• Energy Research
• 7. Clean energy close

Abstract The high penetration combined with the intermittent nature of large-scale distributed generation challenges the stability of power systems. Recently, it has been proposed that thermostatically controlled loads, such as heating, ventilation and air conditioner (HVAC) loads, can be controlled to provide efficient and effective frequency regulation (FR) service with a minimum impact on consumers’ comfort compared to the costly conventional spinning reserve. This paper proposes a progressive load control strategy which enables residential HVAC loads to implement primary and secondary frequency regulation. In particular, a state-shift priority approach is proposed to efficiently choose responsive loads and avoid frequent activations of load control. A state-shift time based random recovery approach is developed to mitigate load rebound after the control period. With the proposed load control strategy, the aggregation of HVAC units can behave like a synchronous generator to provide FR service. As a result, the required spinning reserve can be reduced. The dynamic simulation results indicate that HVAC units can provide a reliable and smooth FR service quickly via the proposed progressive load control strategy based on state-shift priority.

In this paper, a fuel-based distributed generator (DG) allocation strategy is proposed to enhance the distribution system resilience against extreme weather. The long-term planning problem is formulated as a two-stage stochastic mixed-integer programming (SMIP). The first stage is to make decisions of DG siting and sizing under the given budget constraint. In the second stage, a post-extreme-event-restoration (PEER) is employed to minimize the operating cost in an uncertain fault scenario. In particular, this study proposes a method to select the most representative scenarios for the SMIP. First, a Monte Carlo Simulation (MCS) is introduced to generate sufficient scenarios considering random fault locations and load profiles. Then, the number of scenarios is reduced by the K-means clustering algorithm. The advantage of scenario reduction is to make a trade-off between accuracy and computational efficiency. Finally, the SMIP is solved by the progressive hedging algorithm. The case studies of the IEEE 33-bus and 123-bus test systems demonstrate the effectiveness of the proposed algorithm in reducing the expected energy not served (EENS), which is a critical criterion of resilience.

High penetration of renewable energy may cause considerable system frequency deviations. Electric vehicles (EVs) offer alternative potentials for frequency regulation with rapid responding speed. However, for many EVs under centralized control, existing modeling methods may achieve accurate control results at the cost of high computational complexity, a high real-time communication requirement due to many individual control signals, and possible disturbances to charging preferences. This paper focuses on solving these crucial issues. At the system level, we use the state-space method to further simplify the model for an EV aggregator (EVA) with limited data measurements from EVs. During frequency regulation, the reduced EVA model predicts the EVA’s regulation capacity and generates a simplified global control signal for the probabilistic control of individual EVs. In cooperation with the ramp rate of conventional generation, the EVA implements a progressive state recovery strategy to reduce the disturbance of regulation service to EVs’ charging preferences. The EVA decentralizes partial control authorities to individual EVs by applying response functions at the user side. Based on operating states and laxities, an individual EV adjusts the response process to ensure its charging preference under probabilistic control. Comparative simulations validate the effectiveness of this EVA modeling and control method.

Demand response (DR) has been widely regarded as an effective way to provide regulation services for smart grids by controlling demand-side resources via new and improved information and communicat ...

Existing models featuring numerous electric vehicles (EVs) for centralized frequency regulation achieved high accuracy at the expense of heavy computational workloads and a high real-time communication requirement. This paper develops a state space model (SSM) that provides a probability to realize the real-time power control of aggregated EVs with high accuracy and computational efficiency but a low real-time communication requirement. The SSM, a reduced model based on the state space method, accurately describes aggregated EVs with different connecting states and various state-of-charge (SOC) states. Considering heterogeneous charging characteristics and random traveling behaviors of EVs, the SSM realizes the state transition prediction and the regulation capacity estimation with the Markov state transition method, which has a much higher computational efficiency than the existing models. The SSM is used for the frequency regulation, and the SOC adaptive coefficient is implemented to derive the identical control signal and improve the prediction accuracy. The SSM lowers the real-time communication requirement by replacing some real-time processes with offline processes. Meanwhile, the identical control signal is more suitable for real-time dispatching because it broadcasts the control signal to individual EVs globally. Simulation results indicate that the SSM achieves the high prediction accuracy with much higher computational efficiency. Comparison results are conducted to validate the effectiveness of SSM for real-time frequency regulation.

Large-scale wind and solar power integration are likely to cause a short-term mismatch between generation and load demand because of the intermittent nature of the renewables. System frequency is therefore challenged. In recent years, it has been proposed that a part of the residential load can be controlled for frequency regulation with little impact on customer comfort. This paper proposes a thermostatic load control strategy for primary and secondary frequency regulation, in particular, using heating, ventilation, and air-conditioning units and electric water heaters. First, daily demand profile modeling indicates that these two loads are complementary in the daytime and can provide a relatively stable frequency reserve. Second, the progressive load recovery is specifically considered in the control scheme. The random switching and cycle recovery method is proposed for mitigating power rebound after switching the air conditioners on again. The proposed control strategy can organize a large population of thermostatic loads for the provision of a frequency reserve. Consequently, the requirement of a spinning reserve is reduced. Finally, the proposed control strategy is verified by the dynamic simulation of IEEE RTS 24-bus system.

Demand response (DR) has been widely recognized as an important approach to balance the power grid and reduce peak load of power systems. In order to better estimate the capability and the expense of peak load reduction through DR, we need to obtain the residential load profile and customers’ attitudes toward DR programs. Based on a large-scale online survey collected among over 1500 customers from New York and Texas in the U.S., this paper investigates the relationships among household appliance activities (e.g., electric water heater and air conditioner), load profiles, and incentive-based DR (IBDR) participation for peak load curtailment through reward payment. The daily load profiles of major home appliances are developed. Additionally, this paper estimates the expense of reducing the yearly peak of the local grid load. Finally, this paper addresses the importance of investigating the multifaceted factors of affecting IBDR participation and provides useful suggestions to utility companies when implementing DR programs.