Nitin Narang

Scheduling short-term hydrothermal generation using predator prey optimization technique

Abstract Paper presents predator–prey based optimization (PPO) technique to obtain optimal generation scheduling of short-term hydrothermal system. PPO is a part of the swarm intelligence family and is capable to solve large scale non-linear optimization problems. PPO based algorithm combines the idea of particle swarm optimization concept with predator effect that helps to, maintain diversity in the swarm and preventing premature convergence to local sub-optimal. In this paper first of all feasible solution is obtained through random heuristic search and then thermal and hydro power generations are searched for optimum hydrothermal scheduling problem using PPO. Variable elimination method is implemented to handle the equality constraint by eliminating variable explicitly. These eliminated variables are considered by penalty method restricts slack units with in limits. Slack thermal generating unit for each sub-interval handles power balance equality constraint and slack hydro units handle water equality constraint. The performance of the proposed approach is illustrated on, fixed-head and variable-head hydrothermal power systems. The results obtained from the proposed technique are compared with the existing technique. From the numerical results, it is experienced that the PPO based approach is able to provide a better solution.

Multi-objective Short-term Hydrothermal Generation Scheduling Using Predator–Prey Optimization

Abstract In this article, a predator–prey-based optimization technique is applied to obtain the scheduling of a hydrothermal system with cascaded reservoirs, minimizing economic and gaseous pollutants and emission objectives. These objectives are mutually conflicting and are equally important. Predator–prey optimization is a stochastic optimization technique based on the particle swarm optimization concept having an additional predator effect that helps to explore the search area more efficiently due to the fear created by the predator. A heuristic search technique is applied for generating an initial feasible solution. The direct substitution method is implemented to handle the equality constraints, whereby dependent variables are determined from the equality constraint. Inequality constraints of dependent variables are taken care by incorporating an additional objective function represented by the fuzzy membership index, and other variables are set to their limits on violation. Fuzzy methodology has been exploited for solving a decision-making problem involving the multiplicity of objectives and selection criterion for the best compromised solution. The solutions obtained from the proposed technique are compared with other existing techniques, and results are found to be satisfactory. The proposed method has the capability to escape from local optimum solutions due to its predator effect, and it is easy to implement.

Combined heat and power economic dispatch using integrated civilized swarm optimization and Powells pattern search method

PSEUDO code of proposed technique.Display Omitted An optimization technique that embeds CSO and Powells search (PS) method is proposed.The proposed technique is applied to solve combined heat and power dispatch problem.CSO is having attributes of PSO and society civilization algorithm.Initially, search is performed by CSO and then PS is applied to improve the solution.The test systems having valve point loading effect and POZs constraint. An integrated technique that embeds civilized swarm optimization (CSO) and Powells pattern search (PPS) method is proposed to search economic dispatch of combined heat and power (CHP) dispatch problem. In the proposed technique, CSO is selected as global search technique and PPS is undertaken as a local search technique. Civilized swarm optimization is having attributes of particle swarm optimization (PSO) and society civilization algorithm (SCA). In CSO, mutually interacting societies forms the civilization. The positions of society particles are updated through the guidance of own leader along with their best positions. The best performing particle of CSO is further improved by PPS method based on a certain set criterion. The PPS method is based on the conjugate search direction method and does not require the gradient or Hessian matrix of the function to be optimized. The CHP dispatch problem has a mutual dependency of demand and heat-power capacity of generating units, so it requires an effective constraint handling strategy. In this work, variable reduction strategy with exterior penalty method is applied to satisfy equality constraints. The proposed technique is tested on five CHP test systems considering valve-point loading effect, prohibited operating zones constraint, and transmission losses. The obtained results are compared to the results reported in the literature and found satisfactory. Further, for verification of statistical performance of the proposed technique, t-test and Wilcoxon signed rank test is also performed.

Short-term hydrothermal generation scheduling using improved predator influenced civilized swarm optimization technique

Abstract An improved predator influenced civilized swarm optimization (IPCSO) technique is proposed to solve short-term conventional hydro-thermal generation scheduling (HTGS) and profit-based HTGS (PB-HTGS) problems. In the proposed IPCSO technique, prey swarm is divided into the number of societies and each society is influenced by its own predator’s effect. In every society, prey particles interact with each other and the best performing prey particle acts as a society leader. The predator particle chases the society leader and society leader tries to escape from it. In this process, predator effect improves the exploitation capability of the algorithm by searching around the respective society leader. Further, society leader of each society interacts with each other and helps to improve the performance of society leaders. The best performing society leader becomes the leader of civilization. For HTGS problem, a multi-chain cascaded hydro model is undertaken along with consideration of water transport delay between reservoirs. The problem is formulated with due consideration of thermal unit valve point effect, prohibited operating zones on reservoir discharge rate and ramp rate limits on thermal unit power generation. The technique is tested on three HTGS systems and one PB-HTGS system. The obtained results have been compared with the results reported in the literature and found satisfactory. The statistical analysis of the results is carried out to verify the robustness of the proposed technique. Further, a nonparametric test is also applied to compare the performance of the proposed technique.

Civilized swarm optimization for combined heat and power economic emission dispatch

This paper presents civilized swarm optimization for solving combined heat and power economic emission dispatch problem. The problem is a nonlinear constrained multiobjective optimization problem. Civilized swarm optimization to handle economic emission dispatch as a true multiobjective optimization problem with equality, inequality, and feasible operating region constraints. The CSO algorithm is illustrated for the test system and the test results are compared with the real-coded genetic algorithm (RCGA), non-dominated sorting genetic algorithm II (NSGA-II), strength Pareto evolutionary algorithm 2 (SPEA2) and lexicographic optimization is to verifying the superiority of the presented approach for lower total cost and emission.

Hydro-thermal generation scheduling using integrated gravitational search algorithm and predator–prey optimization technique

Abstract In this research work, an integrated optimization technique has been proposed by coordinating gravitational search algorithm (GSA) and predator–prey optimization (PPO) in a suitable manner to improve the search capability of algorithm. The integrated technique is applied to obtain the optimum generation schedule of hydro-thermal generation system considering some of the practical constraints and transmission losses. For the hydro-thermal systems, the multi-chain hydro model has been undertaken with due consideration of water transport delay between reservoirs. In PPO algorithm, the search is performed by considering the experience of other prey particles along with the effect of predator particle. The predator effect helps to avoid any possible stagnation of global best prey on local optima due to the fear created by predator particle. In PPO algorithm, the quality of the solutions has not been considered while updating the position of prey or predator, whereas in GSA, the agent direction is computed based on the overall force, and it is proportional to the quality of the solutions. Further, GSA is memory less and agent direction is not influenced by best positions. In the proposed integrated technique, the position of agent/prey is directed by overall force around themselves, global best prey position and predator effect. The proposed integrated technique is tested on three hydro-thermal systems. A penalty-free constraint handling approach is employed to satisfy all equality and inequality constraints. The results obtained from proposed technique have been compared with the results reported with the existing technique, and it is experienced that proposed technique is able to provide a better solution with improved convergence characteristics. The statistical analysis of results is also done to measure the sensitivity and robustness of the proposed technique.

Heuristic optimization technique for hydrothermal scheduling considering pumped storage unit

In this paper presence of pumped-storage unit is considered for understanding its effect over the optimal scheduling of multi-reservoir cascaded hydrothermal Plants. In Present scenario pumped storage units are becoming a valuable part of power system plants for energy and water conservation and accomplishing the need of high power demands. To maximize the water as fuel input in hydroelectric system pumped storage units are added with an advantage of operating in generating as well as pumping mode. Hydrothermal scheduling is an important aspect and is performed to minimize the operating cost of thermal power generation but considering a pumped storage unit other practical problems are also resolved. The purpose of this research is to implement a heuristic optimization technique for optimal economic scheduling of hydrothermal units considering a pumped storage unit. In proposed heuristic optimization technique, global best solution obtained from Particle Swarm Optimization (PSO) technique is further improved by applying different mutation strategies. The modified global best solutions are compared and most improved global best solution is chosen as a final solution and used for further iterations. The feasibility and efficiency of heuristic optimization technique be validated through a test system containing four hydro plants, three thermal plants and single pumped storage unit. The results demonstrate that the heuristic optimization technique can get a better solution in comparison with PSO technique.