In this paper, Brent method is proposed to solve DYNAMIC ECONOMIC DISPATCH (DED) problem with transmission losses. The proposed algorithm involves the selection of the values of incremental fuel costs (lambda) and then the evaluation of optimal lambda is done by Brent method. The constraint of ramp rate limits distinguishes the DED problem from traditional static ECONOMIC DISPATCH (ED) problem. The DED problem divides the entire DISPATCH period into a number of small time intervals and then static ECONOMIC DISPATCH problem is solved in each interval by incorporating the ramp rate limits. The proposed method has been tested on 6- and 15- units. The simulation results of the proposed method are compared with conventional lambda iterative method. The simulation results show that the proposed method achieves qualitative solution with less computational time than the conventional lambda iterative method.

In solving the electrical power systems ECONOMIC DISPATCH (ED) problems, the goal is to schedule the committed generating units outputs so as to meet the required load demand at the minimum operating cost while satisfying all units and system equality and inequality constraints. This paper presents a different approach to ECONOMIC DISPATCH problems with particle swarm optimization (PSO) technique. In the proposed method, in order to achieve better control on the algorithm’s exploration and exploitation capabilities, particles velocity is dependent on both particle’s fitness and time. Perturbation module is adopted to perform perturbation on some particles and provide extra diversity for it to jump out from local optima and avoid premature convergence. The proposed P-PSO method solves both the ED problem with no nconvex/no nsmooth cost functions due to valve-point loading and the ED problem that takes into account nonlinear generator characteristics such as ramp-rate limits and prohibited operating zones in the power system operation. The performance of the P-PSO method is evaluated on four different power systems, and compared with that of other effective optimization methods in terms of the solution quality and convergence characteristics. The outcome is very encouraging and suggests that the P- PSO algorithm is very efficient to solve power system ECONOMIC DISPATCH problem.

ECONOMIC DISPATCH at minimum production cost is one of the most important subjects in the power system operation, which is a complicated nonlinear constrained optimization problem. To solve the DYNAMIC ECONOMIC DISPATCH problem, it is assumed that a thermal unit commitment has been previously determined. Since DYNAMIC ECONOMIC load DISPATCH was introduced, several methods have been used to solve this problem. However, all of those methods may not be able to provide an optimal solution and usually getting stuck at a local optimal.In this paper, an Improved Particle Swarm Optimizer (IPSO) has been proposed to solve DYNAMIC ECONOMIC load DISPATCH problem. This algorithm is applied to a complex DYNAMIC ECONOMIC DISPATCH problem for 6-unit power systems with a 24-h load demand at each 1-h time intervals. Comparing IPSO results with other methods' results that reported in literature shows the ability of IPSO to reach better solutions.

DYNAMIC ECONOMIC load DISPATCH is one of the most important roles of power generation’s operation and control. It determines the optimal controls of production of generator units with predicted load demand over a certain period of time. ECONOMIC DISPATCH at minimum production cost is one of the most important subjects in the power network’s operation, which is a complicated nonlinear constrained optimization problem. Since DYNAMIC ECONOMIC load DISPATCH was introduced, several intelligent methods have been used to solve this problem. In this paper, an Improved Particle Swarm Optimizer (IPSO) and Water Cycle optimizer (WCO), as swarm-based optimization algorithms, have been proposed to solve DYNAMIC ECONOMIC load DISPATCH problem and their results compare with each other. These algorithms are applied to a DYNAMIC ECONOMIC DISPATCH problem for 6-unit power systems with a 24-h load demand at each one hour time intervals. The goal of the research is categorized in two parts; first of all, introduction of application of new heuristic method for solving ECONOMIC load DISPATCH problem and second, comparison between two swarm-based algorithms. Obtained results show that WCO is very fast and also reach to better results and minimum.

Nowadays, demand response programs (DRPs) play an important role in price reduction and reliability improvement.In this paper, an optimal integrated model for the emergency demand response program (EDRP) and DYNAMIC ECONOMIC emission DISPATCH (DEED) problem has been developed. Customer’s behavior is modeled based on the price elasticity matrix (PEM) by which the level of DRP is determined for a given type of customer. Valve-point loading effect, prohibited operating zones (POZs), and the other non-linear constraints make the DEED problem into a nonconvex and non-smooth multi-objective optimization problem. In the proposed model, the fuel cost and emission are minimized and the optimal incentive is determined simultaneously. The imperialist competitive algorithm (ICA) has solved the combined problem. The proposed model is applied on a ten units test system and results indicate the practical benefits of the proposed model. Finally, depending on different policies, DRPs are prioritized by using strategy success indices.

Grey Wolf optimizer is a well-known metaheuristic technique but it faces the problem of premature convergence, trapping in local optima and poor balance between exploration and exploitation. Hence, various modifications have been proposed over past few years. One such modification is modelling the prey position as DYNAMIC in nature as shown in GWOLF. However, still it has been observed that to get better solution, GWOLF needs to be modified too, hence an improved version of GWO (IGWO) is proposed. IGWO has the advantage of both GWO and GWOLF. In this paper, IGWO is used to solve DYNAMIC ECONOMIC DISPATCH (DED) problem. IGWO is having a better balance between exploitation and exploration for the complex problem such as DYNAMIC ECONOMIC DISPATCH (DED) taking into account of valve-point effect, transmission losses and ramp-rate limits with and without Electric Vehicles (EVs). The efficiency of the algorithm is demonstrated on solving different DED problems for 5 generator and 15 generator test systems with and without losses along with different charging profile distribution of electric vehicles. The results showcased by IGWO is compared with the other algorithm. The results obtained by IGWO algorithm adopted in solving DYNAMIC ECONOMIC DISPATCH problem is giving competitive results as compared to the results given by other algorithm present in literature.

This paper deals with the DYNAMIC ECONOMIC and emission DISPATCH during a day as an important challenge in engineering. On the other hand, renewable energy provides an undeniable contribution to the energy supply. Therefore, to create an efficient model, the probability wind energy models have been proposed. In principle, this problem has several limitations and to bring it to reality, practical and nonlinear constraints such as power balance, ramp rate, prohibited zone, non-smooth cost function, and production constraints have been considered. Since these functions i.e. emission, cost, and wind models are conflicting in nature, to solve this problem, a multi-objective virus colony search algorithm (VCS) based on Pareto theory has been proposed. To improve the performance of the virus colony search algorithm, the chaos theory has been employed that eliminates the weakness of the standard algorithm, ie the speed of convergence and increased number of iterations of the algorithm to achieve the optimal solution. Chaos theory refers to the nature of complex systems with unpredictable behavior. Using the stochastic properties of chaos system, Chaos theory can improve the quality of population distribution in search space and enhance algorithm convergence function. The fuzzy decision function is also used to select the best solution from the set of solutions. The proposed model and method are applied to different systems and in some cases are compared with other methods in the articles. The results show an improvement in the performance of the proposed algorithm. The results also show that the presence of renewable resources has reduced production costs and thus increased network security.

One of the most important issues in power systems is providing the optimal demand for electrical energy’s customers with minimal costs. This goal with non-convex formulation in real-time will be difficult to solve. In this paper, the optimal appropriation of power plants during 24 hours of a day is formulated as an optimization problem which solved by a new modified optimization algorithm. The objective is finding the optimal schedule of the online power plants over a especial time horizon while satisfy the generation unit and ramp-rate constraints. The proposed optimization algorithm work based on the guidance of best solution in the each iteration to enhance searching progress in local and global domains. In addition, by using the chaos theory the local search is enhanced. Also, effect of wind turbine with considering wind speed is investigated in the DED problem solution. The effectiveness of the proposed algorithm is demonstrated on 10-unit, 30-unit, 54-unit and 5-unit with wind power system for a period of 24 hours. The simulation results obtained by the proposed MABC algorithm are compared with the available results for other methods in the literature. In terms of solution quality, the proposed algorithm is found to be better compared to other algorithms.