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Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

This paper proposes an analytical method that incorporates the time of use (TOU) strategy into the reliability evaluation of power system. The TOU electricity price strategy enables the savings of electricity cost, but its impact on power system reliability has not been thoroughly studied. The proposed method constructs a peak-flat-valley period partition model and develops an equal step length iteration algorithm to obtain the optimal period partition. A price-based demand response is modeled to calculate the hourly load using an apportionment method. Using the particle swarm optimization algorithm, a TOU-based optimization model with a penalty is constructed to find the optimal electricity price. Then, an analytical “electricity price–reliability” model is developed to evaluate the reliability of the power system considering TOU. The effectiveness of the proposed method is investigated through the case study on the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

Energy production of a wind farm is greatly affected by the wake-effect. In wind farm planning, it is essential to optimize the layout of wind turbine generators (WTGs) to alleviate the impact of wake-effect. This paper presents a new wind farm layout optimization model with an objective function to maximizing equivalent power of the wind farm. The proposed model incorporates the discrete joint probability distribution and characteristics of the wind, wind energy model, and the wake-effect. A particle swarm optimization algorithm is used to solve the optimization problem. In the optimization process, geometric analysis is conducted to determine the upstream WTGs and to calculate the horizontal and deviating distances among WTGs along the wind direction. An effective look-up table is constructed to directly determine the upstream/downstream WTGs. Then the optimal layout of a wind farm can be obtained. The proposed technique is applied to the wind farm layout optimization and the modified Roy Billinton Test System and IEEE Reliability Test System for reliability analysis of a generating system containing a wind farm. Simulation results demonstrate the validity and flexibility of the proposed technique in the layout optimization of WTGs in a wind farm. [ABSTRACT FROM AUTHOR]