Frequency Security Constrained Energy Management in an Isolated Power System

To alleviate air pollution and energy shortage issues, an increasing amount of renewable energy sources (RESs), such as wind power and solar photovoltaics (PVs), has been integrated into modern power systems. However, the large penetration level of renewable energies leads to the reduction of inertia as RESs are normally connected to the power grid through power electronics converters, which do not inherently provide the inertia as synchronous generators do. To solve the lack of inertia issue, this paper proposes the method of using flywheel energy storage systems (FESSs) to provide the virtual inertia and frequency support. As compared with batteries, flywheels have a much longer lifetime and higher power density. By regulating the speed of the flywheel in proportion to the grid frequency, the flywheel serves as an energy buffer that absorbs and releases its kinetic energy to provide inertia support. Furthermore, the design methods of the virtual inertia emulated by FESSs are described in detail. Finally, the feasibility of the proposed method is verified by simulations, and the simulation results validate the improvement of frequency regulation in terms of frequency nadir and rate of change of frequency (RoCoF).

With the increasing incorporation of Renewable Energy Sources (RESs) like solar Photovoltaic (PV) systems, maintaining frequency stability has turned out to be a significant challenge owing to decreased system inertia. Despite numerous developments in Load Frequency Control (LFC), existing solutions largely overlooked the issue of Under Frequency Load Shedding (UFLS) relay failure during rapid frequency decline, which led to widespread blackouts. To address this critical gap, a novel intelligent control framework integrating the Fuzzy Doubleton Parabolic Inference System (FDPIS) and the Proportional Quad-Alpine Integral Derivative (PQAID) controller for UFLS relay failure mitigation and enhanced LFC in Solar-PV systems is proposed. Primarily, the Direct Current (DC) power from the solar module is fed into the DC-DC boost converter and Maximum Power Smoothstep Point Tracking (MPSPT) algorithm. A capacitor bank failure is detected using FDPIS, and voltage stabilization is ensured through a Savitzky-Golay Dynamic Polynomial-Z Voltage Restorer (SGDP-ZVR). To predict electrical load demand accurately, a hybrid Deep Learning (DL) model, Deep Dualplus Softshrink Pan–Long Short Term Inverse Parzen Memory (2DSP-LSTIPM), is employed, delivering a high accuracy of 98.98 % with a Root Mean Squared Error (RMSE) of 0.002. When demand exceeds thresholds, transmission overload is mitigated using an Inductive Snubber Cubic Circuits–STATCOM (IS2C-STATCOM). The frequency deviation is identified via FDPIS, followed by the Rate Of Change Of Frequency (ROCOF) analysis. If a UFLS relay failure is detected, then the PQAID controller is activated to ensure stable operation. The proposed PQAID achieves a peak time of 1.91 ms, significantly outperforming traditional PID, PI, and PD controllers in transient and overshoot metrics. Simulation results on the HEDGW dataset assess the proposed approach’s robustness and low time complexity. The system demonstrates superior relay fault detection (fuzzification/defuzzification times of 452ms/463ms) and faster rule generation (597 ms) compared to conventional fuzzy systems. Overall, the proposed methodology provides a comprehensive, real-time, and scalable solution for enhancing frequency stability, relay fault mitigation, and load management in solar PV-based smart grids. • Integrates FDPIS and PQAID for real-time UFLS relay failure detection and mitigation in solar PV systems. • Proposes novel 2DSP-LSTIPM deep learning model achieving 98.98 % demand prediction accuracy with RMSE of 0.002. • Introduces SGDP-ZVR for voltage stabilization during capacitor bank faults using Savitzky-Golay filtering. • Deploys IS2C-STATCOM for efficient transmission overload control with fast reactive power regulation. • Enables seamless SCADA/EMS integration via OPC-UA protocol for smart grid compatibility and deployment.

Integrating non-dispatchable renewable energy sources (RES) such as wave farms into electric power systems presents inherent challenges. Specifically, the variability of the resource and its non-dispatchable) nature introduces power imbalances and power system stability problems in the system over both short and medium terms. This issue is particularly relevant for isolated power systems, such as those found on several European islands, where interconnection with the mainland is lacking. Therefore, although wave energy has significant potential for integration into island power systems due to resource availability, determining the maximum RES penetration that such systems can sustain while maintaining stability is not straightforward. Power system stability is closely tied to frequency. A well-established solution for improving the stability of isolated systems when integrating non-dispatchable RES is to install devices capable of responding to over- or under-frequency events, such as energy storage systems (ESS). The literature extensively discusses ESS control techniques for frequency regulation, with one of the most straightforward approaches being to operate the ESS according to a droop control strategy (similar to a statism). In this method, the power dispatched or stored by the ESS increases proportionally to the deviation from the frequency reference value. This paper presents a case study analyzing the frequency stability of an island power system equipped with an energy storage system (ESS) when integrating a wave farm. Frequency stability is evaluated based on two variables: wave energy penetration and the ESS droop control parameter. The case study is based on the island of El Hierro (Canary Islands, Spain), an isolated electrical system with a very high penetration of RES. The island’s electrical generation system consists of a wind farm, a pumped-storage hydroelectric power plant, and conventional generation from a diesel power plant. The analysis is conducted through simulations in MATLAB Simulink. A wave farm is modeled to evaluate the generated power and its associated oscillations, based on the wave energy resource at various locations along El Hierro’s coast. Additionally, an aggregated inertial dynamic model of the electrical power system is employed to assess the impact of the wave energy farm's generation on the system’s frequency and the aging that the frequency excursions introduce in the elements of the system. The Spanish Grid Code is considered to ensure compliance with frequency regulation mechanisms in isolated systems. Simulations are performed across different scenarios, varying both the nominal power of the wave farm and the ESS droop control parameter, in order to evaluate the ESS capacity. The results identify the optimal droop control parameter for each wave farm size to ensure frequency remains within acceptable thresholds. Furthermore, the minimum ESS capacity required for each wave farm size is determined from the simulations. These findings provide a basis for ESS and wave farm dimensioning, which could be translated into economic terms to maximize revenue (this aspect is beyond the scope of the present work).

Frequency regulation of autonomous microgrids with intermittent renewables is a tedious task, which requires additional support from energy storage systems (ESS). Ideally, the system requires a faster‐acting ESS to arrest the initial frequency deviation and high energy capacity ESS to restore the frequency to its nominal value. Pico hydel energy storage (PHES) is one such high energy capacity ESS developed from the agricultural pumping systems. However, PHES has a slower response and cannot switch rapidly between pumping and generation modes. Hence, this paper incorporates a battery with PHES to achieve a faster dynamic response and absorb high‐frequency power dynamics. Additionally, a frequency regulation strategy with synchronverter control is proposed to mimic synchronous machines, especially to reduce the rate of change of frequency (ROCOF) and frequency nadir. Firstly, a small‐signal modelling approach is proposed, and it was utilized to assess the system stability through eigenvalue analysis. Subsequently, transient simulations were carried out to gauge the performance of the synchronverter strategy with vector control for frequency regulation. As a result, the frequency nadir and ROCOF were substantially reduced with the synchronverter strategy compared to the vector control based on the simulation results.

A planning framework and operation strategy for energy storage are developed to limit the rate of change of frequency (RoCoF) within the industry requirements in power systems with high renewable energy penetration. The planning framework utilizes a two-step algorithm for the capacity estimation of energy storage. In the first step, the size is calculated analytically based on the frequency dynamics. While in the second step, the estimated size is used as an initial guess in the region reduction iterative algorithm (RRIA) to calculate the accurate required capacity. Furthermore, an economical comparison is carried out among the available rapid responsive storage technologies to find out the most suitable technology for virtual inertia support (VIS). Next, a wind speed forecast informed coordinated control strategy is developed for the storage and wind turbine to provide VIS. An ensemble neural network technique is used for the wind speed forecasting. The coordinated control strategy utilizes the information of forecasted wind speed and the state of energy storage to improve the utilization of storage while limiting the RoCoF within desired window. Simulation results depict the accuracy of the estimated capacity and the effectiveness of the coordinated control strategy.

The integration of renewable energy sources (RESs) such as solar photovoltaic (PV) and wind energy has become a promising solution as the world shifts toward clean energy. Solar PV and wind resources are increasingly replacing conventional synchronous generators, leading to reduced system inertia and increased vulnerability to frequency instability during disturbances. To address this challenge, this study proposes a novel synthetic inertia provision strategy using a battery energy storage system (BESS) integrated alongside solar PV. The proposed method dynamically compensates for the loss of inertia by considering the variability of solar PV output due to changes in irradiance and temperature. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed strategy significantly improves system stability, with a reduction in the rate of change of frequency (RoCoF) by up to 86.6% and an improvement in frequency nadir by 0.91% under high PV penetration scenarios. These results confirm that incorporating synthetic inertia via BESS enhances the frequency response and resilience of low-inertia power systems with high renewable penetration.

The power output of photovoltaic systems (PV) in Malaysia is highly intermittent due to its location near the equatorial region that is susceptible to many passing clouds. High intermittency of PV power output increases the power mismatches between the power generation and demand. Consequently, it leads to frequency issues such as frequency excursions and a high rate of change of frequency (ROCOF). Several methods have been proposed to mitigate the frequency issues, and demand side management (DSM) has been central due to its lower deployment costs than other methods such as flywheel- and battery-based energy storage systems. DSM controls the electrical consumption of loads at the distribution level to regulate the system frequency. Conventionally, researchers have designed the DSM load controller based on actual PV power output. However, such data may not be commonly available compared to solar irradiance. Thus, this paper presents a solar irradiance-based load controller for frequency regulation under high intermittency of PV. The preliminary results show that the proposed controller has a comparable performance versus a load controller that uses the actual PV power output data for frequency regulation. The proposed controller also can maintain the system frequency well within the statutory limit of 50 Hz ± 1 % and reduce the ROCOF by 0.033 Hz/s compared to the baseline when no load controller is used.

Short-term frequency instability is one of the major concerns in power systems with high percentage of converter-interfaced renewable energy sources. Energy storage system (ESS) has proven to be a viable solution for the problem of short-term frequency instability by fast frequency response (FFR). However, the appropriate location, size, and operating strategy of ESS are the main challenges for FFR. Power injection at some buses in large grids may lead to angular separation and instability. In addition, oversizing ESS could lead to huge investments without appropriate returns and under sizing may jeopardizes grid stability. Capacity estimation of ESS for FFR considering the suitable location and overall deployment strategies are missing in the current literature for large power grids. Hence, this research proposes a technique to place and size ESS for better FFR in power grids. The proposed technique consists of two steps. In the first step, a methodology based on frequency dynamic signature (FDS) is developed to identify the most suitable location. In the second step, the required capacity of the ESS is estimated based on a step reduction iterative algorithm (SRIA). SRIA and FDS consider the complete dynamics of power system components that affect the frequency dynamics of the system. The proposed methodology is thoroughly verified for various operating conditions in IEEE 39-bus using DIgSILENT PowerFactory.

Virtual inertia emulation and RoCoF control of a microgrid with high renewable power penetration

Guest Editorial: Situational awareness of integrated energy systems

Droop control, inertia control, and PD control are three typical frequency response (FR) strategies of grid-tied inverters interfaced renewable energy sources (RES) to restrain the power system frequency indicators, i.e., frequency deviation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \omega $ </tex-math></inline-formula> ) and the rate of change of frequency (RoCoF). Due to the highly limited inverter capacity, the FR performance in power systems with high RES penetration should be optimized. To this end, this paper first analyzes the impact of high-penetration renewable energy injection on power system frequency stability, and derives the system frequency expressions in the absence of an FR scheme, and in the presence of droop/inertia/PD controls that are representative for various virtual synchronous generator-based FR schemes. Under the same constraint of available FR capacity, control parameter ranges of these FR schemes are obtained, and their optimal conditions for restraining the maxima of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \omega $ </tex-math></inline-formula> and RoCoF are identified and compared analytically. Comparison conclusions are finally proved by the provided experiment and simulation results.

Due to the shortage and environmental pollution of fossil fuel sources, conventional generating units are substituted with renewable energy sources. Thus, the total inertia decreases while the rate of change of frequency (ROCOF) increases, affecting the system frequency and stability. In this paper, a supplementary control, washout filter, is added to the primary frequency control of a doubly-fed induction generator (DFIG)-based wind turbine for better frequency regulation. In order to achieve the best performance (lowest frequency deviation with keeping the system stable), a new optimization technique called Mayfly Algorithm (MA) is used to find the optimum values of the washout filter&#x2019;s parameters. Compared with the traditional method, a great improvement has been presented in the system frequency response under different load perturbations. Also, the results show that the washout filter&#x2019;s parameters have an adverse impact on the frequency response and system stability if they aren&#x2019;t determined accurately. MATLAB/SIMULINK program is used to test the performance of the improved method under different scenarios.

11 - Risk-averse scheduling of virtual power plants considering electric vehicles and demand response

The global power grid is undergoing a major transformation as a result of the growing utilization of renewable energy sources (RESs). The incorporation of these RESs is leading to a decline in the use of conventional synchronous generators and an increase in the use of inverter-based RESs. This shift is causing a decrease in the grid's inertia, presenting substantial difficulties for grid operators to maintain frequency stability. The purpose of this study is to examine the impact of declining inertia on the frequency behavior of the grid, as RESs become more prevalent. In order to measure the impact, this study utilizes eigenvalue analysis of power grids to assess the sensitivity of grid frequency and rate of change of frequency (RoCoF) characteristics, as well as the lowest frequency, to variations in grid inertia resulting from changes in RES penetration. Moreover, we have introduced indices that quantify the impact of RES penetration on the grid frequency behaviour in comparison to a grid without RES. To showcase the proposed approach, this research presents case studies using the modified IEEE 39 bus test system, which demonstrate the ability to assess the impact of a high RESs penetration on grid frequency characteristics.

&lt;span lang="EN-US"&gt;Large-scale integration of inverter-based renewables is displacing synchronous machine generation, causing a reduction in the inertia of electrical power systems. This reduction is reflected in an increase in the rate of change of frequency (RoCoF). Additionally, the variation of the RoCoF will depend on the uncertainty associated with the generation of non-conventional renewable energy sources. For the planning of the operation of the system, it is essential to know the range of variation of the RoCoF when there are disturbances in the system and uncertainties in the generation of non-conventional sources of renewable energy. This paper proposes to establish the calculation of a confidence interval of the RoCoF variation that considers these uncertainties. So, this paper proposes a method to consider these uncertainties based on the probabilistic point estimate method (PEM); considering multiple renewable non-conventional sources with correlated or uncorrelated behavior in their powers injected into the system. On the other hand, as there are different proposals to calculate the RoCoF, this paper presents the application of the uncertainty model with three different RoCoF proposed calculation methods.&lt;/span&gt;