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This paper proposes an opposition-based greedy heuristic search (OGHS) strategy to solve multi-objective thermal power dispatch problem as a non-linear constrained optimization problem considering operating cost and pollutant emissions as competing objectives. The optimization problem is solved to find global solution, in case any one objective function is non-convex and non-differentiable. To generate initial population opposition-based learning is applied to select good candidates by exploring the search space extensively. Further, opposition-based learning is exploited for migration to maintain the diversity in the set of feasible solutions. Proposed method applies mutation strategy by perturbing the genes heuristically and seeking better one. This concept introduces parallelism and makes the algorithm always greedy for better solution. The greediness and randomness pulls the algorithm towards the global solution. The algorithm is also self sufficient without the need of tuning any parameter that effects acceleration of the algorithm. Fuzzy-theory is employed for decision-making that selects best solution from available non-inferior solutions. Feasible solution is also achieved heuristically that modifies the generation-schedule and avoids violation of operating generation limits. Proposed method has been implemented to analyze economic and multi-objective thermal power dispatch problems considering ramp-rate limits, prohibited-operating-zones, valve-point-loading effects, multiple-fuel options, environmental effects, and exact transmission losses encountered in realistic power system operation. The validity of proposed method is demonstrated on medium and large power systems. Proposed optimization technique is emerged out to compete with existing solution techniques. Wilcoxon signed-rank test for independent samples also proves the supremacy of proposed algorithm OGHS.

Harmony Search (HS) is a population based metaheuristics search algorithm inspired from the musical process of searching for a perfect state of harmony and has ability to escape from local minima, does not require differential gradients and initial value setting for the variables and free from divergence and has strong ability on exploring the regions of solution space in a reasonable time. However, it has lower exploitation ability in later period and it easily trapped into local optima and converges very slowly. To improve the exploitation ability of HS algorithm in later stage and provide global optimal solution, a memetic algorithm approach considering Harmony Search and Random Search is presented in the proposed research to solve unit commitment problem of electric power system. The proposed memetic algorithm is tested for IEEE benchmarks consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is tested for unimodal and multimodal benchmark functions and is compared with others well known evolutionary, heuristics and meta-heuristics search algorithms and it has been found that performance of proposed hybrid algorithm is much better than classical Harmony Search Algorithm and Improved Harmony Search Algorithm as well as recently developed algorithms.

This paper presents best weight pattern evaluation approach to solve multiobjective load dispatch (MOLD) problem which determines the allocation of power demand among the committed generating units, to minimize a number of objectives. Operating cost, minimal impacts on environment, active power loss, are the objectives undertaken to be minimized subject to physical and technological constraints. MOLD problem is decomposed in two stages and is solved sequentially. In first stage, a optimization problem having multiple objectives which are function of only active power generation like operating cost, gaseous pollutant emissions, is solved to get optimal dispatch of active power generation, subject to meet the active power demand, generators’ capacity constraint and transmission active power line flow limits. In second stage, the system real power loss which is a function of reactive power generation is minimized, to get optimal reactive power generation, subject to meet reactive power demand, reactive power generators’ capacity constraint and transmission reactive power line flow limits, when active power generation is known in prior from first stage. The validity of the proposed method is demonstrated on 11-bus, 17-lines and 30-bus, 41-lines IEEE power systems.

In the paper, a fuzzy decision making methodology is presented to decide the generation schedule of long-term hydrothermal problems with explicit recognition of statistical uncertainties in system production cost data, NOx emission data, system load demand and hydro reservoir water inflows. In deciding the optimal operation, three objectives operating cost, NOx emission and unsatisfied load demand over the whole of the planning period are simultaneously minimised. Specific technique is put forth to convert the stochastic models into their deterministic equivalents. The weighted minimax method is used to simulate the tradeoff relation between the conflicting objectives in the non-inferior domain. The fuzzy set theory is exploited to choose the best operating point over the tradeoff curve. An efficient decomposition technique is applied to reduce the complexity of the problem. In each subproblem, thermal generations are obtained by using simplified method and water discharges are adjusted using Conjugate gradient method. The validity and effectiveness of the method has been demonstrated by analysing a power system consisting of 3-hydro and 4-thermal plants.

A multiobjective thermal power dispatch problem is formulated using noncommensurable objectives such as operating costs and minimal emission. A sensitivity measure is chosen whereby the effects of variations in the nominal conditions describing a given multiobjective problem can be measured and incorporated as a performance index to be minimized. A nonlinear programming problem provides the framework for examining the objective constraint level in an ϵ-constant form of the multiobjective optimization problem. The dispersion index is chosen as the sensitivity measure for the investigation of the effects of random variations in the model parameters of the optimal solution. A sensitivity trade-off is exploited for the multiobjective problem that represents the trade-off between sensitivity and objective level. Validity of the method has been demonstrated by analysing a three-generator sample system.