Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options

This paper presents a new metaheuristic optimization algorithm, the firefly algorithm (FA), and an enhanced version of it, called chaos mutation FA (CMFA), for solving power economic dispatch problems while considering various power constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. The algorithm is enhanced by adding a new mutation strategy using self-adaptation parameter selection while replacing the parameters with fixed values. The proposed algorithm is also enhanced by a self-adaptation mechanism that avoids challenges associated with tuning the algorithm parameters directed against characteristics of the optimization problem to be solved. The effectiveness of the CMFA method to solve economic dispatch problems with high nonlinearities is demonstrated using five classic test power systems. The solutions obtained are compared with the results of the original algorithm and several methods of optimization proposed in the previous literature. The high performance of the CMFA algorithm is demonstrated by its ability to achieve search solution quality and reliability, which reflected in minimum total cost, convergence speed, and consistency

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)

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

Optimal economic dispatch for carbon capture power plants using chaos-enhanced cuckoo search optimization algorithm

Accelerated global demand for low carbon operation of power systems have stimulated interest in Low Carbon Technologies (LCTs). The increased deployment of LCTs within power systems is fundamental to the emission abatement of power system. Carbon Capture Power Plant (CCPP) technology has a good potential for future low carbon emission. Existing Economic Dispatch (ED) formulations do not consider the flexibly-operated CCPPs. Flexible operation of Carbon Capture and Storage (CCS) units transforms conventional power plants in such a way that emission output and power output could be separately controlled. The resulting CCPPs have to be optimized in order to take advantage of the incentives available in both power and carbon markets. This thesis proposes an improved mathematical modelling for flexible operation of CCPPs. The developed work possesses simple and practical variables to appropriately model the flexible operation control of the CCPPs. Using this proposed model a new emission-oriented ED formulation is developed. With this new formulation, the thesis also proposes the concept of decoupling the emission and economic outputs and then quantifies its significance for power system operations. In addition to that, a new Metaheuristic Optimization Technique (MOT) named as Chaos-Enhanced Cuckoo Search Optimization Algorithm (CECSOA) has been developed to improve global optimum result for ED problem. The algorithm has been tested using standard test systems with varying degrees of complexity. The results proved that the CECSOA is superior to the existing techniques in terms of ability to obtain global optimal points and the stability of the solutions obtained. Simulation results also showed the possibility of $1.09 million of annual operational cost savings based on a practical power system located in the Greek island of Crete by applying this methodology in comparison with conventional techniques such as Genetic Algorithm. Further results showed that for a carbon price of 20$/tCO2 and a 60% of system capacity utilization, total emission of a power system is reduced by 10.90% as compared to a “business-as-usual” scenario. In terms of optimal ED for CCPPs, results showed that for carbon prices as low as (~ 8 – 10 $/tCO2), it is economically viable to operate a post-combustion CCS unit

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

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

Optimizing Economic Load Dispatch with Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique

Recently, renewable energy (RE) has become a trend in power generation. It is slowly evolving from an alternative energy source into the main energy source. The technology is currently working as an auxiliary to the existing generators. Demands for electricity is expanding rapidly nowadays, which require generators to run near its operation limit. This activity put grieve risk to the generators. Nonetheless, the extensive analysis should be conducted upon RE integration into the existing power system. This paper assesses its economic impact on the power system. Setting up RE technology such as photovoltaic and wind turbine are costly, yet may reduce generator’s fuel cost in the long run. Thus, economic load dispatch (ELD) is conducted to compute the operating cost of power system with the integration of RE system. In this study, the operating cost represents the fuel cost of conventional fossil-fuel generators. Furthermore, a novel optimization technique namely Differential Evolution Immunized Ant Colony Optimization is proposed as the optimization engine. Comparative studies are conducted to assess the performance of the proposed approach