D.P. Kothari

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.

Synergic predator-prey optimization for economic thermal power dispatch problem

Synergic PPO optimization algorithm is proposed to solve ELD problems.The SPPO blends the psychology with preys behavior to avoid predator.Small, medium and large size test power system are simulated.Multi-fuel test power system with transmission losses is also tested.Newton-Raphson procedure is applied to obtain transmission losses. This paper introduces a synergic predator-prey optimization (SPPO) algorithm to solve economic load dispatch (ELD) problem for thermal units with practical aspects. The basic PPO model comprises prey and predator as essential components. SPPO uses collaborative decision for movement and direction of prey and maintains diversity in the swarm due to fear factor of predator, which acts as the baffled state of preys mind. In the SPPO, the decision making of prey is bifurcated into corroborative and impeded parts. It comprises four behaviors namely inertial, cognitive, collective swarm intelligence, and preys individual and neighborhood concern of predator. The prey particle memorizes its best and not-best positions as experiences. In this research work, to improve the quality of prey swarm, which influence convergence rate, opposition based initialization is used. To verify robustness of proposed algorithm general benchmark problems and small, medium, and large power generation test power system are simulated. These test systems have non-linear behavior due to multi-fuel options and practical constraints. The constraints of prohibited operating zone and ramp rate limits of power generators are handled using heuristics. Newton-Raphson procedure is exploited to attain the transmission losses using load flow analysis. The outcomes of SPPO are compared with the results described in literature and are found satisfactory.

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.

Multi-objective thermal power load dispatch using chaotic differential evolutionary algorithm and Powell’s method

In this work, a chaotic differential evolutionary and Powell’s pattern search (CDEPS) algorithm is proposed to solve multi-objective thermal power load dispatch (MTPLD) problem. The chaotic differential evolutionary method is responsible for the diversification, and Powell’s pattern search is dedicated to exploitation. Further, the performance of two CDEPS variants based on Gauss map and Tent map is investigated. The proposed MTPLD solution procedure either identifies a solution close to Pareto front or diversifies the existing Pareto frontier and finally selects a suitable compromising solution among the available options. In order to select the best compromising solution, a combination of surrogate worth trade-off approach and fuzzy theory is proposed as choice of objectives is ambiguous. The uniformity of Pareto front is evaluated by exploiting a quality measure approach. The performance analysis is done using generalized benchmark test functions and complex MTPLD problems. The ability of CDEPS to diversify Pareto front is verified by uniformity analysis of Pareto front. The one-sample Wilcoxon’s test and two-sample Mann Whitney’s test are used to analyze the experimental results. The exhaustive analysis shows that the Tent map-based CDEPS has better ability to generate quality generation schedule with uniform Pareto front quality and faster convergence rate.

Optimal location of Distributed Generation using micro-genetic algorithm

The introduction of Distributed Generation (DG) devices for power system increases the stability, reduction in losses and increase in the cost of generation. In this paper Micro Genetic Algorithm (MGA) a non conventional optimization technique is used to optimize the various parameters. The various parameters taken into consideration are their type, location and size of the DG devices. The simulation on a distribution system with steady state basis was performed by modelling DG with different types. The results are compared and justified with another search method like Micro Genetic Algorithm (MGA). The results reveal the benefits of this method, which makes it challenging for solving simultaneous optimization problems of DG device in a power system network.

Security Constrained Stochastic Multi-objective Optimal Power Dispatch

A stochastic multi-objective line security constrained problem is formulated to minimize non-commensurable objectives viz. operating cost, polluting gaseous emission and variance of active power generation and reactive power generation, with explicit recognition of statistical uncertainties in the thermal power generation cost coefficients, gaseous emission coefficients, power demands and hence power generations and bus voltages, which are considered random variables. Specific technique is put forth to convert the stochastic models into their respective deterministic equivalents. Fuzzy set theory has been exploited to evaluate the different objectives that are quantified by defining their membership functions. Security of transmission lines with respect to expected active power flow is considered in the form of fuzzy objective function. The solution set of such formulated problems is non-inferior due to contradictions among the objectives undertaken. The weighting method is used to simulate the trade-off relationship between the conflicting objectives in the non-inferior domain. Generally, the weights are either simulated or searched in the non-inferior domain. In the paper Evolutionary search technique is implemented to search the x91preferredx92 weightage pattern in the non-inferior domain, which corresponds to the x91bestx92 compromised solution. Among the non-inferior solutions, the system operator selects the x91preferredx92 optimal operating point that provides maximum satisfaction level of the most under achieved objective in terms of membership function and is termed as fitness function. The validity of the proposed method has been demonstrated on an IEEE system comprising of 11-nodes, 17-lines and 5-generators.

Multiobjective thermal power load dispatch using adaptive predator–prey optimization

Abstract This paper presents an adaptive predator–prey optimization (APPO) to solve multiobjective thermal power dispatch problem considering objectives of operating cost and pollutant emission. In APPO the fear factor from a predator is a function of cognitive and social behavior of prey. It ensures continuous mobility (magnitude and direction) of prey, resulting in better diversification of solutions. The velocity of prey is maintained in the limits by recognizing the reinforcement and inhibition aspects. The multiobjective optimization problem is handled by weighting method, whereby the weight pattern assigned to the objectives has been undertaken as a decision variable. This results in non-inferior solutions at each swarm move. In order to select a best-compromised solution, the fuzzy theory is used. The performance of the proposed algorithm is investigated on six power system test problems. The proposed method provides better results in terms of lesser fuel cost and pollutant emission. The better satisfaction level of conflicting objectives, well distributed Pareto front, acceptable solution in a single trial run and insensitivity to parameter variations are observed in comparison to other existing methods reported in the literature.

Non-interactive approach to solve multi-objective thermal power dispatch problem using composite search algorithm

Abstract This paper deals with a non-interactive approach to solve multi-objective thermal power load dispatch (MTPLD), where either decision maker is not involved or preference information is available in prior. To reduce the computational complexities due to generation of Pareto-front and selection of satisficing solution, this paper adopts no-preference approach. A satisficing function to resolve the conflict of non-commensurable objectives is proposed, which reformulates the MTPLD problem as scalar thermal power load dispatch (MTPLD) problem. Owing to ambiguous or vague in objectives, the proposed method exploits fuzzy theory. MTPLD problems’ satisfying solution is obtained by implementing hybrid chaotic differential evolution algorithm and Powell’s pattern search algorithm (CDEPS). The chaotic differential evolution algorithm is responsible for the diversification of feasible solutions and provides global solution. Whereas Powell’s pattern search, method improves the exploitation by performing local search. The paper investigates the performance of two CDEPS variants based on Gauss map and Tent map, respectively. The performance of the proposed solution procedure is analyzed using generalized benchmark test functions and complex MTPLD problems. The exhaustive analysis using non-parametric significance test and descriptive statistics shows that the Tent map based CDEPS solution procedure has better ability to generate quality generation schedule and faster convergence rate.

Integrated particle swarm optimization variants for economic load dispatch problem

According to no free lunch theorem, a single search technique cannot perform best under different conditions. The integration of principally similar search techniques is one of the option, that has been explored to effectively investigate the search area. In order to avoid stagnation at local minima and to enhance the search capability of particle swarm optimization, this paper proposes a technique that integrates predator prey optimization and anti-predatory particle swarm optimization. The integrated algorithm utilizes the capability of particle swarm optimization to find high quality solution, the capability of anti predatory particle swarm optimization to avoid search in worst solution regions and capability of predator prey optimization to escape from local minima under the effect of predator. The proposed algorithm has been implemented to solve economic dispatch problem. The different practical constraints such as ramp rate limits, prohibited operating zone(s) along with power balance constraint and generator limit are undertaken. The multiple fuel system is considered with transmission losses. The proposed algorithm is tested on various models of ED problems and compared with results reported in literature and found satisfactory.