Hybrid Generation System Research Articles

Impact of electric circuit configurations on power generation in a photovoltaic and thermoelectric generator hybrid system

Energy harvesting using a thermoelectric generator (TEG) leverages the Seebeck effect to generate electricity from temperature differences. As the demand for electrical energy rises, especially in zero-energy buildings and building conversions, there is an increasing focus on recovering untapped energy sources within buildings. This study aims to analyze the thermal characteristics of a hybrid energy harvester that integrates a TEG with phase change materials (PCM), with the objective of recovering and generating power from wasted sunlight and heat within a building envelope. Additionally, we experimentally investigated the circuit configuration of the TEG to optimize energy harvesting. The experimental results highlighted distinct power generation patterns depending on the TEG's location. To achieve maximum power output, TEGs exhibiting similar power generation patterns should be configured in series to ensure optimal performance. Implementing the maximum power circuit configuration resulted in a 70 % increase in power generation compared to a non-optimized configuration. Moreover, configuring an equal number of TEGs in series yielded a 94.4 % increase in power generation compared to a scenario where all TEGs were connected in series on a single panel.

Modelling long-term operational dynamics of grid-connected hydro- photovoltaic hybrid systems

The development of variable renewable energy sources (VRESs), such as wind and solar photovoltaic (PV), has become a strategic choice for ensuring the security of electricity supplies and the decarbonization of power systems. The hybridization of hydropower with VRESs is an important measure for guaranteeing the smooth and cost-effective integration of VRESs into the power system. However, the highly variable weather- and climate-driven spatial and temporal fluctuations in VRES performance have significant potential to affect the optimal trajectory of hydropower operations. Therefore, it is essential to understand how hydropower responds to VRESs over long-term horizons, thereby allowing informed operation decisions to be made for hybrid generation systems (HGSs). In this study, a long- and short-term nested operation model is developed for hydropower operations considering PV integration. Specifically, the long-term model determines the day-by-day hydropower production over many years, and the short-term model simulates the day-ahead generation scheduling and real-time operation processes of the HGS. Performance metrics are established for evaluating the complementary operation in comparison with separate operation of hydropower and PV plants. China's two representative hydro-PV HGSs (Zhongyu and Longyangxia) are selected as case studies. The results indicate that the hybrid generation of hydropower and PV could, on average, increase total electricity generation by 11.33 %, reduce the load loss rate by 9.31 %, and decrease the PV curtailment rate by 36.60 %. However, hydro-PV hybridization may lead to an increase of 9.22 % in the water spillage rate and a decrease of 0.094 kWh/m3 in the water–electricity conversion coefficient. Therefore, hybrid generation of hydropower and PV could further enhance energy yield and supply reliability. Also, the importance of improving hydropower flexibility is highlighted.

Hybrid system for electrical energy generation for the sustainable functioning of the transport infrastructure facility

Hybrid system for electrical energy generation for the sustainable functioning of the transport infrastructure facility

Load-frequency and voltage control for power quality enhancement in a SPV/Wind utility-tied system using GA & PSO optimization

Load Frequency Control (LFC) and Voltage Control (VC) are critical aspects of hybrid generation systems. In this work, the performance comparison of three different control approaches for LFC and VC: Genetics Algorithm (GA)-tuned Proportional Integral Differentiator (PID), Particle Swarm Optimization (PSO)-PID, and a conventional PID controller is presented. Especially, the performance is assessed and analyzed for convergence speed and computational complexity for each approach. Mathematical framework for each approach is discussed, including the required equations for hybrid generation system. It is reported that the traditional PID controller exhibits fast convergence due to its direct adjustment of control parameters. Simulation results reveal that it requires manual tuning and has low computational complexity. In contrast, the GA-PID utilizes a GA optimization process which automatically tunes the PID gains. Although, it may require multiple generations to converge to the optimal solution, however, it offers better control performance. Moreso, it comes at the cost of higher computational complexity compared to the traditional PID controller. In contrast, the PSO-PID employs an algorithm for parameter optimization. It converges faster than the GA-PID but still requires more iterations than the traditional PID controller. Similar to the GA-PID, it has higher computational complexity due to fitness function evaluation and particle updates. The optimization results provide insights into the convergence speed and computational complexity trade-offs between the three control approaches. Practitioners in the field of hybrid energy systems can utilize the outcomes to make informed decisions based on their specific requirements and available computational resources.

A novel solar power generation hybrid system comprising evacuated U-tube solar collector and thermally regenerative thermocapacitive cycle

While solar energy offers a promising solution for sustainable energy, there’s a continuous drive to explore alternative methods of solar power generation. This paper introduces a novel solar power generation hybrid system that merges an angle-independent evacuated U-tube solar collector (EUSC) with a thermally regenerating thermocapacitive cycle (TRTC). Under AM 1.5 conditions, the hybrid system is anticipated to achieve a power density of 44.33 W m−2, accompanied by an efficiency of 4.43 %, underscoring its great prospect for practical applications. To unlock the performance potential, considerable parametric studies are conducted to find out diverse strategies to boost the hybrid system’s efficacy, encompassing the elevation of EUSC’s inlet temperature, reduction of external environmental temperature, moderation of fluid velocity within EUSC, and enhancement of thermal conductivity in the TRTC. Through meticulous optimization of thermal conductivity between TRTC and the environment, the hybrid system’s theoretical efficiency can attain a maximum of 9.38 %. A gradient-based local sensitivity analysis pinpoints that the EUSC’s internal fluid flow velocity is the most sensitive while the thermal conductivity coefficient between the TRTC and EUSC is the least sensitive. These results uncover key information critical for efficiently designing and operating the hybrid system.

Operational Robustness Assessment of the Hydro-Based Hybrid Generation System under Deep Uncertainties

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS’s robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS’s robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power.

PSO-enhanced discrete-time integrated sliding mode-based control of three-level NPC converter for grid-connected PV-FC distributed generation system

This study proposes the control of a three-level NPC converter applied in a PV-FC hybrid generation system based on discrete-time integral sliding mode control (DISMC) combined with the particle swarm optimization (PSO) technique. First, the comprehensive depiction and modeling of the system's main components are initially presented. Then, the controller's detailed design procedure is given. The sliding manifold is designed to have a fast dynamic response, and its stability analysis is verified using the Lyapunov direct method. Next, the optimization procedure is introduced to calculate the optimal values of the DISMC gains. Furthermore, a power management strategy is examined within the proposed control system to maximize the utility of the power produced by the hybrid system; the control is done through the designed PSO-DISMC to allow decoupled control of the active and reactive powers in two distinct modes of operation (the feeder-flow control (FFC) and the unit-power control (UPC) modes). The simulation of the approach is conducted in MATLAB/Simulink, and the findings demonstrate the effectiveness and robustness of the proposed control strategy.

Numerical energy evaluation of an optimized hybrid energy harvesting system (CPV-TEG) considering the effects of solar tracking

Currently, the energy demand at a global level is accelerated, and the use of non-renewable energies increases the problems related to global warming. Solar technologies, mainly photovoltaics, have been used as an alternative, but conversion efficiency is still low. Hybrid generation systems, which integrate photovoltaic and thermoelectric technologies, seek to provide a solution to efficiency and high costs per area unit. These systems need high tracking precision to concentrate solar energy, which requires robotic tracking systems, which generate energy waste due to the tracking action. The studies and developments do not contemplate in their analysis the losses due to tracking or the precision required by the optical concentration element. The present research develops a novel methodology for the design of optimized hybrid energy harvesting systems, integrating the concentrated photovoltaic and thermoelectric generator technologies and including the effects of solar tracking in the energy sense. In addition, a process for the numerical evaluation of hybrid systems is presented, considering thermal and tracking simulations to estimate the global energy balance. Consequently, the design methodology and numerical evaluation are implemented, comparing three concentration configurations. Additionally, a focus control device is designed for a high concentration index and evaluated. The mean total electrical efficiency of the hybrid system is 40.6763%, 38.84%, 13.3439%, and 35.0484%, respectively. With global gain estimates of 189.48 Wh, 201.83 Wh, 47.43 Wh, and 141.91 Wh. Finally, the results are compared with other developments, concluding that the hybrid system has high energy production. In conclusion, the proposal increases the overall performance of hybrid energy harvesting systems, allowing the development of future research regarding novel concentration devices, cooling methods, harvesting systems, and absorbing designs, among other innovations in more realistic energy evaluation terms.

Capacity configuration optimization of photovoltaic‐battery‐electrolysis hybrid system for hydrogen generation considering dynamic efficiency and cost learning

AbstractGreen hydrogen production via photovoltaic (PV)‐electrolysis is a promising method for addressing global climate change. The battery provides a stable power supply for the PV‐electrolysis system. Hence, this study proposes a robust model for configuring the capacity of a PV‐battery‐electrolysis hybrid system by considering the dynamic efficiency characteristics and cost learning curve effect of key equipments. As a segmented function, the dynamic efficiency of electrolysis is incorporated into the robust model, which describes the hydrogen production efficiency based on power fluctuations. A learning curve model is developed based on historical data from 2012 to 2020 to predict future capital expenditure. Major results are as follows: (1) The use of dynamic efficiency characteristics can reflect the real‐time status of the electrolysis more accurately, and make the capacity configuration more reasonable compared with fixed efficiency. (2) Considering the effect of the learning curve, by 2050, the capital expenditure of the PV panel and proton exchange membrane (PEM) electrolysis can be dropped to 2981 and 1992 CNY/kW, respectively. (3) The optimal case considering uncertainty currently is a 1 MW PV panel equipped with 242 kW electrolysis and 2276 kW battery.

A novel concentrated photovoltaic and ionic thermocells hybrid system for full-spectrum solar cascade utilization

Concentrated photovoltaic-thermoelectric generator (CPV-TEG) hybrid system can harvest the low-grade waste heat derived from photovoltaic cells to generate more electric energy. However, it is economically unfeasible due to the expensive cost of thermoelectric generators and higher photovoltaics temperature. Ionic thermocell based on thermogalvanic effect has a more inexpensive cost and ionic Seebeck coefficient of 1– 2 orders of magnitude higher than thermoelectric generators, which is promising for full-spectrum solar cascade utilization. In present work, a novel concentrated photovoltaic and ionic thermocells hybrid system (CPV-iTEC) is reported, aiming to comprehensively evaluate its feasibility. The ionic thermocell adhered directly to the photovoltaic panel consists the π-type multi-channels, electrodes and p/n-type redox electrolytes flowing in multi-channels, which can simultaneously cool photovoltaic panel and output electric energy for waste heat harvesting. A three-dimensional numerical model is proposed and validated by preliminary experiment to investigate the effects of structural and operating parameters on the performance of CPV-iTEC during energy, exergy and economic analyses. Results show that the optimal structural parameters of ionic thermocell are the 2 mm channel height and 1 mm width, showing a 7.18% higher energy efficiency than that of CPV-TEG at 10 concentration ratios. The presence of ionic thermocell can significantly decrease the photovoltaic panel temperature from 351.30 K to 325.14 K, and thus broaden the available concentration ratio from 1– 21 to 1– 39 compared with CPV-TEG, reaching a higher output power of 16.61 W. During the optimization of advanced redox electrolytes/electrodes, the energy efficiency can reach 49.63% at 21 concentration ratios, which is 5.06% higher than that of CPV-TEG, and show a 22.65% lower overall cost. The application of ionic thermocell in the full-spectrum solar cascade utilization of concentrated photovoltaic system is proved to be feasible with significant advantages of low cost, high performance, and flexible operation.

Hydrogen reduction-based energy management strategy of hybrid fuel cell/PV/battery/supercapacitor renewable energy system

Fuel cells can be used in sustainable cities for a variety of purposes, including emergency backup power systems, electric vehicle recharging, and the production of clean, efficient energy for buildings. Additionally, fuel cells can support distributed energy systems, improving energy management, and lowering dependency on conventional energy sources. However, the slow time response of proton exchange membrane fuel cell (PEMFC) during high-level load variation is an issue that needs addressing. To solve this, battery storage and supercapacitor can be integrated with a hybrid generation system including fuel cell, photovoltaic (PV). It's important to have effective energy management strategies (EMSs) to ensure the photovoltaic (PV) array, PEMFC, batteries, and supercapacitors function optimally. An EMS distributes the load demand among these components while maintaining high efficiency and low hydrogen consumption. This paper proposes an effective EMS for a hybrid generating system DC microgrid (MG) having PV, FC, battery, and SC based on recent Harris hawks optimizer (HHO) to manage the energy between the equipment while minimizing the total hydrogen consumption and enhancing the system efficiency. The research work compared several algorithms, including external energy maximization strategy (EEMS), equivalent consumption minimization strategy (ECMS), frequency decoupling and state machine control (FDSMC), proportional integral (PI), Cuckoo search (CS), and grey wolf optimizer (GWO), with the proposed HHO. The EEMS-based HHO algorithm outperformed the others resulting in 18.95 %, 38.77 %, 47.34 %, 34.43 %, 33.9 %, and 20.38 % hydrogen consumption reduction compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. Moreover, efficiency improved by 7.62 %, 7.77 %, 17.15 %, 11.21 %, 2.17 %, and 5.12 % compared to PI, FDSMC, ECMS, EEMS, CS, and GWO, respectively. The obtained findings demonstrated the robustness and efficacy of the suggested HHO-EMS in obtaining the lowest hydrogen consumption of the DC microgrid that was provided.

Five-Port Isolated Bidirectional DC-DC Converter for Interfacing a Hybrid Photovoltaic–Fuel Cell–Battery System with Bipolar DC Microgrids

This paper introduces a novel five-port, three-input, dual-output isolated bidirectional dc-dc converter (FPIBC) topology with an effective controller for power-sharing and voltage-balancing in bipolar dc microgrids (BPDCMGs). The proposed converter acts as the interface for the integration of a hybrid generation system comprising a solid oxide fuel cell (SOFC), a photovoltaic (PV) system, and a battery into BPDCMGs. It employs a reduced number of circuit elements compared with similar multiport converter topologies suggested for BPDCMG applications. Symmetrical bipolar output voltages are ensured by a voltage-balancing circuit composed of a fully controlled switch and four diodes. The FPIBC is equipped with different controllers for output voltage regulation and balancing, power sharing, maximum power point tracking of the PV, the optimum operating region of the SOFC, and constant-current, constant-voltage charging of the battery. To verify the viability and effectiveness of the proposed system, a simulation model was developed with a 4.2 kW SOFC, a 3.7 kW PV, and a 140 V 10.8 Ah battery in MATLAB/Simulink. The performance of the FPIBC was evaluated through extensive case studies with different operational modes, including battery charge/discharge states and SOFC and PV parameter changes under varying load conditions. In addition, the proposed system was examined using a daily dynamic load profile. According to the simulation results, a peak efficiency of 97.28% is achieved and the voltage imbalance between the output ports is maintained below 0.5%. It is shown that the FPIBC has advantages over previous converters in terms of the number of ports, number of circuit elements, bipolar output voltage, bidirectional power flow, and efficiency.

Distributed ADMM power optimal control for standalone hybrid generation systems

With the rapid development and increased demand for renewable energy sources, standalone hybrid generation systems have become an essential energy solution. Power optimization control is thus critical to achieving the efficient operation and stability of this system. The distributed ADMM (alternating direction method of multipliers)-based approach has the full potential to deal with the power optimization problem of standalone hybrid generation systems. This study uses an optimization algorithm with a Gaussian penalty function, ADMM-ρ, to alternately optimize the power reference values of wind, light, and battery-containing power generation subsystems. The local controller regulates the output power of the converter according to this reference value. This ensures that the wind and photovoltaic power generation subsystem work in load-tracking or maximum power-tracking modes so that the optimal operation of hybrid power generation meets the balance of supply and demand while prolonging the service life of the batteries. Simulation experiments show that the distributed ADMM algorithm can reliably address the power optimization challenge of hybrid power generation systems.

Optimal scheduling for wind-solar-hydro hybrid generation system with cascade hydropower considering regulation energy storage requirements

Large-scale integration of renewable energy into the grid can lead to significant changes in the net load, peak-to-valley difference, peak and valley occurrence time of the power system. As a result, the power of hydropower plants must take a rapid adjustment response. Aiming at the coordinated operation of multiple energy sources, such as wind power, solar power, cascade hydropower station and energy storage pumping station, a coordinated scheduling model is proposed which can fully improve the consumption capacity of wind and solar power by aiming at the maximum power generation, minimum net load fluctuation and minimum wind and solar abandonment. Through the configuration of three different pumping station capacities, the influence of energy storage pumping station capacity on the complementary power generation system is analyzed. When the pumping station capacity is large enough, the output of the wind and solar can be completely consumed. The studies show that the cascade power station and pump energy storage regulation have a strong net load filling valley effect, which can effectively reduce the impact of wind and solar access on system operation, maintain the efficient and stable operation of the unit, and ensure the absorption rate of renewable energy.

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems can enhance asset flexibility, enabling various services and optimizing financial performance. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary or too complex for most asset owners to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run).This work presents a deep learning-based battery sizing optimization tool for hybridizing generation facilities. The tool uses deep learning technique to predict revenue over a broad search space of potential battery sizes, estimate capital and operations costs (including accounting for battery degradation), and calculate financial performance of each potential battery system investment; an output is a recommendation of battery that maximizes financial performance. The tool is tested and validated for hydropower generation and is publicly available on Idaho National Laboratory's GitHub page (https://github.com/idaholab/Hydro_Hybrids), documented in Zenodo (https://zenodo.org/record/7562692#.Y9Q7anbMKUm), and accessible through an intuitive web app (hydrohybrids.inl.gov). This tool will help a greater cross-section of generation owners consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

Enhanced exergy analysis of a solar-biomass hybrid system for micro-scale combined heat and power generation

Enhanced exergy analysis of a solar-biomass hybrid system for micro-scale combined heat and power generation

Parametric assessment of a solid oxide fuel cell-organic Rankine cycle hybrid system for clean power generation

Parametric assessment of a solid oxide fuel cell-organic Rankine cycle hybrid system for clean power generation

Analysis of Wave Energy Conversion for Optimal Generator Sizing and Hybrid System Integration

Analysis of Wave Energy Conversion for Optimal Generator Sizing and Hybrid System Integration

The Frequency Regulation Strategy for Grid‐Forming Wind Turbine Generator and Energy Storage System Hybrid System in Grid‐Connected and Stand‐Alone Modes

This paper proposes a coordinated frequency regulation strategy for grid‐forming (GFM) type‐4 wind turbine (WT) and energy storage system (ESS) controlled by DC voltage synchronous control (DVSC), where the ESS consists of a battery array, enabling the power balance of WT and ESS hybrid system in both grid‐connected (GC) and stand‐alone (SA) modes. The newly developed GFM framework, i.e., DVSC, is adopted in both WT and ESS, which utilizes DC voltage dynamics for synchronization purposes. In this paper, the GC mode and SA mode are transferred by changing the status of the series‐connected switch, and it is necessary to meet the grid connection conditions when the system is transferred to the GC mode, namely, voltage, frequency, and phase sequence. For the GC mode, the inertia response from WT can be realized using the reserved energy of the DC capacitor, while the ESS serves to eliminate the steady‐state frequency deviation and reduce the DC voltage fluctuation of WT using the designed secondary frequency regulation scheme. For the SA mode, the proposed strategy can keep the power balance without external power sources. The small‐signal model of the WT and ESS hybrid system is derived. The stability analysis in both GC and SA modes is fully conducted utilizing the modal analysis method, where the impacts of control parameters on stability are assessed. The performance of the proposed strategy in a weak system is evaluated. Simulation studies are carried out under various power system contingencies to verify the effectiveness of the proposed strategy and validate the correctness of the theoretical analysis.

DESIGN OF SIMULINK-BASED GRID CONNECTED PV CELLS AND WIND-BASED ENERGY GENERATING SYSTEM

Renewable energy sources have displaced traditional energy sources, with significant advances in photovoltaic (PV) and solar cell technology propelling these renewable energy sources (RES) to the forefront of all other RES. PV-based systems have begun to be widely employed in home applications that are connected to the electrical grid, and grid-connected PV power generating systems are now commonplace all over the world. PV producing devices have also been utilized to supplement wind turbines. As a result, hybrid generation systems based on PV and wind technology are being researched for power generation. MATLAB/SIMULINK was used to create a gridconnected PV cell and wind-based energy generation system in this study