An Intelligent Dynamic Security Assessment Framework for Power Systems With Wind Power

Nowadays, solar and wind power generation is an important part of the power sources in many countries all over the world. However, due to the variability and uncertainty of these sources, stable operation of the power system with large solar and wind power penetration is a big challenge for system operators. In order to enhance the contribution efficiency of solar and wind power in the power market, a strong ancillary service system is essential and must be reasonable priced. The ancillary services considered in this paper are spinning reserve, non-spinning reserve. In addition, a model for calculating and comparing generation costs for energy and ancillary services, with and without penetration of solar and wind power in the power market have been proposed. Consequently, this model has been developed and validated with a case study. The results show that in a power system with large proportion integration of solar and wind power, the energy and ancillary services generation costs for the power system are high. Besides, the feed-in tariffs of solar and wind power are more expensive than the traditional electricity generation, which leads to high locational marginal prices of the electricity market.

The increasing complexity of power systems as well as the rapid expansion of renewable energy resources and deregulation of electricity industry lead to continual demand for operating power systems at higher efficiencies. The modern power systems are pushed to operate close to their secure limits. When subjected to a severe contingency, the stressed power systems may lose its dynamic security, resulting in catastrophic consequences such as cascading failure and even wide-spread blackout. Under this new reality, online and real-time power system stability assessments have great impacts on avoiding blackout events. The traditional stability methods are mainly based on time-domain simulation, which are not fast enough to satisfy the online and real-time application requirements. In recent years, with the development of advanced real-time system monitoring and communication techniques, the data-driven methods have been identified as powerful tools to help power systems counteract large disturbance and avoid instability. To solve above threats, this Ph.D. research aims to develop data-analytics methodologies from power grid measurement data to recognize the system stability, covering both pre-fault dynamic security assessment (DSA) and post-fault real-time short-term voltage stability (STVS) assessment areas under some extreme scenarios and practical issues. Specific objectives of this Ph.D. research are to develop an intelligent system for the future smart grid. Based on several kinds of promising advanced data-analytics technologies, including randomized learning, incremental broad learning (BL), transfer learning (TL), adversarial machine learning (ML), and generative adversarial network (GAN), etc, an intelligent system will be developed to interpret the power grid stability characteristics, and to support stability-constrained operation control actions. Hence, it can make power systems achieve seven goals, including extensibility, efficiency, robustness, security, personalization, reliability, and effectiveness. The outcomes of this Ph.D. research will enable a more secure, intelligent, and knowledge-transparent future of the next-generation smart grid. The main contributions through this Ph.D. research are as follows: Firstly, the temporal-adaptive data-driven methods are proposed based on optimized hybrid ensemble learning mechanism for online STVS assessment. Compared to the traditional data-driven stability methods, the proposed methods can balance the tradeoff between the stability accuracy and speed and select the most suitable Pareto set. Secondly, in the area of DSA, an online updating DSA method is developed by using incremental BL technique. The incremental BL method only needs to calculate the pseudoinverse of new state matrix without the computations of the whole output weights matrix. In this way, it does not need re-training the whole model to continuously maintain and improve the online DSA accuracy. Besides, the proposed method can achieve under three different increment scenarios, including increment of enhancement hidden nodes, features, and new training instances. Thirdly, in the area of missing phasor measurement unit (PMU) data, two different fully robust data-driven stability methods are developed, respectively for pre-fault DSA and post-fault STVS assessment. The former one is based on GAN without observability-constrained feature subsets to ensure its availability under any PMU missing scenario. The latter one is based on super-resolution perception and incremental BL, which can achieve both satisfactory stability accuracy and speed. Fourthly, in order to accurately quantify the robustness of the ML-based model under adversarial perturbations on safety-critical power system stability problem, an adversarial robustness verification method for data-driven stability models is proposed. Besides, a model-free and attack-independent robust index are defined to evaluate the ability of the ML-based stability models against adversarial examples, which can be used to select the candidate model and provide formal robustness guarantee for stability application in practice. Rigorous mathematic proof is provided for the robust index under both linear and non-linear binary scenarios. Moreover, the proposed robust index can be used to mitigate the adversarial examples via adversarial training. Finally, three different TL-based DSA methods are proposed to use one known fault to evaluate other related but different unknown faults. The first two methods aim to reduce the distribution differences between the trained data and unknown data with the complete unlearned faults. The third one considers the different faults with incomplete data scenario. Through applying the adversarial training process, the DSA model trained by one fault can work for a different but related fault by confusing domain discriminator. With the property of feature extractor network, the proposed integrated TL-based method can solve the missing data issue for the unknown faults. All the methodologies have been tested on benchmark power systems to verify their effectiveness, and comparative and comparative studies with existing methods in the literature are conducted where applicable.

This paper proposes a robust day-ahead scheduling method for a multi-carrier energy system (MES), which would enhance the flexibility of power systems with a large sum of variable wind power. We build an MES model and propose an optimal MES schedule which helps MES reduce wind power curtailment in power systems. At first, electricity and natural gas networks are coordinated at the transmission (regional) level for accommodating the large penetration of wind power in regional MES. The distribution (district) level MES coordinates energy conversion and storage to jointly supply the electricity, natural gas, and heat loads. The transmission level MES is modeled using detailed network equations while the distribution level MES is modeled as a device with multiple input/output ports using the linear branch-flow-based energy hub model. A two-stage robust model is established to consider the variability of wind power at the two MES levels. The proposed problem is solved by a nested column-and-constraint (C&CG) generation method. The first-stage problem which schedules the hourly unit commitment is solved in the outer loop, while the inner loop solves the second-stage problem to realize the worst scenario. Several acceleration strategies are utilized to enhance the computational performance of the nested C&CG. Numerical results offered for a 6-bus 3-node system and a modified IEEE 118-bus 10-node system show the effectiveness of the proposed MES model and solution technique for enhancing the power system flexibility.

In this paper, implementation of power system dynamic security assessment (DSA) into a time domain simulation environment is described. The paper discusses general characteristics of DSA in time domain and presents a framework for software implementation. The implemented DSA system is user oriented and enables simple investigation of power system security in normal or contingency provoked system conditions. Using continuous processing, DSA is fully automated and can process any number of cases without user interaction, however, with some user established security constraints. If a security violation is detected, the contingency is documented and more detailed case study is possible. The paper presents methodology of implementation of DSA into well established simulator PSStradeNETOMAC, an overview of the considered power system security constraints and visualization options developed. The paper concludes with a case study. The DSA system was tested for performance on a generic single processor computer.

The 100% renewable energy targets from the policymakers for the future grids have drawn a significant amount of interest. The high renewable penetration made future large interconnected grids (LIGs) more volatile and harder to understand using historical observations. This led to the need to study future LIGs using a wide range of future-year operating conditions and contingencies. However, existing planning tools are not sufficient for the dynamic security assessment (DSA) of these future LIGs due to a lack of detailed modeling capabilities and computational limitations when processing a wide range of scenarios. This paper addresses these two challenges by proposing; 1) an efficient modeling framework that can generate large grid’s dynamic data with cascade behaviors for a wide range of scenarios. This data is generated by respecting the constraints of production cost models and their respective AC power flow dynamic simulation models at an hourly resolution; 2) an unsupervised machine learning(ML)-based approach for fast scanning the DSA data. The proposed approach uses feature engineering techniques and fast Fourier transforms to transform the time series signals into visually distinguishable frequency domain signals for DSA. The proposed simulation framework is used to generate 1.485 terabytes of dynamic simulation data for the 2028 WECC system containing 4455 scenarios. The proposed ML framework used the 2028 WECC system to demonstrate its effectiveness and speed in identifying critical scenarios.

Preface. Contributors. 1 The Real-Time and Study-Mode Data Environment in Modern SCADA/EMS ( Sudhir Virmani and Savu C. Savulescu ). 1.1 Introduction. 1.2 SCADA/EMS Architectures. 1.3 Integrating Stability Applications with the SCADA/EMS. 1.4 References. 2 Overview of Key Stability Concepts Applied for Real-Time Operations ( Savu C. Savulescu ). 2.1 Introduction. 2.2 In Search of the Stability Limits. 2.3 Transient and Voltage Stability Limits. 2.4 Steady-State Stability Limits. 2.5 Concluding Remarks. 2.6 References. Annex 1-1. Reactive Power Steady-State Stability Criterion dDELTAQ / dV. 3 LIPA Implementation of Real-Time Stability Monitoring in a CIM Compliant Environment ( Loris Arnold, Janos Hajagos, Susan M. Manessis, and Anie Philip ). 3.1 Introduction. 3.2 Static and Dynamic Security Assessment at LIPA. 3.3 Benchmarking the Real-Time Stability Application. 3.4 Practical Experience and Outlook. 3.5 References. 4 Real-Time Stability Monitoring at the Independent System Operator in Bosnia and Herzegovina ( Dusko Vickovic and Roland Eichler ). 4.1 Introduction. 4.2 Interim Implementation of Real-Time Stability Assessment at NOS BiH. 4.3 Real-Time Stability Assessment in the New SCADA/EMS Environment. 4.4 Conclusions and Recommendations. 4.5 References. Annex 4-1. TSL, TTC, and the Stability Envelope. Annex 4-2. Siemens Implementation of the Continuation Power Flow. 5 Experience with Real-Time Stability Assessment at Transelectrica ( Horia S. Campeanu, Cornel Erbasu, and Cornel Aldea ). 5.1 Introduction. 5.2 Security Assessment Philosophy and Criteria. 5.3 Real-Time Steady-State Stability Assessment and Monitoring. 5.4 Off-Line Stability Tools in Support of System Operations. 5.5 Conclusions and Outlook. 5.6 References. 6 Implementation of Online Dynamic Security Assessment at Southern Company ( Kip Morison, Lei Wang, Fred Howell, James Viikinsalo, and Alan Martin ). 6.1 Introduction. 6.2 DSA Implementation Fundamentals. 6.3 Transient Security Assessment Implementation at Southern Company. 6.4 Conclusions. 6.5 References. Annex 6-1. Further Details of the DSA Software and Hardware Architecture. Description of the Core DSA Software. Online DSA Implementation Using DSATools. 7 Online Security Assessment for the Brazilian System A Detailed Modeling Approach ( Jorge L. Jardim ). 7.1 Introduction. 7.2 Security Criteria and Functions. 7.3 Solution Methods and Architecture. 7.4 Practical Implementation Aspects. 7.5 User Interface And Performance. 7.6 Concluding Remarks. 7.7 Acknowledgments. 7.8 References. 8 Dynamic Network Security Analysis in a Load Dispatch Center ( Guenther Beissler, Olaf Ruhle, and Roland Eichler ). 8.1 Introduction. 8.2 Siemens Approach to Dynamic Security Assessment. 8.3 Case Studies: Challenges, Implementation Approach, and Solution Features. 8.4 References. Annex 8-1. Further Dynamic Simulation Capabilities. Time Frame for Dynamic Simulations. Simulation in the Frequency Domain. Eigenvalue and Modal Analysis. 9 Real-Time Transient Security Assessment in Australia at NEMMCO ( Stephen J. Boroczky ). 9.1 Introduction. 9.2 Transient Security Assessment at NEMMCO. 9.3 Performance and Reliability. 9.4 Experience, Benefits, and Outlook. 9.5 References. 10 Online Voltage Security Assessment in the Hellenic Interconnected System ( Costas Vournas, George Christoforidis, and Thierry Van Cutsem ). 10.1 Introduction. 10.2 The Control Center of HTSO. 10.3 Online VSA in the Hellenic System. 10.4 Use of Online VSA For Arming Load-Shedding Protection. 10.5 Conclusion. 10.6 References. Annex 10-1. Quasi-Steady-State Simulation. Principle of the QSS Approximation. Handling of Frequency in QSS Simulation. QSS Model of the Synchronous Machine and its Regulations. Numerical Integration of the QSS Model. 11 The Real-Time Supervision of Transmission Capacity in the Swedish Grid ( Lars Sandberg and Klas Rouden ). 11.1 Introduction. 11.2 Prior and Current Application Development at SVK. 11.3 Voltage Security Assessment with SPICA. 11.4 Benefiting from the Knowledge of the Current Transmission Capacity. 11.5 Additional SPICA Functionality. 11.6 Summary. Appendix A Dimo's Approach to Steady-State Stability Assessment: Methodology Overview, Numerical Example, and Algorithm Validation ( Roberto D. Molina Mylius, Martin Cassano, and Savu C. Savulescu ). A.1 Methodology Overview. A.2 Numerical Example Independent Testing of Algorithm Implementation. A.3 Benchmarking the Methodology. A.4 Conclusions. A.5 References. Appendix B SIME: A Comprehensive Approach to Transient Stability ( Mania Pavella, Daniel Ruiz-Vega, and Mevludin Glavic ). B.1 Introduction. B.2 Basic Formulation. B.3 Preventive SIME. B.4 Emergency SIME. B.5 Postface. B.6 References. Notation. Abbreviations and Acronyms. Appendix C Detection and Evaluation of Stability Constrained ( Marius Pomarleanu and Savu C. Savulescu ). C.1 Introduction. C.2 Approach. C.3 Conclusions. C.4 References. Index.

With increasing heating demand and installed capacity of wind farms, combined heat and power (CHP) systems with inflexible generation are confronted with a major challenge for the efficient utilization of the wind power. Distributed heat sources (DHSs), such as electric boilers and heat storage, offer an effective way to enhance wind power penetration in a combined heat and power system by reducing the electric-heat coupling of the CHP unit. In this paper, an assessment approach for the wind power penetration of a CHP system with DHSs is proposed, and a two-stage multiple types of DHSs optimal configuration strategy to realize the minimum construction cost at a certain permissible wind power curtailment rate is established. After that, the proposed strategy is applied to a case study of a practical CHP system with in-depth analysis and detailed discussion, which reveals the effectiveness of the proposed strategy and indicates its promising applications for DHS configuration.

This paper presents a system-wide approach for contingency severity screening intended as a practical and scalable precursor to full dynamic security assessment (DSA). DSA is the standard practice used by Transmission System Operators (TSOs) to ensure that power systems remain stable following disturbances, with key variables—such as voltage, frequency, and thermal loading—kept within defined dynamic performance limits. However, applying DSA comprehensively across all credible contingencies and operating points is prohibitive, making a pre-screening stage indispensable. Commonly used static-security filters offer limited insight to transient dynamics, motivating the development of alternative screening strategies. To support early-stage analysis and prioritization, three static indicators are devised as proxies for dynamic behaviour, grounded in the power–angle characteristics determining transient stability. For eachn−1 contingency, these indicators provide a severity estimate and serve as inputs to a supervised learning model trained on labels derived from time-domain simulation. This approach aims to reflect how each disturbance might impact system stability, consistent with the objectives of DSA. Contingency rankings derived from these estimates serve to guide further analysis, enabling TSOs to focus dynamic simulations on the most critical scenarios.

The liberalization of the electricity market induces a large variety of scenarios that may lead power systems close to their operation limits. This supports the need for on-line dynamic security assessment (DSA) of the grids, in order to provide operators with a clear insight of the current network state. The on-line application of DSA to a realistic network needs adequate methods to screen the large amount of contingencies to be examined by DSA tools. This paper proposes some practical heuristic indices for Transient Stability contingency pre-filtering and ranking in an on-line DSA session.

A major drawback of current dynamic security assessment (DSA) software used to analyze the transient and dynamic responses to a large number of potential power system disturbances in a transient time frame is the long simulation time required to complete the calculations, making it impractical for real-time applications. This paper describes an attempt to dramatically reduce the time for DSA calculations so that DSA analyses can be converted from offline studies to routine, online use to aid grid operators in their real-time controller analyses. Using a new distributed computational architecture among other techniques, a modified version of DSA is developed which performs calculations fast enough for real-time applications to energy management systems (EMS) that operate today's grid systems.

Artificial neural networks (ANN) based pattern recognition for steady state (SSA), transient (TSA) and dynamic security assessment (DSA) of the power systems is presented. Conventional methods for security assessment take lot of time to assess the security of power systems when all contingencies are considered. Artificial neural networks based pattern recognition is used to solve SSA, TSA and DSA and its effectiveness against conventional methods is presented. In addition contingency ranking and generator severity ranking is used to measure the severity of various contingencies which allows an operator to take preventive control (generator rescheduling) or to be ready for corrective actions. Direct methods are fast compared to numerical methods but proper choice of Lyapunov function is difficult. ANN based PR for SSA, TSA and DSA effectiveness is compared with other conventional methods like pattern recognition which confirms that ANN not only gives accurate results but also is adaptive to the topology changes of the power system. In addition contingency ranking and generator severity ranking has been addressed which allows the operator to take preventive control actions like generator rescheduling.

Unit commitment is important in economy and security of power system operation. However, Large penetration of wind power integrated into power system increases the uncertainty on unit commitment. How to efficiently accommodate the uncertainty has been researched widely. In this paper, a new interval unit commitment (UC) formulation based on interval optimization is proposed in which the uncertain wind power is described as fluctuant intervals. Then, the interval UC with uncertainty is transformed into a deterministic form by using order relation of interval number and degree of satisfaction. The transformed model is a two-stage and multi-objective optimization problem. Therefore, the intergeneration projection genetic algorithm is developed to deal with the transformed deterministic model. Finally, the case study with IEEE 39-bus system demonstrated the effectiveness and advantages of the proposed model.

The rapid expansion of wind power gives rise to a number of challenges for power system operators and electricity market participants. The key operational challenge is to efficiently handle the uncertainty and variability of wind power when balancing supply and demand in ths system. In this report, we analyze how wind power forecasting can serve as an efficient tool toward this end. We discuss the current status of wind power forecasting in U.S. electricity markets and develop several methodologies and modeling tools for the use of wind power forecasting in operational decisions, from the perspectives of the system operator as well as the wind power producer. In particular, we focus on the use of probabilistic forecasts in operational decisions. Driven by increasing prices for fossil fuels and concerns about greenhouse gas (GHG) emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing electricity supply portfolio in many parts of the world. The U.S. Department of Energy (DOE) has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the U.S. wind power capacity would have to reach more than 300 gigawatts (GW). The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas (GHG) emissions by 20%, increase the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared to 1990. Meanwhile, China is the leading country in terms of installed wind capacity, and had 45 GW of installed wind power capacity out of about 200 GW on a global level at the end of 2010. The rapid increase in the penetration of wind power into power systems introduces more variability and uncertainty in the electricity generation portfolio, and these factors are the key challenges when it comes to integrating wind power into the electric power grid. Wind power forecasting (WPF) is an important tool to help efficiently address this challenge, and significant efforts have been invested in developing more accurate wind power forecasts. In this report, we document our work on the use of wind power forecasting in operational decisions.

Dynamic Voltage Security Assessment By Fuzzy Severity Index

Minimum energy storage (ES) and spinning reserve (SR) for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.