Northeast Power Coordinating Council Research Articles

A simple way to integrate distributed storage into a wholesale electricity market

Current plans to decarbonize the electric supply system imply that the generation from wind and solar sources will grow substantially. This growth will increase the uncertainty of system operations due to the inherent variability of these renewable sources, and as a result, more reserve capacity will be required to provide the ramping (flexibility) needed for reliable operations. This paper assumes that all of the increased uncertainty comes from wind farms on the grid, and it shows how distributed storage managed locally by aggregators can provide the ramping needed without introducing a separate market for flexibility. This can be accomplished when the aggregators minimize the expected daily cost of the energy purchased from the grid for their customers by submitting optimal bids into the wholesale market with high and low price thresholds for discharging and charging the storage. This model is illustrated using a stochastic multi-period security constrained optimal power flow together with realistic data for a reduction of the network in the Northeast Power Coordinating Council region of the United States. The results show that the bidding strategy for distributed storage provides ramping to the grid just as effectively as storage managed by a system operator.

A Dataset for Electricity Market Studies on Western and Northeastern Power Grids in the United States

Efficient electricity market operations and cost-effective electricity generations are fundamental to a low-carbon energy future. The Western Electricity Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) systems were built to provide efficient electrical grid simulation solutions for their respective U.S. regions. Data reuse for electricity economic studies remains a challenge due to the lack of credible and realistic economic data. This paper delivers a comprehensive dataset containing generator aggregations, generator costs, transmission limits, load distributions, and electricity prices for the WECC and NPCC systems based on real-world grid operation data at year 2020, including power plant geographic locations, generation profiles, regional power flow interchanges, and load distributions in both regions. The electricity price from the developed dataset is simulated based on the other items in the dataset, and we show that the variation of the simulated electricity price reasonably aligns with the real-world electricity price in both the WECC and NPCC regions. Overall, the developed dataset is of interest for various electricity market and economic studies, such as the economic dispatch and locational marginal price (LMP) analysis.

Real-time Power System Topology Recognition through Convolutional Neural Networks

This paper investigates the status of a transmission line (on/off) using a 140-bus Northeast Power Coordinating Council (NPCC) test system model [1]. The application of the software package ANDES [2] to obtain a database when solving this problem in the transient process of the power system is considered. The Deep Learning Neural Networks (DLNN) [3] were proposed to solve the problem, in particular, a convolutional neural network (CNN), the input variables of which are voltage and current phasors obtained from phasor measurement units (PMU). Calculations to determine the state of lines were performed using a program developed in the Julia language using the Flux package (a machine learning library that includes functions for creating CNN models). The results of the studies are presented.

Optimal Secondary Frequency Regulation With ON-OFF Loads in Power Networks

Load-side participation can provide support to the power network by appropriately adapting the demand when required. In addition, it enables an economically improved power allocation. In this study, we consider the problem of providing an optimal power allocation among generation and ON-OFF loads within the secondary frequency control timeframe. In particular, we consider a mixed-integer optimization problem, which ensures that the secondary frequency control objectives (i.e., generation–demand balance and the frequency attaining its nominal value at steady state) are satisfied. We present analytic conditions on the generation and ON-OFF load profiles such that an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -optimality interpretation of the steady-state power allocation is obtained, providing a nonconservative value for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> . Moreover, we develop a hierarchical control scheme that provides ON-OFF load values that satisfy the proposed conditions. We study the interaction of the proposed control scheme with the physical dynamics of the power network and provide analytic stability guarantees. Our results are verified with numerical simulations on the Western System Coordinating Council (WSCC) 9-bus system and the Northeast Power Coordinating Council (NPCC) 140-bus system, where it is demonstrated that the proposed algorithm yields a close to optimal power allocation.

Frequency Regulation With Thermostatically Controlled Loads: Aggregation of Dynamics and Synchronization

Thermostatically controlled loads (TCLs) can provide ancillary services to the power network by aiding existing frequency-control mechanisms. TCLs are, however, characterized by an intrinsic limit cycle behavior, which raises the risk that these could synchronize when coupled with the frequency dynamics of the power grid, i.e., simultaneously switch, inducing persistent and possibly catastrophic power oscillations. To address this problem, schemes with a randomized response time in their control policy have been proposed in the literature. However, such schemes introduce delays in the response of TCLs to frequency feedback that may limit their ability to provide fast support at urgencies. In this article, we present a deterministic control mechanism for TCLs such that those switch when prescribed frequency thresholds are exceeded in order to provide ancillary services to the power network. For the considered scheme, we provide analytic conditions, which ensure that synchronization is avoided. In particular, we show that as the number of loads tends to infinity, there exist arbitrarily long time intervals where the frequency deviations are arbitrarily small. Our analytical results are verified with simulations on the Northeast Power Coordinating Council 140-bus system, which demonstrate that the proposed scheme offers improved frequency response compared with existing implementations.

Direct Damping Feedback Control Using Power Electronics-Interfaced Resources

This paper proposes a direct damping feedback control method against power system oscillations under small or large disturbances. For a targeted oscillation mode, this control method continuously minimizes the difference between the real-time estimated damping ratio and a desired value by changing power outputs of selected inverter-based resources. The method adopts a proportional-integral controller whose parameters are tuned using a nonlinear single-input-single-output model on damping estimation and control with a single-oscillator equivalent regarding the targeted power system mode. Also, optimization of these controller parameters considers both robustness and time performance in damping control. The paper also proposes utilizing a “zero-th order” parametric resonance phenomenon to simplify the controller design. Tests on a small power system and a 140-bus 48-machine Northeast Power Coordinating Council system validate the effectiveness of the proposed damping controller utilizing battery-based energy storage systems.

Deep Active Learning for Solvability Prediction in Power Systems

Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism and handle limited types of power flow models. In this letter, we propose a deep active learning framework for solvability prediction in power systems. Compared with passive learning where the training is performed after all instances are labeled, active learning selects most informative instances to be labeled and therefore significantly reduces the size of the labeled dataset for training. In the active learning framework, the acquisition functions, which correspond to different sampling strategies, are defined in terms of the on-the-fly posterior probability from the classifier. First, the IEEE 39-bus system is employed to validate the proposed framework, where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the high-dimensional numerical experiments. Then, the Northeast Power Coordinating Council (NPCC) 140-bus system is used to validate the performance on large-scale power systems.

Primary frequency regulation in power grids with on–off loads: Chattering, limit cycles and convergence to optimality

Load side participation can provide valuable support to the power network in case of urgencies. On many occasions, loads are naturally represented by on and off states. However, the use of on–off loads for frequency control can lead to chattering and undesirable limit cycle behavior, which are issues that need to be resolved for such loads to be used for network support This paper considers the problem of primary frequency regulation with ancillary service from on–off loads in power networks and establishes conditions that lead to convergence guarantees and an appropriate power allocation within the network. In particular, in order to assist existing frequency control mechanisms, we consider loads that switch when prescribed frequency thresholds are exceeded. Such control policies are prone to chattering, which limits their practicality. To resolve this issue, we consider loads that follow a decentralized hysteretic on–off policy, and show that chattering is not observed within such a setting. Hysteretic loads may exhibit, however, limit cycle behavior, which is undesirable. To address this, we propose an adapted hysteretic control scheme for which we provide convergence guarantees. Furthermore, we consider a mixed-integer optimization problem for power allocation and propose a suitable design of the control policy such that the cost incurred at equilibrium is within ϵ from the optimal cost, providing a non conservative value for ϵ. The practicality of our analytic results is demonstrated with numerical simulations on the Northeast Power Coordinating Council (NPCC) 140-bus system.

Real-Time Damping Estimation on Nonlinear Electromechanical Oscillation

Real-time damping estimation for a dominant inter-area mode is important for situational awareness of potential angular instability in power systems. Electromechanical oscillations energized by large disturbances often manifest obvious nonlinearities in measurements on first several swings. Traditional methods based on linear system theory often discard first several swings intentionally to avoid nonlinearity; if not, the estimated damping ratios often vary with the length and starting point of the measuring window. By identifying a nonlinear oscillator to fit a dominant mode, this paper proposes a new measurement-based approach utilizing complete post-disturbance data for robust damping estimation independent of the measuring window. Case studies on the IEEE 9-bus system and a 48-machine Northeast Power Coordinating Council system validate the proposed approach for providing accurate and robust damping estimation compared with existing methods including the Prony's method. Meanwhile, three factors influencing damping estimation in practical applications are also addressed, including measurement noises, limited coverage of PMU measurements, and existence of multiple dominant modes.

A distributed scheme for secondary frequency control with stability guarantees and optimal power allocation

Abstract We consider the problem of distributed secondary frequency regulation in power networks such that stability and an optimal power allocation are guaranteed. This is a problem that has been widely studied in the literature, where two main control schemes have been proposed, usually referred to as ’Primal-Dual’ and ’distributed averaging proportional–integral (DAPI)’ respectively. However, each has its limitations, with the former incorporating additional information flow requirements which may limit its applicability, and with the existing literature on the latter relying on static models for generation and demand, which is restrictive. We propose a novel control scheme that aims to overcome these issues by making use of generation measurements in the control policy. In particular, our controller relies on practical measurements and allows distributed stability and optimality guarantees to be deduced for a broad range of linear generation dynamics, that can be of higher order. We show how the controller parameters can be selected in a computationally efficient way by solving appropriate linear matrix inequalities (LMIs). Furthermore, we demonstrate how the proposed analysis applies to various examples of turbine governor dynamics by using realistic numerical data. The practicality of our analysis is demonstrated with numerical simulations on the Northeast Power Coordinating Council (NPCC) 140-bus system that verify that our proposed controller achieves convergence to the nominal frequency, an economically optimal power allocation, and improved performance compared to existing schemes used in the literature.

Mitigating Cascading Outages in Severe Weather Using Simulation-Based Optimization

Severe weather events can trigger cascading power outages and lead to significant losses. In this work, we investigate cascading outage mitigation under severe weather conditions. Given day-ahead weather forecasts and component failure models, we aim to identify a set of power lines that can be hardened to minimize the expected impact of potential cascading outages. Since the expected load shedding cannot be expressed as an explicit function of line hardening decisions and system states, we developed a cascading outage simulator to estimate the expected value of load shedding under various initial weather-related disruption scenarios generated using a weather forecast. To avoid massive enumeration of all possible combinations of line hardening decisions and reduce the simulation efforts, we employed an efficient simulation-based optimization approach that quickly identifies the (near) optimal line hardening decisions in the presence of both large simulation noises due to the highly variable initial disturbances and system states, and significant randomness in the subsequent cascades. The algorithm is also able to utilize parallel computing to dramatically reduce computation time to support decision making in preparation for severe weather conditions. We performed a case study on the Northeast Power Coordinating Council (NPCC) 140-bus system model to demonstrate that our approach can significantly improve power grid resilience to adverse weather events.

A novel measurement-based procedure for dynamic equivalents of electric power systems for stability studies using improved sine cosine algorithm

Abstract Dynamic equivalent (DE) is an important process of multi-area interconnected power systems. It allows to perform stability assessment of a specific area (area of interest) at minimum cost. This study is intended to investigate the dynamic equivalent of two relatively large power systems. The fourth-order model of synchronous generators with a simplified excitation system is used as equivalent to the group of generators in the external system. To improve the accuracy of the estimated model, the identification is carried in two stages. First, using the global search Sine Cosine Algorithm (SCA) to find a starting set values, then this set is used as starting point for the fine-tuning made through the Pattern Search (PS) algorithm. To increase the reliability of the model’s parameters, two disturbances are used to avoid the identification based on a specific event. The developed program is applied on two standard power systems, namely, the New England (NE) system and the Northeast Power Coordinating Council (NPCC) system. Simulation results confirm the ability of the optimized model to preserve the main dynamic properties of the original system with accuracy.

A Data Segmentation-Based Ensemble Classification Method for Power System Transient Stability Status Prediction with Imbalanced Data

In recent years, machine learning methods have shown the great potential for real-time transient stability status prediction (TSSP) application. However, most existing studies overlook the imbalanced data problem in TSSP. To address this issue, a novel data segmentation-based ensemble classification (DSEC) method for TSSP is proposed in this paper. Firstly, the effects of the imbalanced data problem on the decision boundary and classification performance of TSSP are investigated in detail. Then, a three-step DSEC method is presented. In the first step, the data segmentation strategy is utilized for dividing the stable samples into multiple non-overlapping stable subsets, ensuring that the samples in each stable subset are not more than the unstable ones, then each stable subset is combined with the unstable set into a training subset. For the second step, an AdaBoost classifier is built based on each training subset. In the final step, decision values from each AdaBoost classifier are aggregated for determining the transient stability status. The experiments are conducted on the Northeast Power Coordinating Council 140-bus system and the simulation results indicate that the proposed approach can significantly improve the classification performance of TSSP with imbalanced data.

Secondary Frequency Control With On–Off Load Side Participation in Power Networks

We study the problem of decentralized secondary frequency regulation in power networks where ancillary services are provided via on–off load-side participation. We initially consider on–off loads that switch when prescribed frequency thresholds are exceeded, together with a large class of passive continuous dynamics for generation and demand. The considered on–off loads are able to assist existing secondary frequency control mechanisms and return to their nominal operation when the power system is restored to its normal operation, a highly desirable feature that minimizes users’ disruption. We show that system stability is not compromised despite the switching nature of the loads. However, such control policies are prone to chattering, which limits the practicality of these schemes. As a remedy to this problem, we propose a hysteretic on–off policy where loads switch on and off at different frequency thresholds and show that stability guarantees are retained when the same decentralized passivity conditions for continuous generation and demand hold. Several relevant examples are discussed to demonstrate the applicability of the proposed results. Furthermore, we verify our analytic results by using numerical investigations on the Northeast Power Coordinating Council (NPCC) 140-bus system.

Stability and Optimality of Distributed Secondary Frequency Control Schemes in Power Networks

We present a systematic method for designing distributed generation and demand control schemes for secondary frequency regulation in power networks such that stability and an economically optimal power allocation can be guaranteed. We consider frequency dynamics given by swing equation along with generation, controllable demand, and a secondary control scheme that makes use of local frequency measurements and a locally exchanged signal. A decentralized dissipativity condition is imposed on net power supply variables to provide stability guarantees. Furthermore, economic optimality is achieved by explicit steady state conditions on the generation and controllable demand. A distinctive feature of the proposed stability analysis is the fact that it can cope with generation and demand dynamics that are of general higher order. Moreover, we discuss how the proposed framework captures various classes of power supply dynamics used in recent studies. In case of linear dynamics, the proposed dissipativity condition can be efficiently verified using an appropriate linear matrix inequality. Moreover, it is shown how the addition of a suitable observer layer can relax the requirement for demand measurements in the secondary controller. The efficiency and practicality of the proposed results are demonstrated with simulations on the Northeast Power Coordinating Council (NPCC) 140-bus and a 9-bus system.

A Two-Stage Feature Selection Method for Power System Transient Stability Status Prediction

Transient stability status prediction (TSSP) plays an important role in situational awareness of power system stability. One of the main challenges of TSSP is the high-dimensional input feature analysis. In this paper, a novel two-stage feature selection method is proposed to handle this problem. In the first stage, the relevance between features and classes is measured by normalized mutual information (NMI), and the features are ranked based on the NMI values. Then, a predefined number of top-ranked features are selected to form the strongly relevant feature subset, and the remaining features are described as the weakly relevant feature subset, which can be utilized as the prior knowledge for the next stage. In the second stage, the binary particle swarm optimization is adopted as the search algorithm for feature selection, and a new particle encoding method that considers both population diversity and prior knowledge is presented. In addition, taking the imbalanced characteristics of TSSP into consideration, an improved fitness function for TSSP feature selection is proposed. The effectiveness of the proposed method is corroborated on the Northeast Power Coordinating Council (NPCC) 140-bus system.

Adaptive Nonlinear Model Reduction for Fast Power System Simulation

The paper proposes a new adaptive approach to power system model reduction for fast and accurate time-domain simulation. This new approach is a compromise between linear model reduction for faster simulation and nonlinear model reduction for better accuracy. During the simulation period, the approach adaptively switches among detailed and linearly or nonlinearly reduced models based on variations of the system state: it employs unreduced models for the fault-on period, uses weighted column norms of the admittance matrix to decide which functions to be linearized in power system differential-algebraic equations for large changes of the state, and adopts a linearly reduced model for small changes of the state. Two versions of the adaptive model reduction approach are introduced. The first version uses traditional power system partitioning where the model reduction is applied to a defined large external area in a power system and the other area defined as the study area keeps full detailed models. The second version applies the adaptive model reduction to the whole system. The paper also conducts comprehensive case studies comparing simulation results using the proposed adaptively reduced models with the linearly reduced model and coherency-based reduced model on the Northeast Power Coordinating Council 140-bus 48-machine system.

A Class of Switching Exploits Based on Inter-Area Oscillations

This work presents a new class of cyber-physical switching attacks that targets power transmission systems. The proposed approach relies on exciting inter-area oscillation modes in a coordinated manner to drive groups of system generators out of step. Our paradigm targets inter-area oscillations by switching a relatively small part (in the order of 2%) of the system load at a (low) frequency that resonates with one of the inter-area oscillation modes observed in the power system. The switching frequency of the targeted mode is chosen through measurement-based analysis of the frequency deviation at a select bus that is observable by the adversary. The inter-area switching attack is implemented as single-load switching and coordinated multi-load switching and is studied with a variety of switching signals. Numerical results show the potential and characteristics of the proposed switching exploitation when applied to the four-machine two-area power system and the Northeast Power Coordinating Council 68-bus system.

Efficient Simulation of Temperature Evolution of Overhead Transmission Lines Based on Analytical Solution and NWP

Transmission lines are vital components in power systems. Outages of transmission lines caused by overtemperature are a major threat to system reliability, so it is necessary to efficiently simulate line temperature under both normal operation conditions and foreseen fault conditions. Existing methods based on thermal-steady-state analyses cannot support the simulation of transient temperature evolution, and, thus, cannot provide timing information needed for taking remedial actions. Moreover, the conventional numerical method requires huge computational efforts and barricades system-wide analysis. In this regard, this paper derives an approximate analytical solution of transmission-line temperature evolution enabling efficient analysis on multiple operation states. Considering the uncertainties in environmental parameters, the region of overtemperature is constructed in the environmental parameter space to realize the overtemperature risk assessment in both the planning stage and real-time operations. A test on a typical conductor model verifies the accuracy of the approximate analytical solution. Based on the analytical solution and numerical weather prediction data, an efficient simulation method for temperature evolution of transmission systems under multiple operation states is proposed. As demonstrated on a Northeast Power Coordinating Council 140-bus system, it achieves over 1000 times of efficiency enhancement, verifying its potential in online risk assessment and decision support.

Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate P – V curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximant method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P – V curve with moderate computational burden. The proposed method is first illustrated in detail on a four-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council 48-geneartor 140-bus power system.