Energy Performance Of System Research Articles

Investigation of Degradation of Solar Photovoltaics: A Review of Aging Factors, Impacts, and Future Directions toward Sustainable Energy Management

The degradation of solar photovoltaic (PV) modules is caused by a number of factors that have an impact on their effectiveness, performance, and lifetime. One of the reasons contributing to the decline in solar PV performance is the aging issue. This study comprehensively examines the effects and difficulties associated with aging and degradation in solar PV applications. In light of this, this article examines and analyzes many aging factors, including temperature, humidity, dust, discoloration, cracks, and delamination. Additionally, the effects of aging factors on solar PV performance, including the lifetime, efficiency, material degradation, overheating, and mismatching, are critically investigated. Furthermore, the main drawbacks, issues, and challenges associated with solar PV aging are addressed to identify any unfulfilled research needs. Finally, this paper provides new directions for future research, best practices, and recommendations to overcome aging issues and achieve the sustainable management and operation of solar energy systems. For PV engineers, manufacturers, and industrialists, this review’s critical analysis, evaluation, and future research directions will be useful in paving the way for conducting additional research and development on aging issues to increase the lifespan and efficiency of solar PV.

Stochastic planning for low-carbon building integrated energy system considering electric-heat-V2G coupling

The low-carbon building has been proposed to mitigate the climate change caused by environmental problems and realize carbon neutrality in urban areas. In addition, the integrated energy system (IES) has been developed to reduce renewable energy curtailment in the power distribution system and improve energy efficiency due to the independent operation of traditional energy systems. In this paper, we propose a stochastic planning method for low-carbon building IES, in which the Vehicle to Grid (V2G) is also considered to further increase the flexibility of low-carbon buildings. The proposed planning method optimizes the investment and operation costs, and CO2 emission of the building IES, to achieve the maximum benefit of the low-carbon building and help realize carbon neutrality. By considering the uncertainty of distributed renewable energy, multi-energy load fluctuation and the random behavior of EV users, a two-stage stochastic programming model is formulated with chance constraints, in which the heuristic moment matching scenario generation (HMMSG) and sample average approximation (SAA) methods are applied to deal with the uncertainties. In the case study, a real IES office building in Shanghai, where photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are included as planning options, is used as the test system to verify the effectiveness of the proposed planning method, and the functions of the ESS and EV in IES are analyzed in detail in different operation scenarios. The case study results show that the proposed planning scheme can reduce the total cost and carbon emission significantly.© 2017 Elsevier Inc. All rights reserved.

Superior Optimization for a Hybrid PEMFC Power System Employing Model Predictions

This research investigated the impacts of model prediction on the optimization of hybrid energy systems using a system consisting of solar panels, batteries, a proton exchange membrane fuel cell (PEMFC), and a chemical hydrogen generation system. A PEMFC has several advantages, such as low operating temperatures, fast response times, high power density, and environmental friendliness, and it can convert hydrogen into electricity. However, because hydrogen costs are an important consideration, the PEMFC is usually integrated with hybrid energy systems to guarantee system sustainability. Therefore, in this study, a whole-year household load and solar radiation data were applied to optimize the system components and power management, thereby reducing the system cost by 42.43% and improving system sustainability by 7.05%. The system responses showed that some hydrogen consumption might be saved if the solar and load profiles could be foreseen. Two prediction models were developed that could accurately forecast the radiation and load profiles. Next, a second-year dataset was employed to verify the effectiveness of the model prediction. The results showed that the system cost was reduced by 40.20% without model prediction and by 44.06% with model prediction compared to the original system settings. Finally, experiments to illustrate the feasibility of the hybrid energy system were conducted using prediction models. Based on the results, the model prediction was deemed effective in improving the performance of hybrid energy systems.

Thermal and energy performance evaluation of a full-scale test cabin equipped with PCM embedded radiant chilled ceiling

The escalating global demand for space cooling has led to the emergence of new cooling technologies, including the phase change material embedded radiant chilled ceiling (PCM-RCC) system. This technology improves energy efficiency and indoor environmental quality, while also offering demand-side flexibility. The present study experimentally evaluates the thermal efficiency and energy performance of a PCM-RCC system in a full-scale test cabin equipped with PCM panels. Here, the transient thermal behaviour of PCM ceiling panels besides the cooling energy delivered during charging-discharging cycles are examined. The indoor thermal comfort and peak electricity demand reduction enabled by the present PCM-RCC are also discussed. The results reveal that chilled water circulation for 4–5 h overnight was sufficient to fully recharge the PCM panels. Over 80% of the occupancy time was classified as “Class B″ thermal comfort according to ISO 7730. The system's daily electricity usage was mostly concentrated during off-peak hours, accounting for ∼70% of the total usage. While the controlling schedule used in this study responded to the transient thermal behaviour of the indoor space and PCM ceiling panels, a more dynamic, predictive schedule is necessary to improve the system's overall efficiency and further enhance indoor thermal comfort in response to the changing environmental conditions.

A comprehensive scheduling model for electric vehicles in office buildings considering the uncertainty of charging load

The access of electric vehicles (EVs) has a profound impact on the operation and scheduling of building energy system. However, due to the fixed schedules of EV charging load, few studies concerned on the coordination strategy between EVs and other potential flexible load in office buildings. In this study, uncertainty sources of EV charging load are extracted from sensitivity analysis based on regression method, and then the uncertainties are introduced into the charging load prediction model based on Monte Carlo method. A optimal scheduling model embedded with the uncertainty of charging load, with the joint operation of building air conditioning system, photovoltaic system and EVs, is proposed for building demand response period. The results show that the operation strategies obtained from the proposed model embrace the advantages of small load variance, high flexibility, low operating cost and large photovoltaic power consumption capacity. The arrival non-guarantee rate is reduced from 12.23% to 6.55% and the charging non-guarantee rate is reduced from 24.03% to 9.39%. On the basis of EV orderly charging and the air conditioning pre-cooling, 7.57–61.67% of the 8:00 startup load can be transferred to night, and the original load variance can be reduced by 3.06–17.81%.© 2017 Elsevier Inc. All rights reserved.

Application of Heat Pumps in New Housing Estates in Cities Suburbs as an Means of Energy Transformation in Poland

This paper presents possible applications of heat pumps in buildings during the energy transformation and decarbonization of a country whose energy sector is highly centralized and based on coal. Contemporary cities are spreading beyond the existing borders and new areas cannot be supplied by the existing centralized district heating system. The only form of energy that is available on the outskirts of cities is electricity, which means that it must be used for all energy needs, including heating. In such a case, the use of heat pumps is perfectly justified in terms of energy, economy and environment, especially when they are coupled with photovoltaic systems. Hypothetical micro housing estate energy systems based on photovoltaics and heat pumps are analyzed in the paper. New options for configuration and operation of the energy systems are considered. Results of a simulation study show that by creating a common local electricity network and a local heating network powered by a central heat pump, the direct use of electricity generated in the local photovoltaic systems increases from 25% to at least 35%, thanks to enabling more even storing and consuming of solar energy during a day, compared to the independent operation of energy systems at individual houses.

Computational study of Cattaneo–Christov heat flux on cylindrical surfaces using CNT hybrid nanofluids: A solar-powered ship implementation

This study examines the potential of using nanofluids in solar thermal energy systems. Nanofluids are known to exhibit high convection heat transfer coefficients, low specific heat, and density, making them ideal for improving the performance of solar thermal energy systems. However, this computational study investigates the application of Cattaneo-Christov heat flux on cylindrical surfaces using carbon nanotube (CNT) hybrid nanofluids, for use in a solar-powered ship. The work utilizes numerical simulations to analyze the heat transfer and fluid flow characteristics of the hybrid nanofluids. The research examines the use of single-walled and multi-walled carbon nanotubes (SWCNT and MWCNT) in engine oil (EO) as the working fluid. The Galerkin weighted residual method (GWRM) is utilized to solve the ordinary differential equations (ODEs) governing the system. The impact of various parameters, such as Cattaneo-Christov heat flux, solar thermal radiation, nonlinear stretching surface, slippery velocity, and porous media on the velocity equation, energy equation, and entropy generation are investigated and elaborated through detailed plots. The findings show that the MWCNT-SWCNT/EO hybrid nanofluid (HNF) exhibits maximum efficiency of around 2.4%, while the minimum efficiency is at 2.7%. This research provides valuable insights into the design and optimization of solar thermal systems for sustainable transportation.

A robust reliability evaluation model with sequential acceleration method for power systems considering renewable energy temporal-spatial correlation

As the penetration of renewable energy increases, it becomes critical to evaluate the power system reliability, while considering the correlations among renewable energy sources (RESs) and their uncertainties. In this paper, the robust reliability evaluation model based on the minimum volume enclosing ellipsoid (MVEE) algorithm is established, and the sequential acceleration method is proposed to improve the convergence. First, the robust reliability evaluation model is built, where the robust state analysis is performed under each sampling. Second, the sequential Cross-Entropy-Latin Hypercube Sampling (CE-LHS) acceleration method is presented, which first obtains the optimal probability distribution of system parameters, and then forces the sampling values to be evenly distributed to overcome the truncated tail effect. Correspondingly, the reliability indices are improved. Third, for state analysis, the robust multi-period optimal load shedding (RMPOLS) model is established, where the dynamic performance of energy storage and system ramping rate constraints are described in detail. Therefore, the periods are strongly coupled, and the impact of RES uncertainties is carefully considered. Besides, to address the temporal-spatial correlation of RESs, the MVEE algorithm is introduced to generate the convex hull of RESs scenarios. Therefore, the RMPOLS model can be transformed into a second-order cone form. Finally, to further reduce the computational complexity, the branch flow constraints scanning strategy is proposed, which can quickly remove inactive constraints in advance. Numerical results prove that the MVEE-based robust reliability evaluation model considers the temporal-spatial correlation of RES and thus improves the system reliability by almost 30 %. Besides, branch flow constraint scanning can eliminate more than 65 % of the SOC constraints. Moreover, CE-LHS can improve the convergence by about 8 times compared with Monte Carlo Sampling (MCS) while the error is less than 1 %.

Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm

AbstractLocal integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms.

Operation Approval for Commercial Airborne Wind Energy Systems

Integrating the operation of airborne wind energy systems safely into the airspace requires a systematic qualification process. It seems likely that the European Union Aviation Safety Agency will approve commercial systems as unmanned aircraft systems within the “specific” category, requiring risk-based operational authorization. In this paper, we interpret the risk assessment methodology for airborne wind energy systems, going through the ten required steps of the recommended procedure and discussing the particularities of tethered energy-harvesting systems. Although the described process applies to the entire field of airborne wind energy, we detail it for a commercial flexible-wing airborne wind energy system. We find that the air risk mitigations improve the consolidated specific assurance and integrity level by a factor of two. It is expected that the framework will increase the safety level of commercial airborne wind energy systems and ultimately lead to operation approval.

Analysis of the Solar Charge Controller using ABCD Framework

Purpose: The renewable energy is getting more and more popular today due to various factors including the benefits of the energy system, contribution to the environment in zero pollution, and flexible adoption for both domestic as well as the industry. Among the various renewable energy sources, the most popular is solar energy which can be easily installed. This type of renewable energy is more popular today. The performance of the solar energy system is affected by the availability of sunlight, the battery backup, and the load. Thus, the efficiency of the solar energy system depends on the above factors. It is very important that every solar energy system needs a charge controller to increase the efficiency of the total system. The solar charge controller has to control the charge stored in the battery backup, load, and switching concepts. A new model of the solar charge controller by the name smart switch is suggested which could improve the efficiency of solar energy by taking smart decisions in charging the battery backup, supplying the solar energy to the load when the battery is getting full, and then managing the energy storage in the battery backup, etc. Design/Methodology/Approach: Developing a conceptual theoretical model by the name smart switch and studying the performance of the same by taking the values in different time intervals. Then analysis of the model is done using the ABCD framework. Findings/Results: The performance of the model is studied by considering the values at various time intervals and the observations are noted in the tabular format. The values entered in the table are from the theoretical performance of the smart switch-a modified solar charge controller. From the table it is proved that the performance of the model is found to be optimal. The performance of the smart switch is analyzed using the ABCD framework. Originality/Value: A new conceptual model is analyzed using ABCD framework by identifying the determinant issues and key attributes for each determinant issue. This analysis is proven to be adaptable in future technology. Paper Type: Conceptual Research.

Techno-economic appraisal and machine learning-based gray wolf optimization of enhanced fuel cell integrated with stirling engine and vanadium-chlorine cycle

Researchers' interest in Fuel Cells (FCs), a cutting-edge technology for producing heat and electricity simultaneously, has grown significantly in recent years. FC technology is a non-renewable yet unconventional distributed energy source with a number of benefits for the economic, environmental, and dependable operation of energy systems. In the present study, a highly efficient FC type is integrated with a Stirling engine and vanadium chlorine scheme. The proposed cycles are efficient not only in power generation but also in hydrogen production since it uses waste heat to generate hydrogen. The system is investigated from technical, economic, and environmental aspects through the scrutiny of parameters affecting the system's performance. A cutting-edge method of machine learning-based optimization with a gray wolf algorithm is used to identify the ideal solution point when different optimization goals are selected. The results indicate that the TOPSIS point matches the maximum efficiency and hydrogen production of 81% and 0.008 kg/s, and the ζ equals 0.21. Also, maximum net power output and hydrogen production of 7500 kW and 0.0045 kg/s happens in the TOPSIS point with the levelized cost of the products of 0.14 $/kWh.

Power Electronics Technology for Large-Scale Renewable Energy Generation

Grid integration of renewable energy (REN) requires efficient and reliable power conversion stages, particularly with an increasing demand for high controllability and flexibility seen from the grid side. Underpinned by advanced control and information technologies, power electronics converters play an essential role in large-scale REN generation. However, the use of power converters has also exposed several challenges in conventional power grids, e.g., reducing the system inertia. In this article, grid integration using power electronics is presented for large-scale REN generation. Technical issues and requirements are discussed with a special focus on grid-connected wind, solar photovoltaic, and energy storage systems. In addition, the core of the energy generation and conversion—control for individual power converters (e.g., general current control) and for the system level (e.g., coordinated operation of large-scale energy systems)—is briefly discussed. Future research perspectives are then presented, which further advance large-scale REN generation technologies by incorporating more power electronics systems.

A comprehensive study on the impacts of isolation heat exchangers on the energy performance of surface water heat pump systems

Surface water heat pumps are included as a subset of ground-source heat pumps for the utilization of shallow geothermal energy. For the surface water heat pump applications in which the location of the heat pump plant is remarkably higher than water surface, the pump energy consumption of an indirect system with isolation heat exchangers (IHE) will be lower than that of a direct system without IHE. However, the entering fluid temperature of heat pumps in an indirect system is higher/lower than that in a direct system in cooling/heating modes due to the existence of IHE, which will bring a certain decrease in heat pump performance. This paper presents a comprehensive study on the impacts of IHE on system energy performance. The energy performance models for indirect and direct systems are developed, and a method for energy-saving judgment between indirect and direct systems is proposed. The case study results show that a direct system is more energy efficient for cooling than an indirect system because the elevation difference between water surface and plant is less than the critical value. Under low surface water temperature condition in winter, an indirect system using antifreeze fluid can eliminate the defect that the heating capacity of heat pumps in a direct system decreases much and auxiliary heat is needed. An optimized system configuration that combines the advantages of direct and indirect systems is proposed. The proposed system can operate without auxiliary heat in winter even though the surface water temperature drops to a low level, and can acquire cooling performance close to that of the cooling systems including chillers and cooling towers.

Investigation on flow and heat transfer in various channels based on triply periodic minimal surfaces (TPMS)

Heat exchangers are one of the key components to ensure the safe and efficient operation of nuclear energy systems. Additive manufacturing offers the processing solutions of advanced heat exchangers based on triply periodic minimal surfaces (TPMS). TPMS has superior mechanical and thermal–hydraulic performance that provides excellent energy-saving potential in the energy systems. In this study, I-WP, Neovius, Fischer-Koch S, Primitive surface-based heat transfer channels with different volume shares are constructed in three dimensions. The enhanced heat transfer mechanisms of TPMS topologies are identified and parametric analysis on the thermal–hydraulic performance is performed. The results indicate that the secondary flows, periodic flow acceleration/deceleration and constant changes in flow directions play key roles in convective heat transfer of TPMS topologies. Based on a large amount of simulation data, empirical correlations for Nusselt number and friction factor in the turbulent regime are established. This study provides a database for the design and application of TPMS-based heat exchangers, which contributes to the energy-saving and sustainable future target.

On the behavior of renewable energy systems in buildings of three Saudi cities: Winter variabilities and extremes are critical

Integration of renewable energy systems emerges as a key enabler for future low-carbon electricity supply. To maintain a secure and effective supply, the design and operation of these systems must carefully account for the influence of weather variability on the availability of renewable energy resources and on energy demand. This study examines the impact of weather variability on the optimal design and operation of renewable energy systems for office buildings in Saudi Arabia. The building electricity demand is supplied by a photovoltaic/wind/battery storage system, with an additional connection to the grid. The optimization process then aims at finding the optimal capacities of the renewable energy system components, so as to strike a trade-off between life cycle cost and CO2 emissions by considering weather datasets with different levels of variability. The results show that accounting for the full weather variability, particularly the extreme weather events, leads to higher investment costs in fully renewable energy system, namely due to increased battery storage (up to 288%). Neglecting extreme events at the design stage, however, leads to a significant performance gap that could be compensated by grid integration, which adds to the overall CO2 emissions, for a negligible change in the system cost. Such extreme weather events are then associated with cloudy and high temperatures days, the impact of which drives down the photovoltaic power output and increases the demand for energy from the grid or from the storage. The study further examines the electricity price impacts on renewable energy system design and operation. With global weather variability challenges at the forefront, the paper findings draw insightful recommendations related to energy policy.

Effects of External Resistance, New Electrode Material, and Catholyte Type on the Energy Generation and Performance of Dual-Chamber Microbial Fuel Cells

In this study, the effects of an external resistance, new electrode material, and non-conventional catholyte on the energy generation and performance of a dual-chamber MFC were evaluated. Ten different resistances (15 Ω–220 kΩ), hydrophilically-treated graphene and graphite electrodes, and a 0.1 M HCl solution as a catholyte were assessed. The results showed that greater energy generation and power density were achieved at an external resistance of 2 kΩ and internal resistance between 2 and 5 kΩ on average; meanwhile, the greatest coulombic efficiency was obtained at the lowest external resistance evaluated (15 Ω). Therefore, it is recommended to operate the MFCs at the external resistance between 2 and 5 kΩ to ensure the maximum power generation of the dual chamber MFCs. Regarding the two electrode materials evaluated as an anode and cathode, hydrophilically-treated graphene was found to be a much better material to enhance the energy production and performance of the MFC system; therefore, its use is suggested in experimental and practical applications. On the other hand, the use of HCl as a catholyte enhanced the performance of MFC (constant and steady potential and greater coulombic efficiency) in most cases.

A digital twin of a local energy system based on real smart meter data

The steadily increasing usage of smart meters generates a valuable amount of high-resolution data about the individual energy consumption and production of local energy systems. Private households install more and more photovoltaic systems, battery storage and big consumers like heat pumps. Thus, our vision is to augment these collected smart meter time series of a complete system (e.g., a city, town or complex institutions like airports) with simulatively added previously named components. We, therefore, propose a novel digital twin of such an energy system based solely on a complete set of smart meter data including additional building data. Based on the additional geospatial data, the twin is intended to represent the addition of the abovementioned components as realistically as possible. Outputs of the twin can be used as a decision support for either system operators where to strengthen the system or for individual households where and how to install photovoltaic systems and batteries. Meanwhile, the first local energy system operators had such smart meter data of almost all residential consumers for several years. We acquire those of an exemplary operator and discuss a case study presenting some features of our digital twin and highlighting the value of the combination of smart meter and geospatial data.

Ensuring energy balance for sudden demand changes in smart grids

With the increasing diversity in the use of electrical energy and the inclusion of renewable electricity power plants in the system, more efficient and more controllable electrical power systems are provided by smart grid technologies. Load balance based on pricing in electrical power systems; It is a strategy that takes into account customer demands, enables customers to use electrical energy at the most attractive price, and provides suppliers with the desired profit, that is, in summary, it coordinates the efficient operation of energy systems. In this study; While load balance is created with smart grid technology, demand-supply-price balance is achieved by using an optimum Fuzzy Logic PI controller within a closed-loop electric power control system model. Within the scope of the study, the simulation results obtained by applying 6 different demand scenarios to the power system model consisting of different types of generation resources were given graphically and analyzed. The different demand scenarios applied include difficult demand conditions such as possible sudden increase or decreases in demand in the electrical power system.

Off-Grid Hybrid Renewable Energy System Operation in Different Scenarios for Household Consumers

Off-grid hybrid renewable energy systems represent the most modern and flexible solutions that can cover a wide range of energy efficiency needs for household consumers. In the current context, these systems must be taken into account by most household consumers, given the fact that the price of energy has increased. This paper proposes an experimental analysis of the behavior of such a system, in real operating conditions, considering two renewable energy sources, wind and photovoltaic sources, using an experimental stand. It can be considered a testing platform for hybrid energy production systems, in that the power installed in the two sources can be scaled up. The platform has the advantage of allowing the implementation of different load and wind curves through a programmable logic controller; in this way, it is possible to evaluate the degree of coverage of the energy consumption produced from renewable sources, in the north-east of Romania, in the Suceava County region. The experimental study also involves an analysis of the storage capacity in relation to the consumption and the electricity produced by the two renewable sources. In this regard, three scenarios differentiated by the state of charge (30%, 50% and 70%) have been established. The results indicate that, for each of the imposed scenarios, the energy required to cover consumption is produced by renewable energy sources (42%, 47% and 53%), to which the energy stored in batteries (39%, 28% and 18%) is added.