Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm

Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques

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

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

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

Aplikasi sistem penuaian air hujan (SPAH) di kawasan perumahan

Pada masa kini, kekurangan sumber air bersih merupakan satu ancaman di kebanyakkan negara, dan tidak terkecuali Malaysia. Keadaan ini adalah disebabkan oleh kaedah penggunaan sumber air yang tidak berkesan serta kemusnahan kawasan tadahan air disebabkan oleh pembangunan pesat yang tidak mematuhi peraturan. Sebagai alternatif untuk mengatasi masalah ini, Sistem Penuaian Air Hujan (SPAH) telah diperkenalkan sebagai kaedah pengurusan terbaik dalam amalan pengurusan air yang berkesan di Malaysia. Sistem ini bertujuan untuk melambatkan aliran air larian permukaan dan untuk menggalakkan penggunaan air secara efisien. Kajian ini dijalankan adalah untuk menentukan pelaksanaan Sistem Penuaian Air Hujan (SPAH), serta untuk menilai keberkesanan pelaksanaan Sistem Penuaian Air Hujan (SPAH). Hasil dapatan kajian ini adalah berdasarkan data yang dikumpul melalui siri-siri temubual yang dilaksanakan dengan enam orang responden iaitu Pemaju harta tanah, Pihak Berkuasa Tempatan, dan Kontraktor. Dapatan kajian menunjukkan bahawa tangki Penuaian Air Hujan yang sesuai untuk unit kediaman adalah dianggarkan bersaiz 200 gelen (50-60m3), dan jumlah kos pemasangan adalah RM 3000.00 seunit. Merujuk kepada keberkesanan pelaksanaan Sistem Penuaian Air Hujan (SPAH), majoriti responden berpuashati bahawa sistem yang digunakan memadai untuk mengawal masalah pembaziran gunaan air serta mampu menjimatkan penawaran air bersih. Diharapkan supaya dapatan kajian ini akan membantu untuk menyeimbangkan kegunaan air dan keperluan pembangunan air untuk generasi akan datang

Economic Load Dispatch Using Bacterial Foraging Technique with Particle Swarm Optimization Biased Evolution

This paper presents a novel modified bacterial foraging technique (BFT) to solve economic load dispatch (ELD) problems. BFT is already used for optimization problems, and performance of basic BFT for small problems with moderate dimension and searching space is satisfactory. Search space and complexity grow exponentially in scalable ELD problems, and the basic BFT is not suitable to solve the high dimensional ELD problems, as cells move randomly in basic BFT, and swarming is not sufficiently achieved by cell-to-cell attraction and repelling effects for ELD. However, chemotaxis, swimming, reproduction and elimination-dispersal steps of BFT are very promising. On the other hand, particles move toward promising locations depending on best values from memory and knowledge in particle swarm optimization (PSO). Therefore, best cell (or particle) biased velocity (vector) is added to the random velocity of BFT to reduce randomness in movement (evolution) and to increase swarming in the proposed method to solve ELD. Finally, a data set from a benchmark system is used to show the effectiveness of the proposed method and the results are compared with other methods