The frequency regulation service (FRS) is playing an increasingly important role in maintaining the power balance between generation and consumption. Moreover, the recent progress in information and communication technologies has enabled residential customers to participate in FRS through direct control over appliances, such as inverter air conditioners (ACs), whose market share is growing rapidly and has made up a large fraction of electricity consumption. Inverter ACs can change compressor's speed continuously to adjust operating power and provide FRS for the system operation. In this paper, the thermal model of a room and the electrical model of an inverter AC for providing FRS are developed. The model of the inverter AC is equivalent to a generator. In this manner, the aggregation of inverter ACs can be controlled just as traditional generators. Besides, a stochastic allocation method of the regulation sequence among inverter ACs is proposed to reduce the effect of FRS on customers. A hybrid control strategy by taking into account the dead band control and the hysteresis control is also developed to reduce the frequency fluctuations of power systems. The effectiveness of the proposed models and control strategies are illustrated in the numerical studies.

Air conditioners (ACs) are widely considered as good candidates to provide operating reserve. Demand response rebound, i.e., the rebound peak of aggregate power, may exist when ACs are controlled by changing the set point temperature. The rebound peak during the recovery period, named lag rebound, may cause significantly higher demand than that prior to the reserve deployment event. The rebound peak during the reserve deployment period, named lead rebound, is rarely considered in previous researches but will constrain the duration time to a short period (e.g., 10 min), which greatly limits the utilization of ACs. This paper proposes an optimal sequential dispatch strategy of ACs to mitigate the lead-lag rebound, and thus realize flexible control of the duration time from minutes to several hours. To quantify the effects of lead-lag rebound, a capacity-time evaluation framework of the operating reserve is developed. On this basis, ACs are grouped to be dispatched in sequence to mitigate the lead-lag rebound. The co-optimization of sequential dispatch on the capacity dimension and time dimension forms a mixed-integer nonlinear bilevel programming problem, in which the consumers' thermal comfort is also guaranteed. Case studies are conducted to validate the proposed strategy.

Thermostatically controlled load (TCL, such as heating, ventilation, and air conditioning system) is a desirable demand-side flexibility source in distribution networks. It can participate in regulation services and mitigate power imbalances from fluctuating distributed renewable generation. To effectively utilize the load flexibility from spatially and temporally distributed TCLs in a distribution network, it is necessary to consider power flow constraints to avoid possible voltage or current violations. Published works usually adopt optimal power flow models (OPF) to describe these constraints. However, these models require accurate topology of the distribution network that is often unobservable in practice. To bypass this challenge, this paper proposes a novel learning-based OPF to optimize TCLs for regulation services. This method trains three regression multi-layer perceptrons (MLPs) based on the distribution network's historical operation data to replicate its power flow constraints. The trained MLPs are further equivalently reformulated into linear constraints with binary variables so that the optimization problem becomes a mixed-integer linear program that can be effectively solved. Numerical experiments based on the IEEE 123-bus system validate that the proposed method can achieve better TCL power scheduling performance with guaranteed feasibility and optimality than other state-of-art models.

Rapidly increasing distributed energy resources (DERs) bring more fluctuating output power to the distribution network and put forward a higher requirement on local regulation resources for maintaining the network's balance. Heating, ventilation, and air conditioning (HVAC) loads account for more than 40% of power consumption in modern cities and have huge regulation potential as flexible loads. However, HVACs equipped with inverter devices have rarely been studied for providing regulation services in the local electricity market (LEM), even though they have exceeded regular fixed-speed HVACs. To address this issue, this article proposes a real-time LEM and a distribution network's optimization framework to exploit the regulation potential of inverter-based HVACs considering multiple DERs. This LEM can avoid iterations in real time and significantly decrease the difficulty related to the participation of small end-users in urban distribution networks. Moreover, in this article, we propose a transactive capacity evaluation method to assist end-users in deciding their inverter-based HVACs regulation capacities in the real-time LEM, which considers buildings’ thermal features, users’ multiple comfort requirements, and dynamic ambient temperature. On this basis, a multilevel bidding strategy is developed for inverter-based HVACs to decrease energy cost, increase fluctuating DERs local utilization rate, and alleviate the distribution network's congestion. Finally, a realistic distribution network is utilized to verify the effectiveness of the proposed methods.