Application of Kalman Filtering for PV Power Prediction in Short-Term Economic Dispatch

The aim of this thesis is to predict the short-term power production of PhotoVoltaic (PV) power plants for the economic dispatch problem with the help of Kalman filtering. The Economic Dispatch (ED) problem in power systems is known as an optimization problem in which the cost of producing energy to reliably supply consumers is minimized, hence the power production is assigned to all the generating units that are dispatchable. Because of the generation cost of renewable energy such as PV is relatively low, it is advantageous to utilize. However, these resources are intermittent. These renewable resources bring a lot of uncertainty into the power system, their power cannot be pre-specified due to their weather dependent properties and therefore it is a big challenge to include them in the ED problem.;For this reason, the work in this thesis will focus on developing a predictive model built on Kalman Filtering for the short-term PV prediction. The model first predicts the solar irradiance and temperature based on an initial guess at each time period. Then, the Kalman filter will refine the results using sensor measurements so that the final estimated outputs from this filter can be used for better prediction in the next period. The PV electric power is then calculated since it is a function of irradiance and temperature.;The proposed methodology has been illustrated using the IEEE 24-bus reliability test system. The real data from National Renewable Energy Laboratory is used in this thesis as the actual outputs that the outputs of the predicting model should get close to. Finally, the performance of the proposed approach is obtained by comparing its results with the results from an available method called the persistent prediction method

GWO-based estimation of input-output parameters of thermal power plants

The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the difference between the actual value and the estimated value of the fuel cost function. The estimated values of parameter that produce the smallest total absolute error were the values of final solution. The simulation results showed that parameter estimation using gray wolf optimizer method further minimized the value of objective function. By using three models of fuel cost curve and given test data, parameter estimation using grey wolf optimizer method produced the better estimation results than those estimation results obtained using least square error, particle swarm optimization, genetic algorithm, artificial bee colony and cuckoo search methods

Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive Power Dispatch

Firefly algorithm (FA) is a newly proposed swarm intelligence optimization algorithm. The original version of FA usually traps into local optima like many other general swarm intelligence optimization algorithm. In order to overcome this drawback, the chaotic firefly algorithm(CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactive power optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PS

Filter Feeding Allogenic Engineering Optimization Algorithm for Economic Dispatch

The main objective of the economic dispatch problem in a power system is to minimize the total thermal fuel cost of the committed generators while satisfying the various system equality and inequality operational constraints. This research developed a new optimization algorithm, named the filter feeding allogenic engineering algorithm, for use in solving the economic dispatch problem. This meta-heuristic algorithm has been inspired by the filter feeding and motile behaviour of allogenic engineers. The newly developed algorithm was formulated using the Matlab software environment, and its performance was tested using the IEEE 39-Bus, 10-Generator system. A comparative analysis was also conducted with the Ant lion optimization heuristic algorithm, and the obtained results indicate that the filter feeding allogenic engineering algorithm yields superior performance

Optimization methods for energy management in a microgrid system considering wind uncertainty data

Energy management in the microgrid system is generally formulated as an optimization problem. This paper focuses on the design of a distributed energy management system for the optimal operation of the microgrid using linear and nonlinear optimization methods. Energy management is defined as an optimal scheduling power flow problem. Furthermore, a technical-economic and environmental study is adopted to illustrate the impact of energy exchange between the microgrid and the main grid by applying two management scenarios. Nevertheless, the fluctuating effect of renewable resources especially wind, makes optimal scheduling difficult. To increase the results reliability of the energy management system, a wind forecasting model based on the artificial intelligence of neural networks is proposed. The simulation results showed the reliability of the forecasting model as well as the comparison between the accuracy of optimization methods to choose the most appropriate algorithm that ensures optimal scheduling of the microgrid generators in the two proposed energy management scenarios allowing to prove the interest of the bi-directionality between the microgrid and the main grid.info:eu-repo/semantics/publishedVersio

Enhanced Bees Algorithm with fuzzy logic and Kalman filtering

The Bees Algorithm is a new population-based optimisation procedure which employs a combination of global exploratory and local exploitatory search. This thesis introduces an enhanced version of the Bees Algorithm which implements a fuzzy logic system for greedy selection of local search sites. The proposed fuzzy greedy selection system reduces the number of parameters needed to run the Bees Algorithm. The proposed algorithm has been applied to a number of benchmark function optimisation problems to demonstrate its robustness and self-organising ability. The Bees Algorithm in both its basic and enhanced forms has been used to optimise the parameters of a fuzzy logic controller. The purpose of the controller is to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. Kalman filtering, as a fast convergence gradient-based optimisation method, is introduced as an alternative to random neighbourhood search to guide worker bees speedily towards the optima of local search sites. The proposed method has been used to tune membership functions for a fuzzy logic system. Finally, the fuzzy greedy selection system is enhanced by using multiple independent criteria to select local search sites. The enhanced fuzzy selection system has again been used with Kalman filtering to speed up the Bees Algorithm. The resulting algorithm has been applied to train a Radial Basis Function (RBF) neural network for wood defect identification. The results obtained show that the changes made to the Bees Algorithm in this research have significantly improved its performance. This is because these enhancements maintain the robust global search attribute of the Bees Algorithm and improve its local search procedure.EThOS - Electronic Theses Online ServiceGBUnited Kingdo