Tsung-Ying Lee

Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO

This paper presents an evolutionary iteration particle swarm optimization (EIPSO) algorithm to solve the nonlinear optimal scheduling problem. A new index called iteration best is incorporated into particle swarm optimization (PSO) to improve the solution quality. The new PSO, named iteration PSO (IPSO), is embedded into evolutionary programming (EP) to further improve the computational efficiency. The EIPSO is then applied to solve the optimal spinning reserve for a wind-thermal power system (OSRWT). Results are used to evaluate the effects of wind generation on the spinning reserve selection of a power system. The OSRWT program considers the outage cost as well as the total operation cost of thermal units to evaluate the level of spinning reserve. The up spinning reserve (USR) and down spinning reserve (DSR) are also introduced into the OSRWT problem. The optimal scheduling of spinning reserve was reached while minimizing the sum of total operation cost and outage cost. Two practical power systems are used as numerical examples to test the new algorithm. The feasibility of the new algorithm is demonstrated by the numerical example, and EIPSO solution quality and computational efficiency are compared to those of other algorithms.

Determination of optimal contract capacities and optimal sizes of battery energy storage systems for time-of-use rates industrial customers

This paper sets up a formulation to determine the optimal contract capacities and optimal sizes of battery energy storage systems for time-of-use rate customers. The advanced multi-pass dynamic programming and expert knowledge base rules are then applied to solve this problem. Two industrial customers of the Taiwan Power Company System are used as examples to test this algorithm. Results show that the optimal sizes of battery energy storage systems and the optimal contract capacities of customers during the life cycle of battery energy storage systems can be achieved by this algorithm; therefore the maximum economic benefits of battery energy storage systems for time-of-use rate customers can be estimated. >

Optimal capacity of the battery energy storage system in a power system

This paper investigates the optimal capacity of a battery energy storage system in a power system. The Taiwan Power Company System is used as an example system to test this algorithm. Results show that the maximum economic benefit of battery energy storage in a power system can be achieved by this algorithm. >

Impact Analysis of Transmission Capacity Constraints on Wind Power Penetration and Production Cost in Generation Dispatch

Wind power penetration in Taiwan power system faces significant barriers due to limited transmission capability. One of the most important future challenges seems to be the management of the integration of fluctuations in the electricity production from wind energy sources. In this paper, a penalty function-direct search method (PF-DSM) is developed to further modify the direct search method (DSM) to improve computation efficiency. The PF-DSM algorithm is used to facilitate economic sharing of generation and reserve across areas, and minimize the total generation cost in the multi-area wind-thermal coordination dispatch problem. Several technique constraints are applied to determine the optimal proportion of wind generator capacity that can be integrated into the multi-area isolated system. Numerical experiments are included to understand the effects of wind generator capacity in production cost analysis and to illustrate the impacts of transmission capacity limits on wind power penetration level in each area.

Effects of Photovoltaic Generation System on the Contract Capacity Selection of Time-Of-Use Rate Industrial Users

This paper investigates the effects of photovoltaic generation system (PVGS) on the selection of contract capacities for time-of-use (TOU) rate industrial users. A benefit cost ratio (BCR) was served to evaluate the economic benefits of PVGS in TOU rate industrial users. Evolutionary programming (EP) is applied to solve the optimal installation capacity of PVGS and the optimal contract capacities for TOU rate industrial users. The impacts of PVGS installation capacity on the selection of contract capacities for TOU rate industrial users were evaluated. To apply the EP to solve the previous problem, an individual which was composed of PVGS installation capacity and TOU rate user contract capacities, was defined. A fitness function evaluates the economic benefits of PVGS was applied to calculate the fitness of individual. After that EP starts to calculate the optimal PVGS installation capacity and TOU rate user contract capacities. Through the cooperation of agents called individuals, the near optimal solution of the previous problem can be effectively reached. Finally, a numerical example was served to demonstrate the feasibility of the new approach, and EP solution quality and computation efficiency were compared to those of other algorithms.

Spinning reserve scheduling of power systems: An ant colony approach

Abstract This paper presents an Ant Colony System (ACS) based optimization approach to solve the optimal spinning reserve problem for a power system. Unit commitment risk is used as an index to evaluate the level of spinning reserve. The outage cost and the fuel cost of thermal units were considered in the unit commitment program. The optimal spinning reserve and unit commitment risk were reached while the sum of fuel cost and outage cost was minimized. A 52 units power system was used as a numerical example to test the new algorithm. The optimal spinning reserve and unit commitment risk could be reached in the testing while satisfying the requirement of the objective function.