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This paper deals with the load behavior of pulp and paper industries after planned or forced outages. Real data of the power consumption following outages have been collected for most of the industries in Sweden. Based on these data load models have been proposed. The economical consequences of disturbances have been evaluated and a method of estimating the costs following a disturbance is presented. The possibility to disconnect parts of the load in a pulp and paper industry is discussed and can result in a reduction of the number of gas turbines necessary for the disturbance power reserve.

Times are changing in the electric utility industry and public groups are taking a more active role in the integrated resource planning (IRP) process by presenting their advice and consultation on matters of public concerns as evidenced in this paper's abstracts that summarize the panel session held during the 1994 Summer Power Meeting in San Francisco, California. Each panelist presented a different perspective on public participation in the IRP process with examples of how their companies developed and implemented this process. This paper summarizes the individual presentations and discussions.

There is an increasing interest in demand-side management (DSM) by utilities and regulators throughout the USA. With this interest, there is an increasing need for DSM evaluation. Regulators expect utility engineers to use least cost planning and integrated resource planning approaches to adjust their generation capacity plans to reflect DSM. Increasingly, utilities are considering DSM to affect their T&D capacity plans. However, major utility DSM programs are less than a decade old. This leaves many utility engineers uneasy. How do they know that DSM will be there when they really need it? To verify and improve the contribution of DSM programs, utility analysts have developed a set of methodologies and procedures for evaluating DSM. The purpose of this panel session was to review these state of the art evaluations and the lessons learned from them so far. The authors explore the differences inherent in evaluating DSM at the T&D level versus the generation level, and review DSM's persistence and reliability in the residential, commercial, and industrial sectors. >

On August 14, 2003, a cascading outage of transmission and generation facilities in the North American Eastern Interconnection resulted in a blackout of most of New York state as well as parts of Pennsylvania, Ohio, Michigan, and Ontario, Canada. On September 23, 2003, nearly four million customers lost power in eastern Denmark and southern Sweden following a cascading outage that struck Scandinavia. Days later, a cascading outage between Italy and the rest of central Europe left most of Italy in darkness on September 28. These major blackouts are among the worst power system failures in the last few decades. The Power System Stability and Power System Stability Controls Subcommittees of the IEEE PES Power System Dynamic Performance Committee sponsored an all day panel session with experts from around the world. The experts described their recent work on the investigation of grid blackouts. The session offered a unique forum for discussion of possible root causes and necessary steps to reduce the risk of blackouts. This white paper presents the major conclusions drawn from the presentations and ensuing discussions during the all day session, focusing on the root causes of grid blackouts. This paper presents general conclusions drawn by this Committee together with recommendations based on lessons learned.

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

This paper analyzes different models for evaluating investments in Energy Storage Systems (ESS) in power systems with high penetration of Renewable Energy Sources (RES). First of all, two methodologies proposed in the literature are extended to consider ESS investment: a unit commitment model that uses the System States (SS) method of representing time; and another one that uses a representative periods (RP) method. Besides, this paper proposes two new models that improve the previous ones without a significant increase of computation time. The enhanced models are the System States Reduced Frequency Matrix (SS-RFM) model which addresses short-term energy storage more approximately than the SS method to reduce the number of constraints in the problem, and the Representative Periods with Transition Matrix and Cluster Indices (RP-TM&CI) model which guarantees some continuity between representative periods, e.g. days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as benchmark. While both system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The RP-TM&CI model, on the other hand, succeeds in approximating both short- and long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.

Merchant transmission planning is considered as a further step towards the full liberalization of the electricity industry. However, previous modeling approaches do not comprehensively explore its social efficiency as they cannot effectively deal with a large number of merchant companies. This paper addresses this fundamental challenge by adopting a novel non-cooperative game-theoretic approach. Specifically, the number of merchant companies is assumed sufficiently large to be approximated as a continuum. This allows the derivation of mathematical conditions for the existence of a Nash Equilibrium solution of the merchant planning game. By analytically and numerically comparing this solution against the one obtained through the traditional centralized planning approach, the paper demonstrates that merchant planning can maximize social welfare only when the following conditions are satisfied: a) fixed investment costs are neglected and b) the network is radial and does not include any loops. Given that these conditions do not generally hold in reality, these findings suggest that even a fully competitive merchant transmission planning framework, involving the participation of a very large number of competing merchant companies, is not generally capable of maximizing social welfare.

In this paper, we study the optimal location planning of renewable distributed generation (RDG) units by taking into account the random uncertainties of renewable generation and load demand. In presence of the random uncertainties, location planning problem is naturally a two-stage stochastic mixed integer nonlinear programming problem, which is hard to solve efficiently. Instead of using traditional sampling methods or robust optimization methods, we propose a novel analytical approach in this paper to solve the problem efficiently and optimally. In particular, analytical expressions are derived for efficiently evaluating the performance of a candidate RDG placement decision. In this way, the stochastic mixed integer nonlinear programming problem is equivalently transformed into a deterministic integer problem, which can be solved efficiently using off-the-shelf tools. Numerical results show that the optimal RDG placement can save up to $4.2\%$ of the long-term average cost and $80.59\%$ of the line losses on the IEEE 13-bus test feeder. In addition, our proposed approach effectively reduces the computational time by $99.51\%$ on the IEEE 123 node test feeder compared with other traditional sampling-based metaheuristic approaches.

In this paper, a new device of FACTS family named as amalgam power flow controller (APFC) is proposed. APFC is the amalgamation of electromagnetic and voltage source based converter devices. The key feature of the model is that it inherits the characteristics of both devices. APFC is an economical FACTS device, which not only controls power flow in a network but also increases the reliability and security of power systems. This paper also proposes a new hybrid approach based modeling technique to develop a mathematical model of APFC. Converter losses of APFC are also incorporated in mathematical modeling. The feasibility and convergence characteristics of the proposed APFC are investigated using benchmark IEEE 30-bus and 118-bus test systems.

For nearly 20 years, the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges that they were originally designed to examine. Additionally, this paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work.

This paper examines the impact of dynamic interactions between the voltage source converters (VSCs) on the small-signal stability of an AC/MTDC (multiterminal dc) power system. The examination is carried out based on a closed-loop interconnected model consisted of an open-loop VSC subsystem and an open-loop subsystem of the rest of MTDC network (RDCN). Open-loop modal coupling is the modal condition that an oscillation mode of open-loop VSC subsystem is close to an oscillation mode of open-loop subsystem of the RDCN on the complex plane. Analysis indicates that the open-loop modal coupling may very likely degrade the small-signal stability of the AC/MTDC power system. If a VSC uses the active power control, the open-loop VSC subsystem is of no oscillation mode. Hence, open-loop modal coupling between the VSCs cannot occur in the MTDC network implementing the master–slave control. Whilst the open-loop modal coupling may introduce instability risk to the MTDC network using the dc voltage droop control. Therefore, the paper reveals the mechanism about how and why the dynamic interactions between the VSCs may degrade the small-signal stability of the AC/MTDC power system from the perspective of the system modal condition—the open-loop modal coupling.

To help the integration of various distributed energy resources (DERs), this paper proposes a transactive approach to the optimal scheduling for prosumers in coupled energy systems. DERs are coordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity market, as well as the wholesale gas market. In the day-ahead electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time balancing market, a VPP adjusts DER schedules to minimize imbalance costs. In this paper, we address the energy conversions between electric power and gas loads and investigate the interacting operations of gas and power systems. Our simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy systems.

Hybrid state estimation determines the states of systems monitored both by conventional SCADA measurements and PMUs. This paper proposes a decentralization method in order to improve the computational performance of hybrid state estimators with minimum loss of accuracy. The proposed method utilizes sensitivity matrix that relates measurement errors to state estimates in order to form the so-called isolated bus groups and observable subislands. Although the proposed decentralization method can be applied to any state estimator, the paper is developed using a least absolute value (LAV) estimator for demonstration purposes, which is known to be robust against bad data. The high computational burden of LAV estimator compared to the well-known weighted least squares estimator is eliminated, thanks to the proposed decentralization method.

Interarea oscillation is one of the main challenges for the secure and stable operation of a large-scale interconnected power system. To address this problem, this paper presents an adaptive supplementary damping controller (SDC) for voltage source converter based high voltage direct current (VSC-HVDC) by using the goal representation heuristic dynamic programming (GrHDP). Compared with conventional heuristic dynamic programming (HDP), GrHDP adds a new goal representation network to generate an adaptive reward signal to achieve better mapping relationship between the system states and the control action. To compensate phase shift between the output and input signals of a large-scale interconnected AC/DC power system, this paper proposes a parallel phase shift channel for the GrHDP. Moreover, the proposed GrHDP-SDC is a model-free control scheme, which does not need the mathematic model of the power system and has quick online learning ability to adapt to the variation of system operating conditions. Case studies are carried out on the 10-machine 39-bus system and 16-machine 68-bus system, respectively. Compared with the conventional lead-lag SDC (LL-SDC) and HDP-SDC, simulation results show that the proposed GrHDP-SDC can always provide satisfactory damping performance over a wide range of system operating conditions and disturbances.

Wind farms commonly cluster in regions rich in wind resources. Thus, correlation of wind speeds from different wind farms should not be ignored when modeling a power system with large wind energy penetration. This paper proposes a probabilistic optimal power flow (POPF) technique based on the quasi-Monte Carlo simulation (QMCS) considering the correlation of wind speeds using copula functions. In this paper, a copula function is used to model the dependent structure of random wind speeds and their forecast errors. QMCS is employed in the sampling procedure to reduce computation burden. The proposed method is applied in probabilistic power flow (PPF). Furthermore, the PPF is used in the POPF problem that aims at minimizing the expectation and downside risk of fuel cost simultaneously. Simulation studies are conducted on a modified IEEE 118-bus power system with wind farms integrated in two areas, and the results show that the accuracy and efficiency are improved by the proposed method.

Clustered unit commitment (CUC) formulations have been proposed to provide accurate and fast approximations to the unit commitment (UC) problem. In these formulations, identical or similar plants are grouped into clusters. This way, the binary commitment variables of all the plants within a cluster can be replaced by a single integer variable. This approach has recently been mainly used for tractably integrating flexibility constraints in generation expansion planning problems. However, a thorough general validation is still missing. In addition, these formulations do not provide commitment schedules on a plant-by-plant level and hence cannot be used directly for operating actual systems or markets. A first contribution of this paper is to show that errors can be introduced both due to the problem formulation and the grouping of non-identical units. A case study is presented in which these errors are quantified under different conditions. Overall, the error in approximating the total cost does not exceed 0.06%. A second contribution of this paper is the development of a hybrid approach which sequentially uses a CUC and a traditional UC model. This approach allows to reduce the computational cost of solving the UC problem while providing a guaranteed feasible and near optimal solution. ispartof: IEEE Transactions on Power Systems vol:33 issue:2 pages:2195-2204 status: published

This paper jointly addresses two major challenges in power system operations: 1) dealing with non-convexity in the power flow equations, and 2) systematically capturing uncertainty in renewable power availability and in active and reactive power consumption at load buses. To overcome these challenges, this paper proposes a two-stage adaptive robust optimization model for the multi-period AC optimal power flow problem (AC-OPF) with detailed modeling considerations, such as reactive capability curves of conventional and renewable generators and transmission constraints. This paper then applies strong second-order cone programming (SOCP)-based convex relaxations of AC-OPF combined with the use of an alternating direction method to identify worst-case uncertainty realizations, and also presents a speed-up technique based on screening transmission line constraints. Extensive computational experiments show that the solution method is efficient and that the robust AC OPF model has significant advantages both from the economic and reliability standpoints as compared to a deterministic AC-OPF model.

Online power distribution system time series analyses are typically divided into uniform intervals (e.g., market-driven) ranging from minutes to hours. This paper presents a constraint-driven approach to refining an initial time window structure at critical times within an injection forecast. Implicit temporal load capability (ITLC), which is formalized in this paper, uses forecasted nodal injection characteristics to identify critical times when control actions are necessary in order to maintain feasibility. ITLC and control selection algorithms are presented. Quasi-static time series and ITLC simulation results are presented using a multiphase, 2556-node distribution test circuit. With ITLC, a subset of the initial time windows are subdivided at the analytically-selected critical times. This approach is more efficient than using a large number of short time windows that span the entire forecast horizon.

Load modelling plays a key role in assessing peak load reduction and has attracted renewed interest recently due to increasing diversity and uncertainty in load characteristic resulted by emergence of new types of loads and more penetrations of distributed renewable generations onto electricity networks. This paper presents a methodology for the development of a static load model based load characteristic profile in medium voltage distribution networks from year-long field measurements. High-resolution load monitoring devices with the 1 s sampling rate installed at 60 primary substations in the U.K. distribution network are used to collect the data for the purpose of load modelling. Load models at these 60 substations comprising domestic, commercial/industrial, and mixed-type load demand are presented in the paper together with relevant model parameters. Field trials (which involve triggering transformer taps to initiate voltage changes) were performed throughout the year on 15 selected substations aimed at load profile validation. The developed load profiles take into account seasonal, weekly, and daily variations of P–V and Q–V characteristics. Representative 24 h (0.5 h) load matrices are developed for all 60 substations and provide an insight into the operational flexibility and network resilience to voltage variations.

This paper presents a distributionally robust planning model to determine the optimal allocation of wind farms in a multi-area power system, so that the expected energy not served (EENS) is minimized under uncertain wind power and generator forced outages. Unlike conventional stochastic programming approaches that rely on detailed information of the exact probability distribution, the proposed method attempts to minimize the expectation term over a collection of distributions characterized by accessible statistical measures, so it is more practical in cases where the detailed distribution data is unavailable. This planning model is formulated as a two-stage problem, where the wind power capacity allocation decisions are determined in the first stage, before the observation of uncertainty outcomes, and operation decisions are made in the second stage under specific uncertainty realizations. In this paper, the second-stage decisions are approximated by linear decision rule functions, so that the distributionally robust model can be reformulated into a tractable second-order cone programming problem. Case studies based on a five-area system are conducted to demonstrate the effectiveness of the proposed method

In this paper, a risk-based security assessment methodology is presented, which allows the assessment of operational security of a power system’s future state under uncertainty deriving from varying topology scenarios (contingencies) and forecast errors (loads and renewable infeeds). The methodology models input uncertaintywith a copula function-based Monte–Carlo (MC) framework. Furthermore, it provides the highest level of accuracy on initiating causes of failures through an AC power flow (AC PF) framework. Finally, it achieves speed in solution by the combination of twomeasures of risk. A fast screening tool, based on severity functions, allows us to quickly screen the system for the most severe states. A detailed analysis tool, based on an AC optimal power flow (AC OPF) framework and the notion of lost load, provides additional valuable information, including remedial actions to steer away from severe system states. This paper presents results from the application of the methodology proving the necessity of such a framework. It is shown that not taking into account stochasticdependence through a proper MC setup seriously underestimates system risk and that an AC framework is needed, as voltage deviations are shown to often be initiators of system collapse.

The uncertain nature of wind power causes difficulties in power system operation scheduling. Probabilistic descriptions of the uncertainty have been studied for decades. However, probabilistic forecasts designed for the regional multiple wind farms are few. Although the traditional methods for the single wind farm can still be used, they have the limitations in capturing the spatial correlations among wind farms, and they are less robust when multivariate observations are not so complete. To improve the forecast quality in this case, we combine the multivariate distribution modeling and probabilistic forecasts in this paper. An advanced model—the regular vine copula, which can describe the wind farms’ dependence structure precisely and flexibly with various bivariate copulas as blocks, is used in this paper. Enough simulation data can be generated from the model, which can be easily used to form the conditional forecast distributions under multiple forecast conditions. A case of 10 wind farms in East China has been used to compare the proposed method with its competitors. The results showed the method's advantages of providing reliable and sharp forecast intervals, especially in the case with limited observations available.

Due to rapid expansions of renewable energy (RE) generations, it becomes more important to assess the feasibility of power system operation under limited controllable resources. Especially, exact evaluation of the system reserve for preserving system security is required under erroneous RE output predictions. This paper proposes a method to evaluate the size of the feasible region of power system operation in control space for the examination of the effective system reserve margin under uncertainties. Predicted RE and demands with their confidence intervals (CIs) are specified to formulate a problem for the evaluation of the size of the worst-case feasible region, where positive size implies feasibility, while negative, infeasibility. The method computes the degree of system security, which is referred to as “Robust Power System Security” in this paper. The problem is formulated as bi-level optimization, which is linearized and transformed into the mixed integer linear programming (MILP) problem. This is a new approach in the treatment of uncertainties. We use DC power flow and linear constrained dynamic economic dispatch problem to demonstrate the effectiveness of the proposed method. The proposed approach is useful in power system planning in analyzing the feasibility of dynamic real-time operation in future circumstance.

The optimal sizing of an energy storage system (ESS) in a power generation system that incorporates photovoltaic (PV) generation is crucial in a power grid for which the reduction of CO2 emissions is important. This problem is particularly challenging when it relates to the power system of a ship because it involves uncertain meteorological and load data along a navigation route. This paper proposes a novel method for multi-objective minimization of investment/replacement cost, fuel cost, and CO2 emissions, to find the optimal size of the ESS considering life-span of the ESS. The generation of power by PV modules on a ship is affected by temporal and geographical variations of irradiation along the navigation route. In particular, operating load conditions and irradiation are uncertain. This paper proposes a novel algorithm for partitioning high-dimensional uncertain data into tractable clusters solved by deterministic optimization method. Case studies of an all-electric ship along a route from Dalian in China to Aden in Yemen are shown to demonstrate the applicability of the proposed clustering-based stochastic optimization method.

This paper proposes an integrated framework for wind farm maintenance that combines i) predictive analytics methodology that uses real-time sensor data to predict future degradation and remaining lifetime of wind turbines, with ii) a novel optimization model that transforms these predictions into profit-optimal maintenance and operational decisions for wind farms. To date, most applications of predictive analytics focus on single turbine systems. In contrast, this paper provides a seamless integration of the predictive analytics with decision making for a fleet of wind turbines. Operational decisions identify the dispatch profiles. Maintenance decisions consider the tradeoff between sensor-driven optimal maintenance schedule, and the significant cost reductions arising from grouping the wind turbine maintenances together—a concept called opportunistic maintenance. We focus on two types of wind turbines. For the operational wind turbines, we find an optimal fleet-level condition-based maintenance schedule driven by the sensor data. For the failed wind turbines, we identify the optimal time to conduct corrective maintenance to start producing electricity. The economic and stochastic dependence between operations and maintenance decisions are also considered. Experiments conducted on i) a 100-turbine wind farm case, and ii) a 200-turbine multiple wind farms case demonstrate the advantages of our proposal over traditional policies.

This paper derives the index of dynamic interactions (IDI), which is the sensitivity of a power system electromechanical oscillation mode (EOM) of concern to the dynamic interactions introduced by a grid-connected double-fed induction generator (DFIG). Based on the IDI, analysis is carried out with the following generally-applicable conclusions obtained: 1) Impact of dynamic interactions introduced by the DFIG increases when wind power penetration increases; 2) as long as the frequency response of an equivalent single DFIG model approximately matches the frequency response of a wind farm around the frequency of electromechanical oscillations of concern, this equivalent dynamic model of single DFIG can be used to represent the wind farm to examine the impact of wind farm on EOMs. Furthermore, an indicator of DFIG control (IDC) is derived for comparing the impact of terminal voltage control and reactive power control implemented by the DFIG. Main merit of the IDC is the computational simplicity without need to know dynamic model of the DFIG to indicate that either the terminal voltage control or the reactive power control is more beneficial to power system stability. An example multi-machine power system with grid-connected DFIGs is presented to demonstrate and validate the analysis and conclusions made in the paper.

Boosting the resilience of power systems is one of the core requirements of smart grid. In this paper, an integrated resilience response framework is proposed, which not only links the situational awareness with resilience enhancement, but also provides effective and efficient responses in both preventive and emergency states. The core of the proposed framework is a two-stage robust mixed-integer optimization model, whose mathematical formulation is presented in this paper as well. To solve the above model, an algorithm based on the nested column-and-constraint generation decomposition is provided, and computational efficiency improvement techniques are proposed. Preventive response in this paper considers generator re-dispatch and topology switching, while emergency response includes generator re-dispatch, topology switching and load shedding. Several numerical simulations validate the effectiveness of the proposed framework and the efficiency of the solution methodology. Key findings include the following: 1) in terms of enhancing power grid resilience, the integrated resilience response is preferable to both independent preventive response and independent emergency response; 2) the power grid resilience could be further enhanced by utilizing topology switching in the integrated resilience response.

One of the most used methods for long-term hydrothermal operation planning is the stochastic dual dynamic programming (SDDP). Using this method, the immediate and future water opportunity cost can be balanced and an economic-dispatch policy can be defined for multiple reservoirs under inflow uncertainty. In this framework, equipment outages and reserve deliverability are generally disregarded, despite their strong impact on the operative plan. However, recent advances in robust optimization have shown how to endogenously account for security criteria in scheduling models with reduced computational burden. Within this framework, reserve deliverability is ensured across the network via the co-optimization of energy and reserves (ancillary services). In this paper, we propose a new multistage model for planning hydrothermal coordination that co-optimizes the nominal energy dispatch and individual up and down reserve allocations. The main goal of this paper is to address a general $n-K$ security criterion, such that, for each inflow scenario, the system is capable of withstanding the loss of up to $K$ components, i.e., generation or transmission assets. The proposed methodology uses the column-and-constraint generation algorithm to efficiently incorporate a compound umbrella set of contingencies in the SDDP algorithm. Results for the Brazilian power system data corroborate the effectiveness of the proposed model.

This paper establishes a closed-loop interconnected dynamic model of a power system with a doubly fed induction generator (DFIG) for wind power generation, wherein, the DFIG and power system are modeled as two open-loop interconnected subsystems. The established model indicates that the impact of the dynamic interactions introduced by the DFIG on the power system's small-signal angular stability is the difference between the closed-loop and open-loop electromechanical oscillation modes (EOMs). The analysis conducted in this study reveals that when an open-loop DFIG oscillation mode (DOM) is close to an open-loop EOM on the complex plane, the modal coupling of the open-loop EOM and DOM can cause strong dynamic interactions between the DFIG and the power system; thus, the DFIG has a significant impact on the power system's small-signal angular stability. An example multimachine power system with grid-connected DFIGs is presented to demonstrate and validate the analysis and conclusions made in the paper.

Anomalous data can negatively impact energy forecasting by causing model parameters to be incorrectly estimated. This paper presents two approaches for the detection and imputation of anomalies in time series data. Autoregressive with exogenous inputs (ARX) and artificial neural network (ANN) models are used to extract the characteristics of time series. Anomalies are detected by performing hypothesis testing on the extrema of the residuals, and the anomalous data points are imputed using the ARX and ANN models. Because the anomalies affect the model coefficients, the data cleaning process is performed iteratively. The models are re-learned on “cleaner” data after an anomaly is imputed. The anomalous data are reimputed to each iteration using the updated ARX and ANN models. The ARX and ANN data cleaning models are evaluated on natural gas time series data. This paper demonstrates that the proposed approaches are able to identify and impute anomalous data points. Forecasting models learned on the unclean data and the cleaned data are tested on an uncleaned out-of-sample dataset. The forecasting model learned on the cleaned data outperforms the model learned on the unclean data with 1.67% improvement in the mean absolute percentage errors and a 32.8% improvement in the root mean squared error. Existing challenges include correctly identifying specific types of anomalies such as negative flows.

This paper presents a distributed, robust, finite-time secondary control for both voltage and frequency restoration of an islanded microgrid with droop-controlled inverter-based distributed generators (DGs). The distributed cooperative secondary control is fully distributed (i.e., uses only the information of neighboring DGs that can communicate with one another through a sparse communication network). In contrast to existing distributed methods that require a detailed model of the system (such as line impedances, loads, other DG units parameters, and even the microgrid configuration, which are practically unknown), the proposed protocols are synthesized by considering the unmodeled dynamics, unknown disturbances, and uncertainties in their models. The other novel idea in this paper is that the consensus-based distributed controllers restore the islanded microgrid's voltage magnitudes and frequency to their reference values for all DGs within finite time, irrespective of parametric uncertainties, unmodeled dynamics, and disturbances, while providing accurate real-power sharing. Moreover, the proposed method considers the coupling between the frequency and voltage of the islanded microgrid. Unlike conventional distributed controllers, the proposed approach quickly reaches consensus and exhibits a more accurate robust performance. Finally, we verify the proposed control strategy's performance using the MATLAB/SimPowerSystems toolbox.

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.

With the increasing use of voltage source converters (VSCs) in power electronics dominated power systems, oscillation phenomena in DC-link voltage control (DVC) timescale (around 10 Hz) among multiple VSCs have occurred. Several studies have tried to analyze these oscillation problems, but all associated with the single VSC situation. To consider the dynamic interactions between VSCs in DVC timescale, especially in the weak grid condition, this paper presents a small-signal model to understand VSC external characteristics based on motion equation concept also featured in synchronous generator (SG). Comparisons of time-domain simulation responses and eigenvalues show that the proposed model can hold the main behaviors of concern. The form of the model is very similar to the rotor motion equation in SG, with which power engineers have been very familiar. In addition, by establishing the relationship between the unbalanced powers and state variables of internal voltage (viz., VSC output voltage), the modeling idea introduced in this paper can be applied to other power electronic devices.

This paper develops enhanced hybrid generic (nonlinear) models of Type-3 and Type-4 wind power plants (WPPs) and extracts the corresponding linear (small-signal) dynamic models for power system transient stability analysis. The models are hybrid in nature since they consider both continuous states and discrete logic-controlled variables. The introduced enhancements include (i) a freezing function to reactivate reactive power emulator of Type-3 and Type-4 WPPs, (ii) active-current command recalculation step for Type-3 WPP and (iii) elimination of an activating logic of PI-controller limits in real current control path. The main feature of the enhanced models is that they can replicate the field-verified responses of the built-in PSS/E software models in any adopted software platform. It should be noted that the generic models described in the technical literature do not necessarily provide such replication. The paper also deduces small-signal dynamic models of Type-3 and Type-4 WPPs and addresses the multiple eigen structures of the linearized enhanced generic model of Type-3 WPP, which has not been comprehensively discussed in the technical literature. The enhanced nonlinear hybrid models and the corresponding linearized models are evaluated and verified based on time-domain simulation studies in PSS/E and MATLAB platforms, using NPCC system as the test bed.

This paper presents a methodology and set of validation criteria for the systematic creation of synthetic power system test cases. The synthesized grids do not correspond to any real grid and are, thus, free from confidentiality requirements. The cases are built to match statistical characteristics found in actual power grids. First, substations are geographically placed on a selected territory, synthesized from public information about the underlying population and generation plants. A clustering technique is employed, which ensures the synthetic substations meet realistic proportions of load and generation, among other constraints. Next, a network of transmission lines is added. This paper describes several structural statistics to be used in characterizing real power system networks, including connectivity, Delaunay triangulation overlap, dc power flow analysis, and line intersection rate. The paper presents a methodology to generate synthetic line topologies with realistic parameters that satisfy these criteria. Then, the test cases can be augmented with additional complexities to build large, realistic cases. The methodology is illustrated in building a 2000 bus public test case that meets the criteria specified.

The screening curve method (SCM) is an intuitive and fast model that estimates the least-cost generation mix for generation planning purposes. It calculates an economically adapted generation mix for a target load duration curve within a few seconds, but lacks many detailed considerations of the generation system. In order to improve SCM, many developments have been made in recent years. However, one of the biggest limitations had always been that the SCM assumed all capacity to be new with no existing capacity. This drawback made SCM less useful compared to other generation planning models. In this paper, we develop a direct way to model existing capacity in SCM. The proposed method is studied in this paper and is illustrated with an example. Finally, an ERCOT year 2030 case is simulated.

Wind power generators will become increasingly useful in modern power systems; therefore, they should be able to support the system frequency in a way similar to that of conventional generators. Droop and virtual inertia are the most reported methods in the literature. However, their impact on the generator and system frequency stability, when the double-mass mechanical dynamics of a wind power generator are considered, has not been addressed thoroughly. In this paper, small-signal modeling, analysis, and eigenvalues studies are used to show that incorporating a wind power generator in the frequency regulation can expose its shaft to forces stimulating its natural resonance frequency dynamics and lead to instability. This paper shows that the mechanical resonance of frequency-regulating wind generators must be studied and enhanced not individually but as a part of the whole power system stability analyses. To overcome the stability problem caused by implementing frequency regulation in wind generators, this paper investigates different alternatives to stabilize the generator dynamics and, at the same time, minimize undesirable interference in the conventional wind power generator controllers. Time-domain simulation results validate the analytical results and discussions.

The PMU data is increasingly used in the grid monitoring system SCADA. As the DFT algorithm used in PMUs fails to distinguish the fundamental frequency signal with low-frequency interference, the low-frequency disturbance (LFD) included in fundamental frequency variable data, which are collected from PMUs, will affect the correctness of calculation and analysis of the power system's steady state. In order to extract the fundamental frequency variable from PMU data, taking low-frequency forced oscillation as an example, a method for filtering LFD in PMU data is proposed. First, the effect of low-frequency oscillation to the fundamental frequency variable is analyzed in this paper. Then, the component of LFD in PMU data is explained. By finding the maximum value and minimum value of voltage or current in PMU data to draw the envelopes and add them together, the mean value or fundamental frequency variable of voltage and current is obtained. The mechanism of error in calculating active power is analyzed when low-frequency oscillation occurs in the power grid. The results of analyzing actual PMU data under low-frequency oscillation verify the correctness of the method proposed in this paper. This method is of great significance to improve the accuracy of calculation in SCADA.

A high penetration of residential photovoltaic (PV) panels can potentially cause a number of operational issues in the distribution circuit, necessitating the requirement to determine the largest PV generation, a distribution circuit can accommodate. This paper presents a Monte Carlo-based hourly stochastic analysis framework to determine a circuit's PV accommodation limit or PV hosting capacity. First, a mathematical formulation of the hosting capacity problem for bus overvoltage concerns is presented, followed by the proposed analysis approach. In the proposed approach, the Monte Carlo method is used to simulate the scenarios of probable PV locations and sizes while the hourly analysis framework is used to include the daily variations in load and PV generation. Next, a method to evaluate the percentage accuracy of the hosting capacity results is presented. The sensitivity of PV hosting results to multiple parameters related to both feeder characteristics and simulation method are evaluated as well. The utility of the proposed framework is presented using an actual 12.47-kV distribution circuit. In sum, by presenting a PV hosting analysis method, accuracy assessment framework, and sensitivity analysis approach, this paper provides a thorough understanding of the PV hosting capacity problem.

This paper presents a modeling approach for capturing closed-loop load behavior in distribution networks using bottom-up time-variant load models and incorporating load-flow feedback for thermal loads. Traditional load models are useful for providing insights into instantaneous changes in power, however, they do not capture the time-variant response to changes in voltage due to the effects of appliances with closed-control loops such as those which are thermostatically controlled. This paper proposes a modeling and co-simulation platform to simulate closed-loop load-modeling using state-of-the-art bottom-up load modeling techniques for the residential sector with a feedback model for time series steady-state load-flow. It incorporates time-variant load models and discrete state-space representation of thermostatically controlled appliances. The developed methodology is used in a conservation voltage reduction case study on an urban distribution network, allowing detailed assessment of the voltage response and analysis of changes in active and reactive power and network losses.

In wind energy conversion systems, the fundamental frequency step-up transformer acts as a key interface between the wind turbine and the grid. Recently, there have been efforts to replace this transformer by an advanced power-electronics-based solid-state transformer (SST). This paper proposes a configuration that combines the doubly fed induction generator-based wind turbine and SST operation. The main objective of the proposed configuration is to interface the turbine with the grid while providing enhanced operation and performance. In this paper, SST controls the active power to/from the rotor side converter, thus, eliminating the grid side converter. The proposed system meets the recent grid code requirements of wind turbine operation under fault conditions. Additionally, it has the ability to supply reactive power to the grid when the wind generation is not up to its rated value. A detailed simulation study is conducted to validate the performance of the proposed configuration.

Electric power systems are going through a major upgrade with the integration of advanced technologies to enable the smarter electric grid (SEG). The SEG will use information and communications technology to have enhanced controllability and will become more interactive. This ongoing change also necessitates educating professionals and future generation of engineers to manage evolving complexity of the electric grid. This paper presents experiences in the design and teaching of a unique multidisciplinary team-taught course on smart grid cyber infrastructure to provide a new generation of engineers with a solid foundation of smart grid concepts and their associated challenges. This paper identifies the course topics covered, learning objectives, and assessment activities for the class as well as lessons learned based on course evaluations obtained from multiple offerings. All of the course material is available in public domain and can be easily adopted at another educational institution.

Enhanced utilization of the existing transmission network is a cheaper and paramount alternative to building new transmission lines. Flexible ac transmission system (FACTS) devices are advanced technologies that offer transfer capability improvements via power flow control. Although many FACTS devices exist in power systems, their set points are not frequently changed for power flow control purposes, which is mainly due to the computational complexity of incorporating FACTS flexibility within the market problem. This paper proposes a computationally efficient method for adjustment of variable impedance-based FACTS set points, which is also compatible with existing market solvers. Thus, the method can be employed by the existing solvers with minimal modification efforts. This paper models FACTS reactance control as injections to keep the initial shift factors unchanged. Next, the paper formulates a dc optimal power flow that co-optimizes FACTS set points alongside generation dispatch. The resulting problem, which is in a nonlinear program, is then reformulated to a mixed-integer linear program. Finally, an engineering insight is leveraged to further reduce the computational complexity to a linear program. Simulation studies on IEEE 118-bus and Polish 2383-bus test cases show that the method is extremely effective in finding quality solutions and being very fast.

This paper describes the modeling and formulation of a variety of deterministic techniques for energy storage devices, namely the PI, H-infinity, and sliding mode controllers. These techniques are defined based on a general, yet detailed, energy storage device model, which is accurate for transient stability analysis. The paper also presents a thorough statistical comparison of the performance and robustness of the considered control techniques, using stochastic dynamic models and a variety of disturbances and scenarios. The case study is based on a 1479-bus model of the all-island Irish transmission system and an energy storage device actually installed in the system. European Commission Horizon 2020 Science Foundation Ireland

This paper proposes a distributionally robust optimization model for solving unit commitment (UC) problems considering volatile wind power generation. The uncertainty of wind power is captured by an ambiguity set that defines a family of wind power distributions, and the expected total cost under the worst-case distribution is minimized. Compared with stochastic programming, this method may have less dependence on the data of exact probability distributions. It should also outperform the conventional robust optimization methods because some distribution information can be incorporated into the ambiguity sets to generate less conservative results. In this paper, the UC model is formulated based on the typical two-stage framework, where the UC decisions are determined in a here-and-now manner, and the economic dispatch decisions are assumed to be wait-and-see , made after the observation of wind power outcomes. For computational tractability, the wait-and-see decisions are addressed by linear decision rule approximation, assuming that the economic dispatch decisions affinely depend on uncertain parameters as well as auxiliary random variables introduced to describe distributional characteristics of wind power generation. It is shown in case studies that this decision rule model tends to provide a tight approximation to the original two-stage problem, and the performance of UC solutions may be greatly improved by incorporating information on wind power distributions into the robust model.

In a recent paper, we proposed a system identification method for constructing reduced-order models for the electro-mechanical dynamics of large power systems, divided into multiple coherent clusters, using Synchrophasors. Every cluster in the actual model was represented as an aggregate generator in the reduced-order model. An aggregate network graph connected one aggregate generator to another. In this paper, we extend this identification approach to differential-algebraic (DAE) models. First, every cluster is associated with a unique terminal bus, referred to as the pilot bus, that couples its internal network to the rest of the system. The proposed algorithm uses Synchrophasor measurements from the pilot buses to identify the dynamic model of the aggregate generator for each cluster using nonlinear least squares while retaining the identity of all the pilot buses. The resulting reduced-order model is in the form of a nonlinear electric circuit described by aggregate differential and algebraic equations. We illustrate our results using two case studies, one for the IEEE 9-bus power system and another for the IEEE 39-bus power system. We also discuss how these reduced-order DAE models may be useful for designing shunt controllers at the pilot buses by using Synchrophasor feedback.