mingxuan Mao ◽  
Pan Duan ◽  
Bei Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Memory Factor ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

Purpose – The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed. Design/methodology/approach – In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching. Findings – The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm. Research limitations/implications – Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved. Practical implications – The proposed algorithm can be used to many practical engineering problems such as tracking problems. Social implications – The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent. Originality/value – The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree.

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