An Effective Algorithm for MAED Problems with a New Reliability Model at the Microgrid

This paper proposes a new framework for multi-area economic dispatch (MAED) in which the cost associated with the reliability consideration is taken into account together with the common operational and emission costs using expected energy not supplied (EENS) index. To improve the reliability level, the spinning reserve capacity is considered in the model as well. Furthermore, the MAED optimization problem and non-smooth cost functions are taken into account as well as other technical limitations such as tie-line capacity restriction, ramp rate limits, and prohibited operating zones at the microgrid. Considering all the above practical issues increases the complexity in terms of optimization, which, in turn, necessitates the use of a powerful optimization tool. A new successful algorithm inspired by phasor theory in mathematics, called phasor particle swarm optimization (PPSO), is used in this paper to address this problem. In PPSO, the particles’ update rules are driven by phase angles to essentially ensure a spread of variants across the population so that exploitation and exploration can be balanced. The optimal results obtained via simulations confirmed the capability of the proposed PPSO algorithm to find suitable optimal solutions for the proposed model.

Application of BARON Solver for Solution of Cost Based Unit Commitment Problem

This paper presents the solution to cost-based unit commitment (CBUC) problem with and without ramp rate limits of thermal power plants using general algebraic modelling system (GAMS) with BARON solver. The BARON solver in GAMS environment takes care of different units and system constraints to find an optimal solution. To validate the effectiveness of the proposed GAMS solution, simulations have been performed on six different systems consisting of 10-units, 20-units, 40-units, 60-units, 80-units and 100-units, respectively. The analysis also includes the valve-point loading along with the ramp rate limits of thermal units. Results obtained with BARON solver in GAMS have been compared with other approaches available in literature. Comparative analysis shows that the performance of GAMS is better as compared to other existing techniques in terms of operating cost obtained and satisfaction level of constraints.

Teaching-Learning-Based Optimization Algorithm for the Combined Dynamic Economic Environmental Dispatch Problem

The Dynamic Economic Environmental Dispatch Problem (DEEDP) is a major issue in power system control. It aims to find the optimum schedule of the power output of thermal units in order to meet the required load at the lowest cost and emission of harmful gases. Several constraints, such as generation limits, valve point loading effects, prohibited operating zones, and ramp rate limits, can be considered. In this paper, a method based on Teaching-Learning-Based Optimization (TLBO) is proposed for dealing with the DEEDP problem where all aforementioned constraints are considered. To investigate the effectiveness of the proposed method for solving this discontinuous and nonlinear problem, the ten-unit system under four cases is used. The obtained results are compared with those obtained by other metaheuristic techniques. The comparison of the simulation results shows that the proposed technique has good performance.

Solving Power Economic Dispatch Problem with a Novel Quantum-Behaved Particle Swarm Optimization Algorithm

This paper proposes the shrink Gaussian distribution quantum-behaved optimization (SG-QPSO) algorithm to solve economic dispatch (ED) problems from the power systems area. By shrinking the Gaussian probability distribution near the learning inclination point of each particle iteratively, SG-QPSO maintains a strong global search capability at the beginning and strengthen its local search capability gradually. In this way, SG-QPSO improves the weak local search ability of QPSO and meets the needs of solving the ED optimization problem at different stages. The performance of the SG-QPSO algorithm was obtained by evaluating three different power systems containing many nonlinear features such as the ramp rate limits, prohibited operating zones, and nonsmooth cost functions and compared with other existing optimization algorithms in terms of solution quality, convergence, and robustness. Experimental results show that the SG-QPSO algorithm outperforms any other evaluated optimization algorithms in solving ED problems.

Solution of Non-Smooth Economic Dispatch Using Interactive Grouped Adaptive Bat Algorithm

This article presents the application of new grouped adaptive Bat algorithm (GABA) based metaheuristic method to improve the solution of economic dispatch (ED) problem considering valve point effect, prohibited zones, ramp rate limits and total power loss. The Bat algorithm is a new swarm intelligence algorithm inspired by the echolocation phenomenon in bats. The Bat algorithm is easy to program, and like many metaheuristic methods has an exploration and exploitation phases which require fine adjustment to achieve the near global solution. A grouped search mechanism is introduced to enhance the performances of the original Bat algorithm. The robustness of the proposed algorithm in term of solution quality and convergence characteristic have been demonstrated of three test systems of various complexities 6 units considering simultaneously the prohibited zones, ramp rate limits and total power loss, 13 and 40 units considering valve point effect. Results show clearly the efficiency and superiority of the proposed algorithm compared with various techniques reported in the recent literature.

A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems

The economic load dispatch (ELD) problem is an optimization problem of minimizing the total fuel cost of generators while satisfying power balance constraints, operating capacity limits, ramp-rate limits and prohibited operating zones. In this paper, a novel multi-population based chaotic JAYA algorithm (MP-CJAYA) is proposed to solve the ELD problem by applying the multi-population method (MP) and chaotic optimization algorithm (COA) on the original JAYA algorithm to guarantee the best solution of the problem. MP-CJAYA is a modified version where the total population is divided into a certain number of sub-populations to control the exploration and exploitation rates, at the same time a chaos perturbation is implemented on each sub-population during every iteration to keep on searching for the global optima. The proposed MP-CJAYA has been adopted to ELD cases and the results obtained have been compared with other well-known algorithms reported in the literature. The comparisons have indicated that MP-CJAYA outperforms all the other algorithms, achieving the best performance in all the cases, which indicates that MP-CJAYA is a promising alternative approach for solving ELD problems.

Economic Dispatch Solution Using Moth-Flame Optimization Algorithm

This paper proposes an application of a recent nature inspired optimization technique namely Moth-Flame Optimization (MFO) algorithm in solving the Economic Dispatch (ED) problem. In this paper, the practical constraints will be included in determining the minimum cost of power generation such as ramp rate limits, prohibited operating zones and generators operating limits. To show the effectiveness of proposed algorithm, two case systems are used: 6-units and 15-units systems and then the performance of MFO is compared with other techniques from literature. The results show that MFO is able to obtain less total cost than those other algorithms.