Abstract
As a result of extensive penetration of wind farms into electricity grids, power systems face enormous challenges in daily operation because of the intermittent characteristics of wind energy. In particular, the load peak-valley gap has been dramatically widened in wind energy-integrated power systems. How to quickly and efficiently meet the peak-load demand has become an issue to practitioners. Previous literature has illustrated that the demand response (DR) is an important mechanism to direct customer usage behaviors and reduce the peak load at critical times. This paper introduces air-conditioning loads (ACLs) as a load shedding measure in the DR project. On the basis of the equivalent thermal parameter model for ACLs and the state-queue control method, a compensation cost calculation method for the ACL to shift peak load is proposed. As a result of the fluctuation and uncertainty of wind energy, a two-stage stochastic unit commitment (UC) model is developed to analyze the ACL users’ response in the wind-integrated power system. A simulation study on residential and commercial ACLs has been performed on a 10-generator test system. The results illustrate the feasibility of the proposed stochastic programming strategy and that the system peak load can be effectively reduced through the participation of ACL users in DR projects.
Highlights
New energy power generation, wind power generation, has been developed rapidly around the world in recent years because it may offer clean and cost-effective electric energy.According to the description from the U.S Department of Energy, by 2030, 20% of the state’s electricity could be generated by wind farms [1]
This paper focuses on unit commitment (UC) optimization in wind-integrated power systems, which employs air-conditioning loads (ACLs) to balance the peak load
Our results show that controlling the usage of ACLs effectively reduces the peak load and increases energy efficiency
Summary
Wind power generation, has been developed rapidly around the world in recent years because it may offer clean and cost-effective electric energy. By contrast, is based on an equivalent thermal parameter model of the home heating system, which physically describes the relationship between temperature and ACL power output. Tin,t refers to the indoor temperature at time t, Tout,t+1 to the outdoor temperature at thermal resistance, C to the equivalent thermal capacity, COP to the coefficient of performance, PAC,t to time (t + 1), e−Δt/RC to the heat dissipation parameters, Δt to the time interval, R to the equivalent the the ACL power output at time t, A to the conduction coefficient, and SAC to the the switching state thermal resistance, C to the equivalent thermal capacity, COP to the coefficient of performance, PAC,t of the air conditioner, where “1” and “0” denote the air conditioner being on and off, respectively. In this study, we do not consider the uncertainty in R and assume it as a constant
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