The optimization field has grown tremendously, and new optimization techniques are developed based on statistics and evolutionary procedures. Therefore, it is necessary to identify a suitable optimization technique for a particular application. In this work, Black Widow Optimization (BWO) algorithm is introduced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch (MAED). The BWO is implemented for two different-scale test systems, comprising 16 and 40 units with three and four areas. The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy???s efficacy. Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h, whereas it is 121,589$/h for four areas with 40 units using BWO. It is also noted that optimization algorithms other than BWO require higher cost value. The best-optimized solution for emission is achieved at 9.2784e+06 tones/h, and it is observed that there is a considerable difference between the worst and the best values. Also, the suggested technique is implemented for large-scale test systems successfully with high precision, and rapid convergence occurs in MAED.

Purpose The ever-stringent environmental regulations force power producers to produce electricity at the cheapest price and with minimum pollutant emission levels. The electrical power generation from fossil fuel releases several contaminants into the air, and this becomes excrescent if the generating unit is fed by multiple fuel sources (MFSs). Inclusion of this issue in operational tasks is a welcome perspective. This paper aims to develop a multi-objective model comprising total fuel cost and pollutant emission. Design/methodology/approach The cost-effective and environmentally responsive power system operations in the presence of MFSs can be recognised as a multi-objective constrained optimisation problem with conflicting operational objectives. The complexity of the problem requires a suitable optimisation tool. Ant lion algorithm (ALA), the most recent nature-inspired algorithm, was used as the main optimisation tool because of its salient characteristics. The fuzzy decision-making mechanism has been integrated to determine the best compromised solution in the multi-objective framework. Findings This paper is the first to propose a more precise and practical operational model for studying a multi-fuel power dispatch scenario considering valve-point effects and CO2 emission. The modern meta-heuristic algorithm ALA is applied for the first time to address the economic operation of thermal power systems with multiple fuel options. Practical implications Power companies aim to make profit by abiding by the norms of the regulatory board. To achieve economic benefits, the power system must be analysed using an accurate operational model. The proposed model integrates total fuel cost, valve-point loadings and CO2 emission, which are prevailing power system operational objectives. The economic advantages of the operational model can be observed through economic deviation indices, and the performed analysis validates that the developed model corresponds to the actual power operation. Originality/value The realistic operational model is proposed by considering total fuel and pollutant emission, and the ALA is applied for the first time to address the proposed multi-objective problem. To validate the effectiveness of ALA, it is implemented in standard test systems with varying generating units (10-100) and the IEEE 30 bus system, and various kinds of power system operations are performed. Moreover, the comparison and performance analysis confirm that the current proposal is found enhanced in terms of solution quality.

Due to the ever stringent environmental regulations, the power producers have been forced to produce electric power at the least price and minimum level of emissions. The generation electrical power from fossil fuel releases several impurities into atmosphere and this become excrescent if the generating unit is fueled with Multiple Fuel Sources (MFS). Inclusion of this issue in the operational task is a welcome perspective. Nevertheless numerous published reports deal only the cost effective operation, this work proposes a more accurate and practical operational model considering valve-point effects, CO2 emission and MFS. This power system operational problem is constructed as a multi-objective non-linear optimization problem which considers conflicting objectives. To address this problem, the modern nature inspired algorithm called Ant Lion Optimizer (ALO) has been chosen as the primal optimizer. The fuzzy decision making mechanism is adopted to determine the best compromised solution in multi-objective framework. Further, the ALO is applied to solve the operational problem taking into consideration the MFS and tie line capacity between different areas of the power system. To validate the effectiveness of ALO, it is implemented on the standard test system comprises of 10 generating units and various kinds of power system operations are performed. Moreover, the comparison and performance analysis confirm that the current proposal is found enhanced in terms of solution quality.

The electrical power generation from fossil fuel releases several contaminants into the air and this become excrescent if the generating unit is fed by Multiple Fuel Sources (MFS). The ever more stringent environmental regulations have forced the power producers to produce electricity not only at the cheapest price but also at the minimum level of pollutant emissions. Inclusion of this issue in the operational task is a welcome perspective. The cost effective and environmental responsive power system operations in the presence of MFS can be recognized as a multi-objective constrained optimization problem with conflicting operational objectives. The modern meta-heuristic algorithm namely, Ant Lion Optimizer (ALO) has been applied for the first time to obtain the feasible solution. The fuzzy decision-making mechanism has been integrated to determine the Best Compromise Solution (BCS) in the multi-objective framework. The intended algorithm is implemented on the standard test systems considering valve-point effects, CO2 emission and tie-line limits.

The Combined Heat and Power Dispatch (CHPD) is an important optimization task in power system operation for allocating generation and heat outputs to the committed units. This paper presents a Grey Wolf Optimization (GWO) algorithm for CHPD problems. The effectiveness of the proposed method is validated by carrying out extensive tests on three different CHPD problems such as static economic dispatch, environmental-economic dispatch and dynamic economic dispatch. Valve-point effects, ramp-rate limits and spinning reserve constraint along with network loss are considered. Standard test systems containing 4, 7, 11 and 24 units are used for demonstration purpose. To validate the performance of the GWO, statistical measures like best, mean, worst, standard deviation, epsilon, iter and sol-iter over 50 independent runs are taken. The simulation experiments reveal that GWO performs better in terms of solution quality and consistency. (C) 2015 Elsevier Ltd. All rights reserved.

This paper has developed a metaheuristic-based methodology to commit and allocate the load demand among the running dual heat and power sources. The proposed two-stage process using Grey Wolf Algorithm (GWA) engages suitable heat and power units and performs the combined dispatch. The method was tested and compared to demonstrate its effectiveness with the support of test systems containing 11, 22, 66 and 88 and a large system containing 110 units. The numerical results revealed that the proposed method can find a solution to the best fit in the Feasible Operating Region (FOR), and moreover, it is cost-effective with respect to other algorithm results.