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This article introduces a method employing demand response (DR) and wind turbine (WT) to decrease the first and second frequency dips in power systems with the massive proliferation of wind resources. Inverter air conditioners (IACs) are utilized to participate in the DR program. The buildings' thermal model and the IACs' electrical model are integrated to characterize a relation between power system frequency and IACs regulation power. An adaptive delay controller is presented to capture the command communication delays from the DR controller to IACs. In addition, a controller is suggested to adopt wind power plants in system frequency regulation. This controller rises the wind plant output power for some seconds and returns it, following frequency recovery, to the initial power. The recovery period causes the second frequency dip, in response to which, a method is introduced to identify the moment of the second dip and alleviate it via DR.

The topological configuration of a bulk power grid is often altered by network investment upgrades, forecasted disasters and random faults, as well as planned operator-triggered transmission line maintenance and controlled switching actions. Such topological variations can drastically change the measurement data distribution from phasor measurement units (PMUs), which may in turn compromise the accuracy of the artificial intelligence (AI)-aided monitoring and control applications using the measurements. For instance, data-driven transient stability assessment (TSA) models that were trained with static network topologies may no longer be accurate for monitoring power grid stability as the network topology changes. Not only would the number of possible topology changes be too vast to train all possible scenarios, but also the training process will render computationally intensive. This paper proposes a model-based transfer learning (TL) approach that integrates a convolutional neural network and a long short-term memory network (ConvLSTM), to efficiently train a new stability prediction model that predicts the system operating states (SOSs) and identify critical generators (CGs) in case of instability when the system undergoes enduring topological changes. Numerical analyses on three test cases including the IEEE 39-bus test system, the IEEE 118-bus test system, and the large-scale 2000-bus synthetic power grid in the state of Texas verify the efficiency of the proposed approach and highlight benefits in training time and accuracy, when compared to the state-of-the-art alternatives. Note to Practitioners—As the national power grid goes through transitions towards digitalization and modernization with emerging technologies, maintaining its reliability and resiliency against environmental stressors and cyber attacks remains an urgent need. The power system topology is expected to change more frequently, sometimes to accommodate the proliferation of heterogeneous distributed energy resources and tackle their intermittence, sometimes event-driven due to disruptive events (e.g., faults), and sometimes operator-triggered for maintenance activities and responsive control to return the system back to its normal operating condition. This article is motivated by the need for an efficient and computationally-attractive approach for online situational awareness and real-time transient stability monitoring and assessment of the power grid under an enduring topological change, where the main goal is to identify the power system operating states (SOSs) and critical generators (CGs) in case of instability. Instead of training a new model for each topological change, this paper proposes an adaptive power system transient stability assessment (TSA) method that uses transfer learning (TL) which in return reduces the training time yet with less data than a newly-trained model for each topology change scenario.

Wildfires have been, recently more-frequently, posing significant threats to the safety and reliability of electric power delivery infrastructure, calling for the development of dedicated risk and vulnerability assessment frameworks. Fragility functions are essential tools for probabilistic risk assessment to estimate the failure likelihood of the system components as a function of hazard intensity. Widely exposed to the environment and atmospheric stressors, overhead power line conductors are among the most susceptible electric delivery infrastructure to approaching wildfires. Beyond the state-of-the-art fragility models, this article proposes novel parameterized wildfire fragility functions that can capture the impact of different environmental conditions and wildfire severity measures on overhead power line conductors. In doing so, wildfire behaviour is comprehensively characterized in order to model the temperature change in overhead conductors in the face of progressing wildfires. A novel parametric fragility model, which is a function of a set of physical and environmental features (e.g., conductor height, landscape slope, the dryness of fuel, fuel depth, wind speed, flame length), is then suggested to assess the failure likelihood of conductors given a permissible safety conductor temperature rise. A set of fire features (e.g., fire line intensity, fire rate of spread, and the angle of the fire approaching the conductors) is embedded in the proposed fragility model to capture the wildfire uncertainties. The numerical results reveal that, unlike the commonly-used fragility models, the proposed parameterized fragility function is able to accurately represent the impact of critical physical, environmental, and fire features in determining the vulnerability of power distribution lines when facing wildfire emergencies.

The aggregated entity formed by the distributed photovoltaic (DPV) and energy storage system has the capability to offer multiple services in the electricity markets, reaping the advantages of both energy arbitrage and frequency regulation. This article focuses on developing a bidding strategy and operation plan for an aggregated entity from a profit pursuit perspective. We propose a multi-stage stochastic programming optimization model that divides the original problem into three stages, allowing for feasible and more realistic decision-making while maintaining the relationships among decisions. The proposed model includes a new multi-service portfolio that balances the profit of the two markets and accounts for uncertainties in market prices, DPV output, and regulation signals. To motivate collaboration among stakeholders, we also develop a profit allocation model rooted in the principles of Nash bargaining theory. This model serves as a compelling incentive for all parties involved, encouraging mutually beneficial cooperation. Finally, a numerical case study demonstrates the validity of the proposed models.

The highway transportation system exhibits immense potential for adaptive functioning due to its distinctive operational attributes. This capability permits to harness more renewable energy, leading to a reduction in energy consumption costs. In this article, an integrative energy system optimization model is introduced from the standpoint of highway service zone operators. To simulate the behavior of electric vehicles on highways, an expanded network flow model is formulated considering driving, queueing, and charging actions. This model comprehensively integrates dimensions of time, space, and the state of charge of the vehicles. Consequently, the proposed model is proficient in representing the spatiotemporal and flexible operational traits inherent to electric vehicles. Through a series of numerical case studies executed on a real-world highway system, the effectiveness of the proposed model is demonstrated. These findings substantiate the potential utility and efficacy of our model in enhancing the operation optimization of energy systems within highway transportation systems.