Since thermal energy generated by air conditionings (ACs) can be stored in the buildings providing the potential of shifting electricity consumption between different time periods, ACs are considered as an important demand response (DR) resource and attract extensive attentions. In order to achieve the compatibility of the inverter ACs with the current dispatch models, this paper attempts to model an inverter AC system as a thermal battery (TB). The comparisons between the TB and the lithium-ion battery are given. In order to protect the end-users' privacy and relieve the computational burden of the centralized control, a hierarchical control framework is designed and an aggregated TB model is proposed to handle the heterogeneity of the inverter ACs. A finite-horizon optimization model is used to compare the operating performances of the aggregated TBs and the lithium-ion batteries. Simulation results demonstrate that the aggregated TB model works well with the power dispatch model developed for the lithium-ion batteries. In other words, with the TB modeling of ACs, lithium-ion batteries can be replaced in current dispatch models providing service for the grid.

This paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user’s information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model.

The increasing of grid-connected variable renewable energy (VRE) on the demand side causes balance problems in the power system. Thus, dealing with the uncertainty, variability, and consequently, flexibility requirement is becoming an urgent challenge to the power system operators. Virtual power plant (VPP), which bundles different types of small-scale distributed energy resources (DERs) into a single unit for optimization will effectively mitigate those uncertainties. An optimal VPP energy management method is proposed in this article for optimal energy and operating reserve (OR) scheduling. The studied VPP is a cluster of dispersed generating units (including dispatchable and stochastic power sources), flexible loads, as well as storage units. VPP operator has to make decisions based on the uncertainty coming from the stochastic VRE, load demand, as well as market electricity price. Thus, a dynamic risk reserve quantification method is proposed to cover both VRE power and load forecast uncertainties, while information gap decision theory is applied in the unit commitment procedures to study the impact of price uncertainty on the decision-making of VPP operators. Finally, the proposed method is implemented and verified with a case study, and optimal decisions are discussed.

Thermostatically controlled loads (TCLs) which possess thermal inertia have attracted additional attention for providing ancillary services to power systems. In this regard, the aggregation model for TCLs is critical for evaluating the performance and optimizing the control of a population of TCLs. We learn by theoretical analyses and simulations that the accuracy of the state space model in most cases is deteriorated by applying longer TCL dispatch periods. Thus, a modified aggregated TCL model is proposed and a new state transition matrix is derived in this paper to improve the TCL modeling accuracy for the provision of grid regulation services. The proposed control model for the aggregated TCL takes into account compressor time delays that would enhance the collective TCL behavior. A probabilistic control strategy is presented to reduce the adverse impacts on the durability of TCL compressors and communication requirements for implementing the TCL control signal. Simulation results demonstrate that the proposed aggregated TCL model with control signals can closely follow the actual TCL behavior and the proposed control strategy can reduce the number of TCL switching for providing ancillary services to power systems.

Service restoration is one of the critical functions to enable the future self-healing distribution system. To restore the distribution system in a timely and reliable manner, the realistic system operating conditions need to be accurately characterized. In this paper, two main factors that have great impacts on distribution system restoration (DSR) in practice are investigated. First, cold load pickup (CLPU), generally caused by thermostatically controlled loads (TCLs), is a common phenomenon after an outage and shaped by the outage duration. However, the time-dependent behaviors of CLPU are rarely considered in literature. In this paper, the operating state evolution of TCLs after an outage is analyzed to characterize time-dependent CLPU. And the time-dependent CLPU is analytically embedded in DSR to accurately represent the actual behaviors of the restored loads. Second, it is difficult to predict loads that fluctuate during DSR due to the lack of real-time measurement data. Accordingly, a robust DSR based on the information gap decision theory (IGDT) is proposed to address this challenge, fully considering the uncertainty of CLPU. The proposed models are tested in IEEE 13-node and 123-node test feeders. Simulation results demonstrate that the time-dependent CLPU model and the uncertainty modeling of CLPU can accurately capture the actual behaviors of loads with TCLs after an outage, which greatly improves DSR decisions in practice.

Thermostatically controlled loads (TCLs), considered an important source of demand response (DR) without sacrificing end users' comfort, have demonstrated their potentials in providing ancillary services for power grid operations. In order to smooth out multi time-scale variabilities of wind power generation, TCLs are modeled and dispatched as virtual generators and batteries according to the designated control methods applied to power systems. However, considering the heterogeneity of distributed TCLs and uncertainties surrounding such loads, it would be difficult to obtain accurate parameters of aggregated generator and battery models quickly in large power systems. To address this problem, a parameter estimation method via HDMR is proposed in this paper to quickly determine critical parameters for aggregated generator and battery models in power system dispatch. The proposed dispatch framework will coordinate multi time-scale models of TCLs for smoothing out wind power variability. A numerical simulation study in this paper demonstrates that aggregated parameters for TCL model can be accurately estimated by the proposed method in which virtual generators and batteries would be able to follow actual behaviors of TCL populations in power system operations.