Because failures in distribution systems caused by extreme weather events directly result in consumers’ outages, this paper proposes a state-based decision-making model with the objective of mitigating loss of load to improve the distribution system resilience throughout the unfolding events. The system topologies including on/off states of feeder lines are modeled as Markov states, and the probabilities from one Markov state to another Markov state throughout the unfolding events are determined by the component failure caused by the unfolding events. A recursive optimization model based on Markov decision processes (MDP) is developed to make state-based actions, i.e., system reconfiguration, at each decision time. To overcome the curse of dimensionality caused by enormous states and actions, an approximate dynamic programming (ADP) approach based on post-decision states and iteration is used to solve the proposed MDP-based model. IEEE 33-bus system and IEEE 123-bus system are used to validate the proposed model.

A data-based Stackelberg market strategy for a distribution market operator (DMO) is proposed to coordinate power dispatch among different virtual power plants (VPP), i.e., demand response aggregators (DRA). The proposed strategy has a two-stage framework. In the first stage, a data-driven method based on noisy inverse optimization estimates the complicated price-response characteristics of customer loads. The estimated load information of the DRAs is delivered to the second stage where a one-leader multiple-follower stochastic Stackelberg game is formulated to represent the practical market interaction between the DMO and the DRAs that considers the uncertainty of renewables and the operational security. The proposed data-driven game model is solved by a new penalty algorithm and a customized distributed hybrid dual decomposition-gradient descent (HDDGD) algorithm. Case studies on a practical demand response project in China and a distribution test system demonstrate the effectiveness of the proposed methodology.

The increasing penetration of renewable generation presents challenges for system frequency regulation due to short-term power fluctuations and system inertia reductions. This paper presents a coordinated control strategy for frequency regulation in which inverter air-conditioning (IAC) units are used to perform primary frequency regulation (PFR) and fixed frequency air-conditioning (FFAC) units are used to perform secondary frequency regulation (SFR). In PFR, the regulation power is provided by adjusting the setpoints of the IAC units. A random number generation method is proposed to stochastically trigger IAC units based on the frequency deviation in real time. Furthermore, a recovery method is presented to stably restore the IAC units to their initial operating states after regulation. In SFR, a constant equivalent duty ratio method is raised to keep the regulation power stable over a long instruction interval. Based on this, the transforming time interval method is presented to determine the ON/OFF status of FFAC units to provide the required regulation power. Additionally, a recovery method for FFAC units is proposed to mitigate the power rebound after regulation. The proposed control strategy achieves an improved frequency regulation effect with fewer communication demands. Dynamic simulations in a six-machine two-area system and an isolated microgrid with wind power verify the effectiveness of the proposed control strategy.

With the increasing integration of uncertain resources, e.g., renewables, electric vehicles, and demand responses, it is imperative to understand the characteristics of loads for power system analysis and control. Challenges of load modeling come from a variety of load components and time-varying compositions. In addition, the existence of outliers in measurements further complicates the problem. This paper proposes a robust time-varying parameter identification technique for composite ZIP and induction motor (IM) load models. A batch-model regression form, including time-varying parameters, is established based on state transition models and observation models with current observations and previous predictions to guarantee data redundancy. To deal with outliers, down-weighting coefficients of measurements are calculated with a projection statistics (PS) approach. Based on the batch-model regression and the down-weighting coefficients, parameter identification at each sample time is formulated as a weighted least squares optimization, which is solved by the Newton-Raphson approach with the previous estimated parameters as the initial iteration values. In addition, parameters’ sensitivities to different outliers in measurements are analyzed. Results on the IEEE 57-bus system and IEEE 118-bus system show that the proposed algorithm can robustly identify time-varying parameters for the composite load models.

Integrated energy system (IES) represents a coordinated operation of constrained natural gas (NG), electric power, and thermal systems in three different time scales. Microturbines (MTs) are increasingly utilized in IES as suppliers of combined cooling, heating and power (CCHP) units, which optimize the usage and the delivery of interdependent NG, electric power, and thermal energy sectors in a distributed IES. In this paper, a dynamic IES model is derived by considering the dynamic interactions of the three energy systems in three time-scales. The dynamic integrated system represented as boundary layer system (BLS) is analyzed using singular perturbation. Simulation results demonstrate the validity of the proposed dynamic integrated model in three time-scales, highlighting the necessity of incorporating the dynamic characteristics of NG, electric power, and thermal energy in IES dynamic analyses.