An alternative method to solve combined economic emission dispatch problems using flower pollination algorithm

Flower Pollination Algorithm (FPA) is a new biologically inspired meta-heuristic optimization technique based the pollination process of flowers. FPA mimics the flower pollination characteristics in order to survival by the fittest. This research presents implementation of FPA optimization in solving Combined Economic Emission Dispatch (CEED) problems in power system which minimize total generation cost by minimizing fuel cost and emission. Increasing in power demand requires effective solution to provide sufficient electricity to customer with minimum cost of operation at the same time considering emission. CEED actually is a multi-objective problem and need complex programming to solve it. The problem becomes complicated when there is practical constraints to be considered as well. To simplify the programming, objective of economic dispatch (ED) and emission dispatch (EmD) are combined into a single function by price penalty factor and analysed using weighted sum method to choose the best compromising result. In this research, the valve point loading effect problem in power system also will be considered. The proposed algorithm are tested on four different test systems which are: 6-generating unit and 11-generating unit without valve point effect with no transmission loss, 10-generating unit with having valve point effect and transmission loss, and lastly 40-generating unit with having valve point effect without transmission loss. The results of these four different test cases were compared with the optimization techniques reported in recent literature in order to observe the effectiveness of FPA. Result shows FPA able to perform better than other algorithms by having minimum fuel cost and emission

Design and Simulation of Multi Objective Power Flow Optimization in IEEE-30 Bus System using Modified Particle Swarm Optimization

Execution and Reliability of OPF algorithms is an important issue of research for gainful power structure control and orchestrating. Perfect Power Flow is driven for restricting the objective work. This objective limit can be single regarded target work or different objective limits. In the present research, we have executed perfect power stream in order to constrain the fuel cost while satisfying the constraints, for instance, the voltages, power yields of the generator kept inside embraced purpose of repression. Some other objective can be used reliant on utility's preferred position and needs. Many streamlined framework models have been combined in the past by various researchers for OPF issue, for instance, Linear Programming, Non Linear Programming, Quadratic Programming, Newton Based Techniques, Parametric Methods, and Interior Point Methods, etc. A wide variety of bleeding edge optimization methodologies like Evolutionary Programming, Genetic Algorithm, PSO Algorithm, etc are proposed recorded as a hard copy for handling OPF issue. In this proposition, we have executed improved particle swarm optimization algorithm to restrain cost limit while keeping goals inside acceptable most extreme. The adjustments in particle swarm optimization is finished by introducing the idea of quantum computing and optimization of quickening coefficients. The proposed algorithm is associated with IEEE-30 bus structure. Resuts indicated unrivaled execution of proposed algorithm as contrasted and contemporary techniques

Solving convex and non-convex static and dynamic economic dispatch problems using hybrid particle multi-swarm optimization

Problem ekonomične otpreme opterećenja ranije se uspješno rješavalo tehnikama rojeva. Međutim, elektroenergetski sustavi složenog ponašanja još uvijek čekaju razvoj robustnog algoritma za njihovu precizniju optimizaciju. Problem ekonomične otpreme uz ograničenja kao što su ograničenja generiranja, ukupna potražnja energije, ograničenja brzine pristupa i zabranjene operativne zone, čini problem složenijim za rješavanje čak i globalnim tehnikama. Za prevladavanje tih komplikacija, predlaže se novi algoritam pod nazivom Hybrid Particle Multi-Swarm Optimization (HPMSO). Predloženi algoritam ima svojstvo dubokog pretraživanja s prilično brzim odzivom. Vrednovanje učinkovitosti predloženog pristupa ispitivalo se konveksnim i ne-konveksnim funkcijama troškova uz ograničenja jednakosti i nejednakosti. Štoviše, slučajevi dinamičke ekonomične otpreme također su bili uključeni u statistička istraživanja za testiranje predloženog pristupa čak i u stvarnom vremenu. Različite studije slučaja provedene su korištenjem standardnih sustava za ispitivanje statičke i dinamičke otpreme. Usporedba predloženog pristupa i prethodnih tehnika pokazala je da se predloženim algoritmom postižu bolji rezultati.Economic Load Dispatch problem has been previously solved successfully with swarm techniques. However, power systems with complex behaviours still await a robust algorithm to be developed for their optimization more precisely. Economic Dispatch problem with constraints such as generator limits, total power demand, ramp rate limits and prohibited operating zones, makes the problem more complicated to solve even for global techniques. To overcome these complications, a new algorithm is proposed called Hybrid Particle Multi-Swarm Optimization (HPMSO). The proposed algorithm has a property of deep search with quite fast response. Convex and Non-convex cost functions along with equality and inequality constraints have been used to evaluate performance of proposed approach. Moreover, Dynamic Economic Dispatch cases have also been included in statistical studies to test the proposed approach even in real time. Different case studies have been accomplished using standard test systems of Static and Dynamic Economic Dispatch. Comparison of proposed approach and previous techniques show that the proposed algorithm has a better performance

Investigating evolutionary computation with smart mutation for three types of Economic Load Dispatch optimisation problem

The Economic Load Dispatch (ELD) problem is an optimisation task concerned with how electricity generating stations can meet their customers’ demands while minimising under/over-generation, and minimising the operational costs of running the generating units. In the conventional or Static Economic Load Dispatch (SELD), an optimal solution is sought in terms of how much power to produce from each of the individual generating units at the power station, while meeting (predicted) customers’ load demands. With the inclusion of a more realistic dynamic view of demand over time and associated constraints, the Dynamic Economic Load Dispatch (DELD) problem is an extension of the SELD, and aims at determining the optimal power generation schedule on a regular basis, revising the power system configuration (subject to constraints) at intervals during the day as demand patterns change. Both the SELD and DELD have been investigated in the recent literature with modern heuristic optimisation approaches providing excellent results in comparison with classical techniques. However, these problems are defined under the assumption of a regulated electricity market, where utilities tend to share their generating resources so as to minimise the total cost of supplying the demanded load. Currently, the electricity distribution scene is progressing towards a restructured, liberalised and competitive market. In this market the utility companies are privatised, and naturally compete with each other to increase their profits, while they also engage in bidding transactions with their customers. This formulation is referred to as: Bid-Based Dynamic Economic Load Dispatch (BBDELD). This thesis proposes a Smart Evolutionary Algorithm (SEA), which combines a standard evolutionary algorithm with a “smart mutation” approach. The so-called ‘smart’ mutation operator focuses mutation on genes contributing most to costs and penalty violations, while obeying operational constraints. We develop specialised versions of SEA for each of the SELD, DELD and BBDELD problems, and show that this approach is superior to previously published approaches in each case. The thesis also applies the approach to a new case study relevant to Nigerian electricity deregulation. Results on this case study indicate that our SEA is able to deal with larger scale energy optimisation tasks

Hybrid optimization algorithm to solve the nonconvex multiarea economic dispatch problem

In this paper, multiarea economic dispatch (MAED) problems are solved by a novel straightforward process. The solved MAED problems include transmission losses, tie-line constraints, multiple fuels, valve-point effects, and prohibited operating zones in which small, medium, and large scale test systems are involved. The methodology of tackling the problems consists in a new hybrid combination of JAYA and TLBO algorithms simultaneously to take the advantages of both to solve even nonsmooth and nonconvex MAED problems. In addition, a new and simple process is used to tackle with the interaction between areas. The objective is to economically supply demanded loads in all areas while satisfying all of the constraints. Indeed, by combining JAYA and TLBO algorithms, the convergence speed and the robustness have been improved. The computational results on small, medium, and large-scale test systems indicate the effectiveness of our proposed algorithm in terms of accuracy, robustness, and convergence speed. The obtained results of the proposed JAYA-TLBO algorithm are compared with those obtained from ten well-known algorithms. The results depict the capability of the proposed JAYA-TLBO based approach to provide a better solution.fi=vertaisarvioitu|en=peerReviewed

Emission Dispatch Problem with Cubic Function Considering Transmission Loss using Particle Swarm Optimization

In this research, authors have exploited particle swarm optimization (PSO) technique for solving the emission dispatch problem. Authors have used cubic function, instead of quadratic function, to solve emission dispatch problem to make the system more robust against nonlinearities of actual power generator. PSO with cubic function reveals better results by optimizing less emission of hazardous gases, transmission losses and showing robustness against nonlinearities than simplified direct search method (SDSM)

Application of Firefly Algorithm for Combined Economic Load and Emission Dispatch

This paper presents an application of Firefly algorithm for multi-objective optimization problem in power system. By economic load scheduling the generations of different plants can be determined such that the total operating cost is minimum. Considering the environmental impacts that grow from the emissions produced by fossil fuelled power plant, the economic dispatch that minimizes only the total fuel cost can no longer be considered as single objective. Application of Firefly algorithm in this paper is based on mathematical modelling to solve combined economic and emissions dispatch problems by a single equivalent objective function. Firefly algorithm has been applied to two realistic systems at different load conditions. Results obtained with proposed method are compared with other techniques presented in literature. Firefly algorithm is easy to implement and much superior to other algorithms in terms of accuracy and efficiency