Penetrated Power System Research Articles

Impact of Communication Time Delay in a 5G Network on Frequency Regulation Performance of a High Renewable Energy Penetrated Power System

Impact of Communication Time Delay in a 5G Network on Frequency Regulation Performance of a High Renewable Energy Penetrated Power System

Identification of reliable locations for wind power generation through a global analysis of wind droughts

Wind droughts, or prolonged periods of low wind speeds, pose challenges for electricity systems largely reliant on wind generation. Using weather reanalysis data, we analyzed the global distribution of and trends in wind droughts using an energy deficit metric that integrates the depth and duration of wind droughts. We identified regions with high power densities, low seasonal variability, and limited weather fluctuations that favor wind power generation, such as the American Midwest, Australia, the Sahara, Argentina, Central Asia, and Southern Africa. Northwestern Europe has high power densities but experiences more frequent and prolonged wind droughts due to higher weather variability. We found little evidence for strong trends in wind droughts over recent decades in most places. Rather, the most severe wind droughts in many places occurred before wind power substantially penetrated power systems, which suggests that historical weather data can be useful in designing reliable wind-reliant electricity systems.

Coordinated restoration of inverter‐based power sources and synchronous generators for the high renewable penetrated power system considering the dynamic frequency regulation capability

AbstractExtensive inverter‐based power sources (IPS) impose significant challenges on the restoration of high renewable penetrated power systems (HRPPS). To enhance HRPPS resilience, the proper utilization of IPSs must be implemented. Combining frequency dynamics of IPSs and synchronous generators, this paper proposes a coordinated restoration method for multi‐type power sources after a major blackout. First, interactions between synchronous generators and IPSs are systematically analyzed. Based on this, output characteristics and constraints of IPSs in the power sources restoration process are quantified. Second, the dynamic frequency regulation capability (DFRC) of restored systems is quantified based on a unified transfer function structure model. Then the maximum power disturbance that restored systems can bear is derived based on DFRC indices including the maximum frequency deviation and the rate of change of frequency. Third, considering interactions between power sources and the DFRC of restored systems, a coordinated restoration optimization model of multi‐type power sources is proposed. Finally, case studies based on a modified IEEE 39‐bus system are simulated to verify the applicability and superiority of the proposed method. Meanwhile, results show that the proposed method for quantifying DFRC is more suitable for HRPPSs than traditional inertia‐based methods.

Voltage Stability Constrained Operation Optimization: An Ensemble Sparse Oblique Regression Tree Method

Voltage stability remains a main challenge for high renewable penetrated power systems. In future power systems, the dispatch needs to consider the voltage stability margin. However, considering static and transient voltage stability is challenging because a complex relationship exists between voltage stability and operation state variables. This paper proposes an ensemble sparse oblique regression tree method for voltage stability constrained operation optimization. First, we train multiple oblique regression trees on voltage stability simulation datasets. The trained trees are then grouped into ensemble using a boosting method to extract accurate and understandable voltage stability rules. Finally, the rules are embedded as mixed-integer linear programming constraints in a linear optimal power flow model. Validation on the IEEE-14 case shows the interpretability and efficiency of the proposed algorithm. Case studies on the Qinghai power grid and IEEE-118 further show that the proposed strategy can effectively improve the voltage stability margin of optimized operation states.

Design flexible renewable energy penetrated power system to address long-run and short-run interactive inference

<p>For power systems with a high penetration of renewable energy, sufficient flexible resources such as energy storage must be combined to achieve sustainable energy development. However, in the planning of flexible resources, external societal factors can significantly change the evolution pathways of these resources. A simulation framework is urgently needed to integrate long-run development factors represented by societal influences, with the short-run operational characteristics within the physical energy framework. We provide technical support for the sustainable development of power systems, making the simulation results more accurate for future energy system planning. To address the lack of data for modeling external societal factors, a long-run modeling method based on system dynamics is proposed, alongside a short-run modeling method considering flexibility assessment and optimization. Long-run external societal factors necessitate a low-carbon system, while short-run concerns involve the actual topology of the power system to investigate high flexibility. We found that the sensitivity of various flexibility resource investments to both flexibility and low carbon in the power system is key to resolve this contradiction. An empirical calculation of the power system is conducted in the 213-bus flexibility test system including real data at 15-minute and 1-minute resolutions. Battery storage becomes the largest flexibility investment about a decade after the introduction of carbon reduction policies. While other flexibility resources particularly demand-side response due to unsaturated flexibility, also become major temporary investment assets. Considering the proposed interactive inference framework, there was a significant reduction in marginal abatement cost, and carbon trading continuously reduces the abatement cost.</p>

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

Optimal Sizing and Siting of BESS in High Wind Penetrated Power Systems: A Strategy Considering Frequency and Voltage Control

Optimal Sizing and Siting of BESS in High Wind Penetrated Power Systems: A Strategy Considering Frequency and Voltage Control

Data-Driven Security and Stability Rule in High Renewable Penetrated Power System Operation

Power systems around the world are experiencing an energy revolution that substitutes fossil fuels with renewable energy. Such a transition poses two significant challenges: highly variable generators that add short-term and long-term difficulties for supply–demand balance, and a high proportion of convertor-based devices that may jeopardize power system security and stability. At the same time, machine learning techniques provide more opportunities to study the complex power system security and stability problems. This article summarizes the machine learning framework to embed security rules into power system operation optimization under high renewable energy penetration. First, we explore how high penetration renewable energy impacts power system security and stability. Then, we review how the complex security and stability boundary of power systems is modeled using various machine learning techniques. Finally, we show how the machine learning model is transformed into optimization constraints that can be embedded into the power system operation model. The framework is substantiated through case studies of practical power systems.

Analysis and prediction of the penetration of renewable energy in power systems using artificial neural network

The curtailment of renewable energy worsens with increasing penetration in power systems, so it is necessary to explore the upper limit value of the penetration of renewable energy (PRE). This paper uses artificial neural networks (ANN) to study the historical data of California independent system operator (CAISO), analyze the fluctuation balance strategy of wind and solar power, and predict the upper limit value of the PRE, which will peak at 40.5% in 2025. In addition, this paper also simulates the inclusion of energy storage unit (ESU) with an installed capacity of 3 GWh in the grid to reduce curtailments, and analyzes the grid operating conditions and the upper limit value of the PRE, showing that the storage units with an installed capacity of only 3% of the average daily power output of solar energy recover 58% of the annual curtailments (2021), and the maximum PRE is 41.2%.

Impact of renewable energy penetration in power systems on the optimization and operation of regional distributed energy systems

Impact of renewable energy penetration in power systems on the optimization and operation of regional distributed energy systems

Frequency stability prediction of renewable energy penetrated power systems using CoAtNet and SHAP values

As the complexity of power systems increases, traditional model-driven methods for online frequency stability prediction (FSP) encounter constraints in both accuracy and efficiency. To enhance the accuracy and efficiency of FSP, an data-driven method using CoAtNet and SHAP values is proposed. By leveraging the combination of convolution and attention mechanisms, CoAtNet addresses the limitation of traditional deep learning approaches that may not be able to extract data features comprehensively. Moreover, selecting all features as input into a deep-learning model may cause a substantial computation burden. It is thus impractical for CoAtNet to perform FSP of large-scale power systems. For this problem, this paper develops a SHAP values-based feature selection method to select the effective features as input. This process greatly reduces the numerical complexity, maintaining a high prediction performance. Additionally, the marginally stable situation of the system frequency is ignored by most researchers. A frequency security index to identify marginally stable situations is thus employed to generate the data labels, which are classed as “absolute security”, “relative security”, and “insecurity”. Finally, verified by the comparison simulation, the proposed model outperforms other models with accuracies of 98.80% on the modified IEEE 39-bus system and 99.04% on the modified ACTIVSg500 system.

Intelligent load frequency control for improving wind power penetration in power systems

After wind power is connected to the grid, because the power of wind power will fluctuate greatly, the frequency deviation of the grid will increase with access to wind power generation, then the challenge of frequency control in power grid is also increasing. The Load frequency control(LFC) is one of the important components of frequency control, the fluctuation of wind power output of the system will put forward a higher demand for load frequency control (LFC) of the power grid so as to increase the proportion of wind power in the system. In this paper, a PI controller whose parameters are set by an intelligent cloud calculation result is designed for LFC of the interconnected power system with large-scale wind power. The parameters in PI controller are obtained through the cloud model whose inputs are the control error and the corresponding rate of change so as to realize the adaptive tuning. The subordinate cloud parameters are designed considering randomness of wind power, which effectively solves the problems of poor parameter robustness in traditional PI control. The simulation of the two-area interconnection system with wind power is carried out to verify the effectiveness of the proposed method in the case of random wind power.

A Novel Fuzzy-Logic-Based Control Strategy for Power Smoothing in High-Wind Penetrated Power Systems and Its Validation in a Microgrid Lab

Wind power generation has undergone significant development in recent decades due to its environmental advantages and its economic competitiveness. However, its increasing level of penetration is not exempt from drawbacks, such as those derived from the fluctuating nature of the wind. To reduce its negative incidence on grid power quality and stability, different techniques have been developed, such as those based on power smoothing. In these techniques, there is a research gap on the adjustment of the time constant that adapts to the needs of the smoothing, avoiding uncertain results, computational efforts and delays in the response of the control. This paper addresses the problem, proposing a novel method for power smoothing in a wind turbine by using a fuzzy-logic-based supercapacitor storage system and time-constant fitting, with a first-order adaptive transfer function. The method considers as input variables the active power generated by the wind turbine and the state of charge of the supercapacitor, both sampled simultaneously. After a computation process, the proposal generates active power set-point values that the supercapacitor must produce to compensate for the intermittency of the wind, seen from the point of connection to the grid. The results were validated experimentally with comprehensive laboratory tests.

A distributed PageRank-based dynamic partition algorithm to improve distributed energy storages participation in frequency regulation

In high renewable energy sources penetrated power systems, a large number of distributed small-capacity energy storages (ESs) are expected to form virtual power plants (VPPs) and provide comparable frequency regulation capacity to conventional power plants. In this paper, a dynamic and distributed partition method is proposed to cluster ESs into VPPs by selecting the electrical distance and local power demands as the index for partitioning. Utilizing the partition indices, the Personalized PageRank (PPR) scores are used to measure the similarity of all ESs with respect to other ESs in parallel. Then Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to partition the ESs into different VPPs. Within the VPPs, the secondary frequency control strategy is adopted to achieve the frequency regulation and state of charge (SoC) balancing. Finally, the performance comparison of the proposed partition method with several classic clustering algorithms is conducted to validate the efficiency and the quality of the partition. And numerical studies are performed in an IEEE 39-bus system to verify that the proposed control method can eliminate the frequency deviation and reduce power losses.

Emergency load shedding strategy for high renewable energy penetrated power systems based on deep reinforcement learning

Traditional event-driven emergency load shedding determines the quantitative strategy by simulation of a specific set of expected faults, which requires high model accuracy and operation mode matching. However, due to the model complexity of renewable power generators and fluctuating power generation, traditional event-driven load shedding strategy faces the risk of mismatching in high renewable energy penetrated power systems. To address these challenges, this paper proposes an emergency load shedding method based on data-driven strategies and deep reinforcement learning (RL). Firstly, the reason for the possible mismatch of the event-driven load shedding strategy in the renewable power system is analyzed, and a typical mismatch scenario is constructed. Then, the emergency load shedding strategy is transformed into a Markov Decision Process (MDP), and the decision process’s action space, state space, and reward function are designed. On this basis, an emergency control strategy platform based on the Gym framework is established for application of deep reinforcement learning in the power system emergency control strategy. In order to enhance the adaptability and efficiency of the RL intelligence agent to multi-fault scenarios, the Proximal Policy Optimization (PPO) is adopted to optimize the constructed MDP. Finally, the proposed reinforcement learning-based emergency load shedding strategy is trained and verified through a modified IEEE 39-bus system. The results show that the proposed strategy can effectively make correct strategies to restore system frequency in the event-driven load shedding mismatch scenario, and have good adaptability for different faults and operation scenarios.

Operational resilience metrics for power systems with penetration of renewable resources

AbstractModern power grid is evolving towards carbon neutrality by deploying increasing amount of renewable energy resources. However, the impact of renewable generation on power system planning and operation is not sufficiently investigated, especially the capability of renewable penetrated power systems to resist and recover from major disturbances, which is a critical concern for system operators. Novel metrics and evaluation methodologies are needed to depict systems’ ability in response to events caused by natural disasters, and quantitatively evaluate system performance in various time scales. In this paper, operational resilience metrics are proposed for power systems with penetration of renewable energy resources based on transient stability principles. A systematic methodology is proposed to quantitatively assess the evolution of system performance during various stages of the disaster process. Based on the proposed metrics, a resilience‐oriented disaster management strategy is designed and validated using the modified IEEE 39‐bus test system. The simulation results demonstrate the validity of the proposed metrics and strategy, and show that the system resilience is enhanced during the mitigation of fault conditions.

Leader Harris Hawks algorithm based optimal controller for automatic generation control in PV-hydro-wind integrated power network

• A novel attempt has been put together to design a Model Predictive Control based on the leader Harris Hawks Optimization approach for a PV integrated LFC system. Further, a combined AVR-LFC loop has been designed for a PV, wind and hydro integrated hybrid power system. • The validation of the controller's better performance has been obtained through a thorough comparative analysis where the transient response attributes from the proposed controller have been compared with the exiting controllers available in the literature. Also, the convergence mobility of the proposed LHHO optimizer has been compared with HHO, WOA, and PSO algorithms. • To further strengthen the frequency stability of the network, the authors have proposed and implemented the virtual inertia and capacitive energy storage devices in association with the LHHO-MPC controller. • The robust nature of the proposed controller algorithm has been assessed under different test case scenarios where the step load perturbation (SLP) values have been varied randomly, and the stochastic load model has been considered. Also, various nonlinearities and reduced inertia cases have been presented to show the efficiency of the proposed LHHO-MPC controller working in coordination with CES and VI units. The work in this manuscript aims at designing a novel control strategy based on the Model Predictive Controller aided with Leader Harris Hawks Optimization (MPC-LHHO) algorithm for the regulation of frequency and voltage in renewable penetrated power systems. Herein, two cases have been considered where the first case discusses the frequency stabilization in a thermal power system linked with the solar photovoltaic (PV) plant. Subsequently, the second case features the simultaneous voltage and frequency regulation in a hybrid interconnected power system including thermal, wind, diesel, photovoltaic, and hydro generation units. Additionally, for both cases, the proposed MPC-LHHO algorithm has been evaluated in coordination with the capacitive energy storage and virtual inertia units. Furthermore, the stability analysis of the controlled PV-thermal system has been conducted using eigenvalues and pole-zero plots. In both cases, the effectiveness of the proposed algorithm has been tested after considering diverse scenarios including random loading, nonlinearities, time delays, reduced inertia and stochastic variations in load. For all these cases the presented algorithm has given a stable response and has ensured proper regulation of voltage and frequency in the renewable integrated power system network.

Modified PSO tuned two stage lead lag STATCOM control action for small signal stability enhancement of variable wind integrated power system

In this work Modified PSO (MPSO) is proposed to tune two stage lead lag control action based on STATCOM for enhancing small signal stability of an intermittent wind source penetrated power system. The Modified PSO has been compared with standard PSO and fractional PSO to tune controller parameters of STATCOM based control action. Multi machine system subjected to sudden and intermittent wind variations have been considered in this work. Subject to this wind penetration the lead lag control action with only PSO and two stage lead lag control action with fractional PSO and modified PSO has been implemented to damp the system oscillations pertaining to initial operating condition. It was found that subject to intermittent nature of wind penetration the rotor angle small signal stability is being hampered and subject to this when the modified PSO tuned two stage lead lag STATCOM control action is applied, it damps the oscillations heavily and the results have been compared with only PSO and fractional PSO control algorithms.

Revamped Sine Cosine Algorithm Centered Optimization of System Stabilizers and Oscillation Dampers for Wind Penetrated Power System

The mapping of damping ratios and damping factors by Sine-Cosine Algorithm (SCA) is significant in the analysis of low frequency oscillations generated in an integrated network having multiple conventional and renewable energy sources. Multiple random models with unknown search spaces and uncertain conditions and constraints, which incorporate the fluctuation of solutions towards or outwards at the initial stage for solving real-time problems can be analyzed by a revamped sine-cosine algorithm (RSCA) model. In this paper, the SCA has been revamped to explore the stability threshold for a stable system under dynamic nonlinear operating conditions. It is achieved with the infusion of suitable mathematical functions for optimizing the parameters of Power System Stabilizers (PSSs) and Doubly Fed Induction Generator (DFIG) system-based oscillation dampers to enhance the Small Signal Stability of power systems through effective damping of low-frequency oscillations (LFOs). These LFOs weaken the effective power generation and demand management cycle in conventional power systems when the system faces conditions like intermittent renewable energy sources, overloading conditions, impulsive faults etc. The small-signal and transient stability studies for various disturbances and different operating conditions are investigated, and the performance of the proposed RSCA for optimized parameter tuning of system stabilizers and oscillation dampers are analyzed on the modified benchmarking systems with wind generators using MATLAB Ⓡ simulations. The efficiency and robustness of the proposed algorithm has been verified under selective critical operating conditions, line outages, and load uncertainties to prove that the low frequency modes are damped with an elevated positive damping ratio and rapid settling time with reduced oscillations. The application of system stabilizers and oscillation dampers optimized through the proposed RSCA shows a reduced settling time in the LFOs created and improved damping ratio of 0.22 for an increase in 20% loading and 50% of wind power generation in the case study of Two Area Four Machine System.

Representative operating scenario selection with algebraic multi-grid clustering for integrated energy systems planning

Recent shifts towards higher renewable penetration in power systems, has resulted in increasing gas-fired generation capacity in systems without hydro or other fast responding alternatives, implying increased electricity and gas infrastructure interdependency. Furthermore, highly efficient combined heat and power (CHP) units lead to heat and electricity interdependency. It is crucial to consider these interdependencies in the expansion planning of energy systems for an effective and reliable investment planning and policy design. In this paper, the problem of integrated expansion planning of electricity, heat, and gas in presence of demand and wind uncertainty is addressed. Representative operating scenario selection with algebraic multi-grid clustering is used to model wind and demand uncertainties, and to address computational complexity of a central planning model. The simulation results on a modified IEEE-118 bus test system with a 14-node gas network shows that the algebraic multi-grid clustering outperforms the classical methods such as kmeans by following the benchmark case more closely.