Real-time Operation Of Power System Research Articles

Transfer learning-based updating method of transient stability assessment model

An updating method for data-driven based transient stability assessment model of power systems is proposed in this paper. To cope with potential faults in the real-time operation of power systems, the parameters of models can be modified online through the proposed method. Firstly, according to the long-short term memory (LSTM) network, transient stability assessment models are initially trained offline by expected faults. Then, the feature distribution differences between expected faults and potential faults are evaluated by the time-sequence maximum mean discrepancy (TMMD). Compared with the traditional maximum mean discrepancy (MMD), the proposed TMMD approach takes the temporal properties of transient stability into account, which can reflect the relationship between fault samples and time series better. Finally, the parameters of models are adjusted by transfer learning to reduce the feature distribution differences, by which the updated models can be more suitable for the different but related potential faults. Therefore, through the proposed model updating method, the extensibility of evaluation models is greatly enhanced, which is more in line with the practical conditions of power systems. In this paper, the effectiveness of the proposed method has been demonstrated by the evaluation results in the IEEE 39-bus system and a realistic power system.

Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction

With the continuous growth of grid-connected installed capacity of wind power, the inevitable gap, volatility and randomness of wind power pose challenges to the stability of real-time operation of power system. Accurate wind power prediction (WPP) can effectively ensure the safe and stable operation of power system. At present, the main input of short-term power prediction based on data-driven model is numerical weather prediction (NWP), and its prediction accuracy leads to the failure to effectively improve the accuracy of short-term power prediction. To solve this problem, this paper proposes a dual NWP wind speed (WS) correction method based on trend fusion and fluctuation clustering. First, the WS trend is used to construct a new input feature, and the NWP WS error distribution is determined to establish a more correct and stable mapping relationship with the NWP WS error. Secondly, the error is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the trend and fluctuation components are defined by Pearson coefficient. The trend curve features in the short 24h period were extracted from the two dimensions of time and value, and the trend and fluctuation curve databases under different scenarios were formed by K-Medoids clustering. Finally, the trend component is modified by the Attention-GRU model, and the corresponding trend component is searched and matched from the database to correct the fluctuation component. The NWP WS is modified by the superposition of the two components and the short-term WPP is made. Two wind farms in Mengxi, China were used for example analysis, and the prediction accuracy was improved by 10 % and 13.1 % respectively, which demonstrated the effectiveness of the proposed method.

Concept of Smart ACR Setting in Real-Time Operation of Power Systems

Concept of Smart ACR Setting in Real-Time Operation of Power Systems

A method for monitoring the load margin of power systems under load growth variations

The load margin is an important safety measure used in operation centers to assess the voltage stability of power systems. Usually this measure is calculated considering a single load growth direction and voltage stability requirements. However, oscillation modes with low damping rates can compromise the small-signal stability of power systems and different load growth directions can also affect load margin estimates. Thus, these two factors must be considered to provide adequate load margin for power system operation. This paper proposes a load margin monitoring scheme based on Artificial Neural Networks (ANN) considering voltage stability requirements and small-signal stability and load growth variations. Thus, the power system operator will have real-time load margin information considering voltage stability requirements and small-signal stability requirements. ANN uses as input data the voltage magnitude and voltage angle measurements provided by Phasor Measurement Units (PMUs) installed in system buses. A method to select a reduced set of buses for the monitoring system is proposed and evaluated. Case studies are presented and the results achieved provide indications of the applicability of the proposed scheme in the real-time operation of power systems.

Frequency restoration reserves procurement with HVDC systems

Frequency control ancillary services play an essential role in the secure real-time operation of power systems. Procurement of these services is critical because of the availability and cost of resources and uncertainty in power generation due to the increasing number of renewable sources. Due to the changing generation mix, it is crucial that the transmission system operator has access to the resources for these services. As the number of HVDC interconnections is increasing in Europe, it is imperative to consider their utilization for reserve procurement. This paper proposes a mathematical model for examining the economic aspects of frequency restoration reserves procurement with HVDC systems. The proposed model is used for analysis on two asynchronous systems connected via an HVDC link. The analysis shows that procurement of frequency restoration reserves with HVDC systems increases social welfare; however, it is highly dependent on the availability of HVDC systems capacity.

Impact of intermittent renewable energy generation penetration on the power system networks – A review

Entrance of intermittent renewable power energy sources has brought in benefits mainly associated with emission reduction to help the climate change cause and reduce pollution. However, entrance of renewable generation sources, mainly wind and solar generation that are intermittent energy sources by nature has not come without its own challenges. Future power grids will see continued elevation of intermittent generation with instantaneous penetration levels that may potentially be as high as 100% for some periods. This paper reviews published works and highlights technical challenges that require further research to ensure that power grids that will be dominated by inverter-based generation or purely powered by inverter-based generation continue to operate reliably and remain stable for all operating scenarios. The paper outlines further research opportunities in areas of power sytem security assessment, voltage and frequency management, power system stability, resource forecasting techniques for unit commitment, behind the meter generators and loads integration to grid, frequency and voltage support, power system flexibility assessment methods, protection schemes enhancement, power quality mitigation techniques and modelling requirements to support real-time power system operation.

Scenario-Based Economic Dispatch With Tunable Risk Levels in High-Renewable Power Systems

This paper introduces an empirical approach to dispatch resources in real-time power system operation with growing levels of uncertainties emerging from intermittent and distributed energy resources in the supply and the demand side. It is shown that by taking empirical data of specific sizes, the dispatch results can lead to a quantifiable and rigorous bound on the risk of violating constraints at the implementation stage. In particular, we formulate the look-ahead real-time economic dispatch problem using the scenario approach. This approach takes empirical data as input and guarantees a tunable probability of violating the constraints according to the input data size. By exploiting the structure of the economic dispatch, we show that in the absence of transmission constraints, the number of samples that is required by the theory does not grow with the size of the problem. In the more general case with transmission constraints, it is shown that the posterior bound on the risk of dispatch can be quantified and can be much smaller than the risk bound before solving the dispatch. Numerical examples based on a standard test system suggest that the scenario approach can provide a practically attractive solution with theoretically rigorous properties for risk-limiting power system operations.

Power generation scheduling considering stochastic emission limits

In some highly populated areas of the planet, air quality is an issue. Since power plants in those areas significantly contribute to air pollution due to SO2 and NOx emissions, it is appropriate to operate such power plants enforcing admissible emission limits. Since emissions directly impact the air quality index, which is highly uncertain 24h in advance, admissible emission limits should be modeled as stochastic parameters.To represent the stochastic nature of such admissible emission limits, we propose a two-stage stochastic programming model for the commitment of power plants. The first stage represents the day-ahead scheduling, and the second stage represents the real-time power system operation under different weather and thus admissible emission limit conditions. We show the effect of stochastic emission limits on power system operations using a simple example and a 118-node case study.

Synchrophasor-based real-time state estimation and situational awareness system for power system operation

State estimation is a critical functionality of energy management system (EMS) to provide power system states in real-time operations. However, problems such as failure to converge, prone to failure during contingencies, and biased estimates while system is under stressed condition occur so that state estimation results may not be reliable. The unreliable results further impact downstream network and market applications, such as contingency analysis, voltage stability analysis, transient stability analysis, system alarming, and unit commitment. Thus, operators may lose the awareness of system condition in EMS. This paper proposes a fully independent and one-of-a-kind system by integrating linear state estimator into situational awareness applications based on real-time synchrophasor data. With guaranteed and accurate state estimation solution and advanced real-time data analytic and monitoring functionalities, the system is capable of assisting operators to assess and diagnose current system conditions for proactive and necessary corrective actions. The architecture, building components, and implementation of the proposed system are explored in detail. Two case studies with simulated data from the subsystems of Electric Reliability Council of Texas (ERCOT) and Los Angeles Department of Water and Power (LADWP) are presented. The test results show the effectiveness and reliability of the system, and its value for real-time power system operations.

Unit Commitment With Continuous-Time Generation and Ramping Trajectory Models

There is increasing evidence of shortage of ramping resources in the real-time operation of power systems. To explain and remedy this problem systematically, in this paper we take a novel look at the way the day-ahead unit commitment (UC) problem represents the information about load, generation and ramping constraints. We specifically investigate the approximation error made in mapping of the original problem, that would decide the continuous-time generation and ramping trajectories of the committed generating units, onto the discrete-time problem that is solved in practice. We first show that current practice amounts to approximating the trajectories with linear splines. We then offer a different representation through cubic splines that provides physically feasible schedules and increases the accuracy of the continuous-time generation and ramping trajectories by capturing sub-hourly variations and ramping of load in the day-ahead power system operation. The corresponding day-ahead UC model is formulated as an instance of mixed-integer linear programming (MILP), with the same number of binary variables as the traditional UC formulation. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total day-ahead and real-time operation cost, and the number of events of ramping scarcity in the real-time operations.

Power System Connectivity Monitoring Using a Graph Theory Network Flow Algorithm

A method of applying network flow analyses during real time power system operation, to provide better network connectivity visualization, is developed and presented. Graph theory network flow analysis is capable of determining the maximum flow that can be transported between two nodes within a directed graph. These network flow algorithms are applied to a graphical representation of a power system topology to determine the minimum number of system branches needed to be lost in order to guarantee disconnecting the two nodes in the system that are selected. The number of system branches that are found serves as an approximate indicator of system vulnerabilities. The method used in these connectivity analyses makes use of well known graph theory network flow maximum flow algorithms, but also introduces a new algorithm for updating an old network flow solution for the loss of only a single system branch. The proposed new algorithm allows for significantly decreased solution time that is desired in a real-time environment. The value of using the proposed method is illustrated by using a detailed example of the 2008 island formation that occurred in the Entergy power system. The method was applied to a recreation of the 2008 event using a 20,000-bus model of the Entergy system to show both the proposed method's benefits as well as practicality of implementation.

Reservoir based learning network for control of two-area power system with variable renewable generation

The penetration of renewable energy sources into the electric power system is rapidly increasing. Integrating variable renewable energy sources into the transmission grid introduces challenges in real time power system operation. This causes power and frequency fluctuations and raises stability concerns. In this paper, a 200MW photovoltaic (PV) plant is integrated into a two-area four-machine power system. In order to maintain the system frequency, a dynamic tie-line power flow control is implemented using predicted PV power as an input to the automatic generation controller in Area 1, which transfers power to Area 2 with PV generation. The prediction performances of two learning reservoir based networks, an echo state network (ESN) and an extreme learning machine (ELM), are investigated for day and night time operations. The experimental study is performed using actual weather data from Clemson, SC and a real time simulation of a utility-scale PV plant integrated power system. Phasor measurement units (PMUs) are used to provide input signals to automatic generation controllers in the two area power system. Typical tie-line power flow control results based on ESN and ELM models are presented to show the impact of predicting PV power in improved automatic generation control with variable generation. ESN and ELM models provide minimal tie-line power flow deviations from reference power flows during day and night time operations, respectively.

Assessment of voltage stability margin by comparing various support vector regression models

Voltage stability assessment and prediction of loadability margin are the major concerns in real-time operation of power systems. This paper proposes a support vector machine (SVM) regression network for the voltage stability assessment for normal condition as well as for contingency cases. The loadability margin of any given operating conditions is obtained for pre-contingency and post-contingency based on the computation of a stability index. SVM takes real and reactive power at all buses of the system and gives the loading margin. The validity of the proposed SVM-based index is tested on IEEE 30 and Indian 181 bus systems. The results of the proposed method are compared with neural network, extreme learning machine, online sequential extreme learning machine and extreme support vector machine regression methods. The feasibility of application of the proposed SVM regression network for real-time stability assessment is discussed. Also, FACTS devices are produced to improve the system loadability and their results are discussed.

Multi‐objective short‐term scheduling of thermoelectric power systems using a novel multi‐objective θ ‐improved cuckoo optimisation algorithm

This study proposes a multi-objective optimal static and dynamic scheduling of thermoelectric power systems considering the conflicting environmental and economical objectives. Meantime, some restrictions such as valve-point effects, prohibited operating zones, multi-fuel options, line flow limits as well as spinning reserve should be taken into account in order to ensure secure real-time power system operation. A novel multi-objective θ-improved cuckoo optimisation algorithm is projected to solve the optimisation problems by defining a set of nondominated points as the solutions. The suggested method moves forward the particles to the problem search space in the polar coordinates as a substitute of the Cartesian one. In addition, in order to achieve better performance and higher-convergence speed, several improvement strategies are utilised. This algorithm is equipped with a novel powerful mutation strategy in order to increase the population diversity and to amend the convergence criteria. Furthermore, a fuzzy-based clustering is used to control the size of the repository and a niching method is utilised to choose the best solution during the optimisation process and to ensure diversity among non-dominated solutions. Performance of the proposed algorithm is tested on 6-, 10-, 14-, 40- and 100-unit test systems and compared with those of other well-known methods.

Impact of frequency-based tariff in power market

System frequency can be a useful signal in real–time power system operation to promote competition along with operational reliability. The commercial mechanism adopted in India, the availability–based tariff (ABT), defines a power tariff structure that allows recovery of capital cost and fuel cost as well as a condition–based price incentive to participants which help to minimise the real power imbalance. We propose a mathematical model for profit–based dispatch and a heuristic technique has been developed to determine the daily scheduling of declared capacity by maximising the profit. The main purpose of the paper is to look at the advocacy rules of ABT more strategically and to develop plans for generation scheduling. Comparative analysis with previously followed two–part tariff system is also carried out to expose the importance and role of incentive/penalty for self regulatory system.

Reserve Constrained Dynamic Environmental/Economic Dispatch: A New Multiobjective Self-Adaptive Learning Bat Algorithm

This paper proposes a new multiobjective self-adaptive learning bat-inspired algorithm to solve practical reserve constrained dynamic environmental/economic dispatch that considers realistic constraints such as valve-point effects, transmission losses, and ramp rate limits over a short-term time period. Furthermore, to ensure secure real-time power system operations, the system operator must schedule sufficient resources to meet energy demand and operating reserve requirements simultaneously. The proposed problem is a complex nonlinear nonsmooth and nonconvex multiobjective optimization problem whose complexity is increased when considering the above constraints. To this end, this paper utilizes a newly developed meta-heuristic bat inspired algorithm to achieve the set of nondominated (Pareto-optimal) solutions. This algorithm is equipped with a novel self-adaptive learning to increase the population diversity and amend the convergence criteria. The initial population of the proposed framework is generated by a chaos-based strategy. In addition, a tournament crowded selection approach is implemented to choose the population such that the Pareto-optimal front is distributed uniformly, while the extreme points of the tradeoff surface are achieved simultaneously. Numerical results evaluate the performances of the framework for real-size test systems.

실시간 전력계통 운영을 위한 데이터 시각화에 관한 연구

This paper describes and suggests the data visualization for real time power system operation based on energy management system. Because real time power system operation performs analysis of the vast amount of on-line data, the operators need intuitive data visualization to find out useful information in the big data. Especially, in emergency situation, the data visualization is able to assist the operators in handling the crisis quickly and efficiently. Therefore, this paper aims to improve displays of output of real time power system operation by visualizing on-line big data. Through this study, we can develop improved visualization technique for real time power system operation, which has highly readable displays of output and intuitive information.

Optimal multi‐area generation schedule considering renewable resources mix: a real‐time approach

One of the key role players in deregulated electricity markets is a retailer. This study proposes a multi-area dynamic economic dispatch (MA-DED) model for a retailer, taking into account hydrothermal generating units, wind power generation and power pool market, to supply the overall demand of the system for a given horizon. The uncertainties in wind power generations, energy prices and demand of the system are also modelled to make the proposed approach more practical in case of real-time operation of practical power systems. Scenario-based approach is adopted for uncertainty modelling. In order to make the proposed MA-DED applicable in real-time operation of power systems, optimality condition decomposition (OCD) technique is employed along with parallel computation ability. The proposed approach is examined on two interconnected power networks, to demonstrate its applicability for real-time scheduling of joint thermal and undispatchable renewable energy resources. DED, multi-area, OCD, real-time, uncertainty modelling, scenario-based approach.

Large-scale branch contingency analysis through master/slave parallel computing

Contingency analysis (CA) requires fast execution time for real-time power system operations. Because CA problems can naturally be divided into separate subtasks, parallel computing helps to speed up the computation time. This paper proposes a master/slave parallel computing architecture and studies the computation of CA in a large-scale power system through high performance computing, adopting a message passing interface for implementation. In particular, although the execution time of CA varies, there is a tradeoff between having an imbalanced workload and “paying” a synchronization penalty for parallel computing: either factor blocks the progress of scalability. The proposed layered dynamic scheduling method is effective to tackle the challenge of high synchronization cost and workload imbalance and have the potential to further scale for the N − 2 contingency analysis.

Application of PMU-Based Information in the Indian Power System

Abstract SCADA/EMS system has been the most commonly used tool for real-time power system operation and control throughout the world. This system has been found to be very useful in steady-state analysis of the power system. The ever-increasing dependence of human society and every country’s economy on electrical energy calls for reliable power delivery. In order to meet these expectations, engineers across the globe have been exploring such new technologies that can improve upon the limitations of SCADA and provide dynamic visibility of the power system. A breakthrough has now been achieved in the form of synchrophasor technology. Synchrophasor measurements using phasor measurement units (PMUs) deployed over a wide area, facilitate dynamic state measurement and visualization of a power system, which are useful in monitoring safety and security of the grid. The Power System Operation Corporation (POSOCO) has taken initiative and implemented a pilot project wherein nine phasor measurement units (PMUs) along with one phasor data concentrator (PDC) were commissioned in the Northern Region (NR) of India. The primary objective of this pilot project was to comprehend the synchrophasor technology and its applications in power system operation. The data received and information derived from the pilot project have been found to be very useful and helped in improving the performance of the grid operation in several ways. The pilot project is operational for the last two years; in the meanwhile, many other initiatives have also been taken in other regions by POSOCO. This article details the utilization of the data collected from the pilot projects and the application of the data in the improvement of Indian power grid.