Mitigate Power Fluctuations Research Articles

Deep Reinforcement Learning Based Optimal Operation of Low-Carbon Island Microgrid with High Renewables and Hybrid Hydrogen–Energy Storage System

Hybrid hydrogen–energy storage systems play a significant role in the operation of islands microgrid with high renewable energy penetration: maintaining balance between the power supply and load demand. However, improper operation leads to undesirable costs and increases risks to voltage stability. Here, multi-time-scale scheduling is developed to reduce power costs and improve the operation performance of an island microgrid by integrating deep reinforcement learning with discrete wavelet transform to decompose and mitigate power fluctuations. Specifically, in the day-ahead stage, hydrogen production and the hydrogen blending ratio in gas turbines are optimized to minimize operational costs while satisfying the load demands of the island. In the first intraday stage, rolling adjustments are implemented to smooth renewable energy fluctuations and increase system stability by adjusting lithium battery and hydrogen production equipment operations. In the second intraday stage, real-time adjustments are applied to refine the first-stage plan and to compensate for real-time power imbalances. To verify the proposed multi-stage scheduling framework, real-world island data from Shanghai, China, are utilized in the case studies. The numerical simulation results demonstrate that the proposed innovative optimal operation strategy can simultaneously reduce both the costs and emissions of island microgrids.

Designing UAV Charging Framework for Forest Area with Microgrid

Unmanned aerial vehicles (UAVs) are suitable for forest fire monitoring, which is critical to prevent unexpected hazards. However, a lack of charging measures is the bottleneck restricting the development of surveillance drones in forest areas. This paper envisions a hierarchical charging framework of heterogeneous drones for forest fire surveillance based on a microgrid with renewable energy. Different replenishment methods of heterogeneous drones, as well as the coordination control strategy of the microgrid in a forest, which are designed to support continuous surveillance, are specified. To improve the transient stability as well as the capacity of fault ride-through in a forest microgrid for the multiple charging of fire surveillance drones, coordination control with a speed regulation strategy for a forest microgrid is proposed in which the substantial kinetic energy generated by the rotation of wind turbines is utilized to mitigate power fluctuations in a timely manner. Simulations are conducted under typical working conditions to verify the effectiveness of the method.

AN IOT-BASED FRAMEWORK FOR EFFICIENT SOLAR POWER GENERATION AND INTEGRATION IN AUTOMOTIVE

Objective: We suggest an Internet of Things (IoT)-based system that uses edge intelligence to anticipate power production effectively and monitors electricity substations and smart solar installations. It ensures dependable and effective power distribution inside industrial Internet of things settings, improving sustainability, safety, and energy management in smart buildings. It also improves decision-making and reduces volatility. Method: Create and execute an IoT-enabled power monitoring system for smart solar panels and substations that incorporates edge intelligence for instantaneous prediction and decision-making. Deploy an IoT-enabled solar charging station for smart homes and Industry 4.0 applications, and use the cloud for sensor data analysis and control. Findings: In order to effectively manage load for commercial, electric, residential, and industrial vehicles, the suggested framework improves the efficiency and dependability of power production and distribution in industrial IoT contexts. The system increases overall efficiency via the mitigation of power fluctuations and eventualities. Furthermore, IoT integration enhances smart building energy management safety and sustainability of energy resources as well as reduced the overall cost by 95% when comparing to the traditional devices. Novelty: For smart solar systems and substations, a novel framework combines edge intelligence with IoT. It includes a sophisticated IoT-based control system that improves power distribution network decision-making. In addition to taking an integrated strategy to energy management and enabling real-time monitoring and prediction of power production in industrial IoT contexts, it emphasizes sustainability, safety, recycling, and reuse in smart buildings.

An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations.

Energy management strategy of fuel cell electric vehicle based on work condition recognition

To reduce hydrogen consumption in fuel cell electric vehicles, mitigate power fluctuations, and maintain the battery system's state of charge, a novel transition process recognition method based on work condition recognition framework of energy management strategy is proposed. This method offers the advantages such as higher recognition rates and faster recognition speed comparing with the traditional condition recognition methods. A comparison is made with the commonly used LVQ recognition method, and the simulations are conducted to demonstrate the superiority of this condition recognition algorithm and the improved performance of the energy management strategy based on the present recognition method under mixed work conditions.

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.

Predictive power fluctuation mitigation in grid-connected PV systems with rapid response to EV charging stations

Currently, renewable energies and electric vehicle charging stations are essential for energy sustainability. However, the variable generation from renewable sources, such as photovoltaic systems, can lead to power peaks that impact the stability of the grid. This challenge is exacerbated by the increasing demand in fast-charging stations. Addressing these demand peaks is crucial to ensure the stability of the electrical grid. This paper introduces the predictive-flex smoother, an innovative method designed to mitigate power fluctuations in grid-connected photovoltaic systems while optimizing energy management in electric vehicle charging stations. The predictive-flex smoother method incorporates a hybrid energy storage system comprising supercapacitors and vanadium redox flow batteries to respond rapidly to electric vehicle charging stations demands, enhance grid electricity purchase optimization, and improve energy quality delivery. The proposed method integrates two control strategies: photovoltaic fluctuation reduction strategy and peak demand reduction strategy for electric vehicle charging stations. By leveraging prediction algorithms and machine learning techniques, the predictive-flex smoother method achieves precise power fluctuation forecasts, allowing efficient utilization of supercapacitors and vanadium redox flow batteries to smooth photovoltaic power fluctuations and reduce electrical vehicles peak demand. Comprehensive experimental investigations and simulations validate the method's performance under various operational conditions. The results demonstrate the effectiveness of the predictive-flex smoother method, significantly improving the quality of power delivered to the grid while reducing costs. The experimental platform, validates the real-time response of the proposed method, with response times under 500 ms. The experimental results further confirm the efficiency of the method in power smoothing and charging strategies with varying electrical vehicles models and connection coefficients.

Reinforcement learning based bilevel real-time pricing strategy for a smart grid with distributed energy resources

The integration of flexible loads, distributed energy resources, and other technologies is becoming common in advance power and energy systems. However, the integration also presents significant challenges due to the increasing complexity and uncertainty. To effectively manage these resources, an adaptive pricing mechanism is needed that can account for their variable availability. Based on this, we propose a new bilevel real-time pricing model that considers different distributed energy resources, uncertainty of renewable energy generation, carbon trading mechanisms, and grid fluctuations. Specifically, the upper-level optimization problem aims to maximize the total profit of the supplier that applies Q-learning algorithm. The lower-level optimization problem addresses the need for each user to make optimal power consumption decisions by constructing an individual Markov Decision Process framework for each user. The bilevel model achieves an effective balance of interests between the supplier and users by simultaneously considering both the upper-level and lower-level optimization problems. Additionally, our model can be efficiently solved using the distributed algorithm without the need to acquire transition probabilities. Simulation results show that the method is highly effective in balancing power supply and demand between the supplier and users, reducing carbon emissions, and mitigating power fluctuations.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems, we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system. First, we employ a strategy that restricts long- and short-term power output deviations to smoothen wind power fluctuations in real time. Second, we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise (SW-ICEEMDAN) strategy to achieve real-time decomposition of the energy storage power, facilitating internal power distribution within the hybrid energy storage system. Finally, we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components. Through simulation validation, we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power. Compared with traditional empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition strategies, the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal. Additionally, the state-of-charge of energy storage components fluctuates within a reasonable range, enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Energy management strategy of urban rail hybrid energy storage system based on fuzzy logic system

Energy management is an important link in the effective functioning of hybrid energy storage systems (HESS) within urban rail trains. This factor significantly impacts the operational stability and economic efficiency of urban rail systems. Safety issues arise from DC bus voltage fluctuations due to varying train conditions. To address these issues, this paper proposes an energy management strategy for the urban rail HESS, which builds upon a traditional double closed-loop control strategy. This strategy incorporates a fuzzy logic system to regulate power distribution parameters and discharge thresholds dynamically. Real-time monitoring of the state of charge (SOC) of the energy storage device informs the decision-making process, utilizing a fuzzy inference system to effectively regulate power and energy distribution in response to the distinct characteristics of batteries and super-capacitors. This approach aims to mitigate power fluctuations within the traction network. To validate the effectiveness of the proposed energy management strategy, the study conducts a comprehensive simulation experiment using MATLAB/Simulink. The simulation results confirm the efficacy of the proposed strategy, highlighting its potential to enhance the stability and overall performance of urban rail train operations.

Suppressing solar PV output fluctuations by designing an efficient hybrid energy storage system controller

The stochastic nature of solar photovoltaics (PV), marked by high-frequency voltage fluctuations due to dynamic climatic conditions such as cloud cover and temperature variations, presents a significant challenge to power quality stability, especially in microgrids. This variability poses a threat to the stability of power electronic devices responsible for power control and monitoring, potentially compromising the power grid's stability. To address this challenge, energy storage systems (ESS) are commonly employed. In this study, we develop a hybrid energy storage system (HESS) incorporating a battery, supercapacitor, and fuel cell. The primary aim is to adjust the inverter voltage for the photovoltaic system using newly developed proportional-integral (PI) and model predictive control (MPC) controllers within the HESS framework. Importantly, this controller eliminates the need for precise knowledge of system parameters and offers robustness, insensitivity to parameter changes, and resilience to time-varying external disturbances, ensuring satisfactory performance. By mitigating power fluctuations, the generated power can be seamlessly integrated into the grid, significantly reducing costs associated with device damage in the power path.Notably, we integrate the proposed photovoltaic system with an RLC series load using an IGBT inverter. To assess the performance of the HESS in the proposed photovoltaic system, four distinct scenarios are examined. These scenarios involve altering the PV system's location and testing two energy storage systems, namely the battery and fuel cell, which are separately designed components of the HESS for a 14-bus microgrid.

Application of nowcasting to reduce the impact of irradiance ramps on PV power plants

Short-term fluctuations in photovoltaic power plants, known as ramps and caused by clouds, challenge grid stability and efficient energy use. These issues are traditionally managed with battery energy storage systems, which, while effective, are expensive. We propose an alternative solution: the use of short-term irradiance forecasts, or nowcasts. Using a photovoltaic power plant in Germany and its simulated model, we demonstrated that nowcasts could cut ramp rate violations by 81%. This led to a reduction in required battery capacity by 71% and the required maximum battery power provided by 48%, at the cost of a 13% curtailment loss, i.e. loss through reduction of power. Our data set of 18 chosen days from 2020 with high variability conditions was scaled up to a year for the economic analysis. From an economic standpoint, nowcasts could lower the Levelized Cost of Electricity by 5.5% from 4.74 to 4.48 EUR cents, and even by 35% to 3.09 EUR cents with ideal forecasting, showing its potential. While nowcasts cannot completely replace batteries, they substantially reduce the need for such storage solutions. This results in cost savings and adherence to grid stability requirements, making nowcasts a complement or partial alternative to battery systems for mitigating power fluctuations in photovoltaic power plants.

Stabilization of isolated hybrid microgrids with electric vehicle-based energy storage systems using a fractional order proportional-integral-derivative control

ABSTRACT Electric vehicles (EVs) are considered to be the most promising technology upgrades in the coming years to reduce the carbon emission, since the EVs could replace the conventional combustion vehicles as well as mitigate power fluctuations in power grids. This paper focuses on addressing the frequency stability issues in isolated hybrid grids (IHGs) by introducing a fractional order proportional-integral-derivative (FOPID) control for grid-connected power converters. A particle swarm optimization (PSO) technique is adopted to optimize the parameters of the controllers. The advantages of the proposed control method over the conventional proportional-integral-derivative (PID) control, proportional-integral with proportional-derivative (PI-PD) control, and proportional-integral-derivative with filter (PIDF) control schemes are verified by simulation case studies. Besides, a modified virtual rotor concept (MVRC) is proposed in this paper to enhance the inertia of the system. Furthermore, the performance of using EVs to regulate the frequency of IHGs is compared to the superconducting magnetic energy storage (SMES) units, capacitive energy storage (CES) units, and redox flow batteries (RFB) under fixed and variable load conditions.

Unit vector template control strategy‐based harmonic mitigation and charging with three phase–three level–three switch Vienna rectifier for Level 3 electric vehicle charging applications

SummaryFast battery charging units act as non‐linear loads, which induce a more harmonic effect in the utility grid. Hence, maintaining the stable voltage, current ripple, and total harmonic distortions within the permissible IEEE std level during the charging time of grid to electric vehicle (G2V) has become an important task for researchers in the future. In order to mitigate power fluctuations during every charging cycle, it will enhance the battery life cycle in electric vehicles (EVs). Three phase–three level–three switch Vienna rectifier with fuzzy logic controller tuned PID controller is used here for AC/DC conversion with minimum switching loss. A fuzzy intelligent control strategy has been employed with the DC link voltage regulator in order to sustain the DC link voltage as constant and progress the voltage/current profile by injecting active power at the point of common coupling. Unit vector template (UVT)‐based control strategy is employed here to identify the fundamental components from the load side and also estimate the reference phase current. This scheme enhances the performance and reduces the mathematical system complexity to diminish the harmonics in the distribution grid. The adaptability and feasibility of this proposed control method have been verified by the MATLAB simulation tool. To validate the response of the Vienna Rectifier with UVT controller are simulated with different unbalanced load conditions and also verified with 30 kW laboratory prototype experimental results using dsPIC30F4011 controller and the switch is IRFP260 with power diode RHRG30120.

Smoothing Intermittent Output Power in Grid-Connected Doubly Fed Induction Generator Wind Turbines with Li-Ion Batteries

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production.

A novel hybrid model for short-term prediction of PV power based on KS-CEEMDAN-SE-LSTM

Accurate prediction of photovoltaic (PV) power plays a pivotal role in ensuring safe and stable grid operation, enhancing power supply quality, and facilitating proactive grid management to mitigate power fluctuations. This paper introduces a novel hybrid model named KS-CEEMDAN-SE-LSTM, which combines K-Shape (KS) clustering, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample entropy (SE), and Long Short-Term Memory (LSTM) techniques. To improve the accuracy of PV power prediction, three advanced preprocessing techniques, KS clustering, CEEMDAN and SE, are employed for feature extraction before prediction. In the proposed approach, KS clustering is employed to capture the similarity features of PV power. Subsequently, CEEMDAN decomposes the PV power into multiple intrinsic mode functions (IMFs) to reduce the influence of non-stationary and noisy features in power prediction. To further enhance prediction accuracy and streamline computational complexity, SE methods and K-means clustering are utilized to reconstruct the decomposed IMFs into new sub-sequences with refined granularity features. Finally, the LSTM model is applied to predict each reconstructed sub-sequence, and the ultimate prediction results are obtained by aggregating the predictions from all sub-sequences. The effectiveness of this method is demonstrated using real-world PV power data collected from China. Comprehensive experiments substantiate that the proposed KS-CEEMDAN-SE-LSTM model outperforms benchmark methods in short-term PV power prediction.

Hybrid energy storage power allocation strategy based on parameter-optimized VMD algorithm for marine micro gas turbine power system

The power system onboard ships is typically a low-inertia, small-capacity isolated grid that is highly susceptible to system disturbances and instability, especially when connected to high power pulse loads. To mitigate power fluctuations and ensure stable operation, a hybrid energy storage system (HESS), which comprises the battery system and flywheel energy storage devices, has emerged as a promising solution. This paper explores the control strategy and coordinated operation of marine gas turbine power generation systems operating under pulse load conditions and presents a novel hybrid energy storage power allocation strategy based on variational mode decomposition (VMD). Specifically, we propose to implement parameter optimization of VMD using an artificial hummingbird algorithm (AHA), which enables effective primary allocation of hybrid energy storage power. To achieve secondary power allocation, we design two fuzzy controllers to optimize the state of charge (SOC) of battery system and speed of flywheel device. To evaluate the feasibility and effectiveness of the proposed control strategy, we establish a theoretical model of a shipboard micro gas turbine (MGT) direct current (DC) power system, which includes an MGT, generator, HESS, and pulse power load. Our simulation results demonstrate that the artificial hummingbird algorithm outperforms the particle swarm algorithm concerning search speed and calculation accuracy for parameter optimization of variational mode decomposition. Moreover, the proposed parameter-optimized VMD method can adaptively decompose pulse load power fluctuations and achieve optimal allocation of the flywheel and battery power in the HESS. Finally, the parallel fuzzy controller design effectively optimizes the operation ranges of the flywheel device and battery system, prevents over-charging and over-discharging of energy storage equipment, ensures that the SOC of energy storage equipment remains within a fixed interval, and reduces frequent and high-power charging and discharging of batteries.

Enhancing the Dynamic Stability of Integrated Offshore Wind Farms and Photovoltaic Farms Using STATCOM with Intelligent Damping Controllers

To build a large-scale renewable energy integrated system in the power system, power fluctuation mitigation and damping measures must be implemented during grid connection. PID damping controllers and traditional intelligent controllers with pole configuration are usually used for improving damping. Integration of large wind power plants and photovoltaic power plants into the power system faces transient power oscillation and fault ride-through (FRT) capability under fault conditions. Therefore, this paper proposes a static synchronous compensator (STATCOM) damper based on a recurrent Petri fuzzy probabilistic neural network (RPFPNN) to improve the transient stability of the power system when large offshore wind farms and photovoltaic power plants are integrated into the power system, suppress power fluctuation, and increase FRT capability. To verify the effectiveness of the proposed control scheme, a three-phase short circuit fault at the connected busbar is modeled in the time domain as part of a nonlinear model. From the comparison of simulation results, the proposed control scheme can effectively slow down the transient fluctuation of power supply to the grid-connected point when the grid is faulty, reach steady-state stability within 1–1.5 s, and reduce overshoot by more than 50%. It can also provide system voltage support at an 80% voltage drop and assist in stabilizing the system voltage to increase FRT capability. It also improves stability more than PID controllers when disturbances are present. Therefore, it maximizes the stability and safety of the power grid system.

External tie line power fluctuations smoothing strategy of new urban power grid

In a net-zero carbon power grid (NZCPG) with a relatively high proportion of renewable energy sources, significant power fluctuations occur, severely constraining the development of both the NZCPG and the New Urban Power Grid (NUPG). In the NZCPG, the proportion of traditional hydro and thermal power units used to mitigate power fluctuations has decreased. It is necessary to explore the load resources in the NUPG to stabilize the power fluctuation between the NUPG and the NZCPG, and to reduce the operating pressure of the NZCPG. Firstly, this paper proposes a method for developing external tie line schedules based on the load operation band (LOB) to relax the constraints on the schedule for the NZCPG. Secondly, A revised load response aggregation model is proposed to improve the aggregation accuracy under uncertainty factors by online correcting their impact on the load response characteristics. Thirdly, considering the flexible loads in NUPG can only compensate for a part of the power fluctuation, this paper proposes a method to judge the power fluctuation that these loads can offset. Two PFS optimal strategies are proposed among NUPG partitions considering deviation penalty and within each NUPG partition reducing the number of participated load-users. This paper focuses on the PFS in bulk NZCPGs other than in the distribution grids and microgrids. Our work can effectively smooth the power fluctuation within NUPG regulation capability meanwhile reducing the number of participated load-users. Fourthly, a method is proposed to judge the power fluctuation beyond NUPG regulation capacity in NUPG. Using the actual load response capacity, the over-amplitude/time layered smoothing strategy with optimal load is proposed, which can offset the power fluctuation of the bulk NZCPG to the largest extent. Finally, the IEEE-39 system is used to verify the effectiveness of the proposed methods.© 2017 Elsevier Inc. All rights reserved.

A multi-energy inertia-based power support strategy with gas network constraints

An integrated energy system with multiple types of energy can support power shortages caused by the uncertainty of renewable energy. With full consideration of gas network constraints, this paper proposes a multi-energy inertia-based power support strategy. The definition and modelling of gas inertia are given first to demonstrate its ability to mitigate power fluctuations. Since partial utilization of gas inertia can influence overall gas network parameters, the gas network is modelled with an analysis of network dynamic changes. A multi-energy inertia-based power support model and strategy are then proposed for fully using gas-thermal inertia resources in integrated energy systems. The influence of gas network constraints on strategy, economy and power outputs is analyzed. Special circumstances where the gas network can be simplified are introduced. This improves the response speed and application value. The feasibility and effectiveness of the proposed strategy are assessed using a real scenario.