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The ability to withstand and recover from disruptions is essential for seaport energy systems, and in light of the growing push for decarbonization, incorporating clean energy sources has become increasingly imperative to ensure resilience. This paper proposes a resilience enhancement planning strategy for a seaport multi‐energy system that integrates various energy modalities and sources, including heating, cooling, hydrogen, solar, and wind power. The planning strategy aims to ensure the reliable operation of the system during contingency events, such as power outages, equipment failures, or extreme weather incidents. The proposed optimization model is designed as a mixed‐integer nonlinear programming formulation, in which McCormick inequalities and other linearization techniques are utilized to tackle the model nonlinearities. The model allocates fuel cell electric trucks (FCETs), renewable energy sources, hydrogen refueling stations, and remote control switches such that the system resilience is enhanced while incorporating natural‐gas‐powered combined cooling, heating, and power system to minimize the operation and unserved demand costs. The model considers various factors such as the availability of renewable energy sources, the demand for heating, cooling, electricity, and hydrogen, the operation of remote control switches to help system reconfiguration, the travel behaviour of FCETs, and the power output of FCETs via vehicle‐to‐grid interface. The numerical results demonstrate that the proposed strategy can significantly improve the resilience of the seaport multi‐energy system and reduce the risk of service disruptions during contingency scenarios. [ABSTRACT FROM AUTHOR]

Safeguarding the nation’s electrical infrastructure and personnel against natural and man-made disasters, and ensuring a continuous, reliable, and resilient supply of energy are among the top priorities for the electric industry. In recent years, wildfires have been remarkably threatening the safety and security of electric power grids demanding innovative frameworks for ensured resilience. Our proposed approach focuses on mitigation of wildfire disruptions that, once occur, can jeopardize the well-being of electrical equipment and the safety of the personnel. The proposed framework first offers a comprehensive wildfire characterization package that can spatiotemporally monitor and analyze the wildfire behaviors, i.e., wildfire intensity, arrival time, and binding paths from ignition points to electrical equipment. This allows power system operators to make proactive decisions before the fire approaches the electrical elements, e.g., power distribution lines. Next, a decision support tool for wildfire management in power grids is proposed such that various local generation resources,—i.e., distributed renewable energy resources and energy storage systems, can be effectively employed to mitigate the wildfire impacts on the power grid. The proposed integrated solution technology ensures a significant reduction in power outages and enhances the safety and resilience of the power grid and the operating personnel. [ABSTRACT FROM AUTHOR]

Wildfires have been growingly recognized as a prominent threat in regions with high temperatures during the summer. Power distribution systems, especially those passing through forest regions, are exposed and highly vulnerable to wildfires. This article provides a general formulation to enhance the operational resilience of power distribution networks equipped with renewable energy resources, e.g., wind and solar energy, micro turbines as well as energy storage systems when exposed to progressive wildfires. The wildfire incident is characterized comprehensively and the dynamic heat balance equations of power distribution branches are used to model the impacts of wildfires on overhead line conductors. A mixed-integer quadratic optimization formulation is applied to optimally operate and coordinate all local energy resources to reduce load outages and enhance the system resilience. The applied framework is evaluated on the IEEE 33-node test system. Comprehensive sensitivity analyses are conducted to assess the efficacy of the applied framework, where the numerical results reveal the resilient operation of power distribution networks in the face of wildfire emergencies. [ABSTRACT FROM AUTHOR]

With the spatial flexibility exchange across the network, mobile energy storage systems (MESSs) offer promising opportunities to elevate power distribution system resilience against emergencies. Despite the remarkable growth in integration of renewable energy sources (RESs) in power distribution systems (PDSs), most recovery and restoration strategies do not unlock the full potential in such resources due to their inherent uncertainty and stochasticity. This paper develops a novel restoration mechanism in PDSs for routing and scheduling of MESSs integrated with stochastic RESs to achieve agile system response and recovery in facing the aftermath of high-impact low-probability (HILP) incidents. The proposed integrated model is presented as a non-convex non-linear stochastic optimization formulation with joint probabilistic constraints (JPCs). The problem is equivalently reformulated to a tractable mixed-integer linear programming (MILP) model that can be solved by commercial off-the-shelf solvers. Case studies on the IEEE 33-node and 123-node test systems demonstrate the effectiveness and scalability of the proposed framework in boosting the system resilience. This is achieved via effective routing and scheduling of MESSs jointly managed with dynamic network reconfiguration in presence of stochastic RESs. [ABSTRACT FROM AUTHOR]

DC-DC Floating Interleaved Boost Converter (FIBC) is recently introduced for converting low-level voltage generated by a renewable energy source to high-level voltage required for AC inverters. Although a desired voltage is expected at the output, designing a proper voltage gain for FIBC is challenging due to different types of uncertainties. For instance, the voltage generated by the energy source and, therefore, the input voltage of FIBC may change by a variety of parameters including external load. Furthermore, parametric uncertainty and measurement noise are other sources which can affect the control procedure. As a result, voltage gain for a fixed switching duty cycle may be uncertain. It demands a robust approach to guarantee the control performance under uncertainties without the need for individually tuning controller for each single converter. In this work, a robust model predictive control is employed to regulate the output voltage at the desired level despite the existing uncertainties. Controller parameters are fixed for any FIBC within the uncertainty range and further tuning is not required for individual converters. In addition, unlike the conventional controllers, the suggested controller is able to handle input-output constraints. Performance of the suggested controller is investigated through simulations carried out in MATLAB and the superiority of the proposed approach is verified over non-robust model predictive framework. [ABSTRACT FROM AUTHOR]

Flexible ramping product (FRP) has been recently launched in a number of electricity markets to enhance the power grid flexibility and accommodate additional deals of uncertainty introduced by renewable energy resources. The FRP procures additional ramp capacities provided by the dispatchable generating units and consumers in the real-time market (RTM) with the aim to enhance the ability of supply to closely follow the net-load at the next immediate time-interval. An important challenge in widespread adoption of this technology is a key question: Does the available energy capacity of the RTM participants contain sufficient ramp capacity to tackle the net-load uncertainties? Indeed, shortage of the systems ramp capacity may not only jeopardize its flexibility, but also may cause drastic fluctuations in the RTM energy prices. To address this challenge, this paper proposes a novel optimization framework to harness the procured spinning reserve services in the day-ahead market (DAM) for FRP provision in the RTM while meeting the system reliability requirements. On this basis, the FRP will be simultaneously procured from the RTM energy and DAM spinning reserve capacities. Furthermore, a new payment mechanism is proposed to compensate the portion of spinning reserve utilized for FRP procurement in the RTM. The proposed framework is simulated on the IEEE 118-bus test system and the results verify its effectiveness in modern electricity markets. [ABSTRACT FROM AUTHOR]

Allocation of the power losses to distributed generators and consumers has been a challenging concern for decades in restructured power systems. This paper proposes a promising approach for loss allocation in power distribution systems based on a cooperative concept of game-theory, named Shapley Value allocation. The proposed solution is a generic approach, applicable to both radial and meshed distribution systems as well as those with high penetration of renewables and DG units. With several different methods for distribution system loss allocation, the suggested method has been shown to be a straight-forward and efficient criterion for performance comparisons. The suggested loss allocation approach is numerically investigated, the results of which are presented for two distribution systems and its performance is compared with those obtained by other methodologies. [ABSTRACT FROM AUTHOR]