In this study, a demand-contributed load frequency control (LFC) strategy is proposed for frequency stabilization in a solar-wind-based autonomous microgrid system (AMGS). The proposed control framework employs a structurally enhanced version of the classical proportional-integral (PI) controller, augmented with a one plus derivative filter (PI-(1 + DF)) scheme. To optimize the controller parameters, a physics-inspired metaheuristic technique known as the Fick's Law Optimization (FLO) is implemented. This controller is designed to address the complex dynamics and uncertainties of the AMGS, which comprises renewable sources (solar and wind), conventional diesel engine generator (DEG), and flexible demand-side contributors such as electric vehicles (EVs), heat pumps (HPs), and freezers. Furthermore, realistic nonlinearities like governor dead band (GDB) and generation rate constraints (GRC) are incorporated into the model to ensure practical relevance. Comparative analysis reveals that the FLO-optimized PI-(1 + DF) controller significantly outperforms recent state-of-the-art algorithms such as the Mine Blast Algorithm (MBA) and the Sine Cosine Algorithm (SCA) in terms of settling time, peak overshoot, and various objective functions. Simulation results conducted in MATLAB/Simulink confirm the efficacy and robustness of the proposed approach, successfully maintaining frequency deviation within acceptable limits even under severe disturbances. Furthermore, robustness tests with ± 50% parametric variations demonstrate the controller's resilience and adaptability in highly uncertain environments. The peak overshoots (Hz) for a ± 50% variation in MG parameters are 0.02, 0.05, and 0.06, while the corresponding undershoots (Hz) are - 0.957, -0.72, and - 0.48. Similarly, for variations in the droop constant (R) the overshoots (Hz) are 0.074, 0.065, and 0.064, and the undershoots (Hz) are - 0.724, -0.725, and - 0.729, respectively.

The growing adoption of renewable energy technologies still faces challenges such as instability, intermittent, and limited energy storage capacity. Diesel engine generators, known for their stability and reliability, remain essential as primary or backup power sources, especially in remote areas. However, conventional diesel generators operating at constant speed are inefficient in fuel consumption and produce high emissions. This study investigates the implementation of a variable-speed diesel generator system using a BLDC (Brushless Direct Current) generator controlled by a fuzzy logic-based controller (FLC). The proposed system adjusts engine speed and the duty cycle of the converter to optimize fuel efficiency while maintaining voltage and frequency stability. Simulation results demonstrate that the system reduces fuel consumption by up to 7.6% (0.86 liters/hour) for a 100 kW generator. Additionally, the FLC effectively stabilizes voltage and frequency during load changes and finally enhancing overall system performance.

Microgrid frequency control faces challenges due to load fluctuations and the intermittent nature of Renewable Energy Sources (RESs). The Load Frequency Control (LFC) scheme has been a profoundly investigated matter for decades for achieving a consistent frequency. This study introduces a novel cascaded Integral-Proportional-Proportional Derivative with Filter (I-P)-PDN controller designed to mitigate frequency deviations in microgrids incorporating Photovoltaic (PV) and Wind Turbine Generator (WTG), Fuel Cells (FCs), Electric Vehicles (EVs), Battery Energy Storage Systems (BESS), and Diesel Engine Generators (DEGs). To optimize the controller's parameters, the recently introduced Black-winged Kite Algorithm (BKA) is employed for its superior search efficiency and quick convergence. Simulation results show that the (I-P) cascaded PDN controller significantly outperforms existing controllers, such as PID, and PI-based models, by reducing frequency deviations, improving settling time, and minimizing overshoot and error indices. There is notable 77% reduction in overshoot (OSH) and 52% decrease in undershoot (USH) in tie-line power variations. Moreover, the Integral Absolute Error (IAE) is reduced by 42.3%, the Integral Time weighted Absolute Error (ITAE) by 85%, and the Integral Squared Error (ISE) by 98%. The study also examines the role of EVs as flexible energy storage, demonstrating their contribution to system resilience and stability. This approach offers a robust solution for effective frequency regulation in modern microgrids, ensuring reliable performance in dynamic conditions.

In the context of maintenance and overhaul carried out when a machine has reached the running hour limit, it is necessary to prepare spare parts according to the manual book instructions to replace the engine parts that must be replaced in order to support the smooth running of the overhaul so that no damage occurs after the diesel engine generator is overhauled, especially the camshaft whose working system continues to receive needs and pressure while the machine is operating, so maintenance must be more for the sake of the diesel engine generator, if there is negligence or replacing spare parts not according to the manual book when doing an overhaul, the consequences that arise are such as erosion of the camshaft. In this case, the researcher used the SHEL method to map the problem and used the USG method for discussion. Data collection techniques in the form of an approach to objects through observation, interviews and literature studies. The purpose of this study was to determine what factors cause erosion of the diesel engine generator camshaft, what impacts are caused by the erosion of the diesel engine generator camshaft and what efforts are made to overcome the erosion of the diesel engine generator camshaft.

In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TIDμ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TIDμ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TIDμ-PIDA controller, which is about 200 times lower than the other controllers that were compared.

This research presents a novel approach for frequency control in an islanded AC Microgrid (MG) system, which integrates multiple renewable energy sources such as solar and wind energy. Variations in the output of these renewable sources can cause fluctuations in both frequency and power within the microgrid. To address these disturbances, a suitable controller is necessary. In this study, a Tilt Integral Derivative (TID) controller is designed for the microgrid, with its parameters optimized and tuned using a novel hybrid Grey Wolf and Cuckoo Search (HGW-CS) algorithm to mitigate these fluctuations. The proposed microgrid consists of various distributed generation (DG) sources, including a Diesel Engine Generator (DEG), Flywheel Energy Storage System (FESS), Battery Energy Storage System (BESS), Micro Turbine (MT), Fuel Cell (FC), and Aqua-electrolyze (AE), all working together to meet the load demand. The performance of the novel HGW-CS-based TID controller is evaluated under different scenarios, demonstrating its robustness and superior performance even in adverse conditions. All simulations were conducted in MATLAB, and the results indicate that the proposed controller outperforms conventional controllers in terms of stability and response to disturbances.

This study focuses on utilising an Automatic Load Frequency Controller (ALFC) within an isolated microgrid to improve the transient response of frequency variations. Addressing the persistent issue of energy shortages requires the expanded deployment of Distributed Generation (DG) systems through the further increased integration of renewable energy sources. A microgrid is a DG that can operate in isolated and grid-connected modes to maximise the benefits of renewable energy. Isolation of the microgrid prevents faults from affecting the microgrid and ensures operational safety. Additionally, intentional islanding is a viable solution for remote area power systems. The extensive integration of renewable energy within microgrids often introduces challenges such as frequency deviations, making frequency stability a critical factor for effective microgrid operation. This research involves modelling an isolated microgrid system that incorporates wind energy, solar energy, a diesel engine generator power generation, a battery energy storage system and biomass energy. The traditional ZN-tuned Proportional-Integral-Derivative (PID) controller is employed to enhance frequency stability. The effectiveness of the ALFC for isolated microgrids is analysed through MATLAB/Simulink simulations under sudden load variations and changes in system inertia. The outcomes of the study are further validated in real-time using an RT simulator, demonstrating the practical application and reliability of the proposed approach.

Selection of phase change material under uncertainty for waste heat recovery in diesel engine generator

Abstract The depletion of fossil fuels, increasing gas emissions and fuel consumption have created a massive need for alternative fuels. Biodiesel is known to have high density and viscosity, which makes it challenging to use in diesel engine generators. One of the methods that can be used to decrease the density, viscosity and emission characteristics is to blend with percentage alcohols. A biodiesel mixture (BDM100) made from waste vegetable oil (WVO) and soybean oil (SBO) has shown some potential in this study. The biodiesel mixture was formed by blending with 15 % ethanol and 15 % butanol, resulting in a biodiesel mixture-ethanol blend (BMET15) and biodiesel mixture-butanol blend (BMBT15). The purpose of this study is to thoroughly evaluate and compare a diesel engine generator’s performance and emission characteristics powered by diesel fuel (D100), BDM100, BMET15, and BMBT15. The experiments were carried out on a single-cylinder diesel engine generator at different speeds. The results indicated that the brake power (BP) of BDM100, BMET15, and BMBT15 decreased by 1.9 %, 17.8 %, and 16.0 % compared to D100. The brake-specific fuel consumption (BSFC) of BDM100, BMET15, and BMBT15 increased by 2.0 %, 17.8 %, and 19.1 % compared to D100. The BDM100 and BMET15 offer 0.4 % and 2.9 % more brake thermal efficiency (BTE) than D100 at maximum speed. The BDM100, BMET15 and BMBT15 decreased hydrocarbon (HC) by 4 %, 26.4 % and 28.8 %, whereas carbon dioxide (CO2) decreased by 9.7 %, 32.4 %, and 28.5 %; Bosch Smoke Number decreased by 21.2 %, 38.1 %, and 29.9 % compared to D100.

HighlightsAn electrically driven traction-assist powered axle was integrated into a triple-axle large-capacity slurry tanker.The traction-assist system reduced tractor drawbar pull, wheel slip, and drawbar power requirements.Specific fuel consumption showed no significant reduction with the implementation of the traction-assist system.Abstract. Hybrid diesel engine-generator systems represent a near-term application that precedes the widespread adoption of fully electric tractors. One such application is a traction-assist system on one or more of the axles of a towed implement. Given the scarcity of published evaluations on such applications, we investigated the effectiveness of this system on a large slurry tanker. The investigation involved testing a 36 m3 tanker with partial and full loads (42.4 and 52.9 Mg) in conditions where traction was favorable, and field slopes were minimal. A randomized and replicated experiment was conducted involving two variables: tanker load (either full or partially full) and traction-assist (engaged or disengaged), resulting in a two-by-two design. The results indicated significant reductions in tractor drawbar pull, wheel slip, and drawbar power requirements occurred with the traction-assist system engaged. However, despite these encouraging results, no significant decrease was observed in specific fuel consumption. The favorable traction conditions might have limited the effectiveness of the traction-assist system in reducing energy consumption. The positive traction and wheel slip results suggest that the system’s greatest potential may lie in soft, wet soil environments and steep terrain. In such challenging scenarios, the traction-assist system will likely demonstrate its full utility by enhancing traction, minimizing tractor wheel slip, and ultimately boosting productivity. Future research should be conducted under these more challenging conditions. Keywords: Electric drive, Slurry, Tanker, Traction, Tractor.

Forklifts (Counterbalance) are work machines that are used to carry and stack specialized or standardized loads with a special attachment connection adapted to the mast section according to the type of work to be done. Forklifts have different load lifting capacities and optional lifting heights. These vehicles cannot reach high speeds due to their nature and safety. According to the development of autonomous driving worldwide, a rapid development is observed in both technology and comfort parameters of warehouse transportation equipment. In the comfort-oriented evaluations of diesel forklifts, the noises originating from the combustion in the engine and the motion transmission in the mechanical units are the main ones; in addition, the noises originating from the wheel and road irregularities constitute the secondary noises originating from the imbalance in the mast system. In this study, the change in the internal and external noise measurements, i.e. the operator and the environment, of a new generation diesel engine forklift according to the ANSI/ITSDF B56 standard was examined. In the unloaded driving evaluation, it was determined that the noise emitted to the environment was 8% less than the noise coming to the operator. In the evaluation of the noise emitted to the environment, it was determined that the idle speed under no-load conditions was 20.7% lower than the maximum speed noise level. In the evaluation of the noise coming to the operator; it was determined that the effect of the load lifted at low speeds was 2.7% higher than the no-load condition, and that the idle speed was 17.6% lower than the maximum speed noise level under the same load condition

Abstract The maritime industry, a major contributor to carbon emissions, is under increasing environmental pressure due to global climate change. This study presents an innovative energy management strategy for hybrid power systems in ocean engineering vessels, based on an improved particle swarm optimisation algorithm. We convert the traditional powered vessel Marine Oil 257 to a hybrid model, and explore the energy storage requirements, system configurations, and control methods for a practical implementation. Post-conversion, the main diesel engine drives the propeller, and is supported by a lithium iron phosphate battery energy storage system in conjunction with the diesel engine and shaft generators to achieve certain energy efficiency and emission reduction goals. In our strategy, the shaft power of the main engine and the active power of the shaft generator are employed as decision variables, and the ship power balance, operational speed limits, generator output constraints, and system reliability are taken into consideration. Real-time optimisation of energy allocation is performed using an improved particle swarm optimisation algorithm in MATLAB. The effectiveness of this approach is validated through a comparative analysis with full-scale experimental data, and it is shown to be a practical pathway for retrofitting traditional power vessels to enhance the energy efficiency and for providing valuable insights for technological advancement.

ABSTRACTRenewable generation plays an important part in today's power system. With the inclusion of the inverter interfaced renewable energy sources (RESs) into a microgrid, the total system inertia decreases and it leads to increased frequency deviation in presence of a disturbance. This paper proposes a cascaded Integral Minus Tilt Integral Derivative with Filter (I–TDN)‐Proportional‐Integral (PI), [(I‐TDN)‐PI] controller for frequency stabilization of a hybrid microgrid in presence of electric vehicles (EV). The microgrid model includes reheated thermal power plant with high degree of non‐linear system such as inverter based RESs like photovoltaic and wind generation systems. A diesel engine generator is incorporated for load frequency control during perturbation in the system frequency. Virtual inertia controller (VIC) with inverter based energy storage system (ESS) is commonly used to improve system inertia and frequency stability of the microgrid. In addition to the ESS, this paper proposes inclusion of the EVs in this VIC. The optimal gains of the proposed cascaded I‐TDN‐PI controller are determined using Mountain Gazelle Optimizer (MGO), a modern metaheuristic optimization algorithm. Sensitivity of the proposed controller is investigated in presence of system nonlinearities, load perturbations, time delay, system parameter variation and RES power fluctuations. The simulation results justify the robustness of the proposed control structure for frequency stabilization of the microgrid.

Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80–90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

A SSA-based CFFOPID drop deloaded tidal turbine controller using HVDC-link

Fluctuations in electricity demand can lead to inefficiencies in diesel generators (DEG) and potentially trigger blackouts, as experienced by PT Indolakto. This study aims to identify the performance parameters of a 2 MW DEG and formulate strategies to prevent blackouts caused by the 2 MW DEG at PT Indolakto. The research used data collection techniques including field observations, interviews, literature studies, and performance test analysis. The results showed that the efficiency of the 2 MW DEG at load reached 44%, indicating there is still reserve power. An overload of 21% from the generator's capacity resulted in a blackout and a system frequency drop of 13.4 Hz from the normal frequency. Although the 2 MW DEG has adequate efficiency, optimization is necessary to prevent blackouts. One strategy is to use the 2 MW DEG as a system reserve for critical loads.

The search for permanent fossil fuel substitutes has become critical due to the declining supply of fossil fuels and the toxic pollution emitted by diesel engines. In this study, diesel engine characteristics have been investigated numerically and experimentally using diesel, biodiesel mixture from waste vegetable oil and soybean oil (BM100) and butanol blends (5%, 10%, and 15%). The experimental work was conducted on the single-cylinder diesel engine generator at different speeds (1000, 1500, 2000, and 2500 rpm) and full load conditions. A commercial Diesel-RK software was used to perform the numerical aspects of the diesel engine. The different percentages of butanol blends were added to biodiesel mixture to form biodiesel mixture-butanol blends. It was discovered that there was good agreement between the experimental and numerical results. The cylinder pressure, heat release rate, brake power, brake-specific fuel consumption, brake thermal efficiency, nitrogen oxide, carbon dioxide, and particulate matter (PM) emissions were all predicted using the numerical technique. Results showed a decrease in carbon dioxide, particulate matter, and brake power. When compared to regular diesel fuel, at maximum speed, there was a decrease in brake-specific fuel consumption and an increase in nitrogen oxide emissions.

Electricity generation in Islanded Urban Microgrids (IUMG) now relies heavily on a diverse range of Renewable Energy Sources (RES). However, the dependable utilization of these sources hinges upon efficient Electrical Energy Storage Systems (EESs). As the intermittent nature of RES output and the low inertia of IUMGs often lead to significant frequency fluctuations, the role of EESs becomes pivotal. While these storage systems effectively mitigate frequency deviations, their high costs and elevated power density requirements necessitate alternative strategies to balance power supply and demand. In recent years, substantial attention has turned towards harnessing Electric Vehicle (EV) batteries as Mobile EV Energy Storage (MEVES) units to counteract frequency variations in IUMGs. Integrating MEVES into the IUMG infrastructure introduces complexity and demands a robust control mechanism for optimal operation. Therefore, this paper introduces a robust, high-order degree of freedom cascade controller known as the 1PD-3DOF-PID (1 + Proportional + Derivative—Three Degrees Of Freedom Proportional-Integral-Derivative) controller for Load Frequency Control (LFC) in IUMGs integrated with MEVES. The controller’s parameters are meticulously optimized using the Coati Optimization Algorithm (COA) which mimics coati behavior in nature, marking its debut in LFC of IUMG applications. Comparative evaluations against classical controllers and algorithms, such as 3DOF-PID, PID, Reptile Search Algorithm, and White Shark Optimizer, are conducted under diverse IUMG operating scenarios. The testbed comprises various renewable energy sources, including wind turbines, photovoltaics, Diesel Engine Generators (DEGs), Fuel Cells (FCs), and both Mobile and Fixed energy storage units. Managing power balance in this entirely renewable environment presents a formidable challenge, prompting an examination of the influence of MEVES, DEG, and FC as controllable units to mitigate active power imbalances. Metaheuristic algorithms in MATLAB-SIMULINK platforms are employed to identify the controller’s gains across all case studies, ensuring the maintenance of IUMG system frequency within predefined limits. Simulation results convincingly establish the superiority of the proposed controller over other counterparts. Furthermore, the controller’s robustness is rigorously tested under ± 25% variations in specific IUMG parameters, affirming its resilience. Statistical analyses reinforce the robust performance of the COA-based 1PD-3DOF-PID control method. This work highlights the potential of the COA Technique-optimized 1PD-3DOF-PID controller for IUMG control, marking its debut application in the LFC domain for IUMGs. This comprehensive study contributes valuable insights into enhancing the reliability and stability of Islanded Urban Microgrids while integrating Mobile EV Energy Storage, marking a significant advancement in the field of Load-Frequency Control.

Nowadays, millions of people in remote areas do not enjoy an uninterrupted power supply due to the lack of connectivity to the main power grid. Under such circumstances, the only feasible way to access electricity is typically local power generation, often relying on diesel engine–generator sets or photovoltaic arrays. However, on many occasions, this configuration does not fully exploit all available local resources, including biomass. Indeed, most isolated areas have access to local biomass production from agricultural activities, which can be used for local electricity generation through gasification. This paper addresses this challenge by developing an innovative optimal sizing tool for hybrid power plants integrating biomass gasifiers, specifically designed for isolated areas with access to local biomass production. The novel approach models the particular features of biomass gasification technologies, including long on/off times or restrictive ramping limits. To this end, an efficient methodology based on representative weeks is proposed, which is combined with a solution strategy based on the multi-cut Benders’ decomposition, thus resulting in a tractable framework that can deal with a huge amount of data efficiently. One of the most salient features of the new proposal is the consideration of local biomass production, which is included in the methodology through an original algorithm. Accordingly, a certain amount of biomass is sourced locally, leading to more accurate and reliable results. The new methodology is applied to a benchmark off-grid community in Ghana. The results demonstrate that the use of gasifiers reduces the project cost notably (by 90%) driven by the reduced biomass cost, which can be supplemented by locally generated biomass from agricultural activities. In addition, this technology constitutes a clean source of energy, reducing the total CO2 emissions by 83% compared to a baseline case in which only diesel generators are used. Moreover, it is demonstrated that biomass gasification can effectively act as base load power generation technology to reliably cover most of the local demand, thereby enabling a clean and inexpensive dispatchable local power generation. Finally, a sensitivity analysis reveals that the economic feasibility of the plant is more sensitive to the biomass cost than the selling price of biochar, resulting in a 33% increment in the total project cost when the price of biomass increases from 0 to 0.4 $/kg. Nevertheless, gasification remains as the predominant power generation technology even under unfavorable prices.

Municipal water supply system (WSS) consist of different pumping stages viz. intake, water treatment plant (WTP) and intermediate pumping station (IPS). Usually, the power supply for WSS is obtained through public power tapping sources. However, this often leads to load shedding and disruption of the water supply. This paper focuses on the concept, considering WSS as a multi-source multi-area microgrid scheme, this includes renewable energy sources (RES) such as solar, wind, etc. Moreover, the study incorporates a Battery Energy Storage System (BESS) and a Diesel Engine Generator (DEG) to provide power supply during peak demand at each pumping station. Frequency control is essential for optimizing system performance. This paper proposes Enhanced Harris Hawks Optimization Algorithm (EHHO) based PID controller for regulating the frequency in the multi-microgrid-based water supply system. The proposed controller is implemented in MATLAB simulation software, and its response is compared with other optimization methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Moreover, implementation and comparison of higher degree order controller such as 3DOF-FOPIDN controller and 3DOF-TIDN controllers are tested under PSO method to observe the performance as well as robustness of the controller. The results indicate that the proposed controller provides better performance in controlling the load frequency deviation, thus improving the efficiency and reliability of the multi-microgrid system for consideration of municipal water supply.