Renewable Energy Generation Systems Research Articles

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

Automated monitoring of natural parameters of renewable energy production systems

The paper discusses the urgent issues of the need to automate the monitoring of natural parameters of renewable energy production systems. Monitoring involves the collec- tion and analysis of various parameters of the natural environment that affect the efficiency and environmental friendliness of systems powered by renewable energy. Based on analytical materials and practical work related to techno-economic and ecological studies concerning the assessment of water reserves in indi- vidual wells, the study focused on the development of technologies and technical means for monitoring. It was found that, in practice, hydrogeological research on groundwaters use outdated methods and technical means for measuring and storing data. A local automated system for monitoring well parameters was developed and constructed. It ensures the operation of the hydrothermal heat pump system at the experimental site of the Institute of Renewable Energy of the National Academy of Sciences of Ukraine. This system was tested in our research. The benefits of this study are revealing the need to create an intelligent monitoring system with the possibility of automated decision-making regarding the management of renewable energy generation systems, depending on changes in the am- bient parameters. A network automated system for monitoring natural parameters was developed, consisting of several wireless sensor subsystems that communicate with each other via wireless connection and transmit information to the gateway. The gateway, in turn, is connected to the computer with a graphical interface for interaction with the system. The results of work indicate that the proposed structures for automated water monitoring are simple and easy to implement. Consequently, the study revealed that one of the prospects is the development of automated monitoring systems with intelligent features, capable of making optimal individual decisions regard- ing the management of renewable energy generation systems under variable environmental parameters. As a result, the data obtained from this study have significant scientific and practical implications for advancing the design methodology of geothermal heat pump wells, focusing on long-term forecasts and assessments of their ecological and economic efficiency.

Aerodynamic effects of an offshore floating photovoltaic platform undergoing pitch and heave motions

The offshore floating photovoltaic (OFPV) platform is a renewable energy generation system that has gained attention in recent years due to several advantages. OFPV exhibits higher sensitivity to wind loads than other offshore platforms, owing to its lightweight design and large aerial coverage. In contrast to the fixed photovoltaic systems, the OFPV platforms experience continuous oscillations induced by waves, which significantly influence the aerodynamic characteristics. In this study, the aerodynamic effects of an oscillating OFPV are investigated using the computational fluid dynamics (CFD) simulation method, including the three-dimensional effects, the Reynolds number effects, and the aerodynamic loads with various kinematic parameters. Some major findings are as follows. Firstly, a large-scale tip vortex exists when OFPV oscillates in pitch and heavy directions, which cannot be detected in two-dimensional simulations. Consequently, the lift coefficient, CL, and the drag coefficient, CD, are highly overestimated in 2-D simulation. Secondly, CL and CD decrease significantly as the Reynolds number increases for cases where Re < 106 while there is only a slight decrease for Re > 106. Thirdly, the aerodynamic forces are highly related to the amplitude of the pitch motion and the phase lag between the pitch and the heave motions. A fitting formula is proposed to calculate CL and CD based on the maximum angle of attack. Lastly, the evolution of the flow fields around the oscillating OFPV is analyzed. The separation of the leading edge mainly causes the aerodynamic force.

Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems.

A novel improved harbor seal whiskers algorithm for solving hybrid dynamic economic environmental dispatch considering uncertainty of renewable energy generation

The originality of this work is introducing a novel Improved Harbor Seal Whiskers Algorithm (IHSWA) as an innovative and improved optimizer for solving complex hybrid dynamic economic-environmental dispatch (HDEED) considering uncertainty of renewable energy generation (REG) and various system constraints. IHSWA is implemented by hybridizing opposition-based algorithm (OBA) and local search algorithm (LSA) approaches to enhance the algorithm's search efficiency and overcome the challenges of other counterpart approaches. IHSWA has an optimal balancing feature between searching modes (exploring and exploiting modes). This feature makes the proposed algorithms more advanced than their counterpart optimizing techniques. The HDEED is modeled using a non-convex and non-smooth objective function, considering various equality and inequality constraints such as ramp rate bounds (RRLs), valve-point effects (VPE), production limits (PL), spinning reserves (SRLs), prohibited operating zones (POZs), and power balance (PB). The proposed approach is verified on a hybrid modified IEEE 57-bus 7-unit power system incorporating solar and wind energy generation. The proposed approach is compared with various modern optimization techniques, such as the traditional Harbor Seal Whiskers Algorithm (HSWA), the Lemurs Optimization Algorithm (LOA), the Nutcracker Optimization Algorithm (NOA), and the Artificial Hummingbird Algorithm (AHA). The incorporation of REG sources produces a significant reduction in operative costs and a remarkable decline in emissions. The convergence rate of the proposed approach indicates that the novel hybrid algorithm converges rapidly towards the optimal solution, achieving the best global optimum solution. The ANOVA test and the Tukey test are used to analyze the results statistically. The IHSWA approach shows superior advanced features with a best fuel cost of 1640.5890 $ and a minimum std. value of 1708.4895. While for emission objective minimizing, IHSWA attained a minimum emission cost and std. value of 1080.1629 kg and 1777.0770, respectively. The statistical analysis of the results approves the stability, reliability, and consistency of the proposed strategy with significant convergence. The appreciated contribution of this work is introducing a novel hybrid approach for solving HDEED efficiently, incorporating REG, and considering various system constraints. IHSWA has advanced features over its counterpart approaches. The contribution holds specific importance for the economic development of countries. It creates the path for increased self-sufficiency by reducing their need for fossil fuels for energy production. The study emphasizes the transition from being reliant on fossil fuels to the development of sustainable urban economies.

Zoning of Degraded Areas Suitable for Implementation of Renewable Energy Generation Systems: Systematic Review

Objective: The objective of this study is to investigate and carry out a systematic review of the literature, with the aim of delving deeper into the topic of zoning and selection of areas for the implementation of renewable energy generating plants. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. The elaboration of the research protocol in four stages: elaboration of questions, choice of databases, definition of the search strategy and inclusion/exclusion criteria, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research comprises the choice of databases and definition of the search string based on the research protocol. Data collection was made by searching Scopus, Science Direct, Web of Science and Energy Citations Database - OSTI. Results and Discussion: The results obtained revealed that 309 studies met the research protocol, of which only 29 studies, after reading the abstract, were ready for the full article reading stage. Seven of these were accepted as relevant to the objective, being classified as accepted and participating in the information gathering to meet the proposed objective. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of georeferencing. These implications may include public and private enterprises that aim for a less environmentally aggressive methodology for selecting sites for renewable energy generation. Originality/Value: This study contributes to the literature by showing the most relevant points and the main gaps in current selection methodologies. The relevance and value of this research are highlighted by representing a new way of thinking about choosing new locations for power plants.

Iot Based Smart Renewable Energy Generation and Irrigation System with Moisture Detection

This research introduces an innovative IoT-based system combining renewable energy with smart irrigation. It uses solar panels and wind turbines to power an irrigation setup while feeding excess energy back to the grid. Soil moisture sensors and machine learning algorithms dynamically adjust watering schedules, optimizing water use and preventing over-irrigation. The system offers remote monitoring and control via mobile devices, enhancing resource efficiency and sustainability. Real-world tests show improved crop yields, reduced resource consumption, and increased energy sustainability, presenting a scalable solution for sustainable agriculture.

A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting

Growing energy demand and increasing environmental challenges underscore the importance of precise forecasts for photovoltaic (PV) operations in renewable energy generation systems. At this stage, it is mainstream to combine both temporal and spatial factors to forecast PV power generation. However, there are fewer studies that consider factors at very large spatial scales. This paper proposes Hybrid Dual Stream ProbSparse Self-Attention Network (HDSPAN), a novel spatial–temporal photovoltaic power forecasting network architecture that can solve the above limitations. The model implements an encoder–decoder approach that extracts the required spatial–temporal information through a dual stream distilling mechanism. In addition, the ProbSparse self-attention mechanism is employed to improve model efficiency and reduce repetitive and redundant information processing. The hyperparameters are optimized using Tree-structured Patzen estimator to improve forecasting outcomes. This paper demonstrates the effectiveness of spatial–temporal PV forecasting by using ERA5 reanalyzed PV data as a case study. Our results show that the HDSPAN model achieves a 10% higher forecasting accuracy compared to the baseline models, significantly advancing PV power forecasting.

Design of a reliable electrical photovoltaic generator based on power converters control

This work proposes a renewable energy generation system that operates with a similar behavior as a synchronous generator in terms of reliability in the power supply and inertial response properties against power changes and grid voltage disturbances. The main contributions of this work are: (1) the renewable energy variability managing by means of a battery energy storage strategy, whose charge and discharge are determined by a forecasted-based power adjustment algorithm, (2) the dynamical modeling of an inverter that includes physical inertia and its corresponding response to power and voltage variations, and (3) the synthesis of a nonlinear optimal control for the inertia-based inverter system where the reactive power injection is regulated. Simulation results for a photovoltaic-based generator are presented to demonstrate the effectiveness of the proposed methodologies.

A novel soft‐switching quadratic high voltage gain trans‐inverse DC/DC converter

AbstractThis paper suggests a new non‐isolated high step‐up DC/DC converter with soft‐switching performance and low input current ripple for renewable energy generation systems. In this introduced circuit, a three‐winding coupled‐inductor combined with a quadratic boost circuit and voltage multiplier cell to obtain high voltage gains. The advanced features of the proposed topology are its ultra‐high voltage gain, soft‐switching performance, low voltage stress across the switching components, continuous input current with low ripple, and the existence of a common ground between the input source and output load. Due to the partial trans‐inverse property in the suggested topology, higher voltage gains can be achieved under smaller turns ratios of the coupled in comparison to the other typical topologies, which leads to efficiency improvement. Moreover, in this introduced circuit, the voltage stress on the single power switch is mitigated using a regenerative clamp circuit. The theory and operating principle of the suggested converter, along with the steady‐state analysis and efficiency interpretation of the proposed converter are discussed in detail. Finally, to verify the theoretical analysis, a 200 W (25 V/400 V) sample prototype is established.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Analysis for the Implementation of Distributed Renewable Energy Generation Systems for Areas of High Vulnerability Due to Hillside Movements: Case Study of Marianza-Cuenca, Ecuador

This research presents a renewable energy system that takes advantage of the energy potential available in the territory. This study emerges as a relevant option to provide solutions to geological risk areas where there are buildings that, due to emergency situations at certain times of the year during deep winter, are a target of danger and where its inhabitants would find it difficult to abandon their properties. The record of mass movements covering the city of Cuenca-Ecuador and part of the province has shown that the main triggering factor of this type of movement comprises the geological characteristics of tertiary formations characterized by lithological components that become unstable in the presence of water and due to their slopes being pronounced. Hybrid systems are effective solutions in distributed electricity generation, especially when it comes to helping people and their buildings in times of great need and the required electricity generation is basic. A hybrid photovoltaic, wind and hydrokinetic system has been designed that supplies electrical energy to a specific area on the opposite geographical side that is completely safe. The renewable energy system is connected to the public electricity grid available on site; however, in the event of an emergency the grid is disconnected for safety and only the hybrid system will work with the support of a battery backup system. In this study, the Homer Pro simulation tool was used and its results indicate that renewable systems that include PV, HKT and WT elements are economically viable, with a COE of USD 0.89/kWh.

Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System

Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System

Analysis of Grid-tied Solar Photovoltaic Energy Generation under Uncertain Atmospheric Conditions Using Adaptive Neuro-fuzzy Control System

The grid-tied photovoltaic (PV) power system has remained the most practical and sustainable configuration among renewable energy generation systems. Although uncertainties persist in solar irradiance and temperature, the grid-tied system faces transient instability issues during maximum power point tracking, adversely affecting power quality and resulting in substantial costs. To overcome this issue, we proposed analyzing the grid-tied system under uncertain atmospheric conditions based on an adaptive neuro-fuzzy control system (ANCS). This control scheme incorporates a hybrid learning algorithm and undergoes evaluation across various operating conditions. The obtained results demonstrate the effectiveness of the learning algorithm in maintaining a fast convergence speed. Consequently, this capability ensures the consistent preservation of sufficient power quality in the power system without any discernible transient impact. Furthermore, the investigation reveals the significant impact of solar radiation and temperature on the performance of the solar grid-tied PV system. Specifically, temperature alone contributes to over 15% power reduction when reaching 45 °C. As the temperature decreases to 5 °C at 1000 W/m2 irradiance, the ANCS influences an increase in the system's power generation from 100.72 kW at 25 °C to 103.01 kW.

Proceeding of catalytic water splitting on Cu/Ce@g-C3N4 photocatalysts: an exceptional approach for sunlight-driven hydrogen generation.

Increasing energy demands and environmental problems require carbon-free and renewable energy generation systems. For this purpose, we have synthesized efficient photocatalysts (i.e., g-C3N4, Cu@g-C3N4, Ce@g-C3N4 and Cu/Ce@g-C3N4) for H2 evolution from water splitting. Their optical, structural and electrochemical properties were investigated by UV-Vis-DRS, PL, XRD, FTIR, Raman and EIS methods. Their surface morphologies were evaluated by AFM and SEM analyses. Their chemical characteristics, compositions and stability were assessed using XPS, EDX and TGA techniques. Photoreactions were performed in a quartz reactor (150 mL/Velp-UK), whereas hydrogen generation activities were monitored using a GC-TCD (Shimadzu-2014/Japan). The results depicted that Cu/Ce@g-C3N4 catalysts are the most active catalysts that deliver 23.94 mmol g-1 h-1 of H2. The higher rate of H2 evolution was attributed to the active synergism between Ce and Cu metals and the impact of surface plasmon electrons (SPEs) of Cu that were produced during the photoreaction. The rate of H2 production was optimized by controlling various factors, including the catalyst amount, light intensity, pH, and temperature of the reaction mixture. It has been concluded that the current study holds promise to replace the conventional and costly catalysts used for hydrogen generation technologies.

Multi-objective capacity optimization method for renewable energy generation systems based on artificial bee colony algorithm

Multi-objective capacity optimization method for renewable energy generation systems based on artificial bee colony algorithm

Multi-objective capacity optimisation method for renewable energy generation systems based on artificial bee colony algorithm

Multi-objective capacity optimisation method for renewable energy generation systems based on artificial bee colony algorithm

Iridium Oxide Nanorods Supported on Sb-Doped SnO2 As Highly Active Oxygen Evolution Catalysts for PEM Water Electrolysis

With the increasing installation of renewable energy generation systems such as solar and wind power, efficient energy storage devices are required to overcome the problem of fluctuations in the availability of electric energy. In particular, the splitting of water by electrolysis to produce storable hydrogen and oxygen appears to be one of the most promising approaches for large-scale energy storage. Proton exchange membrane water electrolysis (PEMWE) has received a great deal of attention due to its ability to respond quickly to renewable energy fluctuations and to operate at high current densities. However, a key limitation of PEMWE is the high cost associated with the use of high loadings of noble metal Ir catalysts at the anode electrode, which is necessary due to the slow kinetics of the oxygen evolution reaction (OER). Therefore, reducing the Ir loading at the OER electrode is essential to realize large-scale applications of PEMWE. This requires the development of highly active OER catalysts to accelerate the reaction or reduce the overpotential. Herein, we report a remarkably active OER catalyst of Ir oxide with nanorod-like structure supported on antimony-doped tin oxide (Sb-SnO2), showing an order of magnitude increase in Ir mass-specific activity than a commercial IrOx catalyst at 1.5 V vs. RHE. We also investigated the temperature dependence of the OER activity, as a way to elucidate the mechanism of enhanced catalysis, from both experimental and theoretical perspectives.The Sb-SnO2 support material (Sb dopant content 4 at.%) with fused-aggregate network structure was prepared by flame pyrolysis method.1 The as-prepared support powder was annealed at 700 oC for 2 h in air using a rotary kiln furnace. Sb-SnO2 supported IrOx nanorods (NRs) catalyst was synthesized by a colloidal method.2 The obtained IrOx NRs/Sb-SnO2 powder was heat-treated at 350 °C in N2 atmosphere for 2 h and at 150 °C in 1% H2 (N2 balance) for 2 h. The Ir loading was determined to be 49.3 wt.% by inductively coupled plasma optical emission spectrometry (ICP-OES). The OER activity was evaluated in 0.1 M HClO4 at 25 to 80 oC using the rotating disk electrode (RDE) technique.2,3 From Figure 1a, it can be seen that the Sb-SnO2 support particles present an interconnected network structure, which is considered to be beneficial to enhance the electron conduction and gas diffusion in the electrocatalytic reaction. On the support, the majority of IrOx nanoparticles are shown to have coalesced, forming rodlike structures. Figure 1b shows OER polarization curves (iR-corrected and normalized to the Ir mass) of the IrOx NRs/Sb-SnO2 catalyst compared to commercial IrOx catalyst (TANAKA Precious Metals). It is clear that the IrOx NRs/Sb-SnO2 catalyst significantly outperforms the IrOx reference catalyst over the entire potential range, and the OER onset potential of the IrOx NRs/Sb-SnO2 catalyst is lower. At 1.5 V (Figure 1c), the IrOx NRs/Sb-SnO2 catalyst exhibits a mass activity (MA) of 0.40 A mg-1 Ir, which is about 10 times higher than that of the IrOx reference catalyst. This dramatically enhanced activity makes it possible to significantly reduce the Ir loading on the anode of PEMWE. Experimental observations and density functional theory (DFT) calculations suggest that the nanorod geometry and strong metal-support interactions may play an important role in lowering the energy barrier for the crucial first step of the OER, thereby enhancing the OER activity. Acknowledgement This work was partly supported by the JSPS KAKENHI (23H02059) and by the project from the New Energy and Industrial Technology Development Organization (NEDO) of Japan.

Strategies for Low CO2-emission Schools in Norway

Regional and national strategies targeting climate change are a driver for reducing greenhouse gas emissions. Municipalities play an important role towards a sustainable society and in restructuring needed to reach the 2030 European Climate targets. They often require that new buildings shall be zero emission and that the building’s carbon footprint shall be reduced compared to a reference building, starting with public buildings. To reach low or zero emission buildings, the production of renewable energy must equal or outweigh the CO2-emissions associated with the building. The energy consumption of the building should be decreased before designing on-site renewable energy generation systems. This is ensured by following a holistic energy design approach: energy conservation, reducing electrical energy, efficient energy management and producing renewable energy. Although Norway has had a focus on low and zero emission buildings, there is a limited number of low CO2-emission schools. These are designed and constructed by various actors. Therefore, the practical experience and knowledge in the industry is scattered and still in development. This paper aims to summarize practical strategies for low CO2-emission schools in Norway. Several case studies were analyzed to identify common traits and interviews were conducted to identify opportunities, challenges and strategies.

Real-time inspection and fault detection for large photovoltaic arrays based on drones and deep learning algorithms

In recent years, the installation of renewable energy generation systems based on photovoltaic (PV) panels has experienced massive increments and PV parks with thousands of panels are now becoming commonplace. Yet, there are some challenges, like inspection and fault detection. Lately, these operations have been approached using drones. This project adds the use of deep learning, more specifically proposes the convolutional neural network (CNN) algorithm, the YOLOv5 model and Real-Time Messaging Protocol (RTMP) protocol to achieve real-time detection of PV panels failures. The YOLOv5 model was trained by sets sorted into 9 different categories including fault and abnormal objects’ coverage. This multi-class classification system was investigated by a variety of evaluation indexes to show effectiveness and accuracy. The system was also examined with its different fault classes. The performance results demonstrate that the mean average precision could reach up to 98% with a good training set, confirming the feasibility of proposed approaches.