Renewable Generation Research Articles

A Multi-Objective Bi-Level Framework to Model Distribution System Operator’s Behavior in the Wholesale and Local Transactive Markets

This paper presents a multi-objective bi-level framework to model the distribution system operator’s (DSO’s) behavior in both wholesale and local electricity markets. The upper level is a multi-objective optimization problem from the DSO point of view including operation cost minimization, flexibility maximization, and peak load minimization. To this end, the DSO could not only participate in the wholesale electricity market at both day-ahead and real-time stages, but also is able to purchase power from renewable generations as well as the private owners. In this regards, the DSO could manage fluctuations of renewable power generations through considering flexibility as an objective function along with cost and peak-shaving. The lower level assigns to the private owner’s profit maximization through interactions with the DSO. The DSO also exploits the demand response potential by implementing load shifting programs. The obtained results reveal that the proposed model could reduce the peak load by 6.05 MW and enhance the system flexibility level by 88%.

Leveraging machine learning for sustainable integration of renewable energy generation

Long-term economic benefits and sustainability are provided by the integration of renewable energy sources (RESs) into electrical networks. However, because of their intermittent nature and reliance on environmental factors, RESs pose issues in production and consumption balance. Because renewable energy sources like wind and solar are unpredictable, forecasting their output is essential for planning purposes and maintaining grid stability. This thesis focuses on developing effective instruments and algorithms to improve renewable energy generation estimates and handle abnormalities in consumption. These tools and algorithms include maximum power point tracking and machine learning models like random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The methods' effectiveness is confirmed by accuracies higher than 80%, which provides speedier and more user-friendly solutions in comparison to the traditional ways. In the end, our effort seeks to offer practical instruments for anticipatory modelling and mitigating intermittentness in renewable energy sources, enabling their assimilation into current power structures to adequately supply energy requirements in a sustainable manner.

Optimization of a Bayesian Game for Peer-to-Peer Trading among Prosumers under Incomplete Information via a CNN-LSTM-ATT

In modern low-carbon industrial parks, various distributed renewable energy resources are employed to fulfill production needs. Despite the growing capacity of renewable energy generation, a significant portion of the power produced by these renewable resources remains unconsumed, resulting in a waste of resources. Within an industrial park, microgrids that both generate and consume energy resources act as energy prosumers. Peer-to-peer (P2P) trading provides an efficient means of utilizing renewable energy among these energy prosumers, who possess both power generation and consumption capabilities. However, within the current market mechanism, each prosumer retains private information that is not disclosed on the network. To address the issue of incomplete information among multiple prosumers during the decision-making process, we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers. The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads. Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics, which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method. These decisions are aimed at minimizing each prosumer's electricity costs. The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.

Temporal asynchrony analysis for dynamic operation of hydraulic-thermal-electricity multiple energy networks based on holomorphic embedding method

Analyzing the operational states of multiple energy networks (MEN) in multi-energy systems is crucial for ensuring system stability. The dynamic operational characteristics of different energy flows pose challenges for computational analysis. Traditional steady-state methods are inadequate for addressing the dynamics of MEN, especially when dealing with temporal discrepancies between hydraulic and thermal flows in thermal networks (TN) and the heterogeneity between TN and electrical networks. Therefore, this paper proposes a novel holomorphic embedding method (HEM) based on multi-stage decomposition method. The developed HEM constructs a time coefficient matrix and utilize inner-outer loop recursion to handle the time lag between thermal flow and hydraulic flow in the TN. Additionally, we reconstruct a holomorphic matrix, integrating hydraulic flow to bridge thermal and electric power flows, thereby improving the operational heterogeneity among different networks. Real-case simulations show that when the Taylor expansion order in HEM is equal to 4, the proposed method achieves a mere 1% discrepancy from actual operational data, enhancing computational efficiency by 60% compared to the Newton-Raphson method. Moreover, in this real-case, the TN exhibits a maximum delay response time of 180s compared to electrical networks. Exploiting this delay time effectively increases renewable energy generation within multi-energy systems by 961.58kW per day.

The performance of renewable-rich wholesale electricity markets with significant energy storage and flexibility

This work investigates the technical and financial performance of deeply decarbonised wholesale electricity markets with uncertain variable renewable generation (VRG), uncertain demand and different types of flexibility. It first finds that different combinations of power reserves (i.e. power generation capacity) and energy reserves (i.e. energy storage capacity), can achieve adequate system reliability at similar total costs. However, use of only energy reserves never achieved adequate system reliability, and this looks to be a fundamental challenge to the reliability of systems with only energy reserves but no power reserves. Two independent aspects of wholesale market operations are then found to be the main determinants of system reliability: the length of the unit commitment (UC) schedule prior to dispatch and the degree of operational uncertainty of VRG during dispatch.It is then argued that reliable, least cost and efficient wholesale markets with dominant renewables need acceptably low levels of operational uncertainty of VRG, sufficient flexibility in generation and/or demand, and UC schedules that are significantly longer than the longest energy storage durations present. Rather than suggesting that innovation in market design is required, these results therefore suggest that different technical approaches should have important, future roles. These include mitigating the operational uncertainty of VRG via several potential means as well as flexible generation, battery storage of a few hours duration and flexible electrolysis. Long duration energy storage, however, may challenge market performance and all participants’ returns even if it supports system reliability.

Optimal placement of renewable distributed generators and electric vehicles using multi-population evolution whale optimization algorithm

AbstractThis research takes on a crucial task- exploring the optimal placement of Renewable Distributed Generators such as Solar Photovoltaic, wind turbines and Electric Vehicles into the Radial Distribution System (RDS). This is a strategic move aimed at minimising power loss (PLoss) and improving the voltage profile and stability index. The RDGs are integrated into RDS with and without considering the uncertainty of the different load demands for 24 h. The probability function of Beta and Weibull distribution functions are employed to attain the solar irradiance and wind speed in a particular region. In addition, EVs are also integrated into RDS, employing meta-heuristic algorithms intended to reduce power loss (PLoss) and improve the voltage profile. The study uses an Indian 28-bus test system mimicking a balanced radial distribution network to integrate distributed generators (DGs) and EV charging stations. The simulated results demonstrate that integrating DGs into power systems has offered considerable benefits, including reduced PLoss, heightened efficiency, decreased dependency on centralised generation, and improved environmental sustainability. It is discovered that the Multi-population Evolution Whale Optimization Algorithm (MEWOA) produces better results than other methods in the literature and is valuable and practical for handling these nonlinear optimisation situations.

Statistical Analysis of Levelized Round Trip Cost of Grid Scale Electrical Energy Storage in Batteries with Different Chemistries

Electrical energy storage is a crucial component of the clean energy transition for integrating high share of renewable electricity generators into the supply mix. In this study, the round-trip costs of grid scale electrochemical energy storage from 2 up to 24 hours for peak power ratings of 1 MW and 10 MW in lithium-ion LFP, lithium-ion NMC, Pb-acid and vanadium redox flow batteries are compared using their currently projected techno-economic characteristics for year 2030. A statistical approach is used for estimating mean costs and quantifying the uncertainties in these estimates, since many of the techno-commercial features are still evolving and are uncertain. Using Monte Carlo simulations to derive the input parameters, the levelized round trip cost of energy storage is found to vary from Rs 10.73±0.77/kWh(e) to Rs 15.96±1.12/kWh(e) for Li-LFP batteries, Rs 14±1/kWh(e) to Rs 20.3±1.46/kWh(e) for Li-NMC batteries, Rs 19.1±1.39/kWh(e) to Rs 58.3±4.28/kWh(e) for Pb-acid batteries and Rs 18.7±1.37/kWh(e) to Rs 44.2±3.19/kWh(e) for Vanadium redox flow batteries, for the range of input values considered in this study. Through sensitivity analysis and calculation of correlation coefficients, the battery installed capital cost and useful lifetime are found to be the two most influential parameters affecting life cycle electrical energy storage cost.

Addressing Uncertainty in Renewable Energy Integration for Western Australia’s Mining Sector: A Robust Optimization Approach

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various risk scenarios. The optimization framework, formulated as a mixed integer linear programming problem and implemented in Python using the Gurobi Optimizer, integrates renewable energy sources such as wind turbines, photovoltaic arrays, and demand response programs alongside traditional diesel generators, boilers, combined heat and power units, and water desalination. The model ensures reliable access to electricity, heat, and water while minimizing operational costs and reducing reliance on fossil fuels. A comprehensive sensitivity analysis further examines the impact of uncertainty margins and the value of a lost load on the total system cost, providing insights into how different risk strategies affect system performance and cost-efficiency. The results are validated through three case studies demonstrating the effectiveness of the proposed approach in enhancing the resilience and sustainability of isolated power systems in the mining sector. Significant improvements in reliability, scalability, and economic performance are observed, with the sensitivity analysis highlighting the critical trade-offs between cost and reliability under varying uncertainty conditions.

Case Studies of Battery Energy Storage System Applications in the Brazilian Transmission System

This paper presents the preliminary results of studies aiming to use a battery energy storage system (BESS) in the Brazilian transmission system. The main objective of the BESS is to solve congestion problems caused mainly by the large increase in variable renewable generation in certain system areas. The studies were conducted based on actual forecasted system scenarios using a full representation of the electric grid available from the Brazilian system operator data base. In this work, only the steady-state modeling was considered as this may be the first stage in the assessment of a new technology. A qualitative economic comparison of the BESS application with other possible solutions to the congestion problems is also included.

Revesting the spatial spillover effects of renewable energy in curbing carbon emissions: Evidence from China

Renewable energy is fundamental to achieving China's peak carbon and carbon neutrality targets. Despite the extremely rapid growth in the share of renewable energy generation in terms of provincial-level regions, the impact of renewable energy generation on carbon emission reduction and whether this association is spatially correlated have not been fully recognized. To address this issue, this paper explores the spatial impact effect of renewable energy applications on carbon emissions using Chinese provincial panel data from 1990 to 2020 and a multi-panel spatial econometric model. The results show that the contribution of emission reduction mainly comes from the provincial clean energy mix, while the exclusion of out-of-province power plants also helps to curb their carbon emissions. In addition, in the context of large-scale penetration of renewable energy, grid infrastructure for cross-provincial power trading will contribute to cross-provincial emission reduction. Regulators should take measures to promote coordination of emission reduction policies among provinces in a unified national carbon market, rather than simplifying the allocation of responsibilities in provincial markets. Based on the findings of this paper, there is a need to rationalize and optimize the geospatial distribution of renewable energy projects in the future, so as to maximize the potential of renewable energy to contribute to carbon emission reduction.

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

AbstractReliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Uncovering Displacement between Nuclear and Renewable Electricity Generation for G7 Countries by Novel Wavelet-Based Methods

ABSTRACT Under the pressure of climate-related issues, importance of clean electricity generation (EG) has been developing in ensuring sustainable environment. Accordingly, countries have been trying to increase clean EG. Although increasing clean EG is important, it is also critical that clean EG alternatives should not cause displacement. Otherwise, there may not be certain progress in stimulating clean EG. So, this study aims to investigate displacement between nuclear and renewable EG types. In this context, the study considers four clean EG sources (i.e. nuclear, hydro, solar, and wind), analyzes G7 countries, uses high-frequency daily data between 1 January 2019 to 30 December 2023, applies novel Wavelet Local Multiple Correlation (WLMC) method as the main model, and performs Wavelet Transform Coherence (WTC) for the robustness check. The study defines that (i) hydro and wind EG have a displacement (supporting) effect on nuclear EG at short (long) term; (ii) solar EG has a supporting (displacement) effect on nuclear EG at short (long) term; (iii) the most critical EG source is nuclear for France and United States and hydro for Great Britain and Japan; (iv) the WTC method reveals the robustness. The study determines that renewable EG has a displacement (supporting) effect on nuclear EG in short (long) term. Therefore, there are certain conflict between nuclear and renewable EG in the short term, whereas they support each other in the long term. Thus, there is a need for comprehensive and balanced energy transition policy so that choices should not cause a displacement among alternatives.

Regional Framework Towards Establishing Treatment, Storage, and Disposal (TSD) Facilities for PV Waste Management in the Philippines: A Case Study of Laguna Province

Abstract. The accelerated adoption of photovoltaic (PV) systems for renewable power generation in the Philippines will yield a tremendous amount of PV waste. However, the country lacks a comprehensive framework for understanding PV module waste volume growth and optimally siting Treatment, Storage, and Disposal (TSD) facilities for PV waste management based on minimum siting factor. The study aims to develop a regional framework that utilizes a geographic information system (GIS)-based approach to find the potential sites and optimal locations of TSD facilities in Laguna, Philippines, based on the estimated PV module waste growth of solar energy systems with service contracts and minimum siting criteria under Department of Environment and Natural Resources (DENR) Administrative Order 2013-22. By 2060, the projected amount of PV module waste in Laguna province will be around 162,230 metric tons based on the waste projection results. The suitable sites cover around 19,720.5 hectares or 11.21% of the total land area of Laguna, which were determined by various siting factors that encompass social, economic, and environmental factors and obtained using binary suitability analysis and vector overlay analysis. The optimal locations of the two TSD facilities were identified using strategic positioning based on hotspot analysis, clustering algorithm, and proximity analysis. Consequently, the distance and GHG emissions of the two optimal locations were calculated. Indeed, the approach will serve as a guide for policymakers and other stakeholders in understanding PV module waste growth and strategically and optimally position TSD facilities on suitable sites for efficient and effective PV waste management.

Geothermo-mechanical energy conversion using shape memory alloy heat engine

AbstractThe shift towards renewable energy sources like geothermal energy has become desirable due to the recurrent energy crisis and global warming challenges influenced by fossil fuels. Geothermo-mechanical energy conversion using shape memory alloy (SMA) heat engines presents a novel and sustainable approach for harnessing geothermal energy. Shape memory alloys, known for their ability to undergo reversible phase transformations driven by temperature changes, are ideal for thermal-to-mechanical energy conversion. This paper explores the design and performance of an SMA heat engine that utilizes geothermal heat sources to drive mechanical work. The engine operates by cycling between the high-temperature geothermal environment and a cooler sink, exploiting the shape memory effect to generate mechanical motion. By integrating geothermal energy and SMA technology, this system offers a potential solution for renewable energy generation, with applications in remote or off-grid locations. The paper also investigates output power and the thermodynamic efficiency. A model is formulated and the engine behavior is simulated. A series of experiments are conducted for engine output power and efficiency. The model is compared to the experimental data for validation. The engine developed a maximum power of 3.5, 8.5, and 11.5 watts at 60, 80, and 90 °C respectively. The proposed SMA-based geothermo-mechanical energy conversion system offers a promising solution for efficient, reliable, and scalable geothermal energy harvesting. This research contributes to the development of innovative, efficient geothermal energy conversion technologies, supporting global renewable energy goals and reducing greenhouse gas emissions. This innovative energy conversion mechanism could play a key role in the future of sustainable power generation.

The Political Economy of Variations in Energy Debt Financing by Two Chinese Policy Banks in Africa

ABSTRACTThis article examines the puzzle of why China's two policy banks, China Development Bank (CDB) and the Export‐Import Bank of China (Eximbank), have lending portfolios for power‐generation projects in Africa that have drastically different levels of carbon dioxide emissions. From the supplier side, Eximbank balances two imperatives: Beijing's ideational ambition as a new development provider to African recipients with sustainability commitments, and China's industrial goal to offshore non‐renewable capacity. In contrast, the CDB prioritizes its commercial interests, which results in the bank lending solely for coal projects. On the demand side, Eximbank's concessional capital has emerged as a second‐best option among international financial sources for renewable and hydropower generation projects. Conversely, CDB's market‐rate lending makes it the fiscal last resort for host countries seeking financial support for thermal‐power projects which are shunned by other financiers. This divergence can be understood through the polycentric development finance model, which captures the parallel decision‐making institutions governing Chinese energy financing in Africa. Specifically, the lending decisions of Eximbank are linked with institutionalized policy processes, translating priorities of Chinese and African state actors. Meanwhile, the loan origination processes of CDB are more independent of state actors, allowing greater autonomy for the financier to pursue commercial interests.

Numerical Study on Combustion Characteristics of a 600 MW Boiler Under Low-Load Conditions

Under the background of achieving carbon dioxide peaking and carbon neutrality, the rapid development of renewable energy power generation poses new challenges to the flexible adjustment capabilities of traditional power plants. To explore the furnace combustion stability and optimal operation modes during deep peak shaving, a simulation of the combustion process under low-load conditions for a 600 MW wall-fired boiler is performed utilizing computational fluid dynamics (CFD) analysis. The impact of burner combination modes on the combustion process within the furnace is explored at 25% and 35% boiler maximum continuous ratings (BMCRs). This study investigates two configurations of burner combinations. One mode operates burners in layers A, B, and C, which include the lower layers of burners on the front and rear walls of the boiler, as well as the middle-layer burners on the rear wall, referred to as OM1. The other mode operates burners in layers A and C, which include the lower layers of burners on the front and rear walls of the boiler, referred to as OM2. The results indicate that OM2 exhibits superior capabilities in orchestrating the distribution of the airflow velocity field and temperature field under the premise of ensuring no more than a 1% decrease in the pulverized coal burnout rate. When OM1 is employed, the airflow ejected from the middle-level burners hinders the upward movement of pulverized coal sprayed from the lower-level burners, causing a larger proportion of pulverized coal to enter the ash hopper for combustion. Consequently, the ash hopper attains a peak mole fraction of CO2 at 0.163. OM2 delays the blending of pulverized coal with air by enhancing the injection quantity of pulverized coal per burner. As a result, the generation of CO in the ash hopper reaches a notable mole fraction of up to 0.108. The decreased furnace temperature promotes the formation of fuel-based NOx during low-load operation. Taking the 25% BMCR as an example, the NOx emissions measured at the furnace outlet are 743 and 1083 ppm for OM1 and OM2, respectively. This study focuses on the impact of combustion combinations on the combustion stability when the boiler is operating at low loads. The findings could enrich previous research on combustion stability and contribute to the optimization of combustion schemes for power plant boilers operating at low loads.

Mitigating Risk and Emissions in Power Systems: A Two-Stage Robust Dispatch Model with Carbon Trading

The large-scale integration of renewable energy sources is crucial for reducing carbon emissions. Integrating carbon trading mechanisms into electricity markets can further maximize this potential. However, the inherent uncertainty in renewable power generation poses significant challenges to effective decarbonization, renewable energy accommodation, and the security and cost efficiency of power system operations. In response to these challenges, this paper proposes a two-stage robust power dispatch model that incorporates carbon trading. This model is designed to minimize system operating costs, risk costs, and carbon trading costs while fully accounting for uncertainties in renewable energy output and the effects of carbon trading mechanisms. This model is solved using the column-and-constraint generation algorithm. Validation of an improved IEEE 39-bus system demonstrates its effectiveness, ensuring that dispatch decisions are both robust and cost-efficient. Compared to traditional dispatch models, the proposed model significantly reduces system risk costs, enhances the utilization of renewable energy, and, through the introduction of a ladder carbon trading mechanism, achieves substantial reductions in carbon emissions during system operation.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

A Microgrid Stability Improvement Method by Applying Virtual Adaptive Resistor Paralleling with a Grid-Connected Inverter

An increase in renewable energy generation in the microgrid can cause voltage oscillation problems. To address this issue, an equivalent circuit of the microgrid was established, including a synchronous generator, grid-connected inverter, and constant power load. Then, the impact of different renewable energy generation ratios, different direct current (DC) voltage loops, and phase-locked loop control bandwidths of the grid-connected inverter on microgrid stability were analyzed. The results indicate that an increase in the renewable energy generation ratio leads to a decrease in the stability margin of the microgrid. A microgrid stability improvement method involving the parallel connection of a virtual resistor with the grid-connected inverter was proposed. The resistance value of the virtual resistor was obtained through an adaptive algorithm. This method ensures the stable operation of the microgrid under different renewable energy generation ratios.

Variable droop gain frequency supporting control with maximum rotor kinetic energy utilization for wind-storage system

To address the emerging frequency stability issue brought by the large replacement of synchronous generators with renewable generations, wind turbine generators are required to possess frequency-supporting capability. However, existing frequency-supporting control strategies lack the assessment of the frequency support capability of wind turbine generators, leading to degraded control performance under various situations. Aiming to solve this problem, this paper proposes a variable-droop-gain control for wind turbine generators with maximum rotor kinetic energy utilization. Firstly, an analytical relationship was established between droop gain, disturbance scale, and rotor speed. Subsequently, the released energy of the wind turbine generator is evaluated, which equals the difference in the rotor kinetic energy under the initial and the post-disturbance steady-state rotor speed. It was proved that the released kinetic energy cannot exceed a certain proportion of total rotor kinetic energy. Accordingly, a variable initial gain scheme is proposed, which determines the initial droop gain as per the disturbance scale for maximizing the kinetic energy release of wind turbines. Moreover, an additional real-time droop gain adjustment rule is added to prevent the over-deceleration of wind turbines. The simulation results show that the proposed scheme may provide the maximum KE release and effectively improve the system frequency nadir while ensuring the safe operation of wind turbine generators.