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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 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.

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.

Mobile power sources (MPSs) have promising potential for spatiotemporal flexibility exchange in power distribution systems (DS). They can be strategically employed to enhance the network resilience when facing the aftermath of high-impact low-probability (HILP) events. This article proposes a novel service restoration formulation that models and accounts for the endogenous uncertainty in the MPSs routing and scheduling decision making. The proposed restoration model is formulated as a mixed-integer nonlinear programming (MINLP) problem with nonconvex continuous relaxation. We derive computationally tractable linearization procedures to reformulate the MINLP model as an equivalent mixed-integer linear programming (MILP) problem. Case studies on the IEEE 33-node and 123-node test systems demonstrate the role of incorporating endogenous uncertainties in the decision-making process and the effectiveness of the proposed restoration scheme in boosting the DS resilience.