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.

The coordination of multiple coupled regulation methods in Internet data centers (IDCs) is proposed in this paper to make a full use of IDCs’ spatial and temporal load regulation potentials for demand response (DR). A concise and analytical IDC load model is proposed to facilitate the DR in IDCs. First, the unified IDC load considering multiple coupled regulation methods is modeled represented by workloads and servers. The extra power consumption in IT equipment due to redundancy requirements in computing resources is separated to obtain an explicit mathematical relationship among geo-distributed IDCs. Second, the compatible IDC load model is derived based on a bottom-up approach, where electrical decision variables representing the regulation are introduced to replace non-electrical variables and demonstrate the coupling among multiple regulation methods. Last, a hierarchical control design is deduced to reveal the potentials of the compatible IDC load model in power system applications. Simulations show that the joint implementation of coupled methods can enhance IDC DR capabilities. Simulation results also verify the efficiency of compatible IDC load model in DR and reveal its advantages in load characteristic analyses, computational efficiency, and sensitive information protection of IDCs.

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.

This paper provides an overview of the challenges and pathways involved in achieving a low-carbon-oriented energy transition roadmap and power system planning strategy. The transition towards low-carbon energy sources is crucial in mitigating the global climate change crisis. However, this transition presents several technical, economic, and political challenges. The paper emphasizes the importance of an integrated approach to power system planning that considers the entire energy system (including both physical and information systems and market mechanisms) and not just individual technologies. To achieve this goal, the paper discusses various pathways toward low-carbon energy transition, including the integration of renewable energy sources into current energy systems, energy efficiency measures, and market-based and regulatory strategies encompassing the implementation of regulations, standards, and policies. Furthermore, the paper underscores the need for a comprehensive and coordinated approach to energy planning, taking into account the socio-economic and political dimensions of the transition process. In addition, the paper reviews the methodologies used in modeling low-carbon-oriented power system planning, including both model-based methods and advanced machine learning-assisted solutions. Overall, the paper concludes that achieving a low-carbon-oriented energy transition roadmap and power system planning strategy requires a multi-dimensional approach that considers technical, economic, political, and social factors.