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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]

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]

This paper presents a novel procedure for energy management system (EMS) that can utilize the flexibility in transmission networks in a practical way. With the proposed enhanced EMS procedure, the reliability benefits that are provided by corrective transmission switching (CTS) in real-time contingency analysis can be translated into significant cost savings in real-time security-constrained economic dispatch. Simulation results show that the congestion cost with consideration of CTS is largely reduced as CTS can relieve potential post-contingency network violations. The effects of integrating CTS in existing EMS procedure on markets are also analyzed. In conclusion, this two-part paper shows that CTS can achieve substantial reliability benefits, as well as significant cost savings. [ABSTRACT FROM AUTHOR]

This paper addresses the transmission network expansion planning problem under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby the worst-case operating cost is accounted for under a given user-defined uncertainty set. This paper differs from previously reported robust solutions in two respects. First, the typically disregarded correlation of uncertainty sources is explicitly considered through an ellipsoidal uncertainty set relying on their variance-covariance matrix. In addition, we describe the analogy between the corresponding second-stage problem and a certain class of mathematical programs arising in structural reliability. This analogy gives rise to a relevant probabilistic interpretation of the second stage, thereby revealing an undisclosed feature of the worst-case setting characterizing robust optimization with ellipsoidal uncertainty sets. More importantly, a novel nested decomposition approach based on results from structural reliability is devised to solve the proposed robust counterpart, which is cast as an instance of mixed-integer trilevel programming. Numerical results from several case studies demonstrate that the effect of correlated uncertainty can be captured by the proposed robust approach. [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]

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 flexibility in transmission networks is not fully utilized in existing energy management systems (EMSs). Corrective transmission switching (CTS) is proposed in this two-part paper to enable EMS to take advantage of the flexibility in transmission systems in a practical way. This paper proposes the following two EMS procedures: first, Procedure-A connects real-time security-constrained economic dispatch (RT SCED) with real-time contingency analysis (RTCA), which is consistent with industrial practice; and second, Procedure-B, an enhanced version of Procedure-A, includes CTS in EMS with the proposed concept of branch pseudo limit used in RT SCED. Part-I of this paper presents the methodology, whereas Part-II includes detailed results analysis. It is demonstrated that Procedure-A can effectively eliminate the base case overloads and the potential post-contingency overloads identified by RTCA, and Procedure-B can achieve significant congestion cost reduction with consideration of CTS in RT SCED. Numerical simulations also illustrate that integrating CTS into RT SCED would improve social welfare. [ABSTRACT FROM AUTHOR]

We present a simple method to calculate the marginal capacity credit of energy limited resources with increased penetration. Energy limited resources are defined as any resource with limited hours of dispatch across a day, month, or year. This includes emission limited resources, time-limited demand response, and diurnal energy storage such as batteries and pumped storage. This paper focuses on 4 h energy limited resources with daily dispatch. The method modifies well-established effective load carrying capability methodology by optimally allocating limited capacity to the highest loss of load hours. Subsequent energy limited resources are iteratively stacked upon the prior solution, creating a marginal capacity credit curve with increased penetration of energy limited resources. Two systems are tested using this method. The initial capacity credit depends on system characteristics such as load and renewable penetration. Regardless, the capacity credit for energy limited resources declines steeply with penetration. [ABSTRACT FROM AUTHOR]

With the increased uncertainty in the power system operation due to growing penetration of highly intermittent energy sources such as wind power, the need for the impact assessment of the renewable penetration on system operating risk and the quantification of benefits of using energy storage technologies is more than ever. A recovery-risk-analysis-based analytical framework for operating risk assessment of wind-integrated bulk power system following a major contingency disturbance is presented in this paper. Two new indices that quantify the recovery risk profile of the bulk power system and its load delivery points following major disturbances are introduced in this paper. The indices obtained from the proposed framework quantify the impact of increasing wind penetration on the system operating risk and the reliability benefits of using fast-responding energy storage system such as flywheel energy storage systems. The proposed methodology is illustrated through several case studies carried out in a test system. [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]

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]

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]

This paper presents a circuit breaker (CB) aging failure model based on condition monitoring. This model is inserted into a basic CB Markov model and applied in substation reliability assessment. The proposed model can accurately incorporate the wearing status and aging process of CBs. The results from applying the proposed method to a utility substation configuration demonstrate that ignoring aging failures of CBs can lead to significant underestimation of substation unreliability. [ABSTRACT FROM PUBLISHER]

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]

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 presents a new security-constrained multiobjective optimal dispatch (SC-MOOD) framework for an economic and reliable operation of microgrids. The framework is developed based on a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The new method considers grid steady-state and dynamic constraints while solving the optimal dispatch in both grid-connected and islanded modes. The constraints consist of power balance, voltage magnitude, line flows, power generation, frequency, and voltage transients. The proposed framework finds the most economic operating solutions to not only minimize the generation cost but also minimize the reliability cost. In this paper, the PaCcET is utilized to solve the SC-MOOD. The PaCcET uses an extraordinary transformation to first transfer all the points from a multiobjective space to a transformed objective space. It then solves a linear combination of transformed objectives using a single-objective optimizer to find all the nondominated points of the original multiobjective space. The performance of the new framework is verified using the simulation results in a microgrid with several distributed energy resources in both grid-connected and islanded modes. The results are then compared with two multiobjective optimization techniques, nondominated sorting genetic algorithm II and multiobjective particle swarm optimization, as two well-known benchmarks. [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]

This paper describes a simple and practical reliability model based on a stationary Markov process for assessing the number of spare parts required for a group of bundles of similar component parts. The proposed model accounts for a number of factors that affect the number of spare parts. The factors include the number of bundles, bundle size, bundle failure rate, time required to repair the failed part or to acquire a new spare part, time needed to install the spare part and any redundancy in the bundle. In addition, the proposed model is capable of handling bundles of different sizes. Two performance criteria namely the group availability criterion and the system total minimum cost criterion can be used in the spare part assessment. The purpose of this paper is to present the new reliability model and to compare its results with other existing probability models. [ABSTRACT FROM AUTHOR]

Decision models used in wholesale electricity markets are advancing to manage adequately distributed storage on the grid and grapple with increasing stochasticity and variability in net-load. The goal of this paper is to address these three issues jointly, by introducing a continuous-time stochastic multi-stage reserve unit commitment. Compared to the conventional unit commitment (UC) formulation, the one we propose a) accommodates storage devices with limited energy capacity, b) addresses load uncertainty through a multi-variate scenario tree, and c) models, through a piece-wise polynomial approximation, continuous-time changes in load and generation. In numerical simulations, we compare the system operating cost of our approach relative to relaxations of our proposed formulation, including the conventional UC. The comparisons show the relative impact of the three modeling pillars of our formulation, aimed at capturing storage constraints, uncertainty, and ramping events with inter-hourly variations. [ABSTRACT FROM AUTHOR]

This paper aims at proposing a mixed-integer linear formulation to incorporate reliability-oriented costs into the expansion planning model of electricity distribution networks. In this respect, revenue lost associated with the undelivered energy caused by network interruptions as well as costs incurred by the widely used reward–penalty regulations is considered as the major reliability-related costs from distribution companies point of view. A set of mixed-integer linear equations is proposed to calculate the most common distribution system reliability indices, i.e., expected energy not served, system average interruption frequency index, and system average interruption duration index. It is found that these equations can also facilitate the formulation of radiality constraint in the presence of distributed generation units. Moreover, application of the proposed method is investigated through various case studies performed on two test distribution networks with 24 and 54 nodes. [ABSTRACT FROM AUTHOR]

Risk of blackouts is more than expected and must be considered in the power system studies. The interest of this paper is to minimize the blackouts risk during minimizing the costs in probabilistic transmission expansion planning (TEP). In this paper, a long-term reliability analysis, which was developed by self-organized criticality theory, is used in a multi-objective optimization to include risk of blackouts in TEP. The objective functions are costs and expected energy not supplied. Also, a new formulation for calculating transmission operating cost as a part of cost function is proposed. The problem is solved by a multi-objective particle swarm optimization to find an optimal Pareto set from which the decision-maker can choose the most desirable one, regarding existing budget. The proposed method is applied to the IEEE 118-bus test system to investigate its applicability and scalability. The results are compared with security-constrained TEP, which uses famous deterministic criterion known as N-1. The proposed approach produces more efficient plans for suppressing cascading blackouts with less network investment. Moreover, the results provide motivation for considering the risk of cascading blackouts in TEP, which afford substantial saving to society. [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]

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]

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]

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]

This paper proposes a generalized analytical approach to evaluate the reliability of active distribution networks. The studies are implemented in the context of renewable-based energy hubs. The reliability of energy demands is determined as a function of reliability characteristics of energy hub input resources and converters as well as hub operating strategies. The framework proposed in this paper involves the main attributes of different distributed generation technologies as well as vehicle-to-grid (V2G)-capable vehicles in the reliability studies. Dealing with the uncertainty in energy services provided by the wind turbine output generation and V2G programs, efficient probabilistic methods are presented to attain the reliability models of these energy resources for the studies. The fluctuations in energy demands are also represented through multistate analytical models. Convolving the probabilistic models of energy hub resources by the load profiles’ models, different reliability indices are calculated taking into consideration possible operating strategies of the energy hubs. Effectiveness of the proposed methodology is validated using extensive numerical studies on an energy hub and the obtained results demonstrate its applicability in adequacy studies of active energy networks. [ABSTRACT FROM AUTHOR]

Electric power systems are facing major challenges because of the increase in penetration of energy resources (ERs). This paper focuses on composite system reliability based planning for ERs, and presents novel clustering techniques based approaches to determine the optimal location, size and year of installation of ERs in the system. The K-means clustering and Fuzzy C-means clustering techniques are applied to the set of reliability indices, Load Not Served per Interruption (LNSI), which are determined using nodal minimal cut sets. The nodal minimal cut sets are obtained using an optimal power flow (OPF) based approach in this paper. Once the optimal sizes and locations of ERs are obtained, the earliest year of penetration is determined using an adequacy check algorithm. Detailed studies presented considering the IEEE RTS demonstrate the applicability of the proposed technique. [ABSTRACT FROM PUBLISHER]

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 paper presents a new approach for reliability evaluation of composite power systems by combining Monte Carlo simulation and multi label k-nearest neighbor (MLKNN) algorithm. MLKNN is a classification technique in which target vector of each instance is assigned into multiple classes. In this paper, MLKNN is used to classify states (failure or success at system or bus level) of a complete power system without requiring optimal power flow (OPF) analysis, except in the training phase. As a result, the computational burden to perform OPF is reduced dramatically. For illustration, the proposed method is applied to the IEEE 30 BUS Test System and IEEE Reliability Test System. The obtained results from various case studies demonstrate that MLKNN based reliability evaluation provides promising results in both classification accuracy and computation time in evaluating the composite power system reliability. [ABSTRACT FROM AUTHOR]

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to determine the optimal routing of medium voltage distribution networks in sparse rural areas. The objective is evaluated through minimizing the net present cost of the network over a selected time period. A problem specific genetic algorithm is proposed to address the optimal network routing problem and compute the optimized topology throughout a geographically constrained region. The proposed method simultaneously considers nonfixed candidate lines to overcome search space restrictions through a variable length encoding structure and the use of Steiner points, and a shortest path algorithm to traverse between point-to-point connections in the constrained region. Geographical restrictions on network routing are considered through the formation of a rasterized map. The network is modeled at the branch level and considers both greenfield and expansion planning to highlight the effects of accessibility restrictions. The optimization model is applied to a real rural distribution network in the South–West of Western Australia. Alternative network topologies are found to provide significant improvements over the existing network and traditional reconfiguration based methods for evaluating the minimum cost sparse rural distribution network. [ABSTRACT FROM AUTHOR]

A fast reliability calculation method based on the fault incidence matrix (FIM) for the complex distribution system is proposed in this paper. First, the M-segment-N-tie-switch (MSNT) reliability calculation unit for the complex distribution network is established and the power supply path matrix is obtained by inversing the node branch incidence matrix of the MSNT unit. Then, three types of FIMs are constructed considering the influence type of each fault event on the load points. Finally, the system reliability indexes are calculated through the algebraic operation between the FIMs and the fault event parameter vectors. The accuracy and the efficiency of this method are verified using the IEEE RBTS bus-6 system. The reliability evaluation method proposed in this paper avoids complicated fault event enumeration and repetitive fault influence range searching, thus, saves the computation time greatly. And more importantly, the analytical expression of the reliability indexes in the matrix form can clearly show the influence of each fault event on each load interruption. So, it is convenient for the reliability sensitivity analysis and helpful for the operator with the reliability improvement planning. [ABSTRACT FROM AUTHOR]

Centralized electricity markets currently do not optimize reactive power and voltage in the market clearing software. Voltage and local reliability (VLR) commitment requirements are mostly identified through out of market operational procedures. Failure to maintain VLR may incur voltage collapse, generation, transformer loss, or even blackout. Midcontinent Independent System Operator (MISO) employs binary constraints, minimum/maximum generation constraints, interface constraints, and manual commitments to address VLR requirement in the market clearing process and ensure adequate commitment for reliability. This paper integrates binary VLR constraints in day-ahead security constrained unit commitment to improve market efficiency. However, VLR constraints may cause uplift cost, and sometimes the associated uplift cost can be significant. An uplift cost allocation method with the consideration of resources commitment reasons is developed in this paper. The proposed approach is considered for implementation in MISO market clearing process and a MISO test case is used to validate the effectiveness of the proposed approach. [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]

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]

This paper presents a reliability evaluation method for a tidal power generation system (TPGS) with a doubly-fed induction generator (DFIG). The key to the method is the modeling of the tidal current speed-dependent failure rates of the rotor side converter (RSC) and grid side converter (GSC). The models for calculating the rotor currents through the RSC and GSC and the rotor current state related failure rates are presented. Based on the Wakeby distribution of tidal current speed, a multistate discrete probability distribution technique for rotor current is developed. Case studies are described using tidal current speed data from four coastal sites in North America. The results indicate that the failure rates of the RSC, GSC, and the entire TPGS vary with tidal current speeds and the probability distributions of tidal current speed. The TPGS suffers a much higher failure risk in the super-synchronous mode than in the idle and subsynchronous modes. The failure rate of the RSC is much higher than that of the GSC. The change trends in the failure rates of RSC and GSC in the operation modes are different. The probability distributions of tidal current speed have significant impacts on the reliability of the TPGS. [ABSTRACT FROM PUBLISHER]

This paper deals with the distribution network reconfiguration problem in a multi-objective scope, aiming to determine the optimal radial configuration by means of minimizing the active power losses and a set of commonly used reliability indices formulated with reference to the number of customers. The indices are developed in a way consistent with a mixed-integer linear programming (MILP) approach. A key contribution of the paper is the efficient implementation of the \mmb\varepsilon-constraint method using lexicographic optimization in order to solve the multi-objective optimization problem. After the Pareto efficient solution set is generated, the resulting configurations are evaluated using a backward/forward sweep load-flow algorithm to verify that the solutions obtained are both non-dominated and feasible. Since the \mmb\varepsilon-constraint method generates the Pareto front but does not incorporate decision maker (DM) preferences, a multi-attribute decision making procedure, namely, the technique for order preference by similarity to ideal solution (TOPSIS) method, is used in order to rank the obtained solutions according to the DM preferences, facilitating the final selection. The applicability of the proposed method is assessed on a classical test system and on a practical distribution system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM PUBLISHER]

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]

Nonsynchronous generators such as photovoltaics (PVs) are expected to undermine bulk power systems (BPSs)’ frequency response at high penetration levels. Though the underlying mechanism has been relatively well understood, the accurate assessment and effective enhancement of the U.S. interconnections’ frequency response under extra-high PV penetration conditions remains an issue. In this paper, the industry-provided full-detail interconnection models were further validated by synchrophasor frequency measurements and realistically-projected PV geographic distribution information were used to develop extra-high PV penetration scenarios and dynamic models for the three main U.S. interconnections, including Eastern Interconnection (EI), Western Electricity Coordinating Council (WECC), and Electric Reliability Council of Texas (ERCOT). Up to 65% instantaneous PV and 15% wind penetration were simulated and the frequency response change trend of each U.S. interconnection due to the increasing PV penetration level were examined. Most importantly, the practical solutions to address the declining frequency response were discussed. This paper will provide valuable guidance for policy makers, utility operators and academic researchers not only in the U.S. but also other countries in the world. [ABSTRACT FROM PUBLISHER]

This paper presents a new method to evaluate the reliability of a power system with high penetration of wind generation, considering the impact of not only the intermittence but also the low inertia characteristic of wind power. As wind generation gradually replaces conventional generation, the system stability and the reliability are negatively affected. Some of the measures employed to deal with the challenges resulting from increasing wind penetration include operating wind generators at lower levels than their available output and providing inertia so that wind generation is able to contribute to system frequency regulation. Apart from these measures, another factor that limits the amount of wind power that can be absorbed into the grid is the imposition of the frequency standard and this also affects the reliability of the system in the presence of wind penetration. The reliability evaluation approach proposed in this paper is developed using discrete convolution and implemented on an IEEE RTS-79 system with a suitable modification. Power system reliability with and without considering the impacts of wind intermittence and low inertia are compared to show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper proposes a method for studying the reliability implications of line switching operations in power systems. Two case studies are conducted on RTS and IEEE 118-bus system to illustrate this method. This method is designed to explore previously overlooked areas in reliability evaluation of line switching operations. Line removal test is proposed to obtain simulation data of the system, and then with risk analysis and impact analysis, six reliability indices are used to evaluate reliability performance of each transmission line in the system. Instead of the traditionally used mean value, this method introduces variance into analysis. Weibull distribution is used to reconstruct distributions of reliability indices which provide worst case scenario comparisons in reliability evaluation. Eventually, with results obtained from the proposed reliability evaluation method, categorization for line switching operations is introduced to classify all transmission lines based on their reliability performance. The categories provide reliability implications of line switching operations and can be used for guidance in actual operations. [ABSTRACT FROM PUBLISHER]

This paper investigates the impacts of transient instability on power system reliability. Traditionally, composite system reliability evaluation has been performed based on steady-state estimation of load curtailments; system dynamics have often been ignored, mostly due to computational complexity. In this paper, three probabilistic transient stability indices are proposed to assess system robustness against dynamic contingencies and to account for system instability in computing reliability indices. A direct method is utilized for transient stability assessment based on computing the energy margin of the system under fault events (energy margins measure the ability of a system to withstand contingencies). Energy margins along with the probability of occurrences of the events are used to update the probabilistic transient stability indices. The dependencies of reliability and stability indices on the fault clearing time are also evaluated. This method is applied on the reduced Western Electricity Coordinating Council and the New England 39 bus test systems. The results indicate the importance of considering the effect of stability in reliability evaluation. [ABSTRACT FROM PUBLISHER]

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]

This paper seeks to improve the computational time needed to solve transmission switching problems. Transmission switching provides an effective way to reduce operating costs in power system operations by altering the topology of the transmission network. However, determining the optimal set of lines to switch creates an enormous computational burden. Transmission switching formulations add binary decision variables for many transmission lines in the system to indicate if they are switched. This creates a very weak formulation that is difficult to solve. Altering the transmission topology by switching lines can affect the reliability of the network, for instance, by creating islands. Additional reliability constraints need to be added to the problem formulation. These constraints can potentially make an already difficult problem even harder. This paper takes a different approach to the transmission switching problem. Rather than preventing islanding by using constraints in the problem formulation, we look at how they can be used to improve the solution process. Specifically, we develop a cutting plane algorithm to generate valid inequalities and fix variables based on the fact that optimal solutions to the transmission switching problem that do not contain islands exist. [ABSTRACT FROM AUTHOR]

This paper presents a new reliability-based incremental phasor measurement unit (PMU) placement method via an enumeration-based integer linear programming optimization approach. By evaluating the proposed reliability indices for node and system observability, incremental PMUs are sequentially placed on the locations where the reliability of entire system's observability can be best improved at each step. As a result, a pre-set desired reliability of the system's observability can be achieved while using the minimal number of PMUs. The proposed method is generic and flexible in a way that different kinds of failures that impact the reliability of system observability can be easily included in the method. The 1st-order and 2nd-order failures of PMUs and lines are considered in this paper. The proposed method is validated using a 4-node system, the IEEE 14-node system and the IEEE RTS 96 test system. Simulation results show that the proposed method is effective and efficient in enhancing the reliability of observability for any system regardless of what the initial PMU placement of the system may be. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic framework for making decisions about the replacement of ageing power equipment. The framework involves three steps: first, to identify the most important and critical components of the system for overall system reliability; secondly, to perform Pareto analysis to relate the replacement of the components to the effect on system reliability indices; finally, to determine the optimum scenario for replacement based on a comparison between the cost of unreliability due to deferring the replacement and the saving on reinvestment cost. The proposed approach is illustrated on a meshed test system modeled using U.K. transmission system parameters, a representative transformer age profile and regulatory energy not supplied values. The results demonstrate the feasibility of the framework for application in the area of power system reliability, and show its feasibility for informing replacement decisions. [ABSTRACT FROM PUBLISHER]

Real-Time Thermal Rating (RTTR) is a smart-grid technology that allows electrical conductors to operate at an enhanced rating based on local weather conditions. RTTR also provides thermal visibility of the network, making system operators aware if the actual rating drops below the static seasonal rating. This paper investigates how using these enhanced variable ratings affects power network reliability. A methodology has been developed to assess network reliability with variable conductor ratings. The effect of failures and uncertainties in the RTTR system are also considered, and the effect of the correlation between conductor ratings due to common weather conditions is built into the model. State sampling and sequential Monte Carlo simulations are used to estimate the reliability of the RBTS 6-bus test network. At low loading levels, the RTTR appears to reduce network reliability but actually illustrates occasions when the existing ratings are being unknowingly infringed. For higher loading, the network reliability is significantly improved by the use of RTTR, with reductions in loss of load expectation of up to 67%. [ABSTRACT FROM PUBLISHER]