CSO Based Solution for Load Kickback Effect in Deregulated Power Systems

Abstract

With increase in power demand, load demand values have also risen to a greater extent. Sometimes, these demands are met with the great difficulties. All these difficulties drive us to seek other alternative ways. One such a way demand response (DR) is considered in this paper, it is a new concept that is introduced in the system in order to reduce peak hour stresses. When implementing the demand response, the main setbacks that arise is the load kickback effect, which the sudden rise in demand during non-peak hours that is caused by the overuse of power by consumers, after their constant reduction of power during peak hours. This paper discusses the various kickback load types, and an effective approach to avoid and tackle kickback effect, by an effective method Cat Swarm Optimization (CSO), which is based on studying the movement of cats. The optimization has been implemented on an IEEE 30 bus and 75 bus Indian utility system, and the results are discussed.

Multi-scenario operation optimization of electric-thermal coupling renewable energy system based on deep reinforcement learning

“…1 includes three thermal power units, two CHP units, a pumped storage power station, a heat storage tank and a wind turbine. The unit datas are from references [8] and [9] . The electricity load and wind power are based on the real operating data of the California Electric Power System CASIO [10] , and the heat load is derived from the Open Power System Data [11] 2017-2018 German users' heat demand.…”