F.N. Lee

Bio: F.N. Lee is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Electric power system & Load management. The author has an hindex of 18, co-authored 30 publications receiving 1487 citations.

Reserve constrained economic dispatch with prohibited operating zones

Abstract: A method is described for solving the reserve constrained economic dispatch problem when some of the online generating units have prohibited operating zone(s). For a unit with prohibited zone(s), the zone(s) divide the operating region between the minimum generation limit (Pmin) and the maximum generation limit (Pmax) into disjoint convex subregions. These disjoint subregions form a nonconvex decisions space and the associated economic dispatch problem is thus a nonconvex optimization problem. As a result, the conventional Lagrangian relaxation (LR) approach (e.g. the lambda - delta iterative approach) cannot be applied directly. The method proposed decomposes the nonconvex decision space into a small number of subsets such that each of the associated dispatch problems is either infeasible or one that can be directly solved via the conventional LR approach. Based on the decomposition, the optimal solution is the least costly one among all the feasible solutions of the associated dispatch problems. Examples are also given to illustrate the proposed method. >

Scheduling direct load control to minimize system operation cost

Abstract: This paper presents a very effective method for direct load control (DLC) dispatch with the objective of minimizing system operational costs. In the paper, the coordination between DLC dispatch and unit commitment is discussed, and the significant potential benefit accrued from using DLC capacity as a part of system spinning reserve is illustrated. This paper also presents an extension of the proposed method to include consideration of capital cost saving.

Short-term thermal unit commitment-a new method

Abstract: A method and an algorithm based on this new method are presented. The effectiveness of the algorithm is illustrated by studying a 20-unit midwestern utility system, the EPRI 174-unit synthetic utility system D, and the EPRI 155-unit synthetic utility system E. The algorithm produces the same unit commitment schedule for the 20-unit system as a frequently used DP-STC algorithm in 15 s of computation time versus 524 s, respectively. The computation time is approximately linear with the number of hours in the unit commitment horizon. For the EPRI 174-unit system the algorithm requires only 205 s of computation time on a VAX 11/780 for a 48-hour horizon. >

The impacts of temperature forecast uncertainty on Bayesian load forecasting

Abstract: This paper presents a short-term load forecast methodology that is suitable for power system operational planning studies. Bayesian estimation is use to predict multiple step ahead peak forecasts using peak and average temperature forecasts as explanatory variables. Herein, the forecast model is developed and illustrated in a case study with utility-derived power system data. Special attention is given to the practical issue of forecasting the electrical load with imperfect weather information.

Risk due to load forecast uncertainty in short term power system planning

Abstract: This paper presents a methodology to analyze the risk of short term power system operational planning in the presence of electrical load forecast uncertainty. As the authors' methodology requires an estimate of the load forecast variance, a Bayesian load forecaster is used in the practical implementation. They express their results as a function of forecast lead time from one to five days into the future, in terms of $/MWH. The risk due to load forecast uncertainty is based on the forecast variance, and found by determining the expected cost of perfect information. They illustrate their risk evaluation method in a case study with utility-derived power system data and temperature forecast data from the US National Weather Service.

Genetic Algorithms + Data Structures = Evolution Programs

Abstract: 1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Evolution Programs for Various Discrete Problems.- 12 Machine Learning.- 13 Evolutionary Programming and Genetic Programming.- 14 A Hierarchy of Evolution Programs.- 15 Evolution Programs and Heuristics.- 16 Conclusions.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- References.

Neural networks for short-term load forecasting: a review and evaluation

Abstract: Load forecasting has become one of the major areas of research in electrical engineering, and most traditional forecasting models and artificial intelligence techniques have been tried out in this task. Artificial neural networks (NNs) have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. Nevertheless, some authors remain skeptical, and believe that the advantages of using NNs in forecasting have not been systematically proved yet. In order to investigate the reasons for such skepticism, this review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting. Our aim is to help to clarify the issue, by critically evaluating the ways in which the NNs proposed in these papers were designed and tested.

Particle swarm optimization to solving the economic dispatch considering the generator constraints

Abstract: This paper proposes a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zone, and nonsmooth cost functions are considered using the proposed method in practical generator operation. The feasibility of the proposed method is demonstrated for three different systems, and it is compared with the GA method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.

A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem

Abstract: This paper presents a new mixed-integer linear formulation for the unit commitment problem of thermal units. The formulation proposed requires fewer binary variables and constraints than previously reported models, yielding a significant computational saving. Furthermore, the modeling framework provided by the new formulation allows including a precise description of time-dependent startup costs and intertemporal constraints such as ramping limits and minimum up and down times. A commercially available mixed-integer linear programming algorithm has been applied to efficiently solve the unit commitment problem for practical large-scale cases. Simulation results back these conclusions

A genetic algorithm solution to the unit commitment problem

Abstract: This paper presents a genetic algorithm (GA) solution to the unit commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple GA algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the varying quality function technique and adding problem specific operators, satisfactory solutions to the unit commitment problem were obtained. Test results for power systems of up to 100 units and comparisons with results obtained using Lagrangian relaxation and dynamic programming are also reported.