Hybrid method for solving the non smooth cost function economic dispatch problem

This article is focused on hybrid method for solving the non-smooth cost function economic dispatch problem. The techniques were divided into two parts according to: the incremental cost rates are used to find the initial solution and bee colony optimization is used to find the optimal solution. The constraints of economic dispatch are power losses, load demand and practical operation constraints of generators. To verify the performance of the proposed algorithm, it is operated by the simulation on the MATLAB program and tests three case studies; three, six and thirteen generator units which compared to particle swarm optimization, cuckoo search algorithm, bat algorithm, firefly algorithm and bee colony optimization. The results show that the proposed algorithm is able to obtain higher quality solution efficiently than the others methods

A cuckoo search optimization scheme for non-convex economic load dispatch

This paper presents a Cuckoo Search (CS) based algorithm to solve constrained economic load dispatch (ELD) problems. The proposed methodology easily deals with non-smoothness of cost function arising due to the use of valve point effects. The performance of the algorithm has been tested on systems possessing 13 and 40 generating units involving varying degrees of complexity. The findings affirm that the method outperforms the existing techniques, and can be a promising alternative approach for solving the ELD problems in practical power system

Solving Economic Dispatch Problem with Valve-Point Effect using a Modified ABC Algorithm

This paper presents a new approach for solving economic dispatch (ED) problem with valve-point effect using a modified artificial bee colony (MABC) algorithm. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. This paper proposes a novel best mechanism algorithm based on a modified ABC algorithm, in which a new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. To demonstrate the effectiveness of the proposed method, the numerical studies have been performed for two different sample systems. The results of the proposed method are compared with other techniques reported in recent literature. The results clearly show that the proposed MABC algorithm outperforms other state-of-the-art algorithms in solving ED problem with the valve-point effect.DOI:http://dx.doi.org/10.11591/ijece.v3i3.251

Rizqi car rental booking system based on mobile applications

Rizqi Car Rental Booking System based on Mobile Application developed to enable customers to book rental cars online that can provide a wealth of information related to car rental reservations. In addition, the system was developed to facilitate the car rental information management of Rizqi Car Rental. Based on observation, this system offer a user feedback module compared to the existing system that does not offer a user feedback module. In order to make the Rizqi Car Rental Booking System based on Mobile Application, the waterfall development process model is used as a process model that acts as a guideline that covers several phases namely planning, analysis, design, and implementation. At the end of the system development, customers can make online car rental reservations that are seen to provide comfort and satisfaction to customers and help the management of Rizqi Car Rental Company booking to run smoothly

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

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

A Comparison of Heuristic Methods for Optimum Power Flow Considering Valve Point Effect

Optimum Power Flow (OPF) is one of the key considerations for planning, generation control and management of electric utility. Hence it is of major importance to solve OPF with minimum cost within reasonable computing time. This paper presents solutions of OPF with Valve Point Effect (OPF-VPE) using Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). When steam valve starts to open in a turbine it changes generation curve. The valve point effect is considered by adding sine component to the quadratic cost function for OPF-VPE. Also, penalty function is added for generator violations. The common parameters of algorithms such as population size and the iteration number are selected same values for the comparison of algorithms for solving OPFVPE. Specific parameters are stated and used for each algorithm. The heuristic algorithms are examined on IEEE-30 bus system and convergence curves are demonstrated with the system results. Performances of each algorithm are discussed as regards optimizing fuel cost, iteration time and other system results

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