Accurate forecasting of household energy load and generation is critical for energy management systems, especially in the context of the rapid development of smart grids and renewable energy. However, privacy concerns often arise when handling sensitive household energy data. Federated learning (FL), as a privacy-preserving distributed learning method, enables collaborative model training across households without exposing sensitive energy data. Nevertheless, traditional federated learning still faces challenges in meeting the personalized needs of households due to the differences in consumption patterns among households, which affects the forecasting accuracy. In this paper, we propose a novel personalized FL method that combines mixture of expert and conformal predictions to improve forecasting accuracy while quantifying prediction uncertainty. Our method utilizes personalized federated learning (PFL) to develop a personalized model for each household that captures its unique consumption behavior. The mixture of experts dynamically integrates global and local personalized models to enhance prediction and adaptability. In addition, uncertainty quantification is achieved through conformal prediction, providing reliable prediction interval. Experiments conducted on two real-world household energy datasets demonstrate that our method outperforms existing approaches in terms of prediction accuracy and uncertainty assessment.

The pervasive adoption of inverter-interfaced resources in 100% renewable power systems fundamentally alters voltage regulation dynamics and renders classical margin - estimation techniques computationally prohibitive. This paper introduces a unified, Jacobian-free Krylov-Arnoldi (JFKA) framework for automated estimation of system static voltage stability (SVS). First, inverter current-limiting behavior is captured by a smooth S-type function, preserving continuous power-flow structure across both grid-forming (GFM) and gridfollowing (GFL) control modes. Building on singularity theory, we derive a Jacobian-free stability indicator that pinpoints the onset of voltage collapse without explicit derivative evaluation. To efficiently solve the resulting large-scale, transcendental power-flow equations, we embed a reduced-order Arnoldi process within a Newton-Krylov solver, yielding rapid convergence and markedly lower memory footprint. Case studies on a modified IEEE-39 bus network with full renewable penetration demonstrate that our method accurately tracks voltage regulation limits under varied load-growth scenarios and automatic mode switches.

Distributed computing faces a persistent multi agent trust dilemma. In the computation process, participants may maliciously attack the system for personal gain by providing false data. Blockchain provides a possible solution for this problem with its immutability and multi-party consensus. However, existing blockchain data throughput has long been queried owing to its exorbitant time and energy costs by consensus mechanisms. This paper proposes a light blockchain structure in distributed computing scenarios. A No-Proof consensus (NPC) mechanism is designed for distributed computing problems with no extra proving process such as Proof-of-Work or Proof-of-Stake. This consensus mechanism notices that the distributed computing result has proven to be valid in the computation process automatically, which does not need to be verified again in the consensus mechanism. Further, the single-threaded data processing ability of the blockchain structure certainly leads to low efficiency when applied to distributed computation problems. An NPC-based blockchain is constructed in this paper to solve this problem. In this structure, the distributed computing is done off chain, and an oracle is designed to upload the computing results to the blockchain asynchronously. Upon the contribution in this paper, a distributed energy trading model is provided as a case study to verify the superiority of the designed blockchain in contrast with other similar structures.

The operation and control of active distribution networks (ADNs) are becoming increasingly important due to the high penetration of renewable energy (RE). The inherent uncertainty of RE can affect the stability and efficiency of ADN operations. To mitigate the inherent uncertainty and rapid variability of the high RE penetration in ADNs, this article uses an online ADN reconfiguration (ADNR) approach to ensure swift responses to RE fluctuations. Unlike traditional deep reinforcement learning (DRL)-based methods, which typically model the ADNR as a Markov decision process (MDP) and rely on historical ADN data to train the DRL agent, this approach may lead to a mismatch between the MDP's characteristics and the actual ADNR and pose challenges in handling scenarios that do not exist in the training data. To address this issue, this article proposes an online-offline DRL framework for online ADNR. Initially, during the offline stage, ADNR is formulated as a state-driven Markov decision process, which incorporates the operational characteristics of the ADN. Following this, a state-driven proximal policy optimization (SD-PPO) algorithm is proposed to enhance the generalization capability of DRL. In the subsequent step, we present the optimized action proximal policy optimization (OA-PPO) algorithm, which performs personalized training based on SD-PPO to further improve DRL performance in the online stage. The proposed approach is applied to three IEEE ADN systems. Numerical results demonstrate the effectiveness of our approach in reducing power loss and enhancing RE accommodation. Furthermore, detailed comparisons with other DRL and traditional ADNR algorithms confirm the superior computational performance of our proposed method.

There has been a growing incidence of adverse weather events leading to substantial power black outs in recent years. Proper hardening of the distribution system significantly improves its resilience to extreme climatic conditions. In this paper, we propose a set of four resilience indices to evaluate the resilience of the distribution system from various perspectives and combine them into a single index to get a holistic measure of distribution feeder resilience. This resilience framework has the capability to analyze realistic performance curves (PCs) of the distribution system with multiple periods of performance degradation and recovery. Additionally, a greedy resilience hardening strategy is proposed which uses the resilience framework and historical storm outage data for determining the set of lines to be hardened to maximally improve the resilience of the distribution feeder. The proposed resilience framework and greedy line hardening strategy are implemented on a real-world distribution feeder (Feeder 91) to demonstrate their efficacy.

Reactive power optimization (RPO) is a key task in the operation and control of active distribution networks (ADNs). The nonlinear power flow constraints and the integers introduced by voltage regulator (VR) constraints make the nonconvex RPO model difficult to solve. In this paper, a RPO model is proposed considering the nonuniform tap ratios of VRs. The three phases in VRs are independently controlled and the phase coupling effects are strictly considered. Moreover, to deal with the bilinear relationship among continuous complex voltage phasors and discrete tap ratios, the voltage phasor matrix is decoupled into real and imaginary parts, and a status variable method is proposed to exactly linearize the bilinear terms. Further, the nonlinear power flow constraints are relaxed to a set of second-order cone constraints without neglecting the coupling effect among the three phases and are proved to be equivalent to the semi-definite constraints, which does not require the rank-1 verification and is more efficient and scalable. Thus, the original nonconvex RPO for three-phase unbalanced ADNs is simplified to a mixed-integer second-order cone programming (MISOCP) model. Case studies validate the model’s effectiveness, and the computational efficiency meets practical requirements. Note to Practitioners—This paper simplifies the complex problem of reactive power optimization for active distribution networks with unbalanced three-phase power flows. We offer a novel RPO model improving the typically nonconvex and nonlinear optimal power flow problem, and addressing the particular challenges imposed by voltage regulators optimization with nonuniform tap ratios. Independent control of the VR phases and accurate incorporation of phase coupling effects have been realized, greatly simplifying the optimization process. The transformation of nonlinear power flow constraints into a computationally efficient set of second-order cone constraints eliminates the need for cumbersome rank-1 verification. Our mixed-integer second-order cone programming model integrates smoothly with standard optimization procedures, improving voltage regulation and network stability. The effectiveness and efficiency of the approach have been validated in case studies, offering a viable tool for industry application.

The multimodal dynamic stability (MDS) is a critical issue for constructing power systems with 100% renewable energy (PSRE). This paper proposes a multimodal unified control (MUC) method for enhancing the MDS in PSRE. First, an improved Heffron-Phillips model is established to demonstrate the mechanism of multimodal dynamic instability (MDI). Next, a third-order external subsystem model of the grid-forming renewable energy source (GFM-RES) is derived by the Lie derivative. Then, the MUC architecture is designed by utilizing the Lyapunov theory in the third-order external system. Finally, the application of the proposed MUC method is verified by analyzing the pertinent results for the modified IEEE 11-bus system with a 100% renewable energy generation.Note to Practitioners—The construction of PSRE has attracted widespread attention from the academic and engineering communities. Dynamic stability is one of the key issues faced in building PSRE. This work presents a MUC of GFM-RESs for improving the MDS of the PSRE. Practitioners should be able to apply the MUC to GFM-RESs like battery energy storage system, wind turbines and photovoltaic units. The MUC is designed using the Lie derivative and Lyapunov theory. From a practical point of view, the MUC achieves the MDS from a control perspective, without additional investment required.

This paper proposes a novel voltage-adaptive strategy (VAS) considering current limits of renewable energy resources (RESs), to enhance the transient stability of the power system with 100% renewable energy (PSRE). First, taking the current limits into account, a new transient stability mechanism is revealed by deriving the fault critical clearing time (CCT) of a PSRE with two RESs. Next, leveraging the Lie derivative and the Lyapunov theory, a novel adaptive control method is proposed, which is more in line with the output saturation characteristic of RESs. Then, VAS is formulated based on the proposed adaptive control method for enhancing the transient stability of PSRE. Finally, the proposed VAS is verified on a modified IEEE 11-bus system with 100% renewable energy generation.Note to Practitioners—Transient control can be considered one of the challenges in power system field for constructing the PSRE, which has significant implications for reducing carbon emissions. This work presents a VAS of RESs taking the current limits into account for improving the transient characteristics of the PSRE. Practitioners should be able to apply the VAS to RESs represented by wind farms and photovoltaic power stations. The VAS implements the transient control through the Lie derivative and the proposed adaptive control law. From a practical point of view, it should be highly emphasized that the proposed VAS can achieve transient control of the PSRE without increasing any investment and bringing negative impacts.

Integrating energy storage systems (ESSs) into the railway power flow controller (RPFC) offers a promising path to enhance the interaction capability and connection compatibility between the railway power systems (RPSs) and the utility. Despite its technical advantages, full consideration must be given to the media-type & configuration of ESS, the rating of RPFC, and the control framework that can compatibly integrate the control & planning for cost-efficiency improvement. For this problem, a novel concept that takes the real-time control and off-line configuration approach in a unified framework is proposed for hybrid ESS-integrated RPFC (HESS-RPFC) in this paper, in which the comprehensive techno-economic performances of the whole system are taken into account. First, a unified analytical power regulation framework is established for HESS-RPFC, in which the functions of energy management (EM) and power quality control are seamlessly integrated. To realize EM, improve RPS’s grid-connection compatibility, and reduce the rating of RPFC, a double-layer power regulation strategy of HESS-RPFC is developed on the proposed framework. Subsequently, a lifetime net benefit-oriented optimization model is formulated and solved, through which, the optimal rating configuration of the whole system and HESS’s mode-triggering thresholds can be obtained. Finally, a comprehensive performance evaluation based on real-measured data is conducted, revealing that HESS-RPFC with the proposed method provides superior economic benefits over single-media ESS schemes, ensures satisfactory grid-connection compatibility for the RPS, and reduces the RPFC’s rating by nearly 46%. Additionally, the real-time execution feasibility of the proposal is demonstrated by hardware-in-the-loop tests.

Optimization problems arise in various domains, ranging from power systems to resource allocation and large network management. An effective solution to these problems in a distributed manner has become a crucial research area due to the increasing scale and complexity of modern systems. In this article, we propose a novel local projection global tracking (LPGT) decentralized algorithm based on the Alternating Direction Method of Multipliers for general optimization problems. Unlike existing distributed methods that require problem-specific adaptations or centralized coordination, LPGT provides tailored solutions for handling generic local equality and inequality constraints, as well as global coupled equality and inequality constraints. Moreover, a projection-based analytical scheme is designed to handle generic local equality and inequality constraints without iterative subproblem solvers, and a fully decentralized deviation tracking mechanism is constructed to enforce both global coupled equalities and inequalities constraints via agent communications, eliminating the need for a central coordinator. Case studies for a local energy trading model are proposed to verify the feasibility and applicability of the algorithm.

This paper proposed sufficient conditions of the zero duality gap for the second-order conic programming approach of the DC optimal power flow that quadratically embeds the transmission loss. The mechanism of the changes for locational marginal price with differently selecting the slack bus is revisited, based on which a load-weighted distributed slack bus method is introduced. Furthermore, a second-order conic programming-based convexification method is proposed by employing the duality analysis. A favorable property of the proposed method is that mild sufficient conditions of the zero duality gap can be derived by the analysis of Karush-Kuhn-Tucker conditions. Numerical results illustrate the effectiveness of the proposed method and physically prove the proposed sufficient conditions. Compared with traditional and similar market-clearing methods, the proposed method has better performance on convergence, robustness, and accuracy. Note to Practitioners—This paper was motivated by the problem of the pricing mechanism in the deregulated electricity market but it is also applicable to rapidly solving the power flow of power systems with sufficient reactive power. Existing approaches use the B-coefficient to estimate the transmission loss in the model non-linearly. In this paper, an improved approach considering transmission loss is proposed, which further introduces the distributed slack bus method and subsequently employs the second-order cone programming algorithm. The proposed method hedges the risk of appearing multiple solutions or lack of accuracy. Rigorous mathematical proofs are derived to support the method. For industrial practice, three sufficient conditions suitable for different kinds of power systems are given, indicating when the proposed approach can be efficiently applied. The proposed method can be integrated into the existing electricity market trading platform for independent system operators and facilitates real-time market clearing and electricity price calculation. Future research is expected to improve the accuracy of the model based on nodal power balance equations.

Security-constrained unit commitment (SCUC) is one of the most fundamental optimization problems in power systems. The objective of SCUC is to minimize the operating cost while respecting both system-wide and generator-specific constraints. It leads to a large-scale and mixed-integer programming (MIP) model with a large number of binary decision variables which is difficult to solve. This paper, based on the convex hull theory of single-unit, proposes a linearization method for the hydro-thermal SCUC problem with decoupled thermal units and variable-head hydro units. Then, the strategy of embedding two types of convex hulls in a multi-unit commitment and the heuristic method of constructing a feasible solution are designed, by which the multi-UC is approximated from large-scale mixed-integer programming to linear programming that can be solved in polynomial time. Finally, we theoretically prove that the optimal solution of the proposed LP model is always better than that of the Lagrangian Relaxation model. Numerical experiments on several large-scale test systems demonstrate the effectiveness and efficiency of the proposed method. Note to Practitioners— This paper proposes a linear programming model for the SCUC problem by lifting up to a higher-dimensional space. It realizes an important innovation in reducing the computational complexity of SCUC from the perspective of linearization. The proposed method can be well applied to large-scale long-term unit commitment problems. To better use this method, the following two properties should be highlighted: 1) the error of the proposed method is less than the Lagrangian relaxation method and decreases with the increasing system scales and 2) the computational efficiency of the proposed method is 10-100 times faster than that of the MIP model. We have tested many practical power systems and find that the error of the proposed LP model is usually very small compared with the precise MIP while the computational performance is significantly improved. In some practical cases, the decision makers usually do not want to find the precise optimal solution while only an approximation under a fast speed, because the boundary condition is imprecise. The proposed method is useful. Besides, for the cases that need the precise optimal solution, the proposed method can provide a high-quality initial solution for the MIP model to accelerate the convergence.

Electric vehicles (EVs) have been gaining great popularity in recent years, but the lack of adequate infrastructure and the long battery charging time have hindered their further development. Therefore, a battery charging and swapping system (BCSS) can solve this problem by arranging battery charging and distributing the battery swapping system (BSS) to various locations while participating in the demand response of both power and transportation networks through time-of-use tariffs and congestion price. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in the continuous-time domain. Specifically, the battery charging system will arrange the optimal battery charging strategy by a rectangle packing algorithm. Furthermore, the logistics system will set up a transportation dispatch model for the battery charging system to deliver the charged batteries from the battery charging system to the battery swapping system and then retrieve them. The combined model is involved in the demand side response considering the price. Simulation studies for different cases verify the effectiveness of the proposed model.

In-depth understanding of the multi-energy consumption behavior pattern is the essential to improve the management of multi-energy system (MES). This paper proposes a data-driven three-stage adaptive pattern mining approach for multi-energy loads, which addresses the issues of complex multi-dimensional time-series mining, uncommon daily loads discovery, typical load classification and parameter setting requiring user intervention. In the first stage, the relative state changes over time between different energy loads are excavated based on Autoplait, which realizes time pattern discovery, segmentation and match for multi-dimensional loads. In the second stage, adaptive affinity propagation (AAP) considering trend similarity distance (TSD) is proposed to classify loads into common and uncommon clusters, where uncommon loads are eliminated and daily pattern is obtained by taking average of common loads. In the third stage, AAP with windows dynamic time warping (WDTW) identifies various profiles to obtain typical pattern of daily loads. Specifically, pattern mining provides the key information of multi-energy loads, which is significant to the applications for the demand side, such as load scene compression, load forecasting and demand response analysis. A case study uses MES data from Arizona State University to verify the effectiveness and practicality of the proposed approach.

Nowadays, rooftop photovoltaic (PV) panels are getting enormous attention as clean and sustainable sources of energy due to the increasing energy demand, depreciating physical assets, and global environmental challenges. In residential areas, the large-scale integration of these generation resources influences the customer load profile and introduces uncertainty to the distribution system's net load. Since such resources are typically located behind the meter (BtM), an accurate estimation of BtM load and PV power will be crucial for distribution network operation. This article proposes the spatiotemporal graph sparse coding (SC) capsule network that incorporates SC into deep generative graph modeling and capsule networks for accurate BtM load and PV generation estimation. A set of neighboring residential units are modeled as a dynamic graph in which the edges represent the correlation among their net demands. A generative encoder-decoder model, i.e., spectral graph convolution (SGC) attention peephole long short-term memory (PLSTM), is devised to extract the highly nonlinear spatiotemporal patterns from the formed dynamic graph. Later, to enrich the latent space sparsity, a dictionary is learned in the hidden layer of the proposed encoder-decoder, and the corresponding sparse codes are procured. Such sparse representation is used by a capsule network to estimate the BtM PV generation and the load of the entire residential units. Experimental results on two real-world energy disaggregation (ED) datasets, Pecan Street and Ausgrid, demonstrate more than 9.8% and 6.3% root mean square error (RMSE) improvements in BtM PV and load estimation over the state-of-the-art, respectively.

Short-term forecasting of non-conforming net load (STFNL) plays a vital role for operating a power system in secure and efficient manner. However, power system load consumption is affected by a variety of external factors and thus includes high levels of volatilities. These volatilities cause STFNL to be a challenging task and inaccurate as more distributed energy resources (DERs) continue to integrate into the power grid. Estimating the average hourly locational distribution of system loads becomes a constant daily challenge to transmission system operators as more non-visible DERs are connected to the distribution system. This paper proposes two commonly used machine-learning and deep learning methods used for load forecasting, i.e., the ensemble bagged and the long short-term memory neural network method. The advantages, features and applications of these methods are used to propose a fusion forecasting model that improves the forecasting accuracy. Additionally, data engineering and preprocessing options are used to increase the accuracy of the proposed model. A comparative study based on real-world transmission grid net load data is performed to verify that the proposed methodology is capable of reaching a relatively higher forecasting accuracy with lower error indices.

With the increasing penetration of renewable energy and flexible loads in smart grids, a more complicated power system with high uncertainty is gradually formed, which brings about great challenges to smart grid operations. Traditional optimization methods usually require accurate mathematical models and parameters and cannot deal well with the growing complexity and uncertainty. Fortunately, the widespread popularity of advanced meters makes it possible for smart grid to collect massive data, which offers opportunities for data-driven artificial intelligence methods to address the optimal operation and control issues. Therein, deep reinforcement learning (DRL) has attracted extensive attention for its excellent performance in operation problems with high uncertainty. To this end, this article presents a comprehensive literature survey on DRL and its applications in smart grid operations. First, a detailed overview of DRL, from fundamental concepts to advanced models, is conducted in this article. Afterward, we review various DRL techniques as well as their extensions developed to cope with emerging issues in the smart grid, including optimal dispatch, operational control, electricity market, and other emerging areas. In addition, an application-oriented survey of DRL in smart grid is presented to identify difficulties for future research. Finally, essential challenges, potential solutions, and future research directions concerning the DRL applications in smart grid are also discussed.

In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system’s vulnerability to cascading outages. A cascading outage simulator (CS), which uses off-line AC power flows, is proposed for generating training data. The initial states are randomly selected and the CS model is deployed for each initial state, where power system generation and loads are adjusted dynamically and power flows are redistributed to quantify the vulnerability metric. Furthermore, the proposed hybrid machine learning model deploys a combined Support Vector Machine (SVM) classification and Gradient Boosting Regression (GBR) to improve the learning precision. The classification model is trained by SVM, which divides the data into two categories with and without load shedding. Then, GBR is adopted only for the data with load shedding to determine the relationship between input power outage states and the vulnerability metric. The proposed vulnerability analysis approach is applied to several test systems and the results are analyzed. Note to Practitioners—The power system vulnerability can be quantified by cascading outage simulations. However, there are two challenges: i) there are a huge number of possible initial states and we cannot enumerate all these initial states for the cascading outage simulation. Neither can we precisely quantify the bus vulnerability. ii) The cascading outage simulation may be time-consuming for large-scale power systems, which is challenging for the online application. To address the above challenges, we expect to design a machine learning technique to predict the power system vulnerability, which can train the model in an offline way and then use it for the online application. Firstly, since there is not enough operation data from practical power systems, we develop a cascading outage simulator, using off-line AC power flows, for generating synthetic training data. Secondly, we observe that the training precision by directly applying the regression model may be very poor because the output of the machine learning model may take on an uneven distribution concerning input parameters. Thus, we propose a hybrid machine learning model with a combined classification and regression method, where the classification model is employed to remove the data without the load shedding, and the regression model then determines the relationship between input power outage states and the vulnerability metric. The proposed model and method have been tested on several systems including a practical large-scale Polish power system to show the effectiveness.

Load aggregators (LAs) play a key role in fully tapping the demand response (DR) resources of small and medium-sized end-users to enable a more flexible power grid. In the ancillary service market, the LA can provide DR to the system by aggregating the resources of its users. In response to the issued DR program, end-users offer to provide DR resources. To help optimize the user bidding strategy, an evolutionary game model is presented here in view of the bounded rationality of bidders. A combined Q-learning and compound differential evolution (CDE) algorithm is proposed to deal with the problems of incomplete information and uncertainties in the opponents’ decision-making, and prevent the evolutionary stable strategy (ESS) from falling into a local optimum. Moreover, a cloud-computing-based framework is designed and agent servers are introduced to protect data privacy. Numerical results show that by adopting the proposed algorithm, the user's bidding price keeps slightly lower than the opponents’ price which guarantees its revenue remains on a high level. This indicates that the proposed algorithm has good adaptability for addressing incomplete information and uncertainties in opponents’ decision-making.

This paper presents a new anti-islanding protection (AIP) scheme for inverters in integration of distributed generation (DG) sources into the grid. The main advantage of this proposed AIP scheme is that it can be embedded into the control of DG inverters without a need of additional part in the hardware of the inverter and any change in the controller. The AIP scheme does not rely on a communication network. It detects the islanding condition based on continuous monitoring of the active power, the reactive power, and the voltage magnitude mismatch, which is shown to be an alternative to the grid impedance measurement technique. The AIP scheme isolates the inverter from the grid after the grid experiences an islanding condition for all possible scenarios of islanding. A detailed modeling of its implementation in a DG inverter is presented and the performance of the AIP scheme is verified by simulations and experiments.

This paper presents a control-loop stability analysis and parameter design of the grid-connected virtual synchronous machine (VSM) inverters that enables the VSM operate in a very weak grid condition. In order to study the stability and design the parameters, a line-frequency-averaged small-signal model of the VSM inverter has been derived. A transfer function of the control-loop has been obtained, including the coupling resulting from the inherent nature of the VSM as both active power and reactive power related to the voltage and frequency. An example of parameter design is presented by using the derived model. It is shown that the designed parameters ensure the stability and robustness of the VSM inverters connected into a weak grid with a wide range of the grid impedance variation.

This paper presents the application of cybersecurity to the operation and control of distributed electric power systems. In particular, the paper emphasizes the role of cybersecurity in the operation of microgrids and analyzes the dependencies of microgrid control and operation on information and communication technologies for cybersecurity. The paper discusses common cyber vulnerabilities in distributed electric power systems and presents the implications of cyber incidents on physical processes in microgrids. The paper examines the impacts of potential risks attributed to cyberattacks on microgrids and presents the affordable technologies for mitigating such risks. In addition, the paper presents a minimax-regret approach for minimizing the impending risks in managing microgrids. The paper also presents the opportunities provided by software-defined networking technologies to enhance the security of microgrid operations. It is concluded that cybersecurity could play a significant role in managing microgrid operations as microgrids strive for a higher degree of resilience as they supply power services to customers.

This paper focuses on the role of networked microgrids as distributed systems for enhancing the power system resilience against extreme events. Resilience is an intrinsically complex property which requires deep understanding of microgrid operation in order to respond effectively in emergency conditions. The paper first introduces the definition and offers a generic framework for analyzing the power system resilience. The notion that large power systems can achieve a higher level of resilience through the deployment of networked microgrids is discussed in detail. In particular, the management of networked microgrids for riding through extreme events is analyzed. In addition, the merits of advanced information and communication technologies (ICTs) in microgrid-based distributed systems that can support the power system resilience are presented. The paper also points out the challenges for expanding the role of distributed systems and concludes that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.

The restructuring of electricity has introduced new risks associated with the security of natural gas infrastructure on a significantly large scale, which entails changes in physical capabilities of pipelines, operational procedures, sensors and communications, contracting (supply and transportation), and tariffs. This paper will discuss the essence of the natural gas infrastructure for supplying the ever-increasing number of gas-powered units and use security-constrained unit commitment to analyze the short-time impact of natural gas prices on power generation scheduling. The paper analyzes the impact of natural gas infrastructure contingencies on the operation of electric power systems. Furthermore, the paper examines the impact of renewable sources of energy such as pumped-storage units and photovoltaic/battery systems on power system security by reducing the dependence of electricity infrastructure on the natural gas infrastructure. A modified IEEE 118-bus with 12 combined-cycle units is presented for analyzing the gas/electric interdependency.

This paper reviews the status of security analyses in vertically integrated utilities and discusses the impact of system security on the operation and the planning of restructured power systems. The paper is focused on the static security rather than the dynamic security of power systems. The paper also discusses assumptions, functions, and calculation tools that are considered for satisfying power systems security requirements. In addition, the security coordination among time-based scheduling models is presented. In particular, real-time security analysis, short-term operation,midterm operation planning, and long-term planning are analyzed. The paper highlights issues and challenges for implementing security options in electricity markets and concludes that global analyses of security options could provide additional opportunities for seeking optimal and feasible schedules in various time scales.