Intermittent Renewable Energy Resources Research Articles

Combined utilization of electricity and thermal storages in a highly renewable energy system within an island society

With the increasing share of intermittent renewable energy resources, the demand for different forms of energy storage increases. In this paper, a comparative analysis was performed on two energy storage solutions: small-scale underground pumped hydro storage (PHS) and high-temperature thermal energy storage (HTTES). Using the PLEXOS energy and power system modeling software, the study analyzed the operation and performance of these storage systems in Åland Island, an autonomous region of Finland. The novelty of the paper is in evaluating the business cases of these storage solutions when operating separately and combined. Business cases are studied by comparing annual energies, operating hours, price paid, and price received of the storage types in different scenarios. The results showed that both storages promoted electrification and renewable energy integration, with the HTTES reducing curtailment by 77 % and the PHS by 4 %. The storage systems were also able to take advantage of lower-cost hours of external electricity markets, with the PHS increasing electricity imports by 0.2 % and the HTTES by 1.5 %. Moreover, the PHS enabled the sale of excess electricity to external markets, leading to an increase in exports by 0.3 %. In contrast, the HTTES increased local consumption of excess production, reducing exports by 1.1 %. Results showed that integrating both storage systems in the same energy system could achieve significant benefits, such as reduced curtailment of renewable energy. However, the study also highlighted that the two systems could compete for the same resources in an open energy market, leading to a decrease in annual energy and net profits for both systems. A PHS exhibits an 11 % reduction in annual energies, when compared with operating alone, whereas for an HTTES, the reduction is 1 %. Similarly, the net profit of the PHS is reduced by 25.2 % and that of the HTTES by 4.9 %. Concluding the business case analysis, changes in annual energy and operation hours are not linearly correlated with revenue and charging cost, and the dynamics of the electricity market and the studied energy system must also be considered. Finally, energy storage can be considered a key element in supporting the energy transition; however, the importance of careful planning must be emphasized when integrating energy storage systems into an energy market.

Theoretical and experimental investigations of vanadium pentoxide–based electrocatalysts for the hydrogen evolution reaction in alkaline media

AbstractA key approach towards better realization of intermittent renewable energy resources, namely, solar and wind, is green electrochemical hydrogen production from water electrolysis. In recent years, there have been increasing efforts aimed at developing noble metal-free electrocatalysts that are earth-abundant and electroactive towards hydrogen evolution reaction (HER) in alkaline electrolytes, wherein an initial water dissociation step is followed by a two-electron transfer cathodic reaction. Although relatively earth-abundant, vanadium-based electrocatalysts have been sparsely reported due to subpar electroactivity and kinetics towards water electrolysis in general and alkaline electrolysis in specific. Herein, we investigate the fine-tuning of orthorhombic V2O5-based electrocatalysts as candidates for HER through a scalable two-step sol–gel calcination procedure. Briefly, surface-induced anionic oxygen deficiencies and cationic dopants are synergistically studied experimentally and theoretically. To that end, first-principle facet-dependent density function theory (DFT) calculations were conducted and revealed that the coupling of certain dopants on V2O5 and co-induction of oxygen vacancies can enhance the catalytic HER performance by the creation of new electronic states near the Fermi level (EF), enhancing conductivity, and modulating surface binding of adsorbed protons, respectively. This was reflected experimentally through kinetically non-ideal alkaline electrochemical HER using Zn0.4V1.6O5 whereby − 194 mV of overpotential was required to attain − 10 mA/cm2 of current density, as opposed to pristine V2O5 which required 32% higher overpotential requirement at the same conditions. The disclosed work can be extended to other intrinsically sluggish transition metal (TM)–based oxides via the presented systematic tuning of surface and bulk microenvironment modulation. Graphical Abstract

An Overview of Hydrogen Energy Generation

The global issue of climate change caused by humans and its inextricable linkage to our present and future energy demand presents the biggest challenge facing our globe. Hydrogen has been introduced as a new renewable energy resource. It is envisaged to be a crucial vector in the vast low-carbon transition to mitigate climate change, minimize oil reliance, reinforce energy security, solve the intermittency of renewable energy resources, and ameliorate energy performance in the transportation sector by using it in energy storage, energy generation, and transport sectors. Many technologies have been developed to generate hydrogen. The current paper presents a review of the current and developing technologies to produce hydrogen from fossil fuels and alternative resources like water and biomass. The results showed that reformation and gasification are the most mature and used technologies. However, the weaknesses of these technologies include high energy consumption and high carbon emissions. Thermochemical water splitting, biohydrogen, and photo-electrolysis are long-term and clean technologies, but they require more technical development and cost reduction to implement reformation technologies efficiently and on a large scale. A combination of water electrolysis with renewable energy resources is an ecofriendly method. Since hydrogen is viewed as a considerable game-changer for future fuels, this paper also highlights the challenges facing hydrogen generation. Moreover, an economic analysis of the technologies used to generate hydrogen is carried out in this study.

Numerical investigation of hydrodynamic damping of a pitching hydrofoil at different flow regimes

Due to the development of intermittent renewable energy resources, hydropower plants are mostly operated under off-design conditions. This may lead to natural frequency excitation shortening the turbine life-span. To accurately estimate the fatigue life, it is necessary to evaluate the hydrodynamic damping parameters. In the present study, different flow regimes and their relationship with hydrodynamic damping are analyzed numerically using the γ - Reθt transition SST ĸ - ω turbulence model. The test case is a NACA0009 hydrofoil pitching around its center of mass. A good agreement between the present and previous numerical results is obtained. Consistent with the literature, hydrodynamic damping coefficient demonstrate consistently two different regions. The phase shift between the displacement and moment increases with the rise of the pitching frequency. After reaching a peak value at a reduced frequency of around κ = 5, the phase shift starts to decrease, and eventually approaches zero again. The damping behavior demonstrates an opposite trend. First, it reduces in spite of the phase shift increase, and after the inflection point, where the flow field changes from the drag mode to the thrust mode, it rises due to the torque development. The maximum of the damping occurs at the low frequencies.

Solar energy and wind power supply supported by battery storage and Vehicle to Grid operations

Worldwide activity in renewable energy is a motive power to introduce technological innovations. Integrating intermittent energy sources such as solar energy and wind power with battery storage and Vehicle to Grid operations has several advantages for the power grid. The first advantage is that energy storage supports the power grid during the periods that the power grid is facing challenges from high peak demand. The second advantage is that using battery storage and Vehicle to Grid operations would shift the power grid load from the peak and busy time to less demand time. And the third advantage uses energy storage and Vehicle to Grid operations to smooth the fluctuating power supply fed into the power grid by intermittent renewable energy resources. This energy storage idea is of particular importance because, in the future, more renewable energy sources are integrated into the power grid worldwide. The research objective includes the results and examines the role and advantages of battery storage and Vehicle to Grid operations integrated into intermittent sources. The battery storage and Vehicle to Grid operations will create a renewable power supply and enhance the power grid reliability, including a large proportion of intermitted renewable energy sources.

Cooperation Mode and Operation Strategy for the Union of Thermal Generating Unit and Battery Storage to Improve AGC Performance

With the growing penetration of intermittent renewable energy resources in power systems, it is a challenge for automatic generation control (AGC) to maintain the required control performance. In recent years, battery energy storage systems (BESSs) have been installed in thermal power plants to provide frequency regulation service bundled with the traditional thermal generating units (TGUs). However, in real projects, the installed BESS typically works as parasitic equipment of the generating unit, which limits the coordination between the TGU and the BESS. In this paper, a complementary cooperation pattern is proposed for the TGU-BESS union to improve the dispatchability of its response to the AGC signal. Then, the correlation between the BESS's energy demand and the TGU's operation states is described by a data-driven method, which combines fuzzy c-means cluster, r-vine copula, and random forest. Finally, a rolling-horizon energy balance strategy is developed to make the control decisions for the TGU-BESS union in response to the stochastic AGC signal. The effectiveness of the proposed method is demonstrated using the historical data from a real union with a 9MW/4.5MWh BESS and a 350MW coal-fired generator.

A systematic approach to modeling synchronous generator using Markov parameters and Takagi–Sugeno fuzzy systems

The problem of precise modeling of Synchronous Generator (SG) devices is demanding and crucial for monitoring, stability, and control of electric grids and microgrids. SGs are known to exhibit nonlinear behavior, and because they may work in a wide range of operating points due to different electric network phenomena like load changes, current intermittent renewable energy resources, temperature variations, topological changes, etc., the modeling task of SG can be a challenging problem. This paper proposes a new method to find a global model for a synchronous generator from the input and output measurement data using state-of-the-art methods in artificial intelligence (AI) like fuzzy clustering, subspace identification, and Takagi–Sugeno (T–S) fuzzy modeling. The fuzzy C-means method is utilized to cluster the measurement data, and then using a subspace identification method based on Markov parameters concepts, the dynamic model of SG for each cluster is obtained in the form of a state space model. A Takagi–Sugeno fuzzy model is applied to determine the corresponding outputs based on the measured inputs and the available state space models. The effectiveness of the proposed approach is illustrated by applying it to the data obtained from simulations on a known fourth-order SG nonlinear model and the data obtained from real-world experimental tests.

Potential Impacts of Climate Change on Renewable Energy and Storage Requirements for Grid Reliability and Resource Adequacy

Abstract This paper provides a study of the potential impacts of climate change on intermittent renewable energy resources and storage requirements for grid reliability and resource adequacy. Climate change models and available regional data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed within a specific U.S. southwest service area as a case study. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources. Results for the extreme climate change scenario show that the projected wind power may decrease by ∼13% due to projected decreases in wind speed. Projected solar power may decrease by ∼4% due to decreases in irradiance and increases in temperature. Uncertainty in these climate-induced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in round-trip efficiency. The hourly energy balance among electrical load, generation, and storage was calculated throughout the year. The annual loss of load expectation (LOLE) was found to increase from ∼0 days/year to a median value of ∼2 days/year due to potential reductions in renewable energy resources caused by climate change and decreased battery performance. Significantly increased battery storage was required to reduce the LOLE to desired values of 0.2 days/year.

Data-driven approach to fuzzy inference system tuning for static security assessment of multi-area power systems with renewable energy

Energy market deregulation and increased integration of intermittent renewable energy resources (RE) have pushed modern power systems to operate close to their security limits, which if violated could be catastrophic. To prevent power system failure and damage, a fast and accurate security assessment tool is required. In this paper, a data-driven fuzzy inference system tuned by hybrid genetic-simulated annealing (HGSA-FIS) is proposed for quick static security assessment (SSA) of a power system under a single component failure. An existing composite security index is modified to make a reliable SSA by distinguishing between severe and non-severe operating scenarios and contingencies. The prediction performance of HGSA-FIS was evaluated on an RTS-GMLC test system with operating scenarios varying with RE generation, load demand, and tie-line flows. The findings demonstrate the robust performance of HGSA-FIS in making static security assessments under varying operating scenarios, exceeding ANFIS in most and ANN in a few scenarios.

A Synthesis Method to Generate Hourly Electricity Production Time-series of Wind Plants in Peru for Long-term Expansion Planning

Plenty of works have treated the system expansion planning problem in the presence of intermittent renewable energy resources like wind. However, most of those proposals have been approached from scenarios of plenty of data, which is not the rule in developing countries, where principal investment actors have recently switched their focus. In contrast to operation problems where existing literature can be successfully applied since it requires short-term historical time-series gathered from the same studied plants, proposals for planning problems are almost impossible to apply because of a lack of information and measurement about renewable resources in places where no renewable plants have been previously installed. In order to fill this information gap, this paper presents a novel methodology to synthesize wind production time-series on an hourly time scale, taking as inputs aggregate data such as monthly wind speed average values and Weibull annual parameters. The methodology comprises four steps, from data gathering to calculating electrical power produced by a wind farm. Three application tests are performed for different places in India, Chile, and Peru to validate the proposed methodology. The results show that the methodology successfully synthesizes time-series of output power, correctly achieves persistence characteristics, and slightly over or underestimates the produced wind energy, having a discrepancy of 6.2% in the yearly total.

Quasi-Oppositional African Vultures Optimization-Based PIλDn Plus PIλ Controller for Frequency Control of an Interlinked Hybrid Power System

One of the challenging tasks in a hybrid power system is to provide an effective control strategy to attenuate frequency and power deviations that occur due to the intermittency of renewable energy resources (RERs) and load demand. To address this issue, we present a novel proportional fractional-order integral derivative filter plus proportional fractional-order integral (PIλDn plus PIλ) controller, which is applied for the load frequency control (LFC) operation of an interlinked hybrid thermal power system. Using quasi-oppositional learning, we also propose the maiden quasi-oppositional African vultures optimization algorithm (QOAVOA), which is used to tune the proposed controller by minimizing the objective function. First, the capability of the proposed QOAVOA tuned PIλDn plus PIλ controller is examined with a two-area thermal power system under load uncertainty and the performance is compared with the results of various optimization-based classical/fuzzy controllers. The sensitivity study is also performed to validate the robustness of the proposed control approach for handling systems’ parametric uncertainties. The suggested LFC mechanism is further validated on a two-area-hybrid thermal power system (2A-hTPS) comprising RERs and distributed generation sources to authenticate the tuning ability of the proposed optimization approach. The dynamic performance of considered 2A-hTPS with the suggested control approach is examined considering various scenarios such as changing load demand, uncertain wind perturbation, and random multi-step load perturbations of the wind turbine generator. The comparative dynamic performance of the suggested controller reveals its superiority over other controllers in terms of transient and steady-state responses. Finally, a few non-linearities are introduced to the test system, and the simulation study with non-linearities validates the potency of the proposed controller.

Improving dynamic stability of wind integrated system by incorporating magnetic energy storage

AbstractThe need to incorporate renewable energy sources in the present grid due to energy demand and environmental issues has led the power industry to face new challenges and unexpected transformations. This new epitome to build a flexible power system with coordination of increasing intermittent renewable energy resources and delivering power efficiently with reliability demand new technologies. This work investigates the coupling of static synchronous compensator (STATCOM) with the most exclusive superconducting magnetic energy storage (SMES) device designed for the improved functioning of a grid network that is incorporated with a wind farm. The widespread integration of intermittent wind generators in the grid is disturbing its stability and reliability. The SMES is interlinked with the grid system via a power electronic interface and chopper for the energy exchange. This work suggests STATCOM as a power electronic interface and a three‐level chopper configuration with novel control scheme for enhancing the functioning of the test system. This integrated controller (STAT‐SMES) has been analyzed for grid‐connected doubly fed induction generator wind farm under different fault conditions and time durations. The results have been compared without any controller, with STATCOM only, and with the proposed STAT‐SMES controller using MATLAB. The simulation outcomes prove that coupled control scheme is effective and better in handling wind farm integration issues.

A Novel Reconfigurable Coordinated Control for Autonomous Low Voltage DC Microgrid

DC microgrids with a small electrical network of locally available energy resources and distributed loads are becoming popular in real-life applications, especially in remote areas and in some specific urban applications. This, in turn demands a serious insight into the control and reliability enhancement of such systems so that the availability of secure and safe energy can be assured to the users. But the integration of intermittent renewable energy resources in dc microgrids lead to a serious concern regarding the stability of the microgrid bus voltage under continuous change in available source power and the loading conditions. While the microgrid management is still a challenge under steady state operating conditions, it becomes much complex under some emergency events like faults, failure of controllers/sources and overloads etc. This paper proposes a novel reconfigurable coordinated control architecture for a Low Voltage DC (LVDC) microgrid comprises of solar photovoltaic, fuel cell based renewable energy sources supported Energy Storage System (ESS) formed with battery and ultra-capacitor banks. The reliability of the system is ensured in the proposed reconfigurable coordinated control with three layered hierarchy viz. Emergency control layer sandwiched between the supervisory and local control layers. The proposed control strategy is implemented through a new Microgrid Bus Controller (MGBC) strengthened by an “emergency” control layer which handles the responsibility in emergency conditions to coordinate the energy sources, loads and controllers. The MGBC facilitates the fault identification and isolation to ensure the reliable operation of the healthy microgrid sections and speedy fault recovery. The MGBC is dedicated for observing the bus parameters and identify the fault location by monitoring the bus branching currents. Some of the key hardware and simulations results validating the proposed architecture along with the details of the control implementation are presented in this paper.

Wind and solar resource complementarity and its viability in wind/PV hybrid energy systems in Machakos, Kenya

Integration of intermittent renewable energy resources provides the potential to mitigate the impact of the variability of independent sources. The intermittent nature creates stability, reliability and power quality problems in power grids. Wind and solar energies are the most viable resources whose complementarity could be deployed in the development of hybrid renewable energy systems to enhance their performance. In this study, wind-solar resource complementarity is investigated to establish its viability in hybrid energy systems in Machakos, a rural-urban town whose geographical location is 1°31′S, 37,016′E in the Eastern region of Kenya. The study findings could be fundamental in energy planning and developments in the area to provide more flexible and dependable hybrid energy systems which would supply energy services under constantly changing conditions. Ground measurements included PVGIS simulation and experimental tools installed at a height of 20 m. Weibull's distribution model and energy potential of wind resource are investigated to characterize wind resource for energy generation. The Wind distribution revealed a positively skewed profile with scale (c) and shape (k) parameter values of 2.68 m/s (≈ 3.0 m/s) and 1.9 at a mean wind speed of 2.47 m/s, respectively. The findings revealed a wind power density of 17 W/m2 at an average wind speed of 3.0 m/s. Further, an annual solar insolation of 2130 kWh/m2 with monthly average of 178 kWh/m2 translating to daily insolation of 5.8 kWh/m2 at an installation capacity of 1 kWp is reported. The months with minimum and maximum solar insolation were July and March which recorded monthly averages of 145 kWh/m2 and 213 kWh/m2 translating to a daily average of 4.7 kWh/m2 and 6.9 kWh/m2, respectively. The understanding gained from this study could be useful to the renewable energy research community and can be extended to stakeholders in PV and wind energy systems for micro grids and utility applications.

Control and Optimisation of Power Grids Using Smart Meter Data: A Review.

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay.

Investigation of Flow-Induced Instabilities in a Francis Turbine Operating in Non-Cavitating and Cavitating Part-Load Conditions

The integration of intermittent renewable energy resources to the grid system requires that hydro turbines regularly operate at part-load conditions. Reliable operation of hydro turbines at these conditions is typically limited by the formation of a Rotating Vortex Rope (RVR) in the draft tube. In this paper, we investigate the formation of this vortex using the scale-resolving methods SST-SAS, wall-modeled LES (WMLES), and zonal WMLES. The numerical results are first validated against the available experimental data, and then analyzed to explain the effect of using different scale-resolving methods in detail. It is revealed that although all methods can capture the main features of the RVRs, the WMLES method provides the best quantitative agreement between the simulation results and experiment. Furthermore, cavitating simulations are performed using WMLES method to study the effect of cavitation on the flow in the turbine. These effects of cavitation are shown to be highly dependent on the amount of vapor in the RVR. If the amount of vapor is small, cavitation induces broadband high-frequency fluctuations in the pressure and forces exerted on the turbine. As the amount of cavitation increases, these fluctuations tend to have a distinct dominant frequency which is different from the frequency of the RVR.

A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System

Nowadays, renewable energy (RE) sources are heavily integrated into the power system due to the deregulation of the energy market along with environmental and economic benefits. The intermittent nature of RE and the stochastic behavior of loads create frequency aberrations in interconnected hybrid power systems (HPS). This paper attempts to develop an optimization technique to tune the controller optimally to regulate frequency. A hybrid Sparrow Search Algorithm-Grey Wolf Optimizer (SSAGWO) is proposed to optimize the gain values of the proportional integral derivative controller. The proposed algorithm helps to improve the original algorithms’ exploration and exploitation. The optimization technique is coded in MATLAB and applied for frequency regulation of a two-area HPS developed in Simulink. The efficacy of the proffered hybrid SSAGWO is first assessed on standard benchmark functions and then applied to the frequency control of the HPS model. The results obtained from the multi-area multi-source HPS demonstrate that the proposed hybrid SSAGWO optimized PID controller performs significantly by 53%, 60%, 20%, and 70% in terms of settling time, peak undershoot, control effort, and steady-state error values, respectively, than other state-of-the-art algorithms presented in the literature. The robustness of the proffered method is also evaluated under the random varying load, variation of HPS system parameters, and weather intermittency of RE resources in real-time conditions. Furthermore, the controller’s efficacy was also demonstrated by performing a sensitivity analysis of the proposed system with variations of 75% and 125% in the inertia constant and system loading, respectively, from the nominal values. The results show that the proposed technique damped out the transient oscillations with minimum settling time. Moreover, the stability of the system is analyzed in the frequency domain using Bode analysis.

A Model-Adaptive Clustering-Based Time Aggregation Method for Low-Carbon Energy System Optimization

Intermittent renewable energy resources like wind and solar introduce uncertainty across multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find the least-cost solution that manages the multi-timescale variability using an optimal portfolio of flexible resources. However, input data that capture such multi-timescale uncertainty are characterized with a long time horizon and high resolution, which brings great difficulty to solving the optimization model. Here we propose a model-adaptive time aggregation method based on clustering to alleviate the computational complexity, in which the energy system is solved over selected representative time periods instead of the full time horizon. The proposed clustering method is adaptive to various energy system optimization models or settings, because it extracts features from the optimization models to inform the clustering process. Results show that the proposed adaptive method can significantly lower the error in approximating the solution of the optimization model with the full time horizon, compared to traditional time aggregation methods.

Fault Ride Through and Intermittency Improvement of Renewable Energy Integrated MMC-HVDC System Employing Flywheel Energy Storage

Modular multilevel converter (MMC)-based high voltage direct current (HVDC) transmission networks integrate remotely located distributed renewable energy resources (RER). The intermittent nature of RER and symmetrical and asymmetrical AC-side low-voltage faults produce operational difficulties. It reduces the MMC’s ability to transfer the rated power, which raises the voltage of the HVDC link. Therefore, the MMC-HVDC network’s fault ride-through (FRT) capabilities and the power fluctuation brought on by RER intermittency are potential challenges to the efficient integration of renewable energy resources. In response, this article suggests an energy control scheme for the flywheel energy storage of the PV-wind-MMC-HVDC system in order to regulate the HVDC-link voltage during low voltage faults at the point of common coupling (PCC) of the AC grids and to address the problem of power fluctuation caused by intermittent RER and sudden load changes. The suggested method eliminates the dynamic braking resistor (DBR) from the HVDC link and seamlessly integrates the RER without actively reducing renewable energy power during low voltage faults. To test the effectiveness of the proposed control method for the flywheel energy storage in reducing excess energy in the HVDC link, symmetrical and asymmetrical low voltage faults have been conducted. In addition, changes in wind speed, solar radiation, and temperature have been made to validate the flywheel energy management system’s performance. A real-time digital simulator (RTDS) and dSPACE-based controller hardware in loop (CHIL) configuration with a complete system have been used to simulate and evaluate the entire system.

Optimal dynamic frequency regulation of renewable energy based hybrid power system utilizing a novel TDF-TIDF controller

ABSTRACT Insertion of intermittent natured renewable energy resources in the microgrid to decline global warming and due to highly inconsistent propulsion load, frequency control of the isolated hybrid micro-grid system (HMGs) is the key point of attraction and necessitates a highly competent and robust control strategy. Hence, in this paper, an efficient frequency control mechanism for HMGs is proposed by utilizing an efficient tilt derivative filter-tilt integral derivative filter (TDF-TIDF) controller. HMGs are composed of solar, wind, fuel cell, freezer, diesel engines, heat pumps, and loads. A recently appeared dragonfly algorithm (DFA) is employed to optimize the suggested controllers’ design variables. The dynamic analysis is exhibited with different types of energy storage units under various conditions. The conditions incorporate perturbations in demanded load and power generation through renewable energy sources. In this study, solar thermal and wind power systems are not contributing to frequency control owing to the plan of their maximum power tracking approach. The dynamic performance of different controllers such as PIDF, TIDF, PDF-PIDF, and TDF-TIDF controllers are evaluated with their design variables tuned by employing genetic algorithm, particle swarm optimization, gravitational search algorithm, quasi harmony search algorithm, grasshopper algorithm and dragonfly algorithm. The output graphs of transient responses and collected statistical data corroborate that the suggested DFA-based TDF-TIDF scheme provides least objective function value (8.1e‒ 07) compared to PDF-PIDF (4.011e‒ 06), TIDF (3.14e‒ 05) and PIDF (5.03e‒ 05), hence, more capable in reducing the oscillations quickly for the system with non-linearities. Moreover, statistical data yielded by DFA is observed superior when evaluated with other existing optimization methods.