Search Results (81)

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with an Attentive Long Short-Term Memory (LSTM) network to enhance forecasting performance in renewable-integrated smart grids. The CNN component captures spatial dependencies from multivariate inputs, comprising meteorological variables and generation data, while the LSTM module models temporal correlations in historical load patterns. An embedded attention mechanism dynamically weights input sequences, enabling the model to prioritise the most influential time steps, thereby improving its interpretability and robustness during demand fluctuations. ST-CALNet was trained and evaluated using real-world datasets that include electricity consumption, solar photovoltaic (PV) output, and wind generation. Experimental evaluation demonstrated that the model achieved a mean absolute error (MAE) of 0.0494, root mean squared error (RMSE) of 0.0832, and a coefficient of determination (R²) of 0.4376 for electricity demand forecasting. For PV and wind generation, the model attained MAE values of 0.0134 and 0.0141, respectively. Comparative analysis against baseline models confirmed ST-CALNet’s superior predictive accuracy, particularly in minimising absolute and percentage-based errors. Temporal and regime-based error analysis validated the model’s resilience under high-variability conditions such as peak load periods, while visualisation of attention scores offered insights into the model’s temporal focus. These findings underscore the potential of ST-CALNet for deployment in intelligent energy systems, supporting more adaptive, transparent, and dependable forecasting within smart grid infrastructures. Full article

The implementation of renewable energy systems (RESs) in the agricultural sector has significant potential to mitigate the negative effects of fossil fuel-based products on the global climate, reduce operational costs, and enhance crop production. However, the intermittent nature of RESs poses a major challenge to realizing these benefits. To address this, thermal energy storage (TES) and hybrid heat pump (HHP) systems are integrated with RESs to balance the mismatch between thermal energy production and demand. In pursuit of clean energy solutions in the agricultural sector, a 3942 m² greenhouse in Yeoju-si, South Korea, is equipped with 231 solar thermal (ST) collectors, 117 photovoltaic thermal (PVT) collectors, four HHPs, two ground-source heat pumps (GSHPs), a 28,500 m³ borehole TES (BTES) unit, a 1040 m³ tank TES (TTES) unit, and three short-term TES units with capacities of 150 m³, 30 m³, and 30 m³. This study evaluates the long-term performance of the integrated hybrid renewable energy and thermal energy storage systems (HRETESSs) in meeting the greenhouse’s heating and cooling demands. Results indicate that the annual system performance efficiencies range from 25.3% to 68.5% for ST collectors and 31.9% to 72.2% for PVT collectors. The coefficient of performance (COP) during the heating season is 3.3 for GSHPs, 2.5 for HHPs using BTES as a source, and 3.6 for HHPs using TTES as a source. During the cooling season, the COP ranges from 5.3 to 5.7 for GSHPs and 1.84 to 2.83 for ASHPs. Notably, the HRETESS supplied 3.4% of its total heating energy directly from solar energy, 89.3% indirectly via heat pump utilization, and 7.3% is provided by auxiliary heating. This study provides valuable insights into the integration of HRETESSs to maximize greenhouse energy efficiency and supports the development of sustainable agricultural energy solutions, contributing to reduced greenhouse gas emissions and operational costs. Full article

This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs. Full article

As a crucial strategy for mitigating climate change and achieving electricity independence, renewable energy sources (RESs) are gaining widespread importance. This study explores achieving electricity autonomy for Nisyros Island, Greece, through RESs. Four scenarios are evaluated, including standalone wind and photovoltaic systems, alongside hybrid options combining both. Each scenario is designed to meet the island’s electricity demands while considering economic feasibility and minimal environmental impact. The research findings are that wind-based scenarios offer the most cost-effective solutions, with a three wind turbine setup emerging as the most economical option for full coverage of electricity demands. Hybrid approaches, particularly those incorporating more wind turbines, are also financially viable. Real-world consumption data are integrated into the analysis, providing valuable insights for Nisyros’ energy future. Overall, the study demonstrates Nisyros’ potential to achieve electricity independence through RESs, with wind resource assessments suggesting that the island could become autonomous. This approach would promote environmental sustainability by reducing the given dependence on fossil fuels. Additionally, it would bring economic benefits for the island’s residents in the renewable energy sector. Furthermore, this work allows for the island to achieve electricity independence through renewable energy in alignment with the EU’s climate goals. Full article

The global transition to renewable energy sources (RESs) is accelerating to combat the rapid depletion of fossil fuels and mitigate their devastating environmental impact. However, the increasing integration of large-scale intermittent RESs, such as solar photovoltaics (PVs) and wind power systems, introduces significant technical challenges related to power supply stability, reliability, and quality. This paper provides a comprehensive review of these challenges, with a focus on the critical role of energy storage systems (ESSs) in overcoming them by evaluating their technical, economic, and environmental performance. Various types of energy storage systems, including mechanical, electrochemical, electrical, thermal, and chemical systems, are analyzed to identify their distinct strengths and limitations. This study further examines the current state and potential applications of ESSs, identifying strategies to enhance grid flexibility and the increased adoption of RESs. The findings reveal that while each ESS type has specific advantages, no single technology can tackle all grid challenges. Consequently, hybrid energy storage systems (HESSs), which combine multiple technologies, are emphasized for their ability to improve efficiency and adaptability, making them especially suitable for modern power grids. Full article

With the advancement of intelligent power generation and consumption technologies, an increasing number of renewable energy sources (RESs), smart loads, and electric vehicles (EVs) are being integrated into smart grids. This paper proposes a coordinated frequency control strategy for hybrid power systems with RESs, smart loads, EVs, and a thermal power unit (TPU), in which EVs and the TPU participate in short-term frequency regulation (FR) jointly. All EVs provide FR auxiliary services as controllable loads; specifically, the EV aggregations operate in charging mode when participating in FR. The proposed coordinated frequency control strategy is implemented by an improved recurrent neural network (IRNN), which combines a recurrent neural network with a functional-link layer. The weights and biases of the IRNN are trained by an improved backpropagation through time (BPTT) algorithm, in which a chaotic competitive swarm optimizer (CCSO) is proposed to optimize the learning rates. Finally, the simulation results verify the superiority of the coordinated frequency control strategy. Full article

As the penetration of renewable energy sources (RESs) in power systems continues to increase, their volatility and unpredictability have exacerbated the burden of frequency regulation (FR) on conventional generator units (CGUs). Therefore, to reduce frequency deviations caused by comprehensive disturbances and improve system frequency stability, this paper proposes an integrated strategy for hybrid energy storage systems (HESSs) to participate in primary frequency regulation (PFR) of the regional power grid. Once the power grid frequency exceeds the deadband (DB) of the HESS, the high-frequency signs of the power grid frequency are managed by the battery energy storage system (BESS) through a division strategy, while the remaining parts are allocated to pumped hydroelectric energy storage (PHES). By incorporating positive and negative virtual inertia control and adaptive droop control, the BESS effectively maintains its state of charge (SOC), reduces the steady-state frequency deviation of the system, and provides rapid frequency support. When the system frequency lies within the DB of the HESS, an SOC self-recovery strategy restores the BESS SOC to an ideal range, further enhancing its long-term frequency regulation (FR) capability. Finally, a regional power grid FR model is established in the RT-1000 real-time simulation system. Simulation validation is conducted under three scenarios: step disturbances, short-term continuous disturbances, and long-term RES disturbances. The results show that the proposed integrated strategy for HESS participation in PFR not only significantly improves system frequency stability but also enhances the FR capability of the BESS. Full article

This paper concerns the application of the AHP (analytic hierarchy process) multi-criteria decision support method for the final selection of the structure of a hybrid power system with RESs (renewable energy sources) and EES (electrical energy storage) from a set of solutions obtained through multi-criteria optimization. These solutions, depending on their position within the Pareto front, may differ significantly in terms of the values of the criteria functions, or may be located very close to each other in the solution space. The role of the expert is to select the final solution, taking into account many additional criteria, often of a subjective nature. The article optimizes the structure of the proposed system using the multi-criteria NSGA-II (Non-dominated Sorting Genetic Algorithm) method, taking into account three technical criteria. The AHP method was used to select the final solution, which allows determination of the ranking of solution variants, taking into account selected additional criteria. In the analyzed case, these are primarily economic indicators, technical conditions, and preferences of the system recipients. In addition to determining the ranking of solutions, a sensitivity analysis was performed, which gives the expert extensive knowledge on the impact of individual criteria on the order of variants in the ranking. It was shown that in the case of selecting hybrid structures of generating systems with EES for a specific type of receiver, the use of the AHP method significantly facilitates making the final decision. Full article

A fuzzy cascaded PI−PD (FCPIPD) controller is proposed in this paper to optimize load frequency control (LFC) in the linked electrical network. The FCPIPD controller is composed of fuzzy logic, proportional integral, and proportional derivative with filtered derivative mode controllers. Utilizing renewable energy sources (RESs), a dual-area hybrid AC/DC electrical network is used, and the FCPIPD controller gains are designed via secretary bird optimization algorithm (SBOA) with aid of a novel objective function. Unlike the conventional objective functions, the proposed objective function is able to specify the desired LFCs response. Under different load disturbance situations, a comparison study is conducted to compare the performance of the SBOA-based FCPIPD controller with the one-to-one (OOBO)-based FCPIPD controller and the earlier LFC controllers published in the literature. The simulation’s outcomes demonstrate that the SBOA-FCPIPD controller outperforms the existing LFC controllers. For instance, in the case of variable load change and variable RESs profile, the SBOA-FCPIPD controller has the best integral time absolute error (ITAE) value. The SBOA-FCPIPD controller’s ITAE value is 0.5101, while sine cosine adopted an improved equilibrium optimization algorithm-based adaptive type 2 fuzzy PID controller and obtained 4.3142. Furthermore, the work is expanded to include electric vehicle (EV), high voltage direct current (HVDC), generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD). The result showed that the SBOA-FCPIPD controller performs well when these components are equipped to the system with/without reset its gains. Also, the work is expanded to include a four-area microgrid system (MGS), and the SBOA-FCPIPD controller excelled the SBOA-CPIPD and SBOAPID controllers. Finally, the SBOA-FCPIPD controller showed its superiority against various controllers for the two-area conventionally linked electrical network. Full article

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition. Full article

The high penetration of renewable energy sources (RESs) and power electronics devices has led to a continuous decline in power system stability. Due to the instability of grid-following converters (GFLCs) in weak grids, the grid-forming converters (GFMCs) have gained widespread attention featuring their flexible frequency and voltage regulation capabilities, as well as the satisfactory grid-supporting services, such as inertia and damping, et al. Notably, the risk of wideband oscillations in modern power grids is increasingly exacerbated by the reduced number of synchronous generators (SGs). Thus, the wideband oscillation suppression method based on adaptive active power droop control of GFMCs is presented in this paper. First, the stability of the hybrid grid-forming and grid-following system is obtained according to the improved short circuit ratio (ISCR), where the GFMC is in parallel at the point of common coupling (PCC) of the GFLC. Then, an adaptive adjustment strategy of the active power droop control is proposed to enhance the oscillation suppression capability across the full frequency range, thereby mitigating the wideband oscillation caused by phase-locked loop (PLL) synchronization in the GFLCs. Additionally, a first-order inertia control unit is added to the active and reactive power droop controllers to mitigate frequency and voltage variations as well as suppress potential mid-to-high frequency resonance. Finally, the wideband oscillation suppression strategy is validated by the simulation and experimental results. Full article

The use of renewable energy sources (RESs) is increasing every day to meet increasing energy demands and reduce dependence on fossil fuels. When designing hybrid renewable energy systems (HRESs), it is necessary to examine their technical, economic, and environmental feasibility. In this study, a new strategy is proposed using the HOMER Matlab Link (ML) connection for an HRES model consisting of a photovoltaic (PV) system, a wind turbine (WT), a biogas generator (BGG), and a battery storage system (BSS) designed to meet the electrical energy needs of Doğanevler village located in the rural area of Bingöl province. The data obtained as a result of the proposed strategy (PS) are compared with HOMER’s loop charging (CC) and load following (LF) optimization results. According to the PS, the optimum capacity values for the HRES components are 10 kW for WT, 10 kW for PV, 8 kW for BGG, 12 kWh for BSS, and 12 kW for the converter. According to the optimum design, 16,205 kWh of the annual energy produced was generated by PV systems, 22,927 kWh by WTs, and 22,817 kWh by BGGs. This strategy’s NPC and LCOE (Levelized Cost of Energy) values are calculated as USD 130,673.91 and USD 0.207/kWh, respectively. For the CC dispatch strategy, the NPC and LCOE values are calculated as USD 141,892.28 and USD 0.240/kWh, while for the LF dispatch strategy, these values are USD 152,456.89 and USD 0.257/kWh. The CO₂ emission value for the system using a BGG was calculated as 480 kg/year, while for the system using a DG, this value increased approximately 57 times and was calculated to be 27,709 kg/year. The results show that the PS is more economical than the other two strategies. The PS provides energy security, reduces costs, and increases environmental sustainability. Finally, a sensitivity analysis was conducted based on the availability of renewable resources, fuel cost, and inflation parameters, and the results were analyzed. Full article

Grid dynamics and control mechanisms have improved as smart grids have used more inverter-based renewable energy resources (IBRs). Modern converter technologies try to improve converters’ capacities to compensate for grid assistance, but their inertia still makes them heavily dependent on synchronous generators (SGs). Grid-following (GFL) converters ensure grid reliability. As RES penetration increases, the GFL converter efficiency falls, limiting integration and causing stability difficulties in low-inertia systems. A full review of grid converter technologies, grid codes, and controller mechanisms is needed to determine the current and future needs. A more advanced converter is needed for integration with more renewable energy sources (RESs) and to support weak grids without SGs and with low inertia. Grid-forming (GFM) inverters could change the electrical business by addressing these difficulties. GFM technology is used in hybrid, solar photovoltaic (PV), battery energy storage systems (BESSs), and wind energy systems to improve these energy systems and grid stability. GFM inverters based on BESSs are becoming important internationally. Research on GFM controllers is new, but the early results suggest they could boost the power grid’s efficiency. GFM inverters, sophisticated smart inverters, help maintain a reliable grid, energy storage, and renewable power generation. Although papers in the literature have compared GFM and GFL, none of them have examined them in terms of their performance in a low-SCR system. This paper shows how GFM outperforms GFL in low-inertia and weak grid systems in the form of a review. In addition, a suitable comparison of the results considering the performance of GFM and GFL in a system with varying SCRs has been depicted in the form of simulation using PSCAD/EMTDC for the first time. Full article

Integrating renewable energy sources (RESs), such as offshore wind turbines (OWTs), into the power grid demands advanced control strategies to enhance efficiency and stability. Consequently, a Deep Fractional-order Wind turbine eXpert control system (DeepFWX) model is developed, representing a hybrid proportional/integral (PI) fractional-order (FO) model predictive random forest alternating current (AC) bus voltage controller designed explicitly for OWTs. DeepFWX aims to address the challenges associated with offshore wind energy systems, focusing on achieving the smooth tracking and state estimation of the AC bus voltage. Extensive comparative analyses were performed against other state-of-the-art intelligent models to assess the effectiveness of DeepFWX. Key performance indicators (KPIs) such as MAE, MAPE, RMSE, RMSPE, and R² were considered. Superior performance across all the evaluated metrics was demonstrated by DeepFWX, as it achieved MAE of [15.03, 0.58], MAPE of [0.09, 0.14], RMSE of [70.39, 5.64], RMSPE of [0.34, 0.85], as well as the R² of [0.99, 0.99] for the systems states [X₁, X₂]. The proposed hybrid approach anticipates the capabilities of FO modeling, predictive control, and random forest intelligent algorithms to achieve the precise control of AC bus voltage, thereby enhancing the overall stability and performance of OWTs in the evolving sector of renewable energy integration. Full article

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method. Full article