Thermostatically controlled loads (TCLs), such as air conditioners (ACs), are important demand response resources—they have a certain heat storage capacity. A change in the operating status of an air conditioner in a small range will not noticeably affect the users’ comfort level. Load control of TCLs is considered to be equivalent to a power plant of the same capacity in effect, and it can significantly reduce the system pressure to peak load shift. The thermodynamic model of air conditioning can be used to study the aggregate power of a number of ACs that respond to the step signal of a temperature set point. This paper analyzes the influence of the parameters of each AC in the group to the indoor temperature and the total load, and derives a simplified control model based on the two order linear time invariant transfer function. Then, the stability of the model and designs its Proportional-Integral-Differential (PID) controller based on the particle swarm optimization (PSO) algorithm is also studied. The case study presented in this paper simulates both scenarios of constant ambient temperature and changing ambient temperature to verify the proposed transfer function model and control strategy can closely track the reference peak load shifting curves. The study also demonstrates minimal changes in the indoor temperature and the users’ comfort level.

Demand response (DR) programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL), plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization model with the combined uncertainties from the wind power output and the demand response scenario is proposed. The proposed model analyzes the demand response behavior of PEL by maximizing the electricity consumption satisfaction and interaction benefit satisfaction of PEL. Finally, a case simulation on the provincial power grid with a 151-bus system verifies the effectiveness and feasibility of the proposed mechanism and models.

Price‐elastic load (PEL) is a typical demand response resource. PEL provides an effective option to address the challenge of wind power output fluctuation. This study proposes a stochastic scheduling model, considering uncertainties of variable wind power and PEL response. A PEL stochastic security‐constrained economic dispatch model using chance‐constrained programming is presented for scheduling. The authors propose that the chance‐constrained transmission interface flow constraints should be considered to control the probability of transmission interface flow limit violation via the parameter of security probability. An approach is suggested to transform the stochastic model into a deterministic quadratic programming problem via linearising the branch flow constraints and transmission interface flow constraints with sensitivity analysis method. Then, the probabilistic transmission interface flow constraints are equivalently transformed into conventional constraints. Case simulations on a provincial power system with 151 buses demonstrate the effectiveness and feasibility of the proposed mechanism, models, and methods.

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.

Short-term load forecasting is an important prerequisite for smart grid controls. The current methods are mainly based on the convolution neural network (CNN) or long short-term memory (LSTM) model to realize load forecasting. For the multi-factor input sequence, the existing methods cannot obtain multi-scale features of the time series and the important parameters of the multi-factor, resulting in low accuracy and robustness. To address these problems, a multi-scale feature attention hybrid network is proposed, which uses LSTM to extract the time correlation of the sequence and multi-scale CNN to automatically extract the multi-scale feature of the load. This work realizes the integration of features by constructing a circular network. In the proposed model, a two-branch attention mechanism is further constructed to capture the important parameters of different influencing factors to improve the model’s robustness, which can make the network to obtain effective features at the curve changes. Comparative experiments on two open test sets show that the proposed multi-scale feature attention mixture network can achieve accurate short-term load forecasting and is superior to the existing methods.