Abstract:In this paper, a two-stage stochastic unit commitment (UC) model considering flexible scheduling of demand response (DR) is proposed. In the proposed UC model, the DR resources can be scheduled: (1) in the first stage, as resources on a day-ahead basis to integrate the predicted wind fluctuation with lower uncertainty; (2) in the second stage, as resources on an intra-day basis to compensate for the deviation among multiple wind power scenarios considering the coupling relationship of DR on available time and capacity. Simulation results on the Pennsylvania-New Jersey-Maryland (PJM) 5-bus system and IEEE 118-bus system indicate that the proposed model can maximize the DR value with lower cost. Moreover, different types of DR resources may vary in the contract costs (capacity costs), the responsive costs (energy costs), the time of advance notice, and the minimum on-site hours. The responsive cost is considered as the most important factor affecting DR scheduling. In addition, the first-stage DR is dispatched more frequently when transmission constraints congestion occurs.

There are many uncertain factors in the modern distribution network, including the access of renewable energy sources and the heavy load level. The existence of these factors has brought challenges to the stability of the power distribution network, as well as increasing the risk of exceeding transmission capacity of distribution lines. The appearance of flexible load control technology provides a new idea to solve the above problems. Air conditioners (ACs) account for a great proportion of all loads. In this paper, the model of dispatching AC loads in the regional power grid is constructed, and the direct load control (DLC) method is adopted to reduce the load of ACs. An improved tabu search technique is proposed to solve the problem of network dispatch in distribution systems in order to reduce the resistive line losses and to eliminate the transmission congestion in lines under normal operating conditions. The optimal node solution is obtained to find the best location and reduction capacity of ACs for load control. To demonstrate the validity and effectiveness of the proposed method, a test system is studied. The numerical results are also given in this article, which reveal that the proposed method is promising.

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Due to the presence of urban heat island effect (UHIE), high humidity and other urban microclimate, temperature of city central area rises. This causes that the actual airconditioning energy consumption (ACEC) in the urban central area is much higher than that in the suburbs. Load control of air-conditioners (ACs) is considered to be equivalent to a power plant of the same capacity, and it can greatly reduce the system pressure to peak load shift. In this paper, a simplified second order transfer function control model of ACs is presented, and its parameters will be influenced by the ambient temperature and urban microclimate. The temperature is obtained by using the temperature inversion algorithm of the heat island effect. Then, the heat index is calculated by combining temperature and humidity. The ambient temperature index of urban central area is modified based on the above microclimate, and the second order linear time invariant model of aggregated ACs is upgraded to the linear time varying model. Furthermore, the consequent parameter changes of the second order transfer function model are studied and the influence of urban microclimate on AC load control is analyzed. The proposed method is verified on numerical examples.

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