A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors  
[PERSON]  
1 Jiangsu Provincial Key Laboratory of Smart Grid Technology & Equipment, Southeast University, Nanjing 210096, Jiangsu, China; [EMAIL_ADDRESS] (J.N. ); [EMAIL_ADDRESS] (B.G.) 1  
[PERSON]  
1 Jiangsu Provincial Key Laboratory of Smart Grid Technology & Equipment, Southeast University, Nanjing 210096, Jiangsu, China; [EMAIL_ADDRESS] (J.N. ); [EMAIL_ADDRESS] (B.G.) 1  
Footnote 1: email: {[EMAIL_ADDRESS]. a report by the Federal Utility Regulatory Commission, demand response (DR) is capable of offsetting 20% of the peak demand in the US [12]. Secondly, without the regulating inertia, the DR resources can respond to the control order rapidly. In addition to the two mentioned advantages, the DR resources can also cope with emergency conditions because of their geographical decentralization. Hence, coordinating the AGC units and DR is a reasonable consideration to mitigate the WPFE impacts in real-time to maximize the wind accommodation. In general, the loads are classified into three types: residential load, commercial load, and industrial load [13]. In this paper, we focus on the residential loads. Among the various residential load types, thermostatic loads such as air conditioner (AC) and water heater (WH) loads, and deferrable loads such as electric vehicle (EV) loads are placed at the top of the merit list of DR appliances due to their greater load shifting potential and larger daily energy demand share [14; 15; 16]. Recently, the load management benefits of controlled appliances for the increased deployment of intermittent renewable generation have been investigated [17; 18; 19; 20; 21]. A stochastic unit commitment model is presented for assessing the reserve requirements resulting from the integration of renewable sources in [17], where alternative DR paradigms for assessing the benefits of demand-side flexibility on absorbing the variability and uncertainty of renewable supply are investigated. [PERSON] et al. [18] considered flexible loads as a storage device in a hybrid power plant of a wind farm to compensate for wind power imbalances and proposed a procedure to derive the strategic offer for a hybrid power plant selling energy in the pool-based market. The joint operation of wind farms and DR producer is demonstrated, and DR is effective in coping with the wind power uncertainties in the intraday market. [PERSON] et al. [19] evaluates thermostat set point control of aggregate electric water heaters (EWHs) for load shifting and providing a desired balancing reserve for the utility at the presence of wind generation. [PERSON] et al. [20] developed a DR model that offers optimal levels for both production and consumption in a smart grid that consists of a wind farm along with conventional sources. Although these studies have studied the DR role in power systems with wind generation, they lack the considerations of the demand side's DRP and security constraints. To the best of the authors' knowledge, until now, few studies have thoroughly investigated the optimization of DR and AGC units to smooth the power imbalance considering the real-time DR potential (DRP) and security constraints. The major contribution of this paper is to develop a mathematical model to mitigate the WPFE impacts through the collaboration of DR and AGC units. It is necessary to evaluate how much power smart appliances including ACs, WHs, and EVs can respond, which is regarded as DRP. The DRP varies with different working conditions of the smart appliances. The operational status has features of continuity and accumulation as a result of their operating characteristics. If the smart appliances respond, the subsequent operational power would change. Therefore, this paper proposes a method of evaluating the DRP quantitatively in real-time. Combining the DR and AGC units, the framework is presented to take advantage of their potential for balancing supply and demand considering the security constraints.