Secure Grid Operation Research Articles

Contingency analysis with warm starter using probabilistic graphical model

Cyberthreats are an increasingly common risk to the power grid and can thwart secure grid operations. We propose to extend contingency analysis to include cyberthreat evaluations. However, unlike the traditional N-1 or N-2 contingencies, cyberthreats (e.g., MadIoT) require simulating hard-to-solve N-k (with k ≫ 2) contingencies in a practical amount of time. Purely physics-based power flow solvers, while being accurate, are slow and may not solve N-k contingencies in a timely manner, whereas the emerging data-driven alternatives are fast but not sufficiently generalizable, interpretable, and scalable. To address these challenges, we propose a novel conditional Gaussian Random Field-based data-driven method that performs fast and accurate evaluation of cyberthreats. It achieves speedup of contingency analysis by warm-starting simulations, i.e., improving starting points, for the physical solvers. To improve the physical interpretability and generalizability, the proposed method incorporates domain knowledge by considering the graphical nature of the grid topology. To improve scalability, the method applies physics-informed regularization that reduces model complexity. Experiments validate that simulating MadIoT-induced attacks with our warm starter becomes approximately 5x faster on a realistic 2000-bus system.

Optimized scheduling of smart community energy systems considering demand response and shared energy storage

Integrated energy systems within communities play a pivotal role in addressing the diverse energy requirements of the system, emerging as a central focus in contemporary research. This paper contributes to exploring optimal scheduling in a smart community featuring multiple smart buildings equipped with a substantial share of distributed photovoltaic sources, shared energy storage, and controllable loads. The study formulates a two-stage scheduling optimization model incorporating multiple stakeholders, explicitly examining the game dynamics between the smart community operator and the customer load aggregator. The weights assigned to each optimization objective are determined using an analytic hierarchy process-entropy weight method to establish a balanced and nuanced approach. The ensuing optimal scheduling encompasses unit output and energy trading considerations, focusing on enhancing the system's economic viability, safety measures, and environmental sustainability. A comprehensive evaluation is conducted to validate the proposed model by comparing its performance with alternative operational strategies. The results show that the designed strategy can reduce the operating cost by 40.22% and increase the level of PV consumption by 22.57% compared to the traditional heat-based strategy, which is effective in mitigating power fluctuations, smartly responding to dispatch peak demand, enhancing new energy integration, and ensuring the security of grid operation.

Detection of False Data Injection Attack in Power System Based on Hellinger Distance

The deep integration of sensor, computer and communication networks has dramatically improved the efficiency and operational performance of the electric grid. However, it also exposes the grid to rising threats of cyber-physical attacks. False data injection attack (FDIA) is one of the most representative attacks. To improve the security of grid operation, we propose a Hellinger-distance -based FDIA detection method by tracking the dynamic characteristics of measurement variations at adjacent moments. First, the irrelevant components of measured data are sieved out by empirical modal decomposition (EMD). Second, the image transform algorithms are used to deal with the mapping of measurement variations to refine the distribution characteristics. Last, the discrepancies between the probability distributions are derived based on Hellinger distance to determine whether FDIA exists. Concerning state-variable attacks on different nodes, the method is tested using the IEEE 14-bus system. The results indicate that the proposed scheme has high-level detection precision for false data injection attacks.

Security‐constrained active power curtailment considering line temperature and thermal inertia

AbstractA novel method is introduced to determine cost‐optimized active power curtailment (APC) of renewable energy sources (RES) considering weather‐dependent dynamic line rating (DLR). A new formulation of the security‐constrained optimal power flow (SC‐OPF) is developed and applied in a case study. The reduction of the required preventive APC in the case for secure grid operation due to the consideration of thermal inertia of overhead power lines is demonstrated. Considering thermal inertia allows relying on curative APC that is activated only after an situation occurs. The overhead power line temperature, calculated with the temperature‐dependent power flow (TDPF), acts as a new limit for line loading and releases unused transmission reserves. The TDPF algorithm is integrated in the open‐source Python library pandapower, providing access to it to the broader community. The power system can be utilized to a greater extent, with a reduced demand for congestion management.

MMC-HVDC High-Frequency Resonance Suppression Strategy Based on Multi-Band Band-Stop Filters

Renewable energy generation is a manifestation of global economic and societal advancement and serves as a fundamental assurance for humanity’s pursuit of sustainable development. However, recent years have witnessed several instances of high-frequency resonance events in high-voltage direct current (HVDC) transmission systems based on modular multilevel converters (MMC), which have resulted in converter station tripping and significant repercussions on the alternating current (AC) grid. This paper addresses the mid-to-high frequency resonance issues prevalent in flexible DC transmission systems employing modular multilevel converters (MMC-HVDC). To tackle these concerns, an impedance model for MMC’s AC side is established. Utilizing impedance analysis, the essential factors contributing to the negative damping characteristics of MMC are identified as delay and voltage feedforward loops, predominantly causing negative damping in the frequency range exceeding 400 Hz. In response, a suppression strategy is proposed, involving the incorporation of a multi-band stop filter and virtual impedance. This strategy ensures that within the 0–2000 Hz frequency range, only the impedance phase within 230–430 Hz slightly surpasses 90°. Consequently, the phase difference between MMC’s positive-sequence impedance and the AC system impedance is reduced from 222° to 174.7°, thus guaranteeing secure grid operation. Lastly, the accuracy and effectiveness of the theoretical analysis and suppression methodology are verified through the development of an electromagnetic transient model in MATLAB/Simulink, considering delay fluctuations of ±10%.

Machine visual technology based steel corrosion evaluation and analysis of power transformation lines: A Zhejiang power grid case study

It can be seen that the corrosion failure of transmission and transformation equipment has increasingly restricted the safe operation of the Zhejiang power grid bottleneck problem. If effective anticorrosion measures are not taken promptly, transmission and transformation equipment serving in various sophisticated atmospheric environments will suffer serious corrosion damage in a relatively short period, which endangers the safe usage of transmission and transformation equipment and the security of grid operation. In this article, through the establishment of transmission and transformation steel components corrosion fracture mechanics model, a standard corrosion spectrum grading software based on DeepLabV3+ image segmentation technology is developed to determine the quantitative assessment method of corrosion damage and assess the corrosion status with safety degree of transmission and transformation equipment. According to the assessment results, the operation and maintenance units are guided to adopt differentiated corrosion maintenance and replacement strategies, so as to reduce corrosion safety hazards and reduce safety accidents and economic losses caused by corrosion, which is of great significance for the safe operation of power grids.

Network-Constrained Peer-to-Peer energy trading for multiple microgrids considering zoning pricing

For multiple microgrids (MMGs) with different zone consumption characteristics in active distribution network (ADN), it is a challenge to combine technical issue about grid operation security and economic issue about energy trading revenue together. To tackle this challenge, a network-constrained peer-to-peer (P2P) energy trading method considering differentiated zoning pricing is proposed for MMGs. The proposed method consists of three parts: 1) a leader–follower energy trading framework considering different consumption characteristics is developed based on Stackelberg game theory with a unique Stackelberg equilibrium (SE); 2) the utility function of distribution network operator (DNO) is conducted by considering different zones buying and selling prices, in order to minimize the operation costs, and to deal with technical issue by reshaping the power flow and on-load tap changer (OLTC) of transformers for power loss reduction and voltage regulation; 3) the utility function of prosumers within MGs is conducted based on differentiated zone prices to maximize their profit by managing the demand response (DR) behavior of shiftable load, battery storage (BS), and plugin electric vehicle (PEV) in charging station (CS). Numerical results for 3-MGs system, modified by IEEE 33-bus distribution network, have verified the effectiveness of the proposed network-constrained and zone pricing leader–follower P2P energy trading method.

Construction of Safety Evaluation Model for Distribution Network with Distributed Generation

With the continuous change in urban power demand, many problems in the urban power system are exposed, and distribution network planning is one of the more prominent ones. In the operation of the distribution network, the access of distributed generation will have a certain degree of impact on power quality, relay protection, current protection of the distribution network, and energy consumption loss. Therefore, in order to improve the evaluation accuracy of the distribution network security evaluation model, obtain a more realistic situation evaluation value, and expand the overall evaluation scope, this paper constructs the distribution network operation security evaluation model under distributed generation access. Firstly, the subjective weight of evaluation is determined, and the evaluation index system of the power grid operation security situation is designed under the background of power access. Secondly, the multi-source extension fuzzy evaluation matrix is constructed, and the model is established by comprehensive weighting processing. Finally, the test experiment is carried out. The test results show that compared with the traditional power grid operation security evaluation model, the power grid situation evaluation value obtained by the distribution network operation security evaluation model constructed in this paper is relatively high. This shows that the model has a good evaluation effect on the operation security of the distribution network, with high evaluation authenticity and small error, and has a high practical application value.

Special Issue on Algorithms in Planning and Operation of Power Systems

Optimal planning and secure grid operation are new challenges facing modern power systems [...]

False data injection attack detection in dynamic power grid: A recurrent neural network-based method

The smart grid greatly facilitates the transmission of power and information by integrating precise measurement technology and efficient decision support systems. However, deep integration of cyber and physical information entails multiple challenges to grid operation. False data injection attacks can directly interfere with the results of state estimation, which can cause the grid regulator to make wrong decisions and thus poses a huge threat to the stability and security of grid operation. To address this issue, we propose a detection approach against false data injection attacks for dynamic state estimation. The Kalman filter is used to dynamically estimate the state values from IEEE standard bus systems. A long short-term memory (LSTM) network is utilized to extract the sequential observations from states at multiple time steps. In addition, we transform the attack detection problem into supervised learning problem and propose a deep neural network-based detection approach to identify attacks. We evaluate the effectiveness of the proposed detection approach in multiple IEEE standard bus systems. The simulation results demonstrate that the proposed detection approach outperforms benchmarks in improving the detection accuracy of malicious attacks.

Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System

An increase in renewable energy injected into the power system will directly cause a fluctuation in the overall voltage and frequency of the power system. Thus, renewable energy prediction accuracy becomes vital to maintaining good power dispatch efficiency and power grid operation security. This article compares the one-day-ahead PV power forecasting results of three models paired with three groups of weather data. Since the number, loss, and matching problem of weather data will all influence the training results of the model, a pre-processing data framework is proposed to solve the problem in this study. The models used are a deep learning algorithm-based artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU). The weather data groups are Central Weather Bureau (CWB), local weather station (LWS), and hybrid data (the combination of CWB and LWS data). Compared to the other two groups, hybrid data showed a 5–8% improvement in measurements. In addition, when it comes to different weather conditions, the advantages of the LSTM model were highlighted. After further analysis, the LSTM model combined with hybrid data showed the most accurate measurements, which was proved through forecasting results for one month. Finally, the results indicate that when the amount of data is limited, using hybrid data and the five weather features is helpful for training the model. Accordingly, the proposed model shows better one-day-ahead PV forecasting.

New energy power system operation security evaluation based on the SWOT analysis

Grid-connection of new energy is highly important in promoting the use of clean and renewable energy. However, it will bring huge risks to the power grid operation security, such as frequency stability, voltage stability, small signal stability, and transient stability, etc.,. In the study, SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis has been employed to construct 24 kinds of internal and external evaluation factors and 8 kinds of improvement strategies, for assessing operation security prospective with new energy power system of HM in China. The weights of SWOT factors are determined with the fuzzy-AHP method. Moreover, the fuzzy-MARCOS approach is used to select the most suitable strategies for power system operation security effective implementation. The reported research reveals that new energy in HM area not only has an ample potential for full development and generating electricity, but also brings operation security problems due to large-scale grid connection. Therefore, 8 kinds of improvement strategies are suggested to encourage the government to exploit and develop new resources, improve the investment pay, power generation and transmission technologies to mitigate the current energy crisis, and increase the energy security for sustainable development of the country. The methodology proposed herein is applicable with a case study concerning the operation security prospective of HM power grid, and all phases of the comparative analysis and sensitivity analysis illustrate the validity of MARCOS method. Furthermore, the ranked order of strategies is obtained as A2 > A6 > A5 > A1 > A8 > A7 > A4 > A3. The three most important strategies are A2, A6 and A5, i.e., “improving the technical establishment to encourage efficient and cheap electricity production”, “strive to build local permanent load, and reduce the risk of long-distance and high-capacity transmission”, “taking advantage of government incentives and investment to modify the irrational energy policies and energy planning”, respectively.

Power Plant Model Parameter Calibration Using Conditional Variational Autoencoder

Accurate models of power plants play an important role in maintaining the reliable and secure grid operations. In this paper, we propose a synchrophasor measurement-based generator parameter calibration method by a novel deep learning method with high computational efficiency. An elementary effects-based approach is developed to identify the critical parameters from a nonlinear system with much better performance than the widely used trajectory sensitivity-based method. Then, synchrophasor measurement-based conditional variational autoencoder is developed to estimate the parameters’ posterior distributions even in the presence of a high-dimensional case with eighteen critical parameters to be calibrated. The effectiveness of the proposed method is validated for a hydro generator with a very detailed model. The results show that the proposed approach can accurately and efficiently estimate the generator parameters’ posterior distributions even when the parameters true values are not in support of the prior distribution.

An advanced method for steady-state security assessment considering dynamic thermal capacities of grid assets

The ( $n-1$ ) criterion is a commonly used criterion for secure grid operation. Violations of this criterion lead to remedial actions in the short term and the construction of additional transmission capacities in the long term to reduce congestions in the grid. An alternative strategy in reducing necessary interventions is to consider the natural dependency of the transmission capacity of an installed grid component from its environmental conditions. The application of dynamic component rating is offering additional transmission capacities for system operation. For the consideration of these capabilities in the day-ahead operation planning, forecasting the trend of the component ratings is needed. The implementation of such methods, especially for overhead lines, is tested by different system operators around the world. A further strategy in enhancing transmission capacity for the operation is to utilize the thermal inertia of a component for small and short exceedances of the ratings without violating the component’s thermal limits. Currently available static emergency ratings of components respect this capability. Component ratings as well as emergency ratings are dependent on environmental conditions. Furthermore, the available emergency operation duration is dependent on the development of the loading current in the recent past. By equipping assets with real-time monitoring systems, utilization of the component’s current transmission capacity is possible for the different operation states. This paper presents a method to consider the thermal transmission capacity of power system components, enabled by real-time monitoring systems, in the contingency analysis. The approach is an assessment at system level combined with a time-domain simulation for the evaluation of the impact of an examined contingency on a component’s relevant thermal parameters. The result of the method is compatible with the current binary statement but extended with an availability duration if a contingency occurs in the examined grid. Introducing this duration can assist the planning of curative remedial actions for the operation. Further, three specific durations were proposed for benchmarking a contingency in the different levels of analysis. These durations are the result of the time-domain simulation. The presented method is capable of analysing a network consisting of assets equipped with a monitoring system and assets without such a system. The second part of this paper presents the operation simulation of an exemplary grid for one operational year to illustrate the effect of the approach on the results of the contingency analysis in day-ahead planning.

A Combined Model Based on CEEMDAN, Permutation Entropy, Gated Recurrent Unit Network, and an Improved Bat Algorithm for Wind Speed Forecasting

Accurate and reliable wind speed forecasting is crucial for wind farm planning and grid operation security. To improve the accuracy of wind speed forecasting, a novel combined model is proposed for wind speed forecasting in this article. First, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and permutation entropy (PE) are employed to decompose the original wind speed time series into the sub-series with obvious complexity different; To overcome the disadvantage of weak generalization ability of single deep learning method when facing diversiform data, a cluster of gated recurrent unit networks (GRUs) with different hidden layers and neurons are applied to capturing the unsteady characteristics and implicit information of each sub-series; The predictions of the GRUs of each sub-series are aggregated into a nonlinear-learning regression top-layer which is consisted of radial basis function neural network (RBFNN), and improved bat algorithm (IBA) is introduced to optimize the parameters of RBFNN; Lastly, the prediction values of each nonlinear-learning top-layer are superimposed to obtain the final prediction values. To validate the effectiveness of the model, 15-min and 1-h wind speed data from the wind farm in Zhangjiakou, China, are used as test cases. The experimental results demonstrate that the proposed combined model can achieve the best performance and stability compared to other models. Such as the performance evaluation indexes (RMSE = 0.3294, MAPE = 2.6169%) are smallest obtained from case study 1, and (RMSE = 0.5876, MAPE = 4.7875%) are smallest obtained from case study 2.

Implementing Secure Routable GOOSE and SV Messages Based on IEC 61850-90-5

Next generation power systems are active networks that handle two-way power flow. They are equipped with extensive communication capabilities to perform dynamic monitoring, protection and control operations. Synchrophasors provide a pseudo real-time representation of grid's current state. Phasor Measurement Units (PMU) placed in different parts of the grid periodically collect synchrophasor data. Then, they send it to a Phasor Data Concentrator (PDCs) through Wide Area Monitoring Systems (WAMS). The entire system formed as PMU Communication Network (PMU-CN) is based on two available frameworks: IEEE C37.118.2 and IEC 61850-90-5. As New York Blackout of 2003 showed that accurate and timely delivery of phasor measurements is vital for secure grid operation. Attacks on PMU-CN may lead to several consequences in the grid and cause physical damage. IEEE C37.118.2 does not specify any security mechanism to mitigate security attacks. To address this gap, security mechanism specified in IEC 61850-90-5 have been implemented using OpenSSL library. A novel toolbox called R-GoSV has been developed to construct PMU messages with cybersecurity mechanisms. Thanks to this tool, custom messages have been transmitted in the network to investigate their effectiveness. Finally, the performance evaluation of the specified security algorithms in terms of computational time sis carried out.

Time series forecasting of solar power generation for large-scale photovoltaic plants

Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 h of operation data taken from a 20 MW grid-connected PV station in China.

Bilateral Electricity Market in a Distribution System Environment

Increasing penetration of proactive agents, including distributed energy resources (DERs) and responsive loads (RLs), in distribution systems motivates methods to incentivize market participation from demand-side resources. The complexity resulting from large-scale integration of proactive agents has challenged the viability of centrally managing grid resources in a distribution system environment and motivated decentralized frameworks to coordinate the transactions. Existing decentralized frameworks are primarily focused on designing transaction mechanisms. They neither include a formal market construct nor a computationally feasible approach for market settlement for a large volume of transactions while ensuring the security of grid operation. This paper presents a distributed computational approach for a distribution system market where the coordination framework is based on agreement on bilateral energy transactions reached by market participants. Using the proposed distributed computational method, participants play a role in determining the set of bilateral transactions that maximizes the social welfare. The distribution system operator (DSO) ensures a secure operating condition of the distribution grid by validating each transaction that is agreed upon. Under a potential violation of security constraints by a proposed bilateral transaction, the DSO runs an optimal power flow (OPF) and recommends a new set of transactions (closest to those previously proposed) while maintaining power system security. The proposed framework is validated using IEEE 123-node test system. The results confirm the feasibility of the proposed approach for distribution systems.

Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting

Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: How can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-the-art methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.

Potential of new business models for grid integrated water electrolysis

Abstract Grid integrated water electrolysers have the potential of coupling electric power systems subjected to high shares of renewable energy sources with sectors of hydrogen demand, thus contributing to European decarbonization goals in future. We therefore investigate the business potential of future electrolyser applications in cross-commodity arbitrage trading by applying a complex power market simulation method for future scenarios and different European countries. Based on this, we evaluate the potential of additional provision of grid services towards grid operators in order to increase the electrolyser utilization ratio. For this, we use a method that identifies measures of transmission grid operators in order to ensure secure grid operation. In this context, uncertain hydrogen prices and different sectors of hydrogen demand are addressed through sensitivities of different hydrogen sales prices. The analysis shows a high dependency of business model efficiency on the hydrogen price. While cross-commodity arbitrage trading can achieve profitability for the transportation sector, applications for the industry sector and natural gas system are less efficient. The results however indicate that for these less efficient applications grid service provision can be an option of increasing the electrolyser utilization ratio thus increasing its profitability.