Secure Operation Of Power Systems Research Articles

Two‐stage stochastic energy and reserve market clearing model considering real‐time offering strategy of renewable energy

AbstractWith the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

Enhanced thermal modeling of power transformers: A comparison of bond graph and IEEE methods considering external environmental factors

The optimal and secure operation of electrical power system is largely affected by the reliability of power transformers in operational and economic terms. The continuous monitoring of power transformer is crucial for ensuring the quality of electrical power supply. Hence, the thermal modeling of power transformer is very essential to prevent their failure. This paper aims to design thermal models of operating power transformer by considering the two external influencing parameters of solar irradiation and wind flow. This paper presents a Bond Graph (BG) method-based design of thermal models for power transformer under operating condition by comparison with the results obtained from IEEE Std. based thermal models and also with the practical readings. The models have been implemented on a 5MVA, 33/11 kV power transformer running at 33 kV Substation, Sardarpura, Jodhpur, by initially considering the input hourly dataset of load and ambient temperature. The designed thermal models were subsequently updated by integrating the impact of solar radiation and wind incident on the surface of power transformer. Comparative study of all the designed thermal models has been done that indicates improved accuracy of the model if the impact of solar radiation and wind flow are included. Also, the effectiveness of the BG based thermal models in accurately predicting the system behaviour validates their applicability for real world engineering applications.

A Bi-Level Peak Regulation Optimization Model for Power Systems Considering Ramping Capability and Demand Response

In the context of constructing new power systems, the intermittency and volatility of high-penetration renewable generation pose new challenges to the stability and secure operation of power systems. Enhancing the ramping capability of power systems has become a crucial measure for addressing these challenges. Therefore, this paper proposes a bi-level peak regulation optimization model for power systems considering ramping capability and demand response, aiming to mitigate the challenges that the uncertainty and volatility of renewable energy generation impose on power system operations. Firstly, the upper-level model focuses on minimizing the ramping demand caused by the uncertainty, taking into account concerned constraints such as the constraint of price-guided demand response, the constraint of satisfaction with electricity usage patterns, and the constraint of cost satisfaction. By solving the upper-level model, the ramping demand of the power system can be reduced. Secondly, the lower-level model aims to minimize the overall cost of the power system, considering constraints such as power balance constraints, power flow constraints, ramping capability constraints of thermal power units, stepwise ramp rate calculation constraints, and constraints of carbon capture units. Based on the ramping demand obtained by solving the upper-level model, the outputs of the generation units are optimized to reduce operation cost of power systems. Finally, the proposed peak regulation optimization model is verified through simulation based on the IEEE 39-bus system. The results indicate that the proposed model, which incorporates ramping capability and demand response, effectively reduces the comprehensive operational cost of the power system.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems

As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by injecting abnormal data into terminal links or devices. The current research on FDIA primarily focuses on detecting its existence, but there is relatively little research on the localization of the attacks. To address this challenge, this study proposes a novel FDIA localization method (GA-CNN-LSTM) that combines convolutional neural networks (CNNs), long short-term memory (LSTM), and a genetic algorithm (GA) and can accurately locate the attacked bus or line. This method utilizes a CNN to extract local features and combines LSTM with time series information to extract global features. It integrates a CNN and LSTM to deeply explore complex patterns and dynamic changes in the data, effectively extract FDIA features in the data, and optimize the hyperparameters of the neural network using the GA to ensure an optimal performance of the model. Simulation experiments were conducted on the IEEE 14-bus and 118-bus test systems. The results indicate that the GA-CNN-LSTM method achieved F1 scores for location identification of 99.71% and 99.10%, respectively, demonstrating superior localization performance compared to other methods.

Intelligent Power System Stability Assessment and Dominant Instability Mode Identification Using Integrated Active Deep Learning.

Simulation analysis is critical for identifying possible hazards and ensuring secure operation of power systems. In practice, large-disturbance rotor angle stability and voltage stability are two frequently intertwined stability problems. Accurately identifying the dominant instability mode (DIM) between them is important for directing power system emergency control action formulation. However, DIM identification has always relied on human expertise. This article proposes an intelligent DIM identification framework that can discriminate among stable status, rotor angle instability, and voltage instability based on active deep learning (ADL). To reduce human expert efforts required to label the DIM dataset when building DL models, a two-stage batch-mode integrated ADL query strategy (preselection and clustering) is designed for the framework. It samples only the most helpful samples to label in each iteration and considers both information contents and diversity in them to improve query efficiency, significantly reducing the required number of labeled samples. Case studies conducted on a benchmark power system (China Electric Power Research Institute (CEPRI) 36-bus system) and a practical large-area power system (Northeast China Power System) reveal that the proposed approach outperforms conventional methods in terms of accuracy, label efficiency, scalability, and adaptability to operational variability.

Quantum model prediction for frequency regulation of novel power systems which includes a high proportion of energy storage

As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge–discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional–integral–derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy.

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.

Voltage stability index: a review based on analytical method, formulation and comparison in renewable dominated power system

In an interconnected complex power system network voltage stability evaluation is indispensable to guarantee secure power system operation. To further increase the system performance and to make the system safer assessment, the voltage stability improvement is obligatory. In the various literature different voltage security assessment method using the voltage stability index has been presented. Different lists proposed in literature can be utilized to realize the weak buses and weak transmission lines to enacting the countermeasures against issues of voltage insecurity. Additionally, the arrangement and measuring of inexhaustible assets, on the web and disconnected observing of force framework and the measure of load to be shed at whatever point essential. This paper shows a survey on the different voltage stability index from various perspectives. The audit results on various record gives a far and wide logical to perceive the impending works in this field and to choose the best index for variety of applications such as voltage security assessment, renewable energy integration, distributed generation (DG) placement and sizing, online monitoring of the power system, and shedding of load.

Carbon Neutrality Computational Cost Optimization for Economic Dispatch With Carbon Capture Power Plants in Smart Grid

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

Ensuring stability and reliability in power systems requires accurate state estimation, which is challenging due to the growing network size, noisy measurements, and nonlinear power-flow equations. In this paper, we introduce the Graph Attention Estimation Network (GAEN) model to tackle power system state estimation (PSSE) by capitalizing on the inherent graph structure of power grids. This approach facilitates efficient information exchange among interconnected buses, yielding a distributed, computationally efficient architecture that is also resilient to cyber-attacks. We develop a thorough approach by utilizing Graph Convolutional Neural Networks (GCNNs) and attention mechanism in PSSE based on Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Unit (PMU) measurements, addressing the limitations of previous learning architectures. In accordance with the empirical results obtained from the experiments, the proposed method demonstrates superior performance and scalability compared to existing techniques. Furthermore, the amalgamation of local topological configurations with nodal-level data yields a heightened efficacy in the domain of state estimation. This work marks a significant achievement in the design of advanced learning architectures in PSSE, contributing and fostering the development of more reliable and secure power system operations.

Research on Fast Frequency Response Control Strategy of Hydrogen Production Systems

With the large-scale integration of intermittent renewable energy generation presented by wind and photovoltaic power, the security and stability of power system operations have been challenged. Therefore, this article proposes a control strategy of a hydrogen production system based on renewable energy power generation to enable the fast frequency response of a grid. Firstly, based on the idea of virtual synchronous control, a fast frequency response control transformation strategy for the grid-connected interface of hydrogen production systems for renewable energy power generation is proposed to provide active power support when the grid frequency is disturbed. Secondly, based on the influence of VSG’s inertia and damping coefficient on the dynamic characteristics of the system, a VSG adaptive control model based on particle swarm optimization is designed. Finally, based on the Matlab/Simulink platform, a grid-connected simulation model of hydrogen production systems for renewable energy power generation is established. The results show that the interface-transformed electrolytic hydrogen production device can actively respond to the frequency disturbances of the power system and participate in primary frequency control, providing active support for the frequency stability of the power system under high-percentage renewable energy generation integration. Moreover, the system with parameter optimization has better fast frequency response control characteristics.

Operation Constraints and Methodology for Stability Preservation in Power Systems with High Penetration of Non-Synchronous Generation

Nowadays, significant changes in the electricity generation mix raise questions about the secure operation of power systems. Most of the newly installed generation capacity is being connected to the grid via power electronic converters, thus can be seen as a non-synchronous generation. The theory of power system operation and control relies heavily on the characteristics of synchronous machines and conventional network structure. Therefore, the effective integration process of the new technologies must include the composition of new physical models for converter dominated large power systems as well as innovative solutions to ensure the secure operation in the future. The focus of this paper is a holistic analysis of the reducing power system inertia to frame up new constraints for system operators through simulation studies on the Institute of Electrical and Electronics Engineers standard 118 bus test system. The different system states and scenarios offer a comparison opportunity between stability preservation possibilities. Minimum inertia constraint calculation methodologies and various objective functions are being discussed. The utilization and effects of synthetic inertia from non-synchronous generators and energy storage systems is also considered to quantify the exact effects.

Artificial Neural Network based FACTS in a Contingency Situation

The two biggest issues facing today's energy management systems are the ongoing monitoring of online voltage stability and the improved loadability of the transmission lines for the current electrical power system. As a result, it is highly difficult and time-consuming to assess online voltage stability under diverse loading conditions. This study describes a practical voltage stability monitoring system that automates online voltage monitoring and alerts the operator before voltage drops by computing line voltage stability indices using an ANN. This study compares evaluations of system voltage stability and loadability at the load with FACTS devices. The results suggest increase in system loadability while ensuring the security of power system operation.

District Cooling System Control for Providing Operating Reserve Based on Safe Deep Reinforcement Learning

Heating, ventilation, and air conditioning (HVAC) systems are well proved to be capable to provide operating reserve for power systems. As a type of large-capacity and energy-efficient HVAC system (up to 100 MW), district cooling system (DCS) is emerging in modern cities and has huge potential to be regulated as a flexible load. However, strategically controlling a DCS to provide flexibility is challenging, because one DCS services multiple buildings with complex thermal dynamics and uncertain cooling demands. Improper control may lead to significant thermal discomfort and even deteriorate the power system's operation security. To address the above issues, we propose a model-free control strategy based on the deep reinforcement learning (DRL) without the requirement of accurate system model and uncertainty distribution. To avoid damaging “trial & error” actions that may violate the system's operation security during the training process, we further propose a safe layer combined to the DDPG to guarantee the satisfaction of critical constraints, forming a safe-DDPG scheme. Moreover, after providing operating reserve, DCS increases power and tries to recover all the buildings' temperature back to set values, which may cause an instantaneous peak-power rebound and bring a secondary negative impact on power systems. Therefore, we design a self-adaption reward function within the proposed safe-DDPG scheme to constrain the peak-power effectively. Numerical studies based on a realistic DCS demonstrate the effectiveness of the proposed methods.

L-M Based ANN for Predicting the Location of DG under Contingency Condition

The continuing monitoring of online voltage stability and the increased loadability of the transmission lines for the existing electrical power system are the two major challenges that today's energy management systems must deal with. As a result, evaluating online voltage stability under various loading situations is extremely challenging and time-consuming. The line voltage stability indices using an Artificial Neural Network (ANN), the system describes online voltage monitoring and warns the operator before voltage dips. The simulation's findings show that the proposed system may boost system loadability while also lowering the cost of installing an electrical power system and guaranteeing the security of power system operation.

Power-System Flexibility: A Necessary Complement to Variable Renewable Energy Optimal Capacity Configuration

Comprehending the spatiotemporal complementarity of variable renewable energy (VRE) sources and their supplemental ability to meet electricity demand is a promising move towards broadening their share in the power supply mix without sacrificing either supply security or overall cost efficiency of power system operation. Increasing VRE share into the energy mix has to be followed with measures to manage technical challenges associated with grid operations. Most sub-Saharan countries can be considered ‘greenfield’ due to their relatively low power generation baseline and are more likely to be advantaged in planning their future grids around the idea of integrating high VRE sources into the grid from the outset. An essential measure for achieving this objective entails exploring the possibility of integrating renewable hybrid power plants into the existing hydropower grid, leveraging on existing synergies and benefiting from the use of existing infrastructure and grid connection points. This study evaluates the potential for hybridizing existing hydropower-dominated networks to accommodate solar- and wind-energy sources. The existing synergy is quantified using correlation and energy indicators by evaluating complementarity at daily, monthly and annual intervals. The proposed metric serves as a tool to improve planning on increasing the VRE fraction into the existing systems with the aim to achieve optimal power mixes. In comparison to cases in which the same kind of resource is over-planted while expanding installed capacity, the results demonstrate that wind and solar resources hold a positive degree of complementarity, allowing a greater share of VRE sources into the grid. The study shows that Kenya bears favorable climatic conditions that allow hybrid power plant concepts to be widely explored and scaled up on a large and efficient scale. The results can be applicable in other regions and represent an important contribution to promoting the integration of VRE sources into sub-Saharan power grids.

The potential impact of climate change on European renewable energy droughts

The daily, seasonal, and interannual variability of solar and wind resources is well-documented, based on evidence from multi-decadal meteorological time series. However, with the growing share of non-dispatchable renewable-based power sources (e.g., wind and solar power), the stable operation of the power system could be undermined by prolonged periods of low availability of these resources. Consequently, this may result in extremely high prices in the energy market or even a power system blackout. This study analyzes the performance of solar, wind, and solar-wind hybrid systems in Europe based on eight regional climate models, considering two possible climate change projections. The resource availability has been evaluated based on the energy drought concept. The total duration of droughts is calculated using daily capacity factors covering the years 1970–2020 (reference period) and 2048–2098 (future period), considering sub-national regions across the whole of Europe. In general, the chosen climate models show a more significant agreement in the occurrence of energy droughts for northern and southern Europe compared to its central part. Assessing the potential for renewable energy droughts is critical to maintaining secure and reliable power system operation in both the present and future climate.

Bayesian deep neural networks for spatio-temporal probabilistic optimal power flow with multi-source renewable energy

Probabilistic optimal power flow (POPF) plays a crucial role in ensuring the economic and secure operation of power systems with multiple fluctuating loads and renewable energy power generations. However, the practical application of POPF faces challenges because of the lack of accurate input scenarios and the heavy computational burden. With the increasing power system uncertainties and frequent changes in topologies, the existing POPF analysis methods struggle to achieve both high precision and efficient training. To address these issues, firstly, this work decomposes the POPF into three stages: 1) the generation of source-load random scenarios; 2) the non-linear mapping derived from the Karush-Kuhn-Tucker conditions; and 3) the non-linear mapping derived from the power flow equations. Secondly, this work proposes two Bayesian deep neural networks to separately solve each stage of the POPF. Multi-collinearity reduction conditional variational autoencoder can generate source-load random scenarios while consistency condition guided Bayesian deep neural network can predict POPF calculation results. The work further conducts numerical simulations on four IEEE benchmark power systems with multiple renewable energy sources, using uncertainty data collected from the Belgian wind power generation, photovoltaic power generation and load dataset. The simulation results demonstrate that: 1) the precision in generating source-load random scenarios has been enhanced, leading to a reduction of up to 128% in the distribution statistical quantities of the accuracy measures; and 2) the mean absolute error of active power injections, and voltage angles as well as the mean absolute percentage error of the voltage amplitudes in the benchmark systems are reduced by at least 0.7% compared to deterministic neural networks.

Management of Var sources for the reactive power planning problem by oppositional Harris Hawk optimizer

Reactive power management has grown more crucial for increased synchronization in modern power systems, since transmission loss minimization is a basic condition for secure power system operation. This paper proposes the Oppositional-based Harris Hawk Optimizer technique as an advanced meta-heuristic nature inspired methodology, which is applied on the conventional Ward Hale 6 bus system and the IEEE 30 bus system. The solution space is further altered by combining HHO with the Oppositional Based Learning technique in order to enhance approximation for the current solution. The suggested OHHO outperforms HHO as well as other optimization methodologies recently published articles, according to simulation results obtained on typical test systems.