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The growing utilization of wind energy resources has been evidenced to have had substantial positive impacts on mitigating climate change consequences. Unlike stationary wind turbines, mobile wind turbines (MWTs) can travel along the local transportation system (TS) via a truck, supplying power to microgrids (MGs). This spatiotemporal flexibility can provide significant benefits, including enhancing system resilience in the aftermath of high-impact low-probability (HILP) events and contributing to energy system decarbonization. In this paper, we propose a two-stage stochastic optimization model for the service restoration problem by coordinating the routing and scheduling of MWTs with hydrogen storage systems (HSSs) in MGs. The first-stage problem focuses on optimizing the deployment of MWTs based on the shortest-path information obtained through Dijkstra’s algorithm. The second-stage problem aims to minimize the expected power outage costs while accounting for wind energy uncertainties estimated through a Monte Carlo simulation approach. Case studies on an integrated transportation and energy network – a central Alabama interstate TS and four IEEE 33-node test power systems – demonstrate the effectiveness of the proposed restoration scheme in boosting MGs resilience and reducing carbon emissions.

This paper evaluates the benefits of cooperative utilization of available flexibility for enhancing the performance reliability of Multi-Microgrid (MMG) distribution systems in emergency situations. Interruptible Load Contracts (ILCs) with voluntary load points, with the goal to reduce the penalty cost of unexpected power interruptions during fault events, are considered as the source of flexibility. In this paper, the business as usual where Microgrid Operators (MGOs) independently plan ILCs for emergency operations is compared with cooperative planning of ILCs shared among multiple MGOs. To do so, a mathematical optimization model is developed to optimally allocate ILCs to each MG considering the presence of other MGs in the MMG distribution system. Decision variables include the optimal location and size of ILCs in each MG for a given incentive rate that motivates customers’ enrollment. The model considers grid topological configuration, capacity, and voltage level as the main technical constraints. The model is formulated as a Mixed Integer Linear Programming (MILP) problem whose solution can be simply achieved via off-the-shelf software packages. Simulation results demonstrate the effectiveness of cooperative planning of ILCs in reducing the overall penalty costs of unexpected outages by 10% to 15% compared to the independent planning approaches, depending on the network characteristics.

Abstract 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 This paper presents a new intra‐day intra‐hourly local flexibility market (LFM) framework to exploit distributed energy resources’ (DERs) flexibility capabilities. A technical virtual power plant (TVPP) operates the LFM in which the DER aggregators participate. The TVPP offers the provided flexibility capabilities to wholesale flexibility market (WFM) as well as compensating the intra‐hourly variability in power distribution network. In the proposed framework, the TVPP clears the LFM by considering hierarchical transactions with the aggregator agents to find the market equilibrium in which the DERs’ flexibility capabilities are optimally exploited while all participating agents make profit by trading flexibility capabilities in the LFM. A bilevel optimization model with multiple lower levels is considered to address different agents’ preferences and transactions in the LFM. In the upper‐level problem, the TVPP aims at maximizing its profit while each lower‐level problem represents an aggregator agent's optimization problem. The proposed model is reformulated into a single‐level mixed integer linear programming problem and is implemented on the distribution network connected to Bus 5 of the Roy Billinton test system (RBTS) as well as a 119‐bus test system. The results demonstrate the effectiveness of the model to utilize DERs’ flexibility and provide revenue opportunities for different agents.

This paper proposes a new planning model to coordinate the expansion of electric vehicle charging infrastructure (EVCI) and renewables in power grids. Firstly, individual electric vehicle (EV) charging behaviours are modeled considering EV customers adopting smart charging services as the main charging method and those using fast charging, super fast charging and battery swapping services as a complementary charging approach. Next, EV aggregation and the associated system economic dispatch model are built. A novel model predictive control (MPC) learning approach is then proposed to iteratively learn the correlation between different types of EV charging loads and the EV interactions with renewables and other generating units in modern power grids of the future. The simulation results demonstrate that the proposed approach can be used to quantify the ratio of different types of charging loads in a region and strategically guide on the integration of EVs and renewables to achieve the clean energy transition goals. The proposed framework can also be used to decide charging capacity needs in a charging demand zone.

The virtual power plant (VPP) breaks the geographical restrictions of environment and resource types by virtue of aggregating the distributed energy resources and dispatchable loads. In view of the impact of large-scale energy consumption by users on the operational safety of VPP, based on the existing demand response (DR) researches, this paper adds a penalty mechanism for the reasonable management of loads. For the difference of resources in multiple VPPs, the current researches ignore the energy interaction between different VPPs, and the external operator is introduced innovatively in this paper to coordinate the real-time complementation of regional energy. Considering the uncertainty from renewable energy and load, a two-stage scheduling strategy for VPP with multi-time scale optimization is proposed, including a long-time scale day-ahead scheduling and a short-time scale real-time scheduling. On the basis of DR and unit constraints, the resource scheduling plan is formulated in the day-ahead stage. In the real-time stage, the energy complementation between VPPs is considered under the framework of rolling optimization for the correction of the previous plan. The scheduling of both stages is aimed at the economic operation of the VPP. Simulation results show that the DR flexibly optimizes the load distribution while bringing economic benefits, and the total profit can be increased by up to 6.65%. Moreover, the results show that the energy interaction under the external operator coordination significantly reduces the total correction cost by up to 8.23%, and the correction cost is decreased with the extension of the rolling optimization scheduling period.

Abstract The price‐based unit commitment (PBUC) problem aims to optimise the power generating units' schedules to meet the system demand with the objective to maximise the generation companies' (GENCOs') profit. State‐of‐the‐art PBUC models have taken into account exogenous uncertainties in renewable generation, demand, and price signals. This study proposes a novel PBUC problem formulation with endogenous or decision‐dependent uncertainty (DDU) in the elastic portion of the demand. The proposed PBUC model is formulated as a mixed‐integer non‐linear programming (MINLP) problem with non‐convex continuous relaxation. A concavification approach is developed to reformulate the non‐convex MINLP model as an equivalent mixed‐integer quadratic programming (MIQP) model whose continuous relaxation is convex. Case studies considering GENCOs owning and operating 3, 12, 19, and 40 generating units demonstrate the efficacy of the proposed DDU‐aware PBUC formulation on the GENCOs' anticipated profits.

Massive proliferation of Variable Energy Resources (VERs) in modern power systems has posed a variety of challenges to the reliable operation of the power grid and has, at times, jeopardized the system flexibility. Flexibility is the system’s ability to respond to and cope with the imbalances between supply and demand while managing the uncertainty and variability of VERs and maintaining the power system’s security and reliability within the acceptable margins. Leveraging the system’s available resources and capabilities, the system operators must take strategic actions to mitigate the impacts of VERs on the grid flexibility at a reasonable cost. The concept of flexibility is somewhat novel, which calls for profound studies and analyses to address different aspects of flexibility, but not limited to definition and characterization of standard metrics and indices to measure the power grid flexibility, flexibility-centred operation and planning models for the power grid, etc. This paper provides a comprehensive review of the state-of-the-art research on power system flexibility, including existing definitions and quantification measures, flexible resources, and flexibility products and services in electricity markets.

This paper presents a new structure for non-isolated and non-inverting DC-DC converters with high voltage gain harnessing the fundamentals of the voltage lift technique. The proposed topology is a suitable structure for low voltage applications. The operation principles, the steady-state relations, and different switching strategies to further improve the voltage gain performance of the proposed converter are described. A hybrid utilization of complementary switching approach and simultaneous switching of two switches is proposed to achieve the highest voltage gain in different duty cycles. Furthermore, a theoretical analysis of power losses is provided. The suggested DC-DC converter architecture features high voltage gain, high efficiency, and low stress on semiconductor devices. In order to demonstrate these advantages, the structure is compared with some recently-presented high step-up converters in terms of efficiency, voltage gain, and voltage stress. Moreover, A 200W laboratory prototype is developed with experiments carried out to validate the given theories and feasibility of the proposed converter topology.

Abstract The concept of flexibility is defined as the power systems’ ability to effectively respond to changes in power generation and demand profiles to maintain the supply–demand balance. However, the inherent flexibility margins required for successful operation have been recently challenged by the unprecedented arrival of uncertainties, driven by constantly changing demand, failure of conventional units, and the intermittent outputs of renewable energy sources (RES). Tackling these challenges, energy storage systems (ESS) as one important player of the new power grids can enhance the system flexibility. It, therefore, calls for an efficient planning procedure to ensure flexibility margins by considering ESS's role in modern power systems. This paper proposes a novel mixed integer linear programming (MILP) model for transmission expansion planning (TEP) framework taking into account the role of compressed air energy storage (CAES) integration on improvements in system flexibility. The proposed framework is housed with a quantitative metric of grid‐scale system flexibility, while a new offline repetitive mechanism is suggested to account for the N − 1 reliability criterion. The model is applied to different test systems, where the numerical results demonstrate the impacts of CAES units on system flexibility, investment plans, and the total costs.

Abstract With the widespread deployment of advanced heterogeneous technologies and growing complexity in our modern society, there is an increasing demand for risk‐aware management and joint operation of interconnected infrastructures and lifeline networks. The coordination between Power and Water Networks (PWNs) is urgently needed as water networks are one of the most energyintensive critical infrastructures. This paper proposes a framework for day‐ahead operation optimization and coordination of the interconnected Joint Power and Water Networks (JPWNs). Unlike the state‐of‐the‐art where PWNs are individually operated in their respective domains, we present an integrated framework for PWNs that conjoins the Optimal Power Flow (OPF) mechanisms in power grids with innovative operation models of the water networks. Piece‐wise linearization is applied to the nonlinear hydraulic operating constraints to convert the proposed optimization model into a mixed‐integer linear programming (MILP) formulation. The suggested framework is applied to a 15‐node water network jointly operated with the IEEE 9‐bus and IEEE 57‐bus test power systems. The simulation results show the effectiveness of the proposed framework, resulting in cost reduction and energy‐saving when both systems’ operation is jointly optimized. The results show that the proposed methodology is scalable and computationally‐efficient when applied to larger‐scale systems.

Abstract This paper proposes analytics for reliability assessment of non‐isolated conventional pulse width modulation DC‐DC (NIDC‐DC) converters. This class of converters consists of conventional Buck, Boost, Buck–Boost, Cuk, Sepic and Zeta topologies. The proposed analytics are founded based on the Markov process principles and can effectively capture the effects of duty cycle, input voltage, output power, voltage gain, components characteristics and aging on the overall reliability performance and mean time to failure of the NIDC‐DC converters. Furthermore, the suggested framework takes both continuous and discontinuous conduction modes of each converter into account, with which the open and short circuit faults in the components are analysed. As an important outcome, the most reliable operation region of the NIDC‐DC converters are obtained with respect to different operational parameters, which is useful in design procedure. Eventually, extensive thermal experiments with an appropriate reflection of reliability metric are conducted to measure the components’ temperatures and verify their performance in different operating conditions.

Abstract Power grid response against high‐impact low‐probability (HILP) events could be enhanced by (a) hardening mechanisms to boost its structural resilience and (b) corrective recovery and mitigation analytics to improve its operational resilience. Planning for structural resilience and attempts to find the optimal location of the Tie switches in radially operated power distribution networks that enable harnessing the network topology for maximised resilience against HILP disasters are focussed. This goal is achieved through a novel resilience‐oriented multi‐objective decision making platform, which employs a k‐PEM based probabilistic power flow (PPF) algorithm. The proposed framework offers a decision making analytic embedded with the fuzzy satisfying method (FSM) that characterises the system resilience features, such as robustness, restoration agility, load criticality, and recovered capacity, to assess different network reconfiguration options and select the optimal solution for implementation. The aforementioned resilience features are formulated in nodal level and then aggregated over the entire system to characterise the system‐level objective functions. The performance of the suggested framework is analysed on the IEEE 33‐Bus test system under a designated HILP event, and the applicability on larger networks has been verified on the IEEE 69‐bus test system. The results demonstrate the efficacy and applicability of the proposed framework in boosting the network resilience against future extremes.

Abstract In recent years, increased integration of renewable energy sources (RES) calls for extensive and costly investments in transmission networks. In response, power system decision‐makers try to apply alternative solutions aimed to decrease the imposed investment costs. In this context, the presence of large‐scale energy storage systems (ESSs) in transmission network can be a practical option for deferring investment in expansion plans of transmission lines, alleviating system congestions, and attaining higher flexibility. In this paper, an efficient model is proposed for co‐planning expansion studies of compressed air energy storage (CAES) units and transmission networks. The associated optimisation formulation of co‐planning problem is expressed as a MILP model, which can be efficiently solved. The proposed model is applied on the Garver as well as RTS test systems and N‐1 criterion is considered to address the system reliability performance in expansion studies. The results demonstrate that the proposed co‐planning framework has a superior performance in expansion plans of transmission systems.

This paper sets forth the reliability analysis of conventional isolated pulse width modulation DC-DC (IDC-DC) converters. The IDC-DC converters are categorized into isolated single-switch DC-DC (ISSDC-DC) or multiple-switch DC-DC (IMSDC-DC) converters. The proposed framework encompasses analyzing the impacts of duty cycle, input voltage, output power, transformer turns ratio, components characteristics and time duration on the overall reliability performance of the IDC-DC converters. The suggested reliability assessment is centered on Markov models characterized by taking into consideration all open and short circuit faults on the components in both continuous and discontinuous conduction modes. We further investigate the self-embedded fault tolerant capability of the IMSDC-DC converters under open circuit fault scenarios on the switches, diodes and blocking capacitors, where we offer new reliability analytics. Along with extensive analyses and comparisons, several experimental results are provided to verify the self-embedded fault tolerant capability of IMSDC-DC converters.

Depending on the application in which power electronic converters (PECs) are deployed, failure processes may endanger the desirable performance of PECs. This paper offers holistic insights on reliability modeling of PECs considering dependencies in two simultaneous failure processes, namely gradual wearing-out degradation and vibration sudden degradation. While sudden and gradual degradation processes may individually affect the useful lifetime of PECs, their mutual interdependencies could significantly accelerate the aging mechanisms. A new analytical model for reliability assessment of PECs is proposed that can capture such mutual interdependence of simultaneous failure processes. The proposed analytics are applied to a DC-DC boost converter in a hybrid electric vehicle exposed to both gradual wearing-out and vibration sudden degradations. Monte Carlo-aided numerical results will demonstrate how critical it is to take into account the mutual interdependence in the failure processes when assessing the reliability performance of PECs, failure to do which may lead to inaccurate useful lifetime estimations and the corresponding maintenance and replacement decisions.

The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare.

Widespread outbreaks of infectious disease, i.e., the so-called pandemics that may travel quickly and silently beyond boundaries, can significantly upsurge the morbidity and mortality over large-scale geographical areas. They commonly result in enormous economic losses, political disruptions, social unrest, and quickly evolve to a national security concern. Societies have been shaped by pandemics and outbreaks for as long as we have had societies. While differing in nature and in realizations, they all place the normal life of modern societies on hold. Common interruptions include job loss, infrastructure failure, and political ramifications. The electric power systems, upon which our modern society relies, is driving a myriad of interdependent services, such as water systems, communication networks, transportation systems, health services, etc. With the sudden shifts in electric power generation and demand portfolios and the need to sustain quality electricity supply to end customers (particularly mission-critical services) during pandemics, safeguarding the nation's electric power grid in the face of such rapidly evolving outbreaks is among the top priorities. This paper explores the various mechanisms through which the electric power grids around the globe are influenced by pandemics in general and COVID-19 in particular, shares the lessons learned and best practices taken in different sectors of the electric industry in responding to the dramatic shifts enforced by such threats, and provides visions for a pandemic-resilient electric grid of the future.

Modern power delivery systems are rapidly evolving with high proliferation of power-electronic (PE)-interfaced distributed energy resources (DERs). Compared to the conventional sources of generation, the PE-inter-faced DERs, e.g., solar and wind resources, are attributed substantially different characteristics such as lower overload capability and limited frequency response patterns. This paper focuses on effective management and control mechanisms for PE-interfaced DERs in power distribution systems with high penetration of renewables, particularly under fault, voltage-sag, load variations, and other prevailing conditions in the grid. Aiming at the solutions to enhance the system performance resilience, we introduce an advanced model predictive control (MPC) based scheme to control the DER units, minimize the impact of transients and disruptions, speed up the response and recovery of particular metrics and parameters, and maintain an acceptable operation condition. The performance of the suggested control scheme is tested on a modified IEEE 34-bus test feeder, where the proposed solution demonstrates its effectiveness to minimize the system transient during faults, with an enhanced grid-edge and system-wide resilience characteristics in voltage profiles.

Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons.

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.