Search Results (21)

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 (TID^μ), 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-TID^μ-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-TID^μ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article

The microgrid is a small-scale, independent power system that plays a crucial role in the transition to carbon-neutral energy systems. Combined heat and power (CHP) systems with energy storage reduce energy waste within microgrids, enhancing energy utilization efficiency. The key challenge for a microgrid integrated with a combined heat and power system is determining the optimal configuration and operation duration under different scenarios to meet users’ electricity and heat demands while minimizing both economic and environmental costs. Thus, this paper presents a bi-objective mathematical model to solve the optimal scheduling problem of the microgrid. The Long Short-Term Memory–Parallel Multi-Objective Energy Valley Optimizer (LSTM-PMOEVO) framework incorporates energy load prediction using LSTM and scheduling planning solved via PMOEVO. These strategies address the challenges posed by unpredictable energy load fluctuations and the complexity of solving such systems. Finally, a public dataset was utilized for the experiments to verify the performance of the proposed algorithm. Comparisons and discussions show that the proposed optimization strategies significantly improve the performance of PMOEVO, demonstrating marked advantages over six classical algorithms. In conclusion, the PMOEVO developed in this paper performs excellently in solving the Scheduling Problem of Biomass-Hybrid microgrids with energy storage considering uncertainty. The work presented in this paper provides a new solution framework for the microgrid-scheduling problem considering uncertainty. In future research, this solution framework will be further advanced for application in real-world scenarios. Full article

The increasing integration of renewable energy-based distributed generation (DG) in modern distribution networks is essential for reducing reliance on fossil fuels. However, the unpredictability and intermittency of renewable sources such as wind and photovoltaic (PV) systems introduce significant challenges for distribution network planning. To address these challenges, this paper proposes a Q-learning-based Distributionally Robust Optimization (DRO) model for expansion planning of distribution networks and generation units. The proposed model incorporates energy storage systems (ESSs), renewable DG, substations, and distribution lines while considering uncertainties such as renewable generation variability, load fluctuations, and system contingencies. Through a dynamic decision-making process using Q-learning, the model adapts to changing network conditions to minimize the total system cost while maintaining reliability. The Latin Hypercube Sampling (LHS) method is employed to generate multi-scenario data, and piecewise linearization is used to reduce the computational complexity of the AC power flow equations. Numerical results demonstrate that the model significantly improves system reliability and economic efficiency under multiple uncertainty scenarios. The results also highlight the crucial role of the ESS in mitigating the variability of renewable energy and reducing the expected energy not supplied (EENS). Full article

This paper addresses the challenge of enhancing power quality in a standalone microgrid powered by wind and battery systems. Fluctuations in wind power generation and unpredictable electricity demand significantly impact power quality. To mitigate these issues, a control strategy utilizing Super Twisting Sliding Mode (STSM) controllers tuned by the Hippopotamus Optimization Algorithm (HOA) is proposed. The HOA algorithm efficiently determines optimal STSM controller parameters, leading to improved system performance and stability. A comparative study was conducted against PI, Fuzzy Logic controllers, and other metaheuristic optimization algorithms (PSO, GWO, WOA). Simulation results, obtained using MATLAB/Simulink, demonstrate the superior performance of the proposed methodology. Specifically, during a simulated abrupt load change, the system exhibited rapid recovery with frequency reaching equilibrium, significantly faster than PI and Fuzzy Logic controllers. Moreover, the DC link voltage remained stable with fluctuations of only 2%, while the three-phase RMS voltages at the Point of Load Bus (PLB) maintained balanced and stable values. These results confirm the enhanced power quality and robust operation achieved with the proposed HOA-tuned STSM control strategy, outperforming other tested methods. The methodology effectively manages both the energy management system and improves power quality in standalone wind and battery-powered microgrids. Full article

The integration of electrolyzers and fuel cells can cause voltage fluctuations within microgrids if not properly scheduled. Therefore, controlling voltage and reactive power becomes crucial to mitigate the impact of fluctuating voltage levels, ensuring system stability and preventing damage to equipment. This paper, therefore, seeks to enhance voltage and reactive power control within reconfigurable microgrids in the presence of innovative power-to-hydrogen technologies via electrolyzers and hydrogen-to-power through fuel cells. Specifically, it focuses on the simultaneous coordination of an electrolyzer, hydrogen storage, and a fuel cell alongside on-load tap changers, smart photovoltaic inverters, renewable energy sources, diesel generators, and electric vehicle aggregation within the microgrid system. Additionally, dynamic network reconfiguration is employed to enhance microgrid flexibility and improve the overall system adaptability. Given the inherent unpredictability linked to resources, the unscented transformation method is employed to account for these uncertainties in the proposed voltage and reactive power management. Finally, the model is formulated as a convex optimization problem and is solved through GUROBI version 11, which leads to having a time-efficient model with high accuracy. To assess the effectiveness of the model, it is eventually examined on a modified 33-bus microgrid in several cases. Through the results of the under-study microgrid, the developed model is a great remedy for the simultaneous operation of diverse resources in reconfigurable microgrids with a flatter voltage profile across the microgrid. Full article

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system. Full article

The feed-in of electricity from renewable energies, such as wind or solar power, fluctuates based on weather conditions. This unpredictability due to volatile feed-in can lead to sudden changes in energy generation so that solutions ensuring grid stability need to be implemented. The cooling sector offers the opportunity to create flexibilities for such balancing, with this study focusing on the thermal flexibilities that can be provided by cooling applications. Various cooling-demand profiles are investigated with respect to their load profile and their impact on flexibility is analysed. In addition to the cooling demand, scenarios of different storage dimensions are considered. As a result, it shows that an increasing base-load level and increasing operating-load duration have a negative effect on flexibility, while an increasing full-load duration is beneficial for flexibility. Storage size also has a strong impact as higher storage capacity and storage performance indicate higher flexibility, whereas above a certain size they only provide little added value. Full article

We face a decentralized renewable energy production scenario, where a large number of small energy producers, i.e., prosumers, contribute to a common distributor entity, who resells energy directly to end-users. A major challenge for the distributor is to ensure power stability, constantly balancing produced vs consumed energy flows. In this context, being able to provide quick restore actions in response to unpredictable unbalancing events is a must, as fluctuations are the norm for renewable energy sources. To this aim, the high scalability and diversity of sources are crucial requirements for the said balancing to be actually manageable. In this study, we explored the challenges and benefits of adopting a blockchain-based software architecture as a scalable, trustless interaction platform between prosumers’ smart energy meters and the distributor. Our developed prototype accomplishes the energy load balancing service via smart contracts deployed in a real blockchain network with an increasing number of simulated prosumers. We show that the blockchain-based application managed to react in a timely manner to energy unbalances for up to a few hundred prosumers. Full article

In recent years, energy challenges such as grid congestion and imbalances have emerged from conventional electric grids. Furthermore, the unpredictable nature of these systems poses many challenges in meeting various users’ demands. The Battery Energy Storage System is a potential key for grid instability with improved power quality. The present study investigates the global trend towards integrating battery technology as an energy storage system with renewable energy production and utility grid systems. An extensive review of battery systems such as Lithium-Ion, Lead–Acid, Zinc–Bromide, Nickel–Cadmium, Sodium–Sulphur, and the Vanadium redox flow battery is conducted. Furthermore, a comparative analysis of their working principles, control strategies, optimizations, and technical characteristics is presented. The review findings show that Lead–Acid, Lithium-Ion, Sodium-based, and flow redox batteries have seen increased breakthroughs in the energy storage market. Furthermore, the use of the BESS as an ancillary service and control technique enhances the performance of microgrids and utility grid systems. These control techniques provide potential solutions such as peak load shaving, the smoothing of photovoltaic ramp rates, voltage fluctuation reduction, a large grid, power supply backup, microgrids, renewable energy sources time shift, spinning reserve for industrial consumers, and frequency regulation. Conclusively, a cost summary of the various battery technologies is presented. Full article

Multi-microgrids (MMGs) revolutionize integrating and managing diverse distributed energy resources (DERs), significantly enhancing the overall efficiency of energy systems. Unlike traditional power systems, MMGs comprise interconnected microgrids that operate independently or collaboratively. This innovative concept adeptly addresses challenges posed by pulsed load effects, capitalizing on the cooperative nature of interconnected microgrids. A coordinated MMG system effectively redistributes and shares the impact of pulsed loads, mitigating voltage fluctuations and ensuring sustained system stability. The proposed cooperative MMG scheme optimizes power distribution and load prioritization, facilitating the seamless allocation of surplus energy from neighboring microgrids to meet sudden surges in demand. This study focuses on DC standalone multi-microgrid systems, showcasing their inherent adaptability, resilience, and operational efficiency in managing pulse, variable, and unpredictable generation deficits. Several experiments on a laboratory-scale DC multi-microgrid validate the system’s robust performance. Notably, transient current fluctuations during pulse loads are promptly stabilized through the effective collaboration of microgrids. Variable load experiments reveal distinct behaviors, shedding light on the profound influence of control strategies. This research reveals the transformative potential of MMGs in addressing energy challenges, with a particular focus on DC standalone multi-microgrid systems. The findings underscore the adaptability and resilience of the proposed cooperative scheme, marking a significant stride in the evolution of modern power systems. Full article

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation. Full article

Microgrids face significant challenges due to the unpredictability of distributed generation (DG) technologies and fluctuating load demands. These challenges result in complex power management systems characterised by voltage/frequency variations and intricate interactions with the utility grid. Model predictive control (MPC) has emerged as a powerful technique to effectively address these challenges. By applying a receding horizon control strategy, MPC offers promising solutions for optimising constraints and enhancing microgrid operations. The purpose of this review paper is to comprehensively analyse the application of MPC in microgrids, covering various levels of the hierarchical control structure. Furthermore, this paper explores the emerging trend of employing MPC across microgrid applications, ranging from converter control levels for power quality to overarching energy management systems. It also investigates the future research perspectives by considering the challenges associated with establishing MPC-based microgrid control. The key conclusion derived from this review paper is that the implementation of MPC techniques in microgrid operations can greatly improve their overall performance, efficiency, and resilience. This paper thoroughly examines the various challenges faced in MPC-based microgrid operations, underscoring the significance of conducting research in advanced artificial intelligence (AI)-based MPC methods. It highlights how these cutting-edge AI techniques can bring about economic benefits in microgrid operations, addressing the complex demands of efficient energy management in a rapidly evolving landscape. The presented insights strive to enhance the comprehension and adoption of MPC techniques in microgrid settings, actively contributing to the ongoing improvement of their operational processes. By shedding light on key aspects and offering valuable guidance, this work aims to propel the advancement and effective utilisation of MPC methodologies in microgrids, ultimately leading to optimised performance and enhanced overall operations. Full article

The share of electricity generation from Variable Renewable Energy Sources (VRES) has increased over the last 20 years. Despite promoting the decarbonization of the energy mix, these sources bring negative characteristics to the energy mix, such as power ramps, load mismatch, unpredictability, and fluctuation. One of the ways to mitigate these characteristics is the hybridization of power plants. This paper evaluates the benefits of hybridizing a plant using an AI-based methodology for optimizing the wind–solar ratio based on the Brazilian regulatory system. For this study, the hybrid plant was modeled using data collected over a period of 10 months. The measurements were obtained using two wind profilers (LIDAR and SODAR) and a sun tracker (Solys 2) as part of the EOSOLAR R&D project conducted in the state of Maranhão, Brazil. After the power plant modeling, a Genetic Algorithm (GA) was used to determine the optimal wind–solar ratio, considering costs with transmission systems. The algorithm achieved a monthly profit increase of more than 39% with an energy curtailment inferior to 1%, which indicates economic complementarity. Later, the same methodology was also applied to verify the wind–solar ratio’s sensitivity to solar energy pricing. The results show that a price increase of 15% would change the power plant’s optimal configuration. Full article

The frequency diversion in hybrid power systems is a major challenge due to the unpredictable power generation of renewable energies. An automatic generation controller (AGC) system is utilised in a hybrid power system to correct the frequency when the power generation of renewable energies and consumers’ load demand are changing rapidly. While a neural network (NN) model based on a back-propagation (BP) training algorithm is commonly used to design AGCs, it requires a complicated training methodology and a longer processing time. In this paper, a bacterial foraging algorithm (BF) was employed to enhance the learning of the NN model for AGCs based on adequately identifying the initial weights of the model. Hence, the training error of the NN model was addressed quickly when it was compared with the traditional NN model, resulting in an accurate signal prediction. To assess the proposed AGC, a power system with a photovoltaic (PV) generation test model was designed using MATLAB/Simulink. The outcomes of this research demonstrate that the AGC of the BF-NN-based model was effective in correcting the frequency of the hybrid power system and minimising its overshoot under various conditions. The BP-NN was compared to a PID, showing that the former achieved the lowest standard transit time of 5.20 s under the mismatching power conditions of load disturbance and PV power generation fluctuation. Full article

Power systems have recently faced significant challenges due to the increased penetration of renewable energy sources (RES) such as frequency deviation due to fluctuations, unpredictable nature, and uncertainty of this RES. In this paper, a cascaded controller called (1+PD)-PID is proposed to reduce the influence of RES uncertainties on the system and to maintain the system’s reliability during fluctuations. The proposed controller is a combination of (1+PD) and PID controllers in order. The output signal of the (1+PD) controller along with the frequency deviation and the power difference between adjacent areas are used as inputs to the PID controller to create the load reference signal. The parameters of the suggested controller are optimally tuned using the African Vulture Optimization Algorithm (AVOA) to ensure the best performance of the controller. A two-area interconnected system with non-reheat thermal power units combined with RES such as solar and wind energy is modeled using MATLAB/Simulink to evaluate the system response. The controller effectiveness is verified by subjecting the studied system to various types of fluctuations such as step load disturbance, variable load perturbation and RES penetration. The obtained simulation results prove that the proposed (1+PD)-PID controller in integration with AVOA offers a significant improvement in the system performance specifications. Moreover, the proposed AVOA-based (1+PD)-PID controller has proven its superiority over other comparable controllers having the least fitness function of 6.01 × 10⁻⁵. Full article