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Reliability evaluation has developed a trend of embedding unit commitment to replace power flow for more accurate operation strategies, while its potential reverse, embedding reliability evaluation in unit commitment, has yet to see equal exploration. This unrecognized, yet meaningful, technical attempt will encounter modeling and computational burden, largely caused by the recursive function and non-convex structure of numerical reliability evaluation equipped with the necessary repair mechanism. To tackle this challenge, this study proposes a linear routine that overcomes the non-convexity of reliability evaluation and accommodates it in unit commitment, which is expected to bring optimal operation and reliability evaluation to a new era by enabling them both conducted at the same pace. [ABSTRACT FROM AUTHOR]

Voltage regulation and proper load-sharing are two main control objectives of DC microgrids which the consensus-based secondary control can be regarded as a feasible approach to achieve them. Using this approach, in addition to the stable voltage regulation, the load is shared according to the nominal potential of the distributed generation units. Nevertheless, the conventional consensus-based secondary control uses the time-triggered communication paradigm that leads to a high communication burden due to transmitting and receiving unnecessary data. In this paper, to prevent excessive data-exchanging, the mentioned approach is upgraded with a novel event-triggered scheme. The system stability analysis under the suggested event-triggered secondary controller is assessed using the Lyapunov theorem. Furthermore, the Zeno-freeness feature of the proposed solution is mathematically proved after assessing uniformly ultimately bounded stability and extracting the current per-unit and voltage derivatives dynamic as the consensus algorithm’s external inputs. The proposed approach is applied to a DC microgrid, and its performance is validated under different scenarios using Simulink/MATLAB environment. Moreover, the experimental tests prove the merits of the suggested method. [ABSTRACT FROM AUTHOR]

Penetration of renewable energy resources (RERs) in the power grid continues to increase as we strive toward a greener environment for the future. While they have many advantages, most RERs possess little or no rotational kinetic energy, thereby threatening the frequency stability of future power grids. Energy storage systems (ESSs) can be used to mitigate this problem, as they are capable of providing virtual inertia to the system. This paper proposes a novel analytical approach for sizing ESSs to provide inertial support to the grid and maintain frequency stability in presence of RERs. This method analytically estimates the total inertia of the system using probability distributions of the outage states of conventional generators and RERs. RERs are modeled as multi-state units, considering the variability of their output power due to changes in weather conditions and also the forced outages of their equipment. The ESS is utilized to provide virtual inertia to the grid in the form of active power once the estimated system inertia falls below the minimum required during a disturbance. The efficacy of the proposed approach is tested on the IEEE 39 bus system and the results obtained by the analytical approach are validated using Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

We present a threshold-based cardinality inimization formulation for the security-constrained economic dispatch problem. The model aims to minimize the operating cost of the system while simultaneously reducing the number of lines operating in emergency operating zones during contingency events. The model allows the system operator to monitor the duration for which lines operate in emergency zones and ensure that they are within the acceptable reliability standards determined by the system operators. We develop a continuous difference-of-convex approximation of the cardinality minimization problem and a solution method to solve the problem. We also present mixed-integer programming reformulation of the cardinality minimization problem. Our numerical experiments demonstrate that the cardinality minimization approach reduces the overall system operating cost as well as avoids prolonged periods of high electricity prices during contingency events. [ABSTRACT FROM AUTHOR]

We propose a chance-constrained formulation for the problem of dimensioning frequency restoration reserves on a power transmission network. We cast our problem as a two-stage stochastic mixed integer linear program, and propose a heuristic algorithm for solving the problem. Our model accounts for the simultaneous sizing of both upward and downward reserves, and uncertainty driven by imbalances, contingencies and available transmission capacity. Our core methodology is further adapted in order to minimize inter-zonal flows and in order to split reserve requirements between automatic and manual frequency restoration reserves. We apply our methodology to the problem of sizing reserves in the four load frequency control areas of the Swedish power system. We demonstrate the benefits of our method in terms of decreasing reserve requirements in the absence of reserve sharing, we analyze the spatial allocation of reserves, and we perform various sensitivity analyses. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

As an important application in the power system operation and electricity market clearing, the network-constrained unit commitment (NCUC) problem is usually executed by Independent System Operators (ISO) in an open-looped predict-then-optimize (O-PO) process, in which an upstream prediction (e.g., on renewable energy sources (RES) and loads) and a downstream NCUC are executed in a queue. However, in the O-PO framework, a statistically more accurate prediction may not necessarily lead to a higher NCUC economics against actual RES and load realizations. To this end, this paper presents a closed-loop predict-and-optimize (C-PO) framework for improving the NCUC economics. Specifically, the C-PO leverages structures (i.e., constraints and objective) of the NCUC model and relevant feature data to train a cost-oriented RES prediction model, in which the prediction quality is evaluated via the induced NCUC cost instead of the statistical forecast errors. Therefore, the loop between the prediction and the optimization is closed to deliver a cost-oriented RES power prediction for NCUC optimization. Lagrangian relaxation is adopted to accelerate the training process, making the C-PO applicable for real-world systems. Case studies on an IEEE RTS 24-bus system and an ISO-scale 5655-bus system with real-world data show that the proposed C-PO can effectively improve the NCUC economics as compared to the traditional O-PO. [ABSTRACT FROM AUTHOR]

This paper proposes a machine learning method for the reliability evaluation of integrated power-gas systems (IPGS) under the uncertain component failure probability distributions. The Random Forest (RF) method is designed to select important features to solve the insufficient quantity of data and the curse of dimensionality problems. The Extreme Gradient Boosting (XGBoost) regression algorithm is developed to quantify the relationship between the uncertain parameters and reliability metrics. Moreover, a ten-fold cross-validation method is employed to further improve the accuracy of the regression model. Simulation results on three test systems show that the proposed method can achieve high accuracy for the reliability evaluation. [ABSTRACT FROM AUTHOR]

A novel distributed energy resources (DER) allocation method focused on grid constraints that avoids topological bias is proposed for distribution networks. A technology-agnostic approach is used, where a non-bias allocation of export capacity (NAEC) not specific to generation type is calculated. Moreover, the proposed NAEC is extended from an export capacity into a hosting capacity (HC) using a statistical approach. The methods are tested using the IEEE 33-bus distribution system, and two typical Irish distribution feeders -one urban, one rural- as case studies. Using a high-resolution year-long quasi-static time series simulation (QSTS) and three different generation profiles, the proposed NAEC method is validated against current practices and state of the art allocation methods in terms of active balancing, security of supply, interactions between users, operational concerns, and fairness. Results show that an equivalent or higher level of DER penetration is achieved using the proposed methodology. There are no additional constraint violations using the NAEC methodology, moreover, time slots with violations are reduced, improving security of supply. Furthermore, results suggest that avoiding topological bias makes the network accessible for more users, and prioritises self-consumption. [ABSTRACT FROM AUTHOR]

Dispatching a fleet of distributed energy resources (DERs) in response to wholesale energy market or regional grid signals requires solving a challenging disaggregation problem when the DERs are coupled within a distribution network. This manuscript presents a computationally tractable convex inner approximation for the optimal power flow (OPF) problem that quantifies a feeder’s aggregate DERs hosting capacity and enables a realtime, grid-aware control policy for DERs in radial distribution networks. The inner approximation is derived by considering convex envelopes on the nonlinear terms in the AC power flow equations. The resulting convex formulation is then used to derive provable nodal injection limits, such that any combination of DER dispatches within their respective nodal limits is guaranteed to be AC admissible. These nodal injection limits are then used to construct a realtime, open-loop control policy for dispatching DERs at each location in the network to deliver fast grid services in aggregate. The IEEE-37 distribution network is used to validate the technical results and illustrate use cases. [ABSTRACT FROM AUTHOR]

The evolution of power distribution grids from passive to active systems creates reliability and efficiency challenges to the distribution system operators. In this paper, an energy management and control scheme for managing the operation of an active distribution grid with prosumers is proposed. A multi-objective optimization model to minimize (i) the prosumers electricity cost and (ii) the cost of the grid energy losses, while guaranteeing safe and reliable grid operation is formulated. This is done by determining the active and reactive power set-points of the photovoltaic and storage systems integrated in the grid buildings. The resulting optimization model is non-convex, thus a convex second-order cone program is developed by appropriately relaxing the non-convex constraints which yields optimal results in most operating conditions. The convexified model is further utilized to develop an algorithm that yields feasible solutions to the non-convex problem under any operating conditions. Moreover, a second novel algorithm to find the operating point that provides fairness between the prosumers and the grid costs is proposed. Simulation results demonstrate the effectiveness and superiority of the proposed scheme in managing an industrial distribution grid compared to a self-consumption approach. [ABSTRACT FROM AUTHOR]

The foreseen development of large-scale Offshore Windfarms (OWFs) further from the shore dictates that the OWF transmission system must be optimally designed based on Dynamic Thermal Rating (DTR) in order to fully utilize the intermittent nature of the wind and to keep the offshore wind cost-competitive. In this paper, a comprehensive, DTR-based, two-stage stochastic model is presented, which has been developed for investment decision support for OWF size and HVAC transmission systems. Complex DTR models for all the critical HV components are made fit for the mixed-integer linear programming problem, while accounting for the stochasticity in wind generation and component availability. The main decisions incorporate the discrete size of OWFs, HV subsea export cable cross-sections and ratings for transformers and shunt reactors. For validation, an actual testcase OWF off the east coast of U.K. has been used. Results indicate that DTR-based iterative design of OWF and its transmission components can significantly improve the business case, even though transmission efficiency and energy delivered are not maximum for the optimal design [ABSTRACT FROM AUTHOR]

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration. [ABSTRACT FROM AUTHOR]

IEEE Standard 1366 offers a method for segmenting reliability performance data to isolate the effects of major events from the underlying year-to-year trends in reliability. Recent analysis by the IEEE Distribution Reliability Working Group (DRWG) has found that reliability performance of some utilities differs from the expectations that helped guide the development of the Standard 1366 method. This paper proposes quantitative metrics to evaluate the performance of the Standard 1366 method in identifying major events and in reducing year-to-year variability in utility reliability. The metrics are applied to a large sample of utility-reported reliability data to assess performance of the method with alternative specifications that have been considered by the DRWG. We find that none of the alternatives perform uniformly 'better' than the current Standard 1366 method. That is, none of the modifications uniformly lowers the year-to-year variability in System Average Interruption Duration Index without major events. Instead, for any given alternative, while it may lower the value of this metric for some utilities, it also increases it for other utilities (sometimes dramatically). Thus, we illustrate some of the trade-offs that must be considered in using the Standard 1366 method and highlight the usefulness of the metrics we have proposed in conducting these evaluations.

This paper proposes an optimization model for the joint planning of sectionalizing switches (SSs), including remote-controlled switches (RCSs) and manual switches (MSs), and tie lines among distribution feeders. Considering a flexible installation of SSs and tie lines (i.e., deployment of SSs on both sides of a line and construction of external and internal tie lines on of a feeder), this model is conducive to developing an economic investment scheme while preserving the expected energy not supplied (EENS) reliability criterion. First, the minimum total cost of investment, maintenance and penalty for EENS is chosen as the objective function. Next, a route analysis method is introduced to analyze particular performances of different deployment schemes for RCSs, MSs, external tie lines and internal tie lines. Considering the differences between overhead lines and underground cables, the lower bound of the restoration time of each load is estimated based on optimal restoration strategies in different fault scenarios. Then, the joint planning problem is formulated as a mixed-integer linear programming (MILP) model with the aid of algebraic operations to calculate the global optimal solution. The effectiveness of the proposed model is verified for several cases in a 54-node distribution system. Numerical results indicate that the proposed planning solution merits higher economy and lower EENS as compared to the schemes offered by other prevalent methods. [ABSTRACT FROM AUTHOR]

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability. [ABSTRACT FROM AUTHOR]

Cellular Base Stations (BSs) are equipped with backup batteries. These batteries have some spare capacity over time while maintaining the power supply reliability, so they are potential flexible resources for power systems. This letter exhibits the insight to explore the BS dispatch potential towards power system frequency regulation. For each BS, the feasible dispatch boundaries of participating in frequency regulation are estimated. Then a framework is proposed to coordinate BSs to provide frequency support. By incorporating massive distributed BSs, the power system frequency performances can be improved. [ABSTRACT FROM AUTHOR]

As more variable renewable energy (VRE) and energy storage (ES) facilities are installed, accurate quantification of their contributions to system adequacy becomes crucial. We propose a definition of capacity credit (CC) for valuing adequacy contributions of these resources based on their marginal capability to reduce expected unserved energy. We show that such marginal credits can incentivize system-optimal investments in markets with installed capacity requirements and energy price caps. We simulated such markets using a LP-based capacity expansion planning model with convexified unit commitment (UC) constraints and ES. The impacts of UC and ES on capacity credits are investigated. Furthermore, we analyze technology and system cost distortions resulting from implementing inaccurate CCs in the capacity market. The results show that ignoring UC constraints can overestimate the CCs for VRE and ES. Building ES increases the CCs of VRE resources with higher capacity factors and a negative correlation with load. Assigning the wrong credit to VRE can significantly distort resource mixes and system cost. Implementing the proposed CCs can, in theory, eliminate those distortions and achieve the same overall optimum as a theoretical market without energy price caps. [ABSTRACT FROM AUTHOR]

This paper proposes a scalable framework for the real-time detection and localization of power line outages in transmission networks. While localizing outages is pivotal for ensuring grid reliability, forming such decisions faces an inherent combinatorial complexity that grows with the grid size and becomes prohibitive even for moderate grid sizes. Hence, designing outage detection and localization algorithms that are amenable to real-time implementation critically hinges on circumventing the computational complexity. This paper proposes a graph-guided quickest change detection (GG-QCD) approach that leverages the grid topology and performs quickest change detection in the spectral domain of the graph underlying grid's topology. The GG-QCD algorithm's key features are that (i) it uses a one-dimensional metric that tests the data's conformity to the grid topology, and (ii) it decouples the detection and localization processes to avoid testing all the lines at all times. Specifically, a lack of such conformity of the data to the system model will be alarming the potential existence of an outage. Once an outage is deemed to exist, an active graph clustering approach will be used to localize the line in outage. The clustering approach will also be relying on the same one-dimensional conformity metric. Overall, this approach will be performing only one test over time when the system is outage-free. Once an outage is detected, it will require $\mathcal {O}(\log (L))$ additional tests to identify the line in outage. This paper presents the theory for GG-QCD and algorithms for outage detection and localization. To evaluate these algorithms’ efficiency and complexity, they are examined in the standard IEEE 30- and 118-bus systems. [ABSTRACT FROM AUTHOR]

While many machine learning approaches have been widely applied to power system online dynamic stability assessment, how to sufficiently learn spatial-temporal correlations from system transients without losing the interpretability is still a challenging issue. In this paper, a novel networked time series shapelet learning approach is proposed to learn spatial-temporal correlations for transient stability assessment (TSA) in an interpretable manner. Specifically, a network impedance-based adjacency matrix is first introduced to characterize spatial networked correlations. Based on graph structural regularization, this matrix is effectively incorporated into the subsequent learning procedure as spatial constraints. Taking time series trajectories acquired from multiple buses as the inputs, networked shapelet learning is heuristically performed to learn critical sequential features, i.e., networked shapelets, for TSA model derivation. With the learning procedure strategically guided by inherent spatial-temporal correlations of the system, the obtained data-driven TSA model is able to perform highly reliable and interpretable online TSA. Numerical test results on the IEEE 39-bus test system and the realistic GD Power Grid in China verify the superior performances of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents an energy storage system (ESS) sizing model and reliability assessment framework to quantify reliability improvements due to ESS of electric energy systems with high penetration of renewables. The model formulation takes into account: (a) variable generation (VG) forced outage rates (FORs), (b) reserve and demand requirements, and (c) conventional generation (CG) operational constraints and practices. Correlation coefficients among wind speed, solar radiation, and demand are computed using the least square estimation method (LSE). Depending on the correlation, probabilistic models for the composite demand are computed and integrated into the ESS sizing model. Subsequently, the ESS optimal sizes are computed along with the ESS optimal charging and discharging schedules. Then, the reliability is quantified using the probabilistic production costing (PPC) method. A case study is presented in which one VG was approximated as independent variable because of its low correlation with demand and other VG. The case study compares the error when treating the VG as independent. [ABSTRACT FROM AUTHOR]

In this paper, we present a data-driven secondary controller for regulating to some desired values several state variables of interest in an inverter-based power system, namely, electrical frequency, voltage magnitudes at critical buses, and active power flows through critical lines. The secondary controller is based on online feedback optimization, leveraging the learned sensitivities of changes in the state variables to changes in inverter active and reactive power setpoints. To learn the sensitivities accurately from data, the feedback optimization has a built-in mechanism for keeping the secondary control inputs persistently exciting without degrading its performance or compromising system operational reliability. To ensure safe and reliable operation, we present an approach based on Gaussian process regression that, by making an inference about the modeling uncertainties not accounted for in the sensitivity-based prediction model, allows the controller to correct the predictions and find safe control actions, for which the prediction errors are more likely to be small. The feedback optimization also utilizes the learned power-voltage characteristics of photovoltaic (PV) arrays to compute DC-link voltage setpoints so as to allow the PV arrays to track the power setpoints. To learn the power-voltage characteristics, we separately execute a data-driven approach that fits a concave polynomial to the collected power-voltage measurements by solving a sum-of-squares (SoS) optimization. We showcase the secondary controller using the modified IEEE-14 bus test system, in which conventional energy sources are replaced with inverter-interfaced DERs. [ABSTRACT FROM AUTHOR]

The integration of the variable renewable energies makes the operation conditions of the power system ever-changeable. Consequently, the power system operational reliability evaluation is increasingly important. This paper introduces the concept of decision-dependent uncertainty (DDU) in the operational reliability evaluation. Unlike the exogenous uncertainties, DDU reveals that the decisions of the system operation could significantly affect the resolution of the uncertainties which influence the reliability metrics. In this paper, the proposed DDU modeling method links the device reliability indices, i.e., the forced outage rate, and the operational-decision variables. The impacts of DDU on operational reliability are analyzed based on a reliability-constrained stochastic unit commitment (UC) model. An adaptive reliability improvement UC (ARIUC) algorithm is proposed to efficiently solve the problem. Case studies underline the necessity of considering DDU in power system operational reliability evaluations. [ABSTRACT FROM AUTHOR]

Due to the development of cyber-physical systems for modernizing power grids, vulnerability assessment has become an emerging focus in power system security studies. With the increasing deployment of cyber-enabled technologies in power systems, modern power system is prevalently exposed to a wide gamut of cybersecurity threats. Thus, there is an urgent need to develop effective cyber risk management mechanisms to mitigate the growing cyberthreats. Recently cyber insurance is emerging as a promising financial instrument for cyber risk management of critical infrastructures such as power grids. In this paper, a new cyber-insurance design framework is proposed to hedge against the risk of massive monetary losses due to potential cyberthreats. Traditionally, insurance companies serve as third-party risk-bearers offering aggregate design of the insurance policy which may stipulate high premiums. However, unusual loss patterns may still lead to excess financial risk for insurance companies. In this paper, coalitional insurance is introduced as a promising alternative or supplement to the traditional insurance plans provided by insurance companies. Under the proposed cyber-insurance model, several transmission operators form an insurance coalition, where the coalitional premiums are derived considering system vulnerabilities and loss distributions. The indemnity which covers the loss of TOs complies with the budget sufficiency. Overall, this study proposes a novel coalitional platform based cyber-insurance design that estimates the insurance premiums via cybersecurity modeling and reliability implication analysis. [ABSTRACT FROM AUTHOR]

Soft open points (SOPs) are power electronic devices which provide interconnection between two feeders in place of normally open points in electricity distribution networks. SOPs can continuously control active power flow between feeders and inject reactive power controllably at both nodes, which can be used to provide substantial capacity support to the system. This paper provides a reliability-based method to quantify the capacity value (the additional load which can be accommodated without reducing reliability) of SOPs using the Effective Load Carrying Capability method within a Monte Carlo Simulation (MCS). Optimization of post-fault active/reactive power injections by SOPs to minimize energy not supplied is formulated (directly in matrix form) as a second-order cone programming problem. This results in very low computational times which enables embedding of the optimization problem within the MCS. The proposed methodology is applied to a modified real-world distribution network considering three different SOP sizes (5 SOPs totalling 2.5, 5, 10 MVA) across three redundancy levels (N-1, N-0.75, N-0.5), and on an unbalanced network with distributed generation. Results demonstrate capacity values ranging from 2.4–12.84 MVA. When operating under a relaxed redundancy level, the capacity value of a given SOP capacity can more than double relative to N-1. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-like function for gas transmission systems with pipelines and compressors, whose stationary points are mapped to gas flow solutions. This energy-like function is a convex function such that a simple unconstrained convex optimization can be solved to precisely track gas flow solutions. Furthermore, a reliable gas flow model is set up which can be reformulated as a constrained convex optimization by using the proposed energy-like function in order to find a reliable gas flow solution and check the feasibility of the gas transmission system in the presence of uncertainties. Case studies are conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A new method is suggested for islanding detection in microgrids with huge penetration of non-dispatchable renewable energy sources. Time synchronised data is collected in the form of variables like voltage phasors, frequency and rate of change of frequency using phasor measurement units from various locations. The method then uses weighted difference principal component analysis for considering nonlinearities among variables and for data condensation. Multi-resolution Teager energy operator (MTEO) is employed to Q statistics obtained to detect islanding events. Further, to eliminate false alarms a difference of the obtained signal with its mean (MTEOD) is computed. When it is compared with a threshold which is derived based on upper control limit of Q, can successfully identify islanding events with no spurious detections. The method implemented in DIgSILENT/Power Factory and MATLAB achieved 100% accuracy, precision and reliability with zero non detection zone. Moreover, islanding location has been precisely identified to attain situational awareness of the microgrid. Immunity to noisy conditions in the microgrid has been demonstrated using a four-variable mathematical model. For a faster online prediction decision tree classifier is utilized. Real time performance and prediction capability was evaluated with decision tree technique which shows promising results. [ABSTRACT FROM AUTHOR]

Multi-energy systems (MESs) make it possible to satisfy consumer's energy demand using multiple coupled energy infrastructures, thus increasing the reliability of the energy supply compared to separate energy systems (SESs). To accurately and efficiently assess and improve the reliability of MESs, this paper proposes a MES reliability and vulnerability assessment method using energy hub (EH) model. The energy conversion, transmission and storage in MESs are compactly and linearly described by EH model, making reliability and vulnerability assessment of MESs tractable. Indices for MES vulnerability assessment are proposed to find the key components for improving MES reliability. Multi-parametric linear programming (MPLP) with a self-adaptive critical region set is proposed to reduce the computational burden caused by iteratively solving LP problems for a large number of samples during the assessment process. The results of a case study show that the proposed reliability and vulnerability assessment method is able to effectively evaluate the energy supply reliability of different energy sectors in MES as well as find the critical component of an MES from reliability perspective to support its planning. The proposed algorithm, i.e., MPLP with a self-adaptive critical region set, can improve the computational efficiency by an order of magnitude compared to the traditional LP method. [ABSTRACT FROM AUTHOR]

The DC microgrid (DC MG) concept enables the hosting of DC-type renewable energy resources. However, their intermittent nature means that a high penetration of renewables can jeopardize supply adequacy and voltage provision during islanding. The work presented in this paper was therefore directed at developing a probabilistic graphical approach based on Bayesian networks (BNs) for the reliability analysis of renewables-dominated DC MGs. The proposed BN model incorporates a family of novel reliability indices for quantifying the impact of a high penetration of renewables on MG reliability, including loss of renewable power supply, rise in voltage, and reversal of power flow. The model is supported by a newly formulated fast and accurate linearized power flow algorithm for probability calculations. The accuracy of the BN model has been verified against a Monte-Carlo simulation (MCS). The effective application of the new BN model for reasoning and impact assessment reveals that a high penetration of renewables affects reliability indices differently. Case study results suggest that the proposed BN model shows promise as a valuable tool for the reliability analysis of renewables-dominated MGs that feature islanding capability. [ABSTRACT FROM AUTHOR]

With the introduction of massive renewable energy sources and storage devices, the traditional process of grid operation must be improved in order to be safe, reliable, fast responsive and cost efficient, and in this regard power flow solvers are indispensable. In this paper, we introduce an Interior Point-based (IP) Multi-Period AC Optimal Power Flow (MPOPF) solver for the integration of Stationary Energy Storage Systems (SESS) and Electric Vehicles (EV). The primary methodology is based on: 1) analytic and exact calculation of partial differential equations of the Lagrangian sub-problem, and 2) exploiting the sparse structure and pattern of the coefficient matrix of Newton-Raphson approach in the IP algorithm. Extensive results of the application of proposed methods on several benchmark test systems are presented and elaborated, where the advantages and disadvantages of different existing algorithms for the solution of MPOPF, from the standpoint of computational efficiency, are brought forward. We compare the computational performance of the proposed Schur-Complement algorithm with a direct sparse LU solver. The comparison is performed for two different applicational purposes: SESS and EV. The results suggest the substantial computational performance of Schur-Complement algorithm in comparison with that of a direct LU solver when the number of storage devices and optimisation horizon increase for both cases of SESS and EV. Also, some situations where computational performance is inferior are discussed. [ABSTRACT FROM AUTHOR]

In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme. [ABSTRACT FROM AUTHOR]

The wide implementation of information and communication technologies (ICT) cause power system operations exposed to cyber-attacks. Meanwhile, the tendency of integrated multi energy vectors has worsened this issue with multiple energy coupled. This paper proposes a two-stage risk-averse mitigation strategy for water-energy systems (WESs), incorporating power, natural gas and water systems against false data injection attacks (FDIA) under water-energy nexus. The FDIA on individual sub-systems is modelled through hampering false data integrity to the systems. An innovative two-stage risk-averse distributionally robust optimization (RA-DRO) is proposed to mitigate uneconomic operation and provides a coordinated optimal load shedding scheme for the nexus system security. A coherent risk measure, Conditional Value-at-Risk is incorporated into the RA-DRO to model risk. A Benders decomposition method is used to solve the original NP-hard RA-DRO problem. Case studies are demonstrated on a WES under water-energy nexus and results show that the effectiveness of the method to mitigate risks from potential FDIA and renewable uncertainties. This research provides WES operators an economic system operation tool by optimally coordinating energy infrastructures and implementing reasonable load shedding to enhance cybersecurity. [ABSTRACT FROM AUTHOR]

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy. [ABSTRACT FROM AUTHOR]

This work explores the benefits of increasing transmission capacity between the US Eastern and Western Interconnections under a high-renewables future. Given the existing “seam” between the two interconnections, a co-optimized infrastructure planning model was developed to assess trade-offs between investments in cross-seam HVDC transmission, AC and DC transmission needs within each interconnection, generation investment costs, and operational costs, at different renewable penetration levels. The model allows existing and candidate generation resources for deliverable planning reserves to be shared throughout the interconnections, a feature which drives identification of least-cost investments. This work is performed using an industry-vetted 169-bus representation of the North American power grid. Results from this analysis indicate that, under high wind/solar growth scenarios, the cost of a macrogrid HVDC transmission is outweighed by the generation-related savings they produce. The presence of other benefits related to grid reliability, resilience, and adaptability, suggest that cross-seam transmission is highly attractive infrastructure. [ABSTRACT FROM AUTHOR]

This paper proposes a new scheme for bulk system restoration considering the available black-start resources (BSRs) in the distribution system (DS). The DS assisted generator start-up sequence (D-GSS) scheme is realized in a decentralized way, so that it does not only benefit the bulk system GSS by utilizing available BSRs in the DS, but also respects the independent operation of the transmission system operator (TSO) and distribution system operators (DSOs). First, the decentralized D-GSS scheme is presented including the interaction of the TSO and DSOs and the corresponding compact models. The models of the decentralized D-GSS scheme are built with the bulk system GSS modeled as a mixed-integer quadratic program (MIQP), the DS multi-step operation modeled as mixed-integer second-order conic programs (MISOCPs) and reliable power supply assessments of renewable energy sources as linear programs (LPs). Finally, a projection function based analytical target cascading (P-ATC) method is developed to iteratively solve the decentralized models with model complexity reduction. The P-ATC algorithm can handle integer variables and improve the computational efficiency of the iterative calculation process. The effectiveness of the proposed method is validated using the small-scale T6D2 system and large-scale T118D5 system, showing good GSS performance and computation efficiency. [ABSTRACT FROM AUTHOR]

Each stakeholder in a power system not only carries some risk, but also creates risks for others and has the ability to mitigate these risks. For some, these risks are purely financial. For others the major concern is the socio-economic risk of an outage, which is not easily translated in monetary terms. This paper analyzes how the interactions of these participants across a complex physical system affect their risk exposure as well as the benefits they derive from this system. It also argues that charging the cost of mitigation measures back to the parties that create outage risks might ultimately reduce this risk and its associated cost. Rules and techniques to implement this approach should be developed and tested. Finally, it proposes directions for research on how making power system operation more risk-aware would help enhance reliability, control cost and facilitate the integration of variable energy resources. [ABSTRACT FROM AUTHOR]

Preventive maintenance is applied in distribution networks to prevent failures by performing maintenance actions on components that are at risk. Distributed generators (DGs) and batteries can be used to support power to nearby loads when they are isolated due to maintenance. In this paper, a novel short-term preventive maintenance method is proposed that explicitly considers the support potential of DGs and batteries as well as uncertainties in the power generated by the DGs. Two major issues are addressed. To deal with the large-scale complexity of the network, a depth-first-search clustering method is used to divide the network into zones. Moreover, a method is proposed to capture the influence of maintenance decisions in the model of the served load from DGs and batteries via generation of topological constraints. Then a stochastic scenario-based mixed-integer non-linear programming problem is formulated to determine the short-term maintenance schedule. We show the effectiveness and efficiency of the proposed approach via a case study based on a modified IEEE-34 bus distribution network, where we also compare a branch-and-bound and a particle swarm optimization solver. The results also show that the supporting potential of DGs and batteries in preventive maintenance scheduling allows a significant reduction of load losses. [ABSTRACT FROM AUTHOR]

With the growing penetration of power electronic converters in power systems, the issue of reliability becomes more critical than ever before. This paper proposes a hierarchical reliability framework to evaluate the electric power system reliability from the power electronic converter level to the overall system level. On the converter level, the reliability model of a power electronic converter is developed based on the power electronic devices it is composed of, for which various hourly based input profiles and converter topologies are considered. On the system level, reliability metrics such as expected energy not served (EENS) and loss of load expectation (LOLE) are estimated through a non-sequential Monte Carlo simulation. Machine learning regression models, such as support vector regression (SVR), and random forests (RF) are implemented to bridge the nonlinear reliability relationship between two levels. The proposed framework is demonstrated through the modified IEEE Reliability Test System (RTS) 24-bus network. Numerical results show power converter reliability should be considered as an important factor when evaluating overall system reliability performance. [ABSTRACT FROM AUTHOR]

Power systems employ reserves to ensure there is adequate generation and storage to meet demand. With the increased use of volatile and uncertain sources of electricity such as solar and wind, the need for reserves is becoming more critical to ensure reliable supply of electricity. Due to the high cost of reserves, methods are required to determine minimal reserves needed. This work proposes a probabilistic data-driven method to determine the minimum reserve size (both power and energy) to satisfy a specified reliability requirement for a bulk power grid. The proposed method is applied to data from two balancing authorities (BPA and CAISO), and the results are compared with those from an alternate deterministic method and to those from the current reserve sizing methods employed by BPA and CAISO. The reserve requirements do not seem to be predicted by simple indicators of volatility, such as installed capacity of wind and solar, which has implications for grid planning in a renewable rich future. [ABSTRACT FROM AUTHOR]

Sectionalizing a distribution system with distributed energy resources (DERs) into multiple microgrids in the case of power outages has been recognized as a promising solution to restore critical loads. Nevertheless, most existing literatures focus on restoring loads by optimally dispatching distributed generators (DGs), where the controllability on the demand side is usually neglected. Moreover, unbalances in the restored microgrids should be limited in specified levels to prevent DGs from tripping to enhance the operational reliability. To address these issues, this paper proposes a novel method to restore a distribution system into multiple microgrids whilst taking the three-phase demand-side management (T-DSM) into account. The T-DSM exploits optimal phase positions of switchable loads to alleviate the unbalance issue from the demand side, which can be coordinated with network partition and DG dispatching to improve the controllability of microgrids during the restoration process. The problem is formulated as a mixed-integer second-order cone programming (MISOCP) which enables applying the off-the-shelve solvers. To improve computational performance, a multi-timescale strategy is developed to enhance its practicality. Simulation results based on the IEEE 34- and 123-bus feeders demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The legacy Optimal Power Flow (OPF) dispatch in electric power grids with high proliferation of renewables can be at risk due to the lack of awareness on major uncertainties, and sudden changes in renewable outputs. This may, in turn, result in conditions where transmission line power flows are significantly exceeded, and subsequent automatic protective actions take place. This letter presents a new generalized joint chance-constrained model for the OPF problem that effectively captures the stochasticity in renewable power generation in the system. In dealing with the complexity, and non-convexity of the proposed optimization model with probabilistic guarantees, we propose a novel tractable Boolean method to transform the model into an equivalent deterministic mixed-integer linear problem, which can be solved quickly, and efficiently by off-the-shelf solvers. Numerical results verify the effectiveness of the proposed model, and the suggested Boolean methodology. [ABSTRACT FROM AUTHOR]

In the absence of demand-side bids for certain reliability products in the wholesale electricity markets, Independent System Operators (ISOs) traditionally use fixed demand requirements with penalty factors to clear the market. This approach does not allow proper tradeoffs between cost and reliability due to the inelasticity of the fixed requirements. Therefore, ISOs have been replacing the fixed requirements with sloped demand curves. However, the economic interpretation of demand curves, especially for multiple coupled products, is largely missing in both theory and practice. This could lead to the misrepresentation of the demand benefit in the market clearing objective. Using a two-product market model, Part I of this paper reveals two distinct interpretations of demand curves, each associated with a specific form of the market clearing problem. This implies that the construction of demand curves and the use of them in market clearing must be consistent with their economic interpretation. In Part II of this paper, we analyze the sequential and iterative market clearing processes currently implemented in some markets to demonstrate their solution sub-optimality. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is proposed. [ABSTRACT FROM AUTHOR]

Following Part I of this paper that reveals two distinct interpretations of demand curves and the corresponding different forms of the market clearing formulation, Part II of this paper analyzes the sequential and the iterative market clearing algorithms currently implemented in some markets. It is shown that these algorithms lead to socially sub-optimal solutions. The root cause of the problem is the lack of a clear representation of the demand benefit. The social surplus optimization based on the proper interpretation of demand curves is then proposed. A small numerical example is used to demonstrate the significant optimality gap between the socially optimal solution and the sequential/iterative solution. The potentially large impacts of the gap on the societal benefit and the individual resource revenues entail a close examination of these algorithms. [ABSTRACT FROM AUTHOR]

With the growing study on the active distribution network, an objective and efficient reliability evaluation method for such cyber-physical systems (CPS) is urgently needed. In this paper, first considering the cyber transmission performance, a new method is proposed for efficient evaluation of cyber link validity based on frequency-time domain transformation. In order to quantify the indirect interdependencies of cyber failures, an unresponsiveness probability model for switches is established. Then, an innovative method is proposed by combining the equivalent method and the minimal path method. Finally, a test system is established to verify the effectiveness of the proposed method. This study is instrumental in informing the efficient planning of contemporary CPS-based distribution networks. [ABSTRACT FROM AUTHOR]

Conventional droop control methods for power-sharing in a multi-terminal high voltage DC (MTDC) grid lead to voltage deviation from the nominal value. Moreover, the power-sharing is inaccurate in the droop-controlled MTDC system. This paper proposes a secondary controller with a distributed architecture to compensate the voltage deviation and to achieve equal power sharing automatically among the converter stations. A distributed observer is used to estimate the average voltage of the converter stations. The proposed distributed controller and observer use local measurements of output powers and voltages, and only communicate with neighboring stations. Therefore, the requirement of the global information in the centralized secondary control schemes is eliminated, which reduces the communication requirement and improves the reliability of the MTDC grid. The case studies illustrate that the proposed distributed controller regulates the stations’ average voltage and shares the power mismatch accurately. Impact of the communication time-delays and the control gain on stability of the MTDC grid are also investigated using the Lyapunov-KrasovskiiLMI condition. It is shown that reducing the control gain can stabilize the MTDC grid in the case of large communication time-delays. [ABSTRACT FROM AUTHOR]

Renewables, such as solar and wind generation, combined with storage are becoming a key part of modern grid. This paper develops probabilistic tools for analysis of grid reliability with such variable generation resources. The developed tools improve speed and accuracy of the reliability analysis compared to usual Monte Carlo methods. This is achieved by using an extension of well known convolution method applicable to interdependent variables. The interdependent distributions are obtained from historical data using Machine Learning of quantile models. The paper presents a novel approach to the analysis of reliability contribution of storage based on these models and related to Information Theory. The developed tools are demonstrated in several example scenarios for ISO-New England service area. [ABSTRACT FROM AUTHOR]

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration. [ABSTRACT FROM AUTHOR]

This paper presents an approach to deal with the uncertainty of generation and load in the optimal island partitioning of the automated radial distribution networks. The proposed approach is based on the concept of interval analysis, Relative Distance Measure (RDM) interval arithmetic, mixed integer linear programming with interval coefficients, and risk management tools. It defines a number of different island partitioning scenarios taking into account dependence (correlation) between loads and DGs. The best island partitioning scenario is obtained by employing the Minimal risk (Minimax) criterion for risk measurement. In this way, the presented approach provides a tool for a decision-maker to determine the island partitioning scenario that minimizes the risk of unsuccessful island operations in the uncertain environment. [ABSTRACT FROM AUTHOR]

This paper addresses a fully distributed hierarchical coordination controller, which minimizes the global total generation cost (TGC) for a cluster of DC microgrids (MGs) through handling the optimal power-sharing between MGs. It consists of local and global tertiary controllers, in which a two-layer communication network is modeled to share information with a reduced complexity as one or some distributed generators (DGs) of each MG are pinned from the local network to broadcast the information between MGs at the global network. Under this control structure, the global tertiary controllers, based on dynamic consensus technique, generate the global optimum incremental cost (IC) reference to attain the entire cluster's economic operation. Moreover, using pinning-based leader-follower consensus algorithm, the local tertiary controllers assign MG average voltage reference at which the ICs of all DGs are matched at the global reference to ensure optimal operation of the cluster with considering DGs generation boundaries. Finally, the nominal voltage set points of each DG can be determined by the secondary controller to guarantee the generation-demand power balance. The feasibility of the proposed controller is demonstrated by simulation and experiment under various test scenarios. [ABSTRACT FROM AUTHOR]

The integration of volatile distributed energy resources (DERs) brings new challenges for the active distribution network maintenance scheduling (DN-MS). Conventionally, the DN-MS is formulated as a deterministic optimization model without considering the uncertainties of DERs. In this paper, the DN-MS is formulated as a multistage stochastic optimization problem, which is cast as a stochastic mixed-integer nonlinear programming model. It aims to reduce the total maintenance cost constrained by the reliability indices. To capture the operational characteristics of active distribution networks, the uncertainties of DERs and post-outage operation strategies of switching devices are incorporated into the model. In general, this type of model is intractable and mainly solved by heuristic search methods with low efficiency. Recently, Monte-Carlo tree search (MCTS) is emerging as a scalable and promising reinforcement learning approach. We propose a stochastic MCTS solution to this problem. In the tree search procedure, a sample average approximation technique is developed to estimate multistage maintenance costs considering uncertainties. To speed up the MCTS, the complicated constraints of the original problem are transformed to penalty or heuristics functions. This approach can asymptotically approximate the optimum with promising computation efficiency. Numerical test results demonstrate the superiority of the proposed method over benchmark methods. [ABSTRACT FROM AUTHOR]