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Aiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively. [ABSTRACT FROM AUTHOR]

Abstract Aiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.

The clearing price in electricity spot market is an important reference guiding market participants to purchase energy. Current electricity price forecasting methods mainly focus on improving numerical accuracy, and the need to optimize economic benefits is ignored. However, higher numerical precision sometimes leads to lower electricity‐purchase gain. To deal with that, this paper proposes a price forecasting method that optimizes economic benefits together with numerical accuracies. A revenue‐optimizing term evaluating the relationship between the predicted price and the cost reference price is introduced to the loss function of the prosumers' forecasting model. A sequence comparison neural network structure is proposed and added to consumers' model, so the forecasting model is trained by also considering price trend. By co‐optimizing numerical precision and electricity‐purchase gain, the prediction is more conducive to reducing the cost of purchasing power. Price data in actual electricity market are used to verify the feasibility and improvement of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract The clearing price in electricity spot market is an important reference guiding market participants to purchase energy. Current electricity price forecasting methods mainly focus on improving numerical accuracy, and the need to optimize economic benefits is ignored. However, higher numerical precision sometimes leads to lower electricity‐purchase gain. To deal with that, this paper proposes a price forecasting method that optimizes economic benefits together with numerical accuracies. A revenue‐optimizing term evaluating the relationship between the predicted price and the cost reference price is introduced to the loss function of the prosumers’ forecasting model. A sequence comparison neural network structure is proposed and added to consumers’ model, so the forecasting model is trained by also considering price trend. By co‐optimizing numerical precision and electricity‐purchase gain, the prediction is more conducive to reducing the cost of purchasing power. Price data in actual electricity market are used to verify the feasibility and improvement of the proposed method.

This paper provides a tutorial overview of robust optimization in power systems, including robust optimization and adaptive robust optimization. We also introduce distributionally robust optimization. For illustration purposes, we describe and analyze a short-term operation problem and a long-term planning one. The operation problem allows identifying the transmission line whose failure has the higher impact on the operation of the system (worst contingency). From a planning perspective, we describe and analyze the problem of identifying which are the most critical transmission lines (vulnerabilities) to be protected against intentional attacks or natural disasters. We provide as well a distributionally robust version of this problem. The operation problem is a robust optimization one, while the planning problem is an adaptive robust optimization one, including a distributionally robust variant. [ABSTRACT FROM AUTHOR]

To date, wind and solar power plants have mainly provided energy. Going forward, with the application of appropriate controls, they can offer additional grid services, such as regulating reserves. Additional grid services can present an opportunity for additional value streams to wind and solar power plants. However, the additional value might not be realized when operator-determined performance targets and settlement adjustments for the delivery of regulation capacity are disregarded. This article studies the participation of variable resources in centralized regulation markets. We derive analytical results for regulation offers in terms of quantity and price considering that a variable resource does not want to risk being disqualified as a regulation provider, incurring losses, or foregoing more profitable opportunities. Our analysis suggests that U.S. operator-determined performance targets impose an upper bound on the quantity of a variable resource's regulation offer; and settlement adjustments for the delivery of regulation affect the price at which variable resources offer and the calculations of opportunity costs in case of imperfect regulation delivery. [ABSTRACT FROM AUTHOR]

Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits. [ABSTRACT FROM AUTHOR]

To ensure powersupply–demand balance under the increasing penetration of wind power, the existing nonanticipative robust unit commitment models (NRUCs) co-optimize the commitment status and the dispatch policy of power sources. Exploiting the fact that the wind power uncertainty reduces over time, this article proposes an NRUC where determining the dispatch policy is delayed until the uncertainty decreases. The proposed NRUC features three decision-making problems sequentially solved under different degrees of uncertainty. The first decision-making problem is formulated as an intractable three-stage robust optimization problem. To solve this problem, a suboptimal approach is developed where a constraint is imposed on the dispatch policy so that the transmission capacity constraint is met regardless of the dispatch level. Results of simulations on a 24-bus and a 300-bus test system show that the proposed NRUC outperforms existing NRUCs regarding feasibility and optimality under currently severe but decreasing wind power uncertainty. [ABSTRACT FROM AUTHOR]

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional approaches for security assessment are often limited by their scope and/or speed, resulting in missing of critical contingencies that could lead to cascading failures. This paper proposes a two-component methodology to enhance power system security. The first component combines an efficient algorithm to detect cut-set saturation (called the feasibility test (FT) algorithm) with real-time contingency analysis (RTCA) to create an integrated corrective action (iCA), whose goal is to secure the system against cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) to secure the system against post-contingency cut-set saturation. The first component is more comprehensive, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. The results obtained by analyzing different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology successfully enhances the scope and speed of power system security assessment during multiple outages. [ABSTRACT FROM AUTHOR]

Global efforts towards de-carbonization have opened the pathway for a test environment of electrical energy storage (EES) topology. In this work, the feasibility of 17 EES facilities applied to 24 individual applications of flexible power networks has been investigated in terms of levelized cost of storage (LCOS) in $/kW. Electricity storage facilities were modelled and evaluated via a life-cycle cost analysis, based on the most realistic EES characteristics and practical applications’ requirements. The results showed that pumped-hydro constitutes the least-cost and most reliable system for large-scale/long-duration applications. Zn-air and vanadium redox (VRB) offer great potential in demand-shifting and reactive support but, due to their wide LCOS range, considerable risk is added in such an investment. Electrochemical double-layer capacitor (EDLC) holds almost the exclusivity in fast-response/frequently-cycled applications, while for medium-term/medium-scale applications and where the large footprint is a prohibitive factor, valve-regulated Pb-acid (VRLA) and hydrogen fuel cells (H2-FC) are more favourable options. However, efficient tools still lack the ability of quantifying all benefits derived from electricity storage, maintaining stakeholders’ concerns for investment. It is apparent that, further research and development implies the decrease of the uncertainty governing the majority of EES technologies, increasing EES implementations and vice versa.

The development of smart grid technologies has resulted in increased interdependence between power and communication systems. Many of the operations in the existing power system rely on a stable and secured communication system. For electrically weak systems and time-critical applications, this reliance can be even greater, where a small degradation in communication performance can degrade system stability. However, despite inter-dependencies between power and communication systems, only a few studies have investigated the impacts of communication system performance on power system dynamics. This study investigates the dependencies of power system dynamics operations on a communication system performance. First, a detailed, dynamic networked microgrid model is developed in the GridLAB-D simulation environment, along with a representative multi-traffic, multi-channel, multi-protocol communication system model, developed in the network simulator (ns-3). Second, a hierarchical engine for large-scale infrastructure co-simulation framework is developed to co-simulate microgrid dynamics, its communication system, and a microgrid control system. The impact of communication system delays on the dynamic stability of networked microgrids is evaluated for the loss of generation using three use-cases. While the example use-cases examine microgrid applications and the impact to resiliency, the framework can be applied to all levels of power system operations.

Abstract The utilization levels of the transmission network can be enhanced by the use of automated protection schemes that rapidly respond to disturbances. However, such corrective systems may suffer from malfunctions that have the potential to exacerbate the impact of the disturbance. This paper addresses the challenge of jointly optimizing the dispatch of generators and protection settings in this context. This requires a holistic assessment of the cyber (protection logic) and physical (network) systems, considering the failures in each part and their interplay. Special protection schemes are used as a prototypical example of such a system. An iterative optimization method is proposed that relies on power system response simulations in order to perform detailed impact assessments and compare candidate solutions. The candidate solutions are generated on the basis of a security-constrained dispatch that also secures the system against a set of cyber failure modes. A case study is developed for a generation rejection scheme on the IEEE reliability test system (RTS): candidate solutions are produced based on a mixed integer linear programming optimisation model, and loss-of-load costs are computed using a basic cascading outage algorithm. It is shown that the partial security approach is able to identify solutions that provide a good balance of operational costs and loss-of-load risks, both in a fixed dispatch and variable dispatch context.

This research presents a robust model predictive controller of two microgrids (MGs) interconnected via tie‐line (TL) power. Power system operations continue to face new challenges due to the large integration of renewable energy generation with an active load demand uncertainty and the management and control of bidirectional power flow between MGs and others uncertainties and system nonlinearities due to the power system operating condition. To handle these uncertainties and system nonlinearities a robust model using predictive control scheme is presented in this paper to achieve the control of the active power between two MGs when considering three disturbances scenario models. In the design process, uncertainties and nonlinearities of the system were taken into account and contributed to the robustness of the closed‐loop system. Robust control scheme development considering power system nonlinearities and uncertainties were used to improve the online close‐loop robustness. The model helped to achieve a desired active power regulation by coordinating the MGs and active power over‐shoot and under‐shoot time were reduced outstandingly. A Comparison was made between open‐loop and close‐loop simulation results, which indicate that the model can handle robustness while settling power deviation of the interconnected system. MATLAB environment was used to solve the optimisation problem. [ABSTRACT FROM AUTHOR]

A new attempt of employing salp swarm algorithm (SSA) to tackle the optimal power flow (OPF) problem is demonstrated in the current study. This aforementioned problem has four fitness functions to be optimized such as (1) the sum of generating units' fuel costs, (2) total network real power losses, (3) entire sum of voltage deviation of load buses, and (4) static voltage stability (VS) of electric power systems. At initial stage, these objective are solved one by one, and at a later stage, different vector objective functions are solved simultaneously by the SSA. The VS study based on a modal analysis is taken into consideration as an objective function. In this issue, the eigenvalues and eigenvectors of a reduced Jacobian matrix due to the reactive power change are figured. The smaller magnitude of eigenvalues indicates the vicinity to system voltage instability. As the magnitude of eigenvalues increases, the incremental voltage decreases, which means strong VS. The output active power of generating units, their voltages, transformers tap setting, and capacitor devices represent the search field. Two electric grids such as IEEE 57- and 118-bus electric networks are demonstrated to examine the performance of the SSA. The effectiveness of the SSA–OPF methodology is compared with that obtained by using other competing optimization methods. Furthermore, statistical performance measures comprising parametric and nonparametric tests are made and the simulation results are extensively verified which indicate a competition of the SSA with others algorithms in solving the OPF problem. [ABSTRACT FROM AUTHOR]

Head‐sensitive forbidden and restricted zones of large hydropower units are challenging the automatic generation control (AGC) as admissible operating zones are discontinuous and variational. This study focuses on the AGC of such a large hydropower plant to ensure the stability, security and timeliness of power grids. A methodology is developed to solve the unit commitment (UC) and load distribution in AGC. This methodology identifies in advance all feasible unit combinations under different water heads and determines admissible operating zones using combinatorial mathematics techniques. A fast strategy that includes the accurate estimation of varying water heads helps to eliminate infeasible unit combinations. Minimising the number of units working in restricted zones and times of unit output passing through forbidden zones is used to optimise the UC, where priority orders of units are introduced to evaluate UC schemes with the same objective value. Finally, a dynamic programming based model is formulated to solve an economic load distribution among operating units. The methodology is applied to the AGC of the Nuozhadu with nine 650 MW units of two different types. Three cases indicate that the number of units working in restricted zones and times of unit output passing through forbidden zones is significantly reduced. [ABSTRACT FROM AUTHOR]

Electric power and natural gas systems typically are operated independently. However, their operations are interrelated due to the proliferation of natural gas-fired generating units. We analyze the independent but interrelated day-ahead operation of the two systems. We use a direct approach to identify operational equilibria involving these two systems, in which the Karush-Kuhn-Tucker conditions of both electric power and natural gas operational models are gathered and solved jointly. We characterize the equilibria that are obtained under different levels of temporal and spatial granularity in conveying information between the two system operators. Numerical results from the Belgian system are used to examine the impacts of different levels of information interchange on prices, operational costs, and decisions in the two systems. [ABSTRACT FROM AUTHOR]

The evolving nature of electricity production, transmission, and consumption necessitates an update to the IEEE's Reliability Test System (RTS), which was last modernized in 1996. The update presented here introduces a generation mix more representative of modern power systems, with the removal of several nuclear and oil-generating units and the addition of natural gas, wind, solar photovoltaics, concentrating solar power, and energy storage. The update includes assigning the test system a geographic location in the southwestern United States to enable the integration of spatio-temporally consistent wind, solar, and load data with forecasts. Additional updates include common RTS transmission modifications in published literature, definitions for reserve product requirements, and market simulation descriptions to enable benchmarking of multi-period power system scheduling problems. The final section presents example results from a production cost modeling simulation on the updated RTS system data. [ABSTRACT FROM AUTHOR]

The interdependency of electric power and natural gas systems requires co-ordinated operational planning. We propose a unit commitment model that integrates a second-order-cone relaxation of a non-convex nonlinear natural-gas flow model that considers pipeline line pack. The model is enhanced by using convex envelopes of bilinear terms, which tighten the relaxation. By fixing the binary variables at their optimal values and linearizing the natural gas-flow-balance equations around the solution that is obtained, we obtain electricity and natural gas locational marginal prices as the dual variables of electricity- and natural gas-flow-balance equations, respectively. The interdependence between these sets of prices is discussed. Numerical results from two test systems validate the solution-quality and computational-efficiency benefits of the proposed modeling methodology. [ABSTRACT FROM AUTHOR]

A vision for an autonomous power grid as an Internet of Things is presented. This vision encompasses the functionality from system operations and market operations perspectives. The vision aspires to a transactive energy grid with intelligence distributed among all components. It introduces the concept of a nested transactive grid to model the distribution grid as a nested set of virtual microgrids that can each act as a market. This facilitates exchanges, including peer-to-peer, participant-to-market, and market-to-market while incorporating the security of the grid. The vision allows to exploit the proliferation of DERs and flexible demands to realize a more resilient grid in both normal and emergency conditions utilizing the transactive grid capabilities. An architecture of the proposed nested model and orchestration of the interactions among the markets are described. A practical use case is also presented. [ABSTRACT FROM AUTHOR]

Global efforts towards de-carbonization have opened the pathway for a test environment of electrical energy storage (EES) topology. In this work, the feasibility of 17 EES facilities applied to 24 individual applications of flexible power networks has been investigated in terms of levelized cost of storage (LCOS) in $/kW. Electricity storage facilities were modelled and evaluated via a life-cycle cost analysis, based on the most realistic EES characteristics and practical applications' requirements. The results showed that pumped-hydro constitutes the least-cost and most reliable system for large-scale/long-duration applications. Zn-air and vanadium redox (VRB) offer great potential in demand-shifting and reactive support but, due to their wide LCOS range, considerable risk is added in such an investment. Electrochemical double-layer capacitor (EDLC) holds almost the exclusivity in fast-response/frequently-cycled applications, while for medium-term/medium-scale applications and where the large footprint is a prohibitive factor, valve-regulated Pb-acid (VRLA) and hydrogen fuel cells (H2-FC) are more favourable options. However, efficient tools still lack the ability of quantifying all benefits derived from electricity storage, maintaining stakeholders' concerns for investment. It is apparent that, further research and development implies the decrease of the uncertainty governing the majority of EES technologies, increasing EES implementations and vice versa. [ABSTRACT FROM AUTHOR]

This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation-based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed. The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical. [ABSTRACT FROM AUTHOR]

A sustainable power grid involves variable and uncertain generation, load and equipment availability. In order to ensure the frequency quality, power system needs to maintain sufficient reserve capacity to meet the demand and generation variations, and to withstand contingency events. To limit the operation risk to an acceptable level, an operation practice driven and control performance based reserve allocation method is proposed in this paper to determine adequate frequency regulation reserve in an island power system with increasing renewable integrations. Analyses of on-line available frequency regulation reserves and control performance data recorded in Energy Management System are conducted to appraise and manage the risk of non-compliance of frequency control standard. The proposed method is based on the conditional probabilities of reaching certain Control Performance Standard 1 (CPS1) score under the availability of Automatic Generation Control ramping and droop response capability. Comparisons of estimated reserve requirements and expected performance of four dynamic reserve allocation schemes to comply with CPS1 are presented. [ABSTRACT FROM AUTHOR]

To provide continuity of balancing demand and generation, renewable sources will be more active than today in near future due to the tendency of massive investment on renewable energy sources (RESs) by countries. However, due to the uncertain and intermittent nature of RESs, RESs would create problems on power system operations such as power quality, efficiency, stability and reliability. Owing to having problems with RESs integration, virtual power plant (VPP) has introduced to make this integration smooth without compromising the grid stability and reliability along with offering many other techno‐economic benefits. This study reviews structures, types, architecture and operations of VPP along with the status of present implementations worldwide. The types of VPP are introduced in details with the optimisation algorithm used with each type. In addition, VPP is linked with the most of the components in power systems such as distributed generation, active prosumers, transmission system operator and distribution system operator, grid services such as fault ride through, reactive power control as well as with the help of technology such as communications, control and optimisations. This study gives a comprehensive outline of transforming microgrid to VPP that is useful for researchers, consumers, prosumers and utility operators. [ABSTRACT FROM AUTHOR]

The malicious data attacks may evade the detection and impose operational impacts. One major impact is the transmission line overloads being intentionally triggered by a judiciously designed false data injection attack. Such overloads can cause physical damages to components or even initiate cascading failures with severe consequences. Thus, it is necessary to establish a mechanism for mitigating the physical overloads due to potential attacks. So, in this paper, we propose a cyber-secured corrective dispatch scheme which can secure the flow levels, i.e., mitigate the physical overloads, against those potential data attacks. The scheme integrates a corrective action for dispatching generators. We propose a solution method which can find the global optimum by adding a finite number of Benders like cuts. We also prove that the Lagrange multipliers can be set as binary variables without changing the optimal solution. The simulations on the IEEE 118-bus system verify the proposed scheme and pave the way for investigating relevant defensive strategies. [ABSTRACT FROM AUTHOR]

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity. • Production cost model of a bulk power system with wave power is developed. • Sensitivity studies to infer impact of wave power on grid operations at varying capacities. • Wave power reduces electricity prices during periods of higher resource availability. • Wave power benefits can percolate inland with system-wide transmission expansion. • Wave power can have added benefits in resilience events like transmission outages. [ABSTRACT FROM AUTHOR]

With the increasing shares of intermittent renewable sources in the grid, it becomes increasingly essential to quantify the requirements of the power systems flexibility. In this article, an adjusted weight flexibility metric (AWFM) is developed to quantify the available flexibility within individual generators as well as within the overall system. The developed metric is useful for power system operators who require a fast, simple, and offline metric. This provides a more realistic and accurate quantification of the available technical flexibility without performing time-consuming multi-temporal simulations. Another interesting feature is that it can be used to facilitate scenario comparisons. This is achieved by developing a new framework to assure the consistency of the metric and by proposing a new adjusted weighting mechanism based on correlation analysis and analytic hierarchy process (AHP). A new ranking approach based on flexibility was also proposed to increase the share of the renewable energy sources (RESs). The proposed framework was tested on the IEEE RTS-96 test-system. The results demonstrate the consistency of the AWFM. Moreover, the results show that the proposed metric is adaptive as it automatically adjusts the flexibility index with the addition or removal of generators. The new ranking approach proved its ability to increase the wind share from 28% to 37.2% within the test system. The AWFM can be a valuable contribution to the field of flexibility for its ability to provide systematic formulation for the precise analysis and accurate assessment of inherent technical flexibility for a low carbon power system.

Though the full AC power flow model can accurately represent the physical power system, the use of this model is limited in practice due to the computational complexity associated with its non-linear and non-convexity characteristics. For instance, the AC power flow model is not incorporated in the unit commitment model for practical power systems. Instead, an alternative linearized DC power flow model is widely used in today’s power system operational and planning tools. However, DC power flow model will be useless when reactive power and voltage magnitude are of concern. Therefore, a linearized AC (LAC) power flow model is needed to address this issue. This paper first introduces a traditional LAC model and then proposes an enhanced data-driven linearized AC (DLAC) model using the regression analysis technique. Numerical simulations conducted on the Tennessee Valley Authority (TVA) system demonstrate the performance and effectiveness of the proposed DLAC model.

Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of the TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major components: the probabilistic data generation, the composite feature selection, and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data. Then, the composite feature selection is used to reduce the dimension of the generated data and identify the features which are highly correlated with TTC. The features are determined by the maximal information coefficients and nonparametric independence screening approach. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through the learned correlation relationship. Extensive numerical results carried out on the modified New England 39-bus test system demonstrate the feasibility of the proposed TTC estimator for online applications. [ABSTRACT FROM AUTHOR]

Abstract Climate change might impact various components of the bulk electric power system, including electricity demand; transmission; and thermal, hydropower, wind, and solar generators. Most research in this area quantifies impacts on one or a few components and does not link these impacts to effects on power system planning and operations. Here, we advance the understanding of how climate change might impact the bulk U.S. power system in three ways. First, we synthesize recent research to capture likely component-level impacts of climate change in the United States. Second, given the interconnected nature of the electric power system, we assess how aggregated component-level impacts might affect power system planning and operations. Third, we outline an agenda for future research on climate change impacts on power system planning and operations. Although component-level impacts vary in their magnitude, collectively they might significantly affect planning and operations. Most notably, increased demand plus reduced firm capacity across generation types might require systems to procure significant additional capacity to maintain planning reserve margins, and regional declines in renewable resources might need to be offset by increasing zero-carbon investment to meet decarbonization targets. Aggregated impacts might also affect operations, e.g., through shifts in dispatching and increased operational reserve requirements. Future research should aggregate component-level impacts at operational timescales, quantify impacts on wind and solar variability, and contextualize climate change impacts within ongoing shifts in the electric power system. Highlights • Climate change might impact electricity demand, generation, and transmission. • We discuss how these impacts might jointly impact planning and operations. • Increased demand plus reduced firm capacity will require additional capacity. • Regional declines in renewable resource will affect decarbonization efforts. • Future research on aggregated component-level impacts is necessary. [ABSTRACT FROM AUTHOR]

This paper considers unbalanced multiphase distribution systems with generic topology and different load models, and extends the $Z$ -bus iterative load-flow algorithm based on a fixed-point interpretation of the AC load-flow equations. Explicit conditions for existence and uniqueness of load-flow solutions are presented. These conditions also guarantee convergence of the load-flow algorithm to the unique solution. The proposed methodology is applicable to generic systems featuring i) wye connections; ii) ungrounded delta connections; iii) a combination of wye-connected and delta-connected sources/loads; and iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. Further, a sufficient condition for the nonsingularity of the load-flow Jacobian is proposed. Finally, linear load-flow models are derived, and their approximation accuracy is analyzed. Theoretical results are corroborated through experiments on IEEE test feeders. [ABSTRACT FROM AUTHOR]

In this paper, we address the risk of hidden N-k line contingencies in power systems by proposing a preventive mitigation strategy to mitigate these hidden contingencies. The strategy comprises a screening model, an evaluation model, and a mitigation cut generating mechanism. Using this strategy, we first screen out the high-risk hidden N-k line contingencies using the screening model, and then mitigate them by iteratively reducing their hidden probabilities assessed in the evaluation model. This is achieved by incorporating mitigation cuts into an enhanced security constrained economic dispatch module, while taking into consideration both reliability and economic criteria. This mitigation strategy can be employed as a preventive action by the system operators. The simulations on the IEEE 118-bus system demonstrate that the proposed approach can reduce the hidden probabilities of these high-risk contingencies below a given threshold with a reasonable increase in the generation cost, and, thus, verify the approach as an effective tool to address the risk of these contingencies. [ABSTRACT FROM AUTHOR]

The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependencies between tens or hundreds of spatially distributed wind farms, e.g., over a region. In this paper, a sparsity-controlled vector autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained mixed integer nonlinear programming (MINLP) problem. It allows controlling the sparsity of the coefficient matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building and forecasting, the original SC-VAR is modified and a correlation-constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set of traditional local methods and spatio-temporal methods. The results obtained show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsity-control ability, sparsity, accuracy, and efficiency. [ABSTRACT FROM AUTHOR]

In cyber-physical power systems, a contingency in the cyber may hamper secure operations of the physical system by changing the information flows. In order to investigate the degree to which a cyber-contingency affects power system operations, we first categorize the potential impacts on the information flow respecting the end-to-end information features in terms of delayed, disordered, dropped, and distorted data. A simplified co-simulation model is then put forth for analyzing the interdependencies between energy and information flows, and obtaining the quantitative relation between information features and power system operations. As the operational characteristics of a cyber system can be entirely captured by all end-to-end information flows, our developed model is hence termed as the information flow based co-simulation model. The information flow based model features in low complexity and covering potential cyber contingencies. Moreover, the quantitative relation can provide requirements for the planning and operation of cyber systems. To illustrate the effectiveness and advantages of the proposed model, a comparison with the commercial communication simulation software OPNET is made. Finally, a wide-area damping control application is taken as an example to demonstrate physical impacts of various cyber-contingencies. [ABSTRACT FROM AUTHOR]