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Recent literature has confirmed the benefits of jointly optimizing and allocating various firm power enablers, such as photovoltaic (PV) overbuilding & proactive curtailment, geographical smoothing, or energy storage. These enablers facilitate the transformation of variable PV power into effectively dispatchable power, thereby firming up PV generation. However, many previous studies on firm PV generation only considered batteries as the energy storage option, which notoriously elevates the overall system costs owing to the short-duration nature of battery storage. Besides, the implications of the anticipated yet uncertain decrease in storage costs on the economic viability of firm PV and system component sizes remain unclear. This work, therefore, introduces hydrogen as a long-duration (e.g. , seasonal) storage option and elucidates the differences between short- and long-duration storage in reducing the cost of firm PV power. Specifically, two facets separate this work from its antecedents: (1) A mixed-integer linear programming model that minimizes the firm kWh premium of the PV–battery–hydrogen system, which possesses the role of both short- and long-duration storage, is proposed to determine the optimal system configuration; (2) the impact of changes in storage options and costs on the energy component ratings is investigated, and the scaling of system economics with these changes is assessed. The analysis reveals that the obtained firm kWh premium stands at 5.42 when the firm 100% PV-supplied system is utilized to fulfill the load demand with an average daily value of 22.04 MWh, while the installation of a 44.81-MWh battery, a 684-kW electrolyzer, and a 540-kW fuel cell, is required to achieve the optimal system costs. Additionally, compared to the future cost change in long-duration storage due to technology updates, the premium is more sensitive to an equal amount of change in the cost of short-duration storage. The results can offer policymakers actionable insights regarding the capacity optimization of PV plants, the strategic deployment of hydrogen systems, and the cost-effective construction of zero-carbon energy networks. [Display omitted] • A model is proposed to optimize the cost of firm PV generation. • The battery, a short-duration storage option, is mainly employed for diurnal storage. • The hydrogen system (long-duration storage) primarily caters to inter-seasonal storage. • The incorporation of long-duration storage lowers the system premium by 10%. • Battery cost reduction diminishes the system cost more than the hydrogen system. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is an essential option for addressing the depletion and environmental repercussions of fossil fuels. However, the performance of PV panels is generally hindered by excessive operating temperatures, leading to decreased efficiency as well as accelerated degradation rates. To address this issue particularly for hot and arid climates, a closed-loop PV cooling system is proposed in this study for continuously regulating the temperature of PV panels throughout both day and night, thereby reducing temperature fluctuations, and enhancing overall efficiency while slowing degradation rates. A 3D transient mathematical model and a degradation rate model are established and verified via comparisons with the experimental results. On this basis, this study focuses on analysing the design of the closed-loop PV cooling system by investigating the effects of various parameters, including the water tank shape, the number of tubes and the water mass flow rate. The overall daily and annual performance and degradation rate of the PV panel in the system are also studied and analysed. The results show the proposed system is well-appropriate for dry and hot regions with significant diurnal temperature variations. On one hand, the PV efficiency and power output can be enhanced through cooling by closed-loop water circulation during the day. On the other hand, the temperature fluctuation of the PV panel during the day and night is effectively reduced, and the PV lifespan is substantially enhanced by up to 33% compared to conventional designs. This study offers valuable insights into the design and performance of closed-loop PV cooling systems and the optimal selection of parameters to improve overall efficiency. • A novel closed-loop PV cooling system with 24-h continuous operation is proposed. • A 3D heat transfer model and a degradation rate prediction model are built and validated. • The novel systems with different configurations are tested, analysed and evaluated. • A remarkable improvement of 8.2% in PV lifespan compared to conventional designs is obtained. [ABSTRACT FROM AUTHOR]

The agricultural limitations in Jordan are due to scarce water resources and are further exacerbated by the arid and hyper-arid climatic conditions. Agrivoltaic systems are the co-location of electricity and agricultural production, optimizing the interplay between the two systems for maximum total production per land area unit. This study investigates the technical potential of agrivoltaic systems in Jordan using geospatial analysis and open data sources and the extent to which they can contribute to the water-energy-food nexus, improving water, food, and energy security while serving national sustainable development goals. The analysis calculated agrivoltaic-suitable zones totaling 9.5% of the country's land area. Further estimations indicate that covering only half of the current irrigated summer tomato fields with PV paneling will fulfill the 50% renewable energy (RE) generated electricity target and potentially save between 4 and 8.6% of the total water budget of the country. • Agrivoltaic (AV): co-locating solar power & crops. 9.5% of Jordan has AV potential. • AVs for energy security: Meeting Jordan's local electricity targets (RE goals). • AVs for water security: Saving a significant portion of Jordan's annual water budget. • Example: 50% tomato fields with AVs can meet 50% RE target & save 4-8.6% water budget. • More research needed: Economic & market potential of AVs in Jordan. [ABSTRACT FROM AUTHOR]

To achieve green and low-carbon development in the logistics industry, logistics operators are promoting the electrification of logistics fleets, which imposes requirements for well-developed charging facilities and integrated renewable energy sources. Due to the specific characteristics of logistics activities, which are different from those of normal vehicles, the infrastructure planning for logistics delivery needs to be considered in coordination with the logistics system's operation. The coupling of planning and operation poses challenges for decision-making. This paper presents a planning–operation coupling optimization framework for low-carbon logistics delivery. The planning level optimizes the location and capacity of charging facilities, photovoltaic (PV), and energy storage systems (ESSs) based on the idea of charging demand matching. The operation level uses deep reinforcement learning (DRL) to simulate the logistics fleet's action patterns, optimize routes and charging behaviors, and extract charging demands. Benefiting from the advantages of DRL, planning and operation can interact well through charging demands, mutual coupling, and iterative adjustment. Numerical experiments based on real-world data show that the proposed framework effectively reduces charging costs and carbon emissions and also provides a benchmark for the cost evaluation of carbon reduction in logistics delivery activities. • A joint planning–operation framework suits the properties of logistics delivery. • The infrastructure planning model adopts the idea of charging demand matching. • DRL is used to extract charging demands and interact with the planning model. • Numerical experiments with real-world city data validate the method's effectiveness. [ABSTRACT FROM AUTHOR]

In a directly-coupled photovoltaic-powered membrane desalination (PV-membrane) system, the solar power supply must exceed a certain threshold for the pump to generate sufficient hydraulic pressure to overcome the membrane's osmotic barrier. Below this threshold, no appreciable membrane flux is produced, meaning PV power is underutilised or dissipated by the system. In this work, a power control management system (PCMS) is designed to harvest the underutilised PV power into a supercapacitor (SC) to, firstly, buffer the periods of solar irradiance fluctuations during daily operation. Secondly, this also fosters a continuous stable operation of the PV-membrane system across multiple solar days. The investigation compared the performance of the PCMS against two other configurations: "directly-coupled" (passive, no SC) and "semi-active" (with SC, but no PCMS). During a three-day test desalinating brackish water (5 g/L salinity), the PCMS enabled an improvement in water quality, with the permeate electrical conductivity (EC) 46% and 23% lower than the other configurations, respectively. The specific energy consumption (SEC) was reduced by 18% and 6%, respectively, during the same period. During a week-long experiment, the PCMS facilitated the production of 6218 L of potable water at an average EC of 0.52 mS/cm, while maintaining a minimum state-of-charge of the SCs to about 40%, allowing for subsequent use. The system also achieved a maximum load-matching efficiency of 99.6%, surpassing the 94.7%–96.6% observed in the other configurations. These results pave the way for maximising daily PV energy utilisation, ensuring steady and dependable clean water production for the community and improving the water-energy nexus. [Display omitted] • Optimise daily energy use of photovoltaic-powered membrane desalination system • Control algorithms: Directly-coupled & Semi-active, with & w/o supercapacitors (SC) • ∼1000 L per day produced during 3–7 day tests performed under different solar days • Control system with SC afforded: 1) 6–18% lower specific energy consumption (SEC); • 2) 42–83% improved water quality; 3) the highest (99.6%) load-matching efficiency [ABSTRACT FROM AUTHOR]

The complementary operation of hydropower, photovoltaic, and wind power can promote the integration of renewable energy resources into the grid. However, the competition of power transmission channels among multiple energy raises surpluses significantly. This study equips a hydrogen production plant for the hydro–wind–PV hybrid system to utilize the power surplus. First, a multi-scale nested joint operation model that considers both long-term and short-term operation strategies is proposed to simulate the operation process of the hybrid system. Then, the potential surplus of the system is evaluated and used as the power source of the hydrogen production plant. Lastly, the optimal hydrogen production plant size is determined based on economic and technical analysis. A case study is performed with the Yalong River basin of China. The results show that (McElroy and Chen, 2017 (1)) The deployment of a 260 MW hydrogen production system has the potential to boost the annual power generation benefit of the hybrid system by 610 million CNY； (2) The fluctuation of hydropower station output is effectively reduced, subsequently mitigating operational risks. Especially during flood season, the power variation coefficient experiences a notable decrease of approximately 75%； Liu and Xu (2022) (3) The deployment of hydrogen production plants is more economically feasible than expanding power transmission channels, particularly when considering long-distance power transmission. Furthermore, given that hydrogen production plants frequently operate under fluctuating power conditions, the electrolyzers and compressors must exhibit prompt responsiveness to these power variations. • Propose surplus energy to hydrogen model for hybrid hydro–wind–photovoltaic power systems • Evaluate the surplus energy of the hybrid systems and the benefit of the hydrogen production plant • Determine the optimal size of the hydrogen production plant with different transmission strategies [ABSTRACT FROM AUTHOR]

Soiling, the deposition of contaminants onto the front glass surface, is a significant challenge for photovoltaic (PV) performance. However, studies on this issue are minimal in urban scenarios despite the surging popularity of PVs. This research systematically measures the soiling losses in subtropical high-density urban environments. A hybrid method combining field exposure and in-lab characterization is proposed. Major soiling modes, temporal patterns of optical loss, and PV output loss are explored within continuous 108-day exposure. Due to the windy climate and low particle concentration, dust deposition and accumulation are insignificant. Meanwhile, abundant rainfall offers a good natural cleaning effect that can remove most dry deposits, including dull leaves, dust, etc. Rainwater residual after evaporation is a major soiling mode, but the optical loss is within 2%. Despite the favorable climate, bio-soiling, especially the bird dropping represents the most harmful soiling mode. The resulting contamination is stubborn, and even hefty rain cannot completely recover the soiling loss. A tiny residual can cause an optical and PV output loss of 2.5% and 15.5%, respectively. Finally, practical suggestions are provided to mitigate local soiling loss. These findings are essential for urban PV project planning and operation. • A hybrid method with merits of the field experiment and in-lab characterization. • Continuous exposure for 108 days on a downtown rooftop in Hong Kong. • Major soiling modes, temporal patterns of optical loss, and output loss. • Bird-dropping induces the most significant loss despite abundant rain wash. • Region-targeted mitigation suggestions to improve local PV performance. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles driven by government plans to strongly electrify mobility will challenge the current distribution grid infrastructure. The related electricity demand increases the risk of overloads and will require significant grid enhancements and infrastructural investments. Our paper evaluates the potential of reducing investment needs through decentralized photovoltaic electricity generation and battery energy storage systems. We use power-flow analyses on representative grid models to test rural, urban, and suburban grids' resilience to higher electric vehicle penetration. We find significant societal benefits with a simultaneous uptake of decentralized generation and estimate savings of up to €3.2 billion solely within the German grid. In rural areas, photovoltaic and battery systems are especially effective for electric vehicle penetrations up to 20%, reducing grid costs by up to 39%. Suburban and urban grids could achieve significant savings for electric vehicle penetrations up to 60%, with cost-saving potentials of up to 51% and 46%, respectively. We recommend that policymakers facilitate decentralized electricity generation to unlock additional benefits from a societal cost perspective, increase the share of sustainable electricity consumption, and improve energy security. [Display omitted] • Estimation of savings potential in EV-related grid cost via PV and battery systems. • Residential generation and storage can significantly reduce grid investments. • Infrastructure cost savings could amount to almost €3.2 billion within the German grid. [ABSTRACT FROM AUTHOR]

Technological factors have significant impacts on the geopolitics of energy transition and climate governance, yet little scholarly attention has been given to international technological competition. This paper constructs a global photovoltaic technological competition network based on patents from 1970 to 2018 to capture the international competitive dynamics at multiple scales. The results reveal a continuous expansion of the global photovoltaic technological competition network. The network has an ideal core-periphery structure, while the competition between peripheral countries has been intensifying. Competition at the regional level is strongly related to Europe due to the high innovation capacity and foreign patenting rate of European countries. The elevating position of East Asia and the rest of the world since 1990 reflects the shifting landscape of global photovoltaic production. The United States and Japan have alternatively dominated the global photovoltaic technological competition, while Germany's influence is declining. Two catching-up players, South Korea and China, have significantly improved their status. The vast majority of developing countries, especially those that rely heavily on fossil fuels, are lagging far behind and exposed to the risk of "carbon lock-in". • A global photovoltaic technological competition network is constructed using complex network theory. • Photovoltaic patenting has formed a pattern of global competition with increasing intensity. • Competition at the regional level is strongly related to Europe. • National competitiveness is geographically uneven and temporally dynamic. • Photovoltaic technological competition may lead to intensifying geopolitical tensions. [ABSTRACT FROM AUTHOR]

The aim of this study is to present and evaluate the performance of a novel photovoltaic (PV) module configuration introduced as the " Desert Module ," developed to enhance the production and efficiency of PV power plants operating in harsh desert locations. This innovative module has been constructed by incorporating various technological solutions to address desert climate conditions such as high temperatures, soiling, and intense UV radiation. To assess the performance of the developed " Desert Module ," a comprehensive evaluation was conducted by comparing its performance to that of a conventional module. The study encompasses three main aspects: (i) indoor characterization, including flash and electroluminescence tests; (ii) performance assessment in a hot semi-arid climate over an eight-month period; and (iii) economic analysis to benchmark the costs of electricity produced from the proposed configuration against conventional alternatives. The results indicate that the Desert Module outperforms the conventional module across all performance metrics, enhancing the average efficiency by 0.5%. In addition, the performance ratio for the Desert Module stands at 0.87, surpassing the 0.82 achieved by the conventional module. Throughout the experiment, the average energy yield was enhanced by 5.8% and the conversion efficiency by 1.95%. Notably, during the hottest period (between July and August), the deviations on the energy yield and the conversion efficiency exceed 7% and 3%, respectively, in favor of the Desert Module , highlighting its resilience to the high temperatures characterizing desert locations. Furthermore, the Desert Module demonstrates a 4.44% reduction in the levelized cost of electricity compared to the conventional module, positioning it as a more economically viable option for large-scale energy generation in desert environments. • Development and Experimental performance testing of a novel PV module configuration adapted to desert climate. • The yield and PR of the Desert Module are 5.8% higher than the regular PV module. • The efficiency of the Desert Module is 1.95% higher than the regular PV module. • −0.85% is the average temperature deviation between the two systems in favor to the Desert Module. • The LCOE of the Desert module is 0,0408$/kWh which is 4.4% lower than the conventional one. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) plays a crucial role in China's energy transition. However, air pollution diminishes solar radiation resources, thereby reducing PV power generation efficiency. This study aims to quantify the impacts of air pollution on PV capacity factors in China while emphasizing the geographically specific potential benefits of improved air quality for the future PV sector. Using a PV power generation evaluation model, this study estimated solar PV capacity factors from 1961 to 2016 under both all-sky and clear-sky scenarios. Also, the spatiotemporal patterns of air pollution-induced capacity factor changes were analyzed. The results indicate that the all-sky and clear-sky PV capacity factors showed consistent annual anomalies, suggesting that air pollution due to aerosol emissions drove PV capacity factor temporal evolutions and trends in China. From 1961 to 1990, the national average clear-sky PV capacity factors for fixed modules with optimal tilt angle showed a significant decreasing trend of −0.0053 per decade, whereas from 1990 to 2016, an upward trend of 0.0030 per decade was observed. Compared with fixed modules, tracking systems suffered from greater productivity losses due to air pollution. In comparison to 1961–1965 averages, the provincial PV capacity factors for 2012–2016 decreased by 0.48–13.54%, 1.15–11.40%, 1.06–14.83%, and 1.31–16.51% for fixed modules with optimal tilt angle, horizontal fixed, one-axis horizontal tracking, and two-axis tracking modules, respectively, with decreases of 7–17% in the central and southeast. Furthermore, according to the provincial PV installation targets projected in China's 14th Five-Year Plan, national solar PV power generation was expected to increase by 81.333 TWh to 1069.997 TWh in 2025 with average PV capacity factors for 1961–1965, compared to 2012–2016 averages. In a scenario of keeping grid parity, additional power generation would offer an economic benefit of approximately 30.102 billion CNY. • Quantifying the long-term impact of air pollution on PV capacity factors in China • PV capacity factors decreased until the late 1980s, followed by an upward trend • Tracking PV systems experienced more significant productivity losses than fixed modules • Additional PV production of 81.333 TWh could be realized in 2025 by air pollution control • Economic benefits from increased capacity factors amount to 30.102 billion CNY in 2025 [ABSTRACT FROM AUTHOR]

In this study, a method for estimating the maximum PV (photovoltaic) cover ratio for plastic greenhouses based on various years of global horizontal irradiance (GHI) data is presented and illustrated with an exemplary site in southeastern Spain. CAMS (Copernicus Atmosphere Monitoring Service) GHI data from 2005 to 2023 were analyzed to estimate the DLI (daily light integral) inside the greenhouses for various PV coverage ratios with East-West or North-South orientation. The conversion from GHI to photosynthetically active radiation is performed with the usage of a regression model from literature based on satellite and measurement data. The shading effect of the PV cover is estimated with a regression model from literature based on radiation distribution simulations in different greenhouse types. The maximum PV cover ratio was derived for different minimal DLI thresholds, corresponding to different crops. The proposed methodology has been tested for the Almería region in southeastern Spain which is characterized by high solar irradiance and can be applied also to other regions with similar climatic conditions. With a required DLI of at least 12 mol/m2/day, a theoretical maximum PV coverage of about 44% is acceptable even in December at the studied site for East-West orientation, while it reaches up to 100% (June) during the year. Further, the maximum PV cover ratios for a DLI threshold range have been calculated and compared with experimental results for plastic greenhouses from literature. In 87.2% of the case studies analyzed from literature, the proposed method showed an agreement in the estimation of the effect of PV shading ratios on marketable crop yields. The study indicates that significant PV cover ratios are theoretically possible even for light demanding crops considering DLI thresholds only and can help to select a useful PV cover ratio in PV greenhouses. • Method to estimate the maximum photovoltaic cover ratios for plastic greenhouses. • Analysis of CAMS global radiation data to estimate the DLI inside greenhouses. • Derivation of maximum PV cover ratio for different minimal DLI thresholds. • Comparison with experimental results for plastic greenhouses from literature. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid approach for switched reluctance motor (SRM) based water pumping system. The proposed hybrid method is combination of both the Northern Goshawk Optimization (NGO) and Finite Basis Physics-Informed Neural Networks (FBPINNs). Hence, it is named as NGO-FBPINNs. The NGO method is employed to better control among the three level boost converter (TLBC) and FBPINNs is predict the optimal control of the TLBC. In this system the water is pumped using a 4-phase SRM driven by a midpoint converter. An intermediary power conversion stage called a TLBC is positioned between the motor-pump and the solar PV array. The proposed method is very efficient because of the TLBC's small inductor size, wide operating voltage range, and low voltage stress across devices. The proposed method implemented in MATLAB platform and its efficiency is compared with various existing techniques like Heap-Based Optimizer (HBO), Wild horse optimizer (WHO) and Particle Swarm Optimization (PSO). The proposed approach NGO-FBPINNs obtains high efficiency than existing approaches. The proposed system's effectiveness is 95% it is higher efficiency than other system. • This Paper Proposes A Hybrid Approach For switched Reluctance Motor (SRM). • Hybrid Approach Is Combination Of Both The Northern Goshawk Optimization (NGO). • The NGO Method Is Employed To Better Control Among The Three Level Boost Converter. • TLBC Is Used As An Intermediate Power Conversion Stage. • The Proposed Method Implemented In MATLAB Platform. [ABSTRACT FROM AUTHOR]

This study aims to move towards sustainable textile industry by controlling water and energy. For this purpose, a novel system is developed based on photovoltaic and electrolyzer systems, which produces hydrogen from the industrial wastewater after pre-treatment using the electrolyzer, and combines it with natural-gas to decrease pollutant emission rate and use it in the heat supply section. In this study, the equipment size are optimized and the system is evaluated from the technical, economic, and environmental aspects. For the case study, five different sites in Iran, including Tabriz, Amol, Torbat Heydariyeh, Yazd, and Sistan were considered. The economic evaluation showed that the levelized cost of electricity varied from 55 $/MWh to 78 $/MWh in different cities of Iran. The levelized cost of hydrogen varied from 6.7 $/kg to 16.3 $/kg in different cities. The levelized cost of heat ranged from 25.5 $/MWh-t to 46.9 $/MWh-t. The environmental advantage of the proposed system indicates that combining hydrogen with natural gas leads to savings of 31,688 Nm3/yrs. of natural gas compared to the conventional system. Overall, the proposed system has the advantage of reducing CO 2 emissions by 640–935 ton-CO 2 /yr. • A novel system is introduced for textile wastewater treatment and hydrogen production. • Blending H 2 to natural gas (NG) leads to savings of 31,688 m3/yrs. NG. • The proposed system is competitive with the NG boiler, in 2030. • The levelized cost of heat ranged from 25.5 $/MWh-t to 46.9 $/MWh-t. [ABSTRACT FROM AUTHOR]

Agrivoltaics or dual use of land for agricultural production systems and photovoltaic energy is experiencing a significant boom since it favours the sustainability and efficiency of both systems, while helping to alleviate the conflict over land use and protecting rural economies. In this context, it is essential to study possible combinations between photovoltaic installations and agricultural operations that optimize the synergies between both production systems. Specifically, since solar irradiance is the common resource shared by both systems, it is necessary to find a balanced distribution of the solar energy to protect agriculture. This work simulates the behaviour of solar irradiance and its interaction with photovoltaic panels and the crop, as well as possible shading, in a photovoltaic plant to study its potential reconversion into an agrivoltaic installation. From this analysis, an innovative methodology is defined to determine the space between the collectors in which the levels of solar irradiance received would be sufficient for adequate crop development. Specifically, the method has been applied to simulate "El Molino" plant, a photovoltaic facility located in Córdoba, Spain with two-axis (azimuthal and elevational) solar trackers and backtracking. For this facility, pentagonal arable streets between the collectors have been identified. Specifically, along the N-S direction these pentagonal areas have a width of 10.5 m, a minimum height of 1.31 m in the lateral areas and a maximum height of 2.81 m in the central. Accordingly, different proposals for crop occupation have been proposed. Likewise, the curve of the percentage of maximum cultivable area as a function of the crop height has been represented, obtaining that for crops with a height of 1.4 m up to 74% of the land between collectors is arable, decreasing this space as the height of the crop increases. Thus, this work represents progress in the possible reconversion and agrivoltaic use of large existing photovoltaic plants, improving their sustainability and contributing to the necessary deployment of agrivoltaics and the fight against Climate Change. • A model to define cultivable space between two-axis solar collectors is presented. • For the study case, pentagonal street profiles for cultivation have been identified. • The 74% of the land between collectors is cultivable for crop under 1.4 m high. • This work boosts the possible agrivoltaic reconversion of existing 2-axis PV plants • This work lets optimize synergies between crop and PV in agrivoltaic plants. [ABSTRACT FROM AUTHOR]

Photovoltaic noise barriers (PVNBs) have the potential to contribute to sustainable urban development by increasing the supply of renewable energy to cities while decreasing traffic noise pollution. However, estimating the power generation of PVNBs at the city or national scale remains a challenge due to the complexities of the urban environment and the difficulties associated with collecting data on road noise barriers (RNBs) and radiation. This study used RNBs, 2.5-dimensional (2.5D) buildings, and hourly time resolution radiation data, to estimate the power generation of PVNBs in 52 of China's major cities. First, hourly building shadows were estimated for each day of the year, covering the period from sunrise to sunset, to identify areas of RNB that are shaded at any given time. Second, hourly clear-sky radiation data were collected and corrected using a radiation correction model to simulate real weather radiation. Finally, utilizing an inclined surface radiation estimation model, the photovoltaic (PV) potential both inside and outside RNBs affected by building shadows was assessed. Subsequently, the power generation of PVNB was estimated based on parameters of mainstream PV systems in the market. The results show that the RNB mileage in 52 selected cities represents 87.7% of China's total RNB mileage. Building shadows often result in a radiation loss of approximately 30% for RNBs reception. The installed capacity and annual power generation of PVNBs in all investigated cities are 2.04 GW and 690.74 GWh, respectively. This study estimates the comprehensive PV potential of potentially exploitable PVNBs in China, offering essential scientific insights to inform and facilitate the strategic development of PVNB projects at both the national and municipal levels. • A framework to assess the photovoltaic (PV) potential of noise barriers is developed. • Building shadows are considered when assessing the PV potential of photovoltaic noise barriers (PVNB). • The RNB mileage of each city is closely and positively related to the PV potential on RNBs. • The annual power generation of PVNBs in China's 52 cities was estimated. [ABSTRACT FROM AUTHOR]

Connecting a large number of distributed photovoltaics (PVs) and energy storage systems (ESSs) to a distribution network enables the mitigation of harmonic issues through grid-connected inverters with active topology. In this paper, we propose an optimization model for harmonic mitigation based on PV-ESS collaboration. The objective function is to minimize the total cost of harmonic mitigation, to ensure that the harmonic emission levels of all nodes in the network do not exceed the limit required by the IEEE standard. The constraint conditions include the fundamental frequency and harmonic power flow, voltage, mitigation performance, power for PV and ESS inverters, and cost constraints. The model is solved to obtain the amount of harmonic compensation currents and the unit price of the harmonic mitigation service of different inverters at different times. Finally, the validity of the proposed model is verified by the modified IEEE 13 node test feeder. • Novel optimization model for harmonic mitigation using PV-ESS. • Minimizes cost while adhering to national harmonic limits. • Includes frequency, voltage, and inverter constraints. • Validated using IEEE 13-node test feeder. • Enables dynamic unit pricing for harmonic services. [ABSTRACT FROM AUTHOR]

The full utilization of solar energy resources along the road is an effective method to solve the energy shortage in transportation. The key to this is an accurate evaluation of solar energy resources, which provides the rationale for the optimal location of road photovoltaic (PV) projects. However, determining the availability of solar energy resources in road areas before route corridor planning remains difficult. To address the issue, this paper developed a standardized method. Firstly, this paper analyzed the critical factors affecting the availability of solar energy resources. They were meteorology, topography, land use type, geology, and location. On that basis, we developed a four-level evaluation indicator system. Then, it proposed a multi-indicator evaluation method based on the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). It used AHP to calculate the weights of each indicator, with solar radiation being the most important. It used GIS to analyze and overlay the spatial distribution of multiple indicators to obtain the map of suitability grades, which could be used to route corridor selection for road PV projects. It also used a case study to demonstrate how the method is applied. The findings show that the method can accurately evaluate the availability of solar energy resources. Furthermore, it studied the appropriate division of evaluation units and it is advised that they shouldn't be larger than 30 m × 30 m. These results provide guidance as a reference for the application of road PV projects and site selection for route corridors worldwide. That will promote the integration of transportation and energy in the future. • The paper addresses the problem of solar energy resources evaluation in road areas. • The paper establishes a universal evaluation indicator system. • The paper proposes a standardized evaluation method based on AHP and GIS. • This method presents the obtained evaluation results as suitability grade map. • This method helps select route corridors with good power generation potential. [ABSTRACT FROM AUTHOR]

Owing to China's escalating demand for renewable energy and carbon emissions reduction, and given its prominent position as one of the fastest-growing nations in photovoltaic (PV) development, a comprehensive assessment of the potential of both centralized and distributed photovoltaic systems in China is crucial. However, current research on PV potential assessment presents several challenges. Therefore, this study presents a five-dimensional assessment model, encompassing geographical, technical, economic, CO 2 mitigation, and realizable potential, to systematically map China's centralized photovoltaic (CPV) and distributed photovoltaic (DPV) potential. The findings underscore the vast PV potential across the country, with areas of 3.11 million km2 and 252.72 thousand km2 deemed suitable for CPV and DPV development, respectively. This corresponds to a technical potential of 416,383.27 and 28,261.53 TWh/yr for CPV and DPV, respectively. Furthermore, this study explored the economic potential of CPV and DPV, revealing that the generation costs of both CPV and DPV in China are in proximity to the feed-in tariffs. With technological advancements and economies of scale, a broader PV potential is projected to be economically feasible by 2030. Full implementation of the current economic potential can yield an annual CO 2 mitigation of 54.20 and 8.39 gigatonnes (Gt) using CPV and DPV, respectively. Notably, China's PV potential exhibits distinct regional heterogeneity, with CPV potential concentrated primarily in the western region and DPV potential distributed predominantly across the eastern provinces. This study identified the most resource-intensive areas in the western region of Inner Mongolia for CPV and in the coastal regions of Guangdong and Shandong provinces for DPV. Considering policy imperatives and the development status, China's PV sector holds tremendous potential to significantly contribute to carbon reduction targets. Based on the findings, policy recommendations are proposed, emphasizing the imperative of jointly developing CPV and DPV systems, as well as implementing phased and planned growth strategies for PV growth. • A five-dimensional assessment estimated China's PV feasibility and CO 2 mitigation. • China has 416,383.27 TWh/yr CPV potential and 28,261.53 TWh/yr DPV potential. • China's CPV and DPV are at a critical point: the LCOE is close to the feed-in tariff. • Photovoltaic development can contribute to China's carbon reduction goals. [ABSTRACT FROM AUTHOR]

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability. • The proposed technique provides universal ability to diagnose different PV configurations with dynamic array faults. • The Isc-Voc normalized GADF transformation method can extract distinguishing features without calibrations. • The CNN-CBAM model can accurately classify and identify the full range of dynamic complicated faults. • The novel fault diagnosis method extends the application under the influence of dustin various scenarios. [ABSTRACT FROM AUTHOR]

The behind-the-meter (BTM) energy management problem has recently attracted a lot of attention due to the increase in the number of residential photovoltaic (PV)-battery energy storage systems (BESSs). In this work, the use of deep reinforcement learning (DRL) combined with a novel heuristic model for real-time control of home batteries is investigated. The control problem is formulated as a finite Markov Decision Process with discrete time steps, where a proximal policy optimization (PPO) algorithm is employed to train the DRL agent with discrete action space. The agent is trained using real-world measured data to learn the policy for sequential charge/discharge tasks, aiming to minimize daily electricity costs. The battery power is calculated using an innovative heuristic model considering the agent's decision and the battery's available capacity, ensuring demand-supply balance through PV self-consumption and load demand shifting. The performance of the model is evaluated by comparing it to four RL agents and two benchmark models based on rule-based and scenario-based stochastic optimization strategies. The results confirm that the presented model outperforms its counterparts, offering €80.38 savings on electricity bills over 46 days of the test data set. This figure exceeds the savings of the rule-based and stochastic models by €15.64 and €19.38, respectively. • An RL-based BESS control that minimizes electricity costs for prosumers. • A heuristic battery power calculation that enables unsupervised and optimal agent training. • The model outperforms four RL agents, a rule-based strategy, and a stochastic programming model regarding solutions. • Successful adaptability to changes in utility policies. [ABSTRACT FROM AUTHOR]

An increasing number of federal and state regulations and financial incentive programs have been enacted in recent years to promote renewable energy systems integration in new buildings. This requires designers to analyze the initial and lifecycle costs of feasible renewable energy systems in order to optimize their integration in buildings during all design phases. The goal of this study is to support designers in generating optimal building schematic designs to maximize the integration and generation of onsite renewable energy. To accomplish this goal, this paper presents the development of a novel multi-objective optimization model that can be used during the schematic design phase to identify optimal building design decisions including dimensions, orientation, location on site, solid-to-void ratio, and the type and size of onsite renewable energy systems. The optimization objectives of the developed model focus on maximizing the generation of onsite renewable energy, maximizing savings-to-investment ratio of all integrated renewable energy, and minimizing both construction and renewable energy costs. An application example of optimizing the schematic design of a 6500 m2 building was analyzed to highlight the model original contributions and its novel capabilities. The results of this analysis highlight the original contributions of the developed model and its novel capabilities that can be used to maximize the generation of onsite renewable energy and its savings-to-investment ratio while minimizing building cost by optimizing building schematic design and renewable energy (RE) technology decisions. • Optimizing building schematic design with renewable energy integration. • Maximizing RE generation, savings-to-investment ration, and minimizing bilding costs. • Complying with building design requirements and renewable energy constraints. • Optimal building design parameters and renewable energy technologies. • Analyzing the impact of design decisions on renewable energy and building costs. [ABSTRACT FROM AUTHOR]

Taking into account almost all kinds of variations and uncertainties to which AC island photovoltaic (PV) microgrid is often subjected, this paper proposes a new nonsingular fast terminal sliding mode control (NFTSMC) strategy for two-stage converters to enhance robustness against those disturbances and improve system dynamic performance. Firstly , to effectively decrease the adverse effect on the system from intermittent solar irradiation and uncertain environmental temperature, a NFTSMC cascaded with perturb and observe (P&O) scheme is put forward for fore-stage DC/DC converter, and PV output voltage is controlled to track its reference and thus maximum power point tracking (MPPT) performance is improved. Then , to significantly decrease the adverse effect on the system from variations of load and LC filter parameters, by incorporating an extended state observer (ESO) into NFTSMC, an ESO-based NFTSMC scheme is proposed for post-stage DC/AC converter, and the load voltage is regulated to track its reference and thus system robustness is strengthened, in which ESO is designed to replace load current sensor and thus reduce hardware cost. By using global terminal attractor without discontinuous switching function, the proposed NFTSMC strategy can not only strengthen system robustness but also improve system dynamic performance. Lastly , on account of the variations of PV generation and load demand, a conventional double-loop PI scheme is put into use for bidirectional DC/DC converter and thus system power balance is ensured. Comprehensive MATLAB simulation and StarSim/Hardware experiment results show that the proposed NFTSMC strategy for two-stage converters can overcome negative effects caused by various internal and external disturbances, and thus enable island PV microgrid to operate reliably and supply high-quality AC power. • For PV island microgrid subjected to various disturbances, the proposed NFTSMC strategy can enhance system robustness • The proposed NFTSMC cascaded with P&O for forestage DC/DC effectively improves PV MPPT performance under various disturbances. • An ESO is designed and incorporated into NFTSMC scheme to remove load current sensor and thus reduce hardware cost. [ABSTRACT FROM AUTHOR]

Agrivoltaics systems have emerged as an approach to alleviate competition for land use between food and energy production. Conducting a thorough analysis of the impact that shading from PV modules can have on crops is crucial for the correct design of the system, as excessive shading can lead to important crop yield reductions. This paper focuses on integrating agrivoltaics systems within super-intensive olive groves in the Mediterranean region. A dual model is used to calculate the suitable transparency of PV modules, representing the area not occupied by PV cells. This model customizes the results based on the site's meteorological parameters and the photosynthetic light-response curve of the olive cultivar. The results indicate that transparency levels vary between 0.57 and 0.71, with the lowest values observed in locations with higher solar radiation, such as Egypt and Tunisia. Using these transparency values and typical 20%-efficiency monocrystalline silicon modules, the annual average energy generation per m2 in the selected locations is 65.9 kWh. Another finding reveals that optimizing the model by considering only the months corresponding to the olive growth cycle can reduce the required transparency, thereby increasing the installed PV capacity by up to 3.5%. Furthermore, the potential deployment of these systems is evaluated in terms of installed PV capacity, energy generation, CO 2 emissions and job creation. The calculations show that installing agrivoltaics systems into 1% of the total olive surface area in the Mediterranean region would: (i) result in a 2.5% increase in the global PV capacity, (ii) generate 1.8% of the current electricity demand in the selected Mediterranean countries, (iii) avoid the emissions of 4 Mt. (CO 2) per year, and (iv) create around 560,000 jobs. • The potential integration of photovoltaics into olive groves is assessed. • Representative locations in top olive producers Mediterranean countries are selected. • Suitable PV transparency levels between 57% and 71% are found. • The average generation with the proposed APV configuration is approximately 65 kWh/year per m2 of ground. • These agrivoltaics systems could lead to the creation of >560,000 jobs. [ABSTRACT FROM AUTHOR]

Active distribution network is encountering serious voltage violations associated with the proliferation of distributed photovoltaic. Cutting-edge research has confirmed that voltage regulation techniques based on deep reinforcement learning manifest superior performance in addressing this issue. However, such techniques are typically applied to the specifically fixed network topologies and have insufficient learning efficiency. To address these challenges, a novel edge intelligence, featured by a multi-agent deep reinforcement learning algorithm with graph attention network and physical-assisted mechanism, is proposed. This novel method is unique in that it includes the graph attention network into reinforcement learning to capture spatial correlations and topological linkages among nodes, allowing agents to be "aware" of topology variations caused by reconfiguration real time. Furthermore, employing a relatively exact physical model to generate reference experiences and storing them in a replay buffer enables agents to identify effective actions faster during training and thus, greatly enhances the efficiency of learning voltage regulation laws. All agents are trained centralized to learn a coordinated voltage regulation strategy, which is then executed decentralized based solely on local observation for fast response. The proposed methodology is evaluated on the IEEE 33-node and 136-node systems, and it outperforms the previously implemented approaches in convergence and control performance. [ABSTRACT FROM AUTHOR]

• Novel approach to simulate time-dependent forecast errors for PV generation. • Based solely on day-ahead PV power forecast and realization. • Stochasticity of PV incorporated in the rolling planning procedure. • Application to the German power market in the European context. • Transmission network and unit commitment restrictions are being considered. This paper investigates the impact of uncertain photovoltaic generation on unit commitment decisions for the German rolling planning procedure employing a large-scale stochastic unit commitment electricity market model (stELMOD). A novel approach to simulate a time-adaptive intra-day photovoltaic forecast, solely based on an exponential smoothing of deviations between realized and forecast values, is presented. Generation uncertainty is then incorporated by numerous multi-stage scenario trees that account for a decreasing forecast error over time. Results show that total system costs significantly increase when uncertainty of both wind and photovoltaic generation is included by a single forecast, with more frequent starting processes of flexible plants and rather inflexible power plants mainly deployed at part-load. Including the improvement of both wind and photovoltaic forecasts by a scenario tree of possible manifestations, the scheduling costs could be significantly reduced in representative weeks for spring and summer. In general, stochastic representations increase the need for congestion management as well as more frequent use of storage in the model, leading to a more realistic depiction of the markets. [ABSTRACT FROM AUTHOR]

Highlights • The basic novel concepts used for the sizing of the solar panel were introduced. • Use of storage could increase the profitability of photovoltaic power generation. • Different incentives could be combined in the control of electrical energy storage. • The size of the solar panel could increase when the community model was applied. Abstract The popularity of small-scale residential energy production using photovoltaic power generation is predicted to increase. Self-production of electricity for self-consumption has become profitable mainly because of high-distribution costs and taxes imposed by the service providers on commercially produced electricity or because of the subsidies which reduce installation costs. Electrical energy storage can be used to increase the self-consumption potential of photovoltaic power. Additionally, electrical energy storage can lead to other benefits such as demand response or avoiding high load peaks. In this study, the profitability and sizing of a photovoltaic system with an associated electrical energy storage are analyzed from an economic perspective. The novel theory of sizing for profitability is presented and demonstrated using case studies of an apartment building and detached houses in Finland. To maximize the benefits, several alternative models for electricity metering and pricing are used and compared. The results demonstrated that the optimal size of the photovoltaic system could be increased by using electrical energy storage and suitable electricity pricing. This could lead to an increasing amount of photovoltaic production in the residential sector. Additionally, it is possible that when all the incentives are taken into account, electrical energy storage in combination with photovoltaic power generation would be more profitable than photovoltaic power generation alone. Photovoltaic power generation also increased the profitability of electrical energy storage, which could mean that the implementation of electrical energy storage in the residential sector could likewise increase. [ABSTRACT FROM AUTHOR]

Highlights • Photovoltaic panel cooling with the use of flat plate heat exchanger. • Width of header significantly affects flow distribution and pressure drop. • Taper has less impact, useful for small adjustments in flow distribution. • V-shaped exit manifold and centered ports results in best performance. • Results are verified with experimental PIV results. Abstract The performance of photovoltaic modules is adversely affected by an increase in photovoltaic cell temperature. Cooling of panels may lead to temperature non-uniformity in the photovoltaic panel, thus limiting the maximum efficiency of the cooled photovoltaic panel. In the current work, the design of a novel heat exchanger that can be used for uniform cooling of photovoltaic modules is presented. For this purpose, a computational fluid dynamics model has been set up. Using the model, the effects of various heat exchanger design parameters (like channel numbers, manifold width, the location of inlet/exit ports, and tapered channels) on its performance are sequentially analyzed resulting in fourteen designs. The performance is quantified by three parameters: top surface average temperature, temperature non-uniformity for photovoltaic module cooling quality, and the heat transfer per unit pumping power. The resulting optimized design is found to be a novel V-shaped heat exchanger design for the photovoltaic module cooling. It has a lower average temperature and temperature non-uniformity, smaller hotspots, and lower pumping power. The optimal design is further examined using experimental particle image velocimetry measurements. [ABSTRACT FROM AUTHOR]

Highlights • PV parameters extraction using a system of non-linear equations. • New concept of shift-factors introduced. • Estimating each PV parameter as a function of both irradiance and temperature. • Variation of each parameter in accordance with PV device theory. • Model validated against PV panel's datasheet as well as field experiments. Abstract The batteries used with standalone PV systems are very sensitive to the current-voltage charging. Thus, for choosing the right battery, a PV system designer needs to make an accurate estimate of the current and voltage outputs of the PV module under consideration. However, this task is challenging due to randomness in the field operating conditions. To achieve this goal, the PV module in this work has been represented by a well-known equivalent one-diode electrical circuit with five unknown PV parameters. Using multiple current-voltage curves at different operating conditions, the proposed methodology extracts the five unknown PV parameters against each current-voltage curve. Each PV parameter is then plotted independently, first against the varying sunlight and then against the varying temperature. Then introducing a novel concept of shift-factors, the proposed methodology couples the two independent plots of each PV parameter and helps predict the value of that PV parameter at any operating condition. Unlike other works from the literature, the variations of these five PV parameters by the proposed methodology have been shown to be in accordance with the PV device theory and the physical behavior of the PV devices. The proposed model has been validated against both the PV panel's datasheet information and the experimental in-field data. With realistic values captured for each PV parameter by the proposed methodology, the validations show a significant increase in the prediction accuracy of the current-voltage output of the PV panel. It has also been shown how this methodology can be used to better understand the behavior of the PV system and aid in its preventive/corrective maintenance. [ABSTRACT FROM AUTHOR]

Highlights • Battery aging rate depends on charging/discharging power and the elapsed life time. • Control strategies minimizing utility costs could lead to higher battery aging costs. • The proposed strategy finds a balance between utility and battery life-cycle costs. • The strategy uses old batteries more aggressively due to the slow aging rate. Abstract This paper presents an aging-aware model predictive control (MPC) approach for operation of sustainable buildings with on-site photovoltaic (PV) and battery systems. On-site batteries can be used to collect excess PV power during low demand hours and to discharge when PV generation is not enough to meet loads. To further increase the value of the PV-battery assets, batteries can be proactively charged/discharged to shed peak demands for utility cost reduction. The battery life span is highly dependent on the operation strategy and aggressive charge/discharge actions lead to faster battery capacity degradation and higher overall operation cost due to more frequent replacement. The control strategy developed in this study incorporates a convex battery capacity loss model, derived from a physically-based degradation model, to capture the battery aging cost in the control optimization. The degradation model used in the control optimization consists of a set of convex and piece-wise linear sub-models to approximate the battery degradation effect at different time instances. A convex MPC problem is solved in the proposed control strategy to seek the optimal balance between the building utility cost and the battery life-cycle cost. Whole month simulation tests were carried out for a typical office building with on-site PV and battery assets. To quantify the benefits of the proposed control approach, three baseline strategies were also simulated that represent the best current practices. Test results showed that conventional battery control strategies seeking minimum utility cost could double the battery degradation rate and lead to a significant increase in the battery life-cycle cost. The proposed strategy could foresee the tradeoff between the utility cost and battery aging cost and make decisions accordingly. It was demonstrated that the proposed control solution could reduce the utility cost by 9% with moderate battery degradation increase, leading to overall building operation cost reduction up to 4%. [ABSTRACT FROM AUTHOR]

Highlights • Battery state of charge profiles are affected by the suppressed demand effect. • In small rural systems the suppressed demand effect impacts directly to reliability. • Considering the suppressed effect demand lead to more sustainable systems design. Abstract Rural electrification programs usually do not consider the impact that the increment of demand has on the reliability of off-grid photovoltaic (PV)/battery systems. Based on meteorological data and electricity consumption profiles from the highlands of Bolivian Altiplano , this paper presents a modelling and simulation framework for analysing the performance and reliability of such systems. Reliability, as loss of power supply probability (LPSP), and cost were calculated using simulated PV power output and battery state of charge profiles. The effect of increasing the suppressed demand (SD) by 20% and 50% was studied to determine how reliable and resilient the system designs are. Simulations were performed for three rural application scenarios: a household, a school, and a health centre. Results for the household and school scenarios indicate that, to overcome the SD effect, it is more cost-effective to increase the PV power rather than to increase the battery capacity. However, with an increased PV-size, the battery ageing rate would be higher since the cycles are performed at high state of charge (SOC). For the health centre application, on the other hand, an increase in battery capacity prevents the risk of electricity blackouts while increasing the energy reliability of the system. These results provide important insights for the application design of off-grid PV-battery systems in rural electrification projects, enabling a more efficient and reliable source of electricity. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

To address climate change, the Chinese government has committed to achieving carbon peaking by 2030. Projecting the wind power and photovoltaic installed capacity is essential for China's low carbon transition as these renewables have been widely recognized as the major energy sources in future. This study proposes a long-term strategic planning approach for wind power and photovoltaic by simulating multiple policies and market scenarios for the national-level energy transitions and incorporating the feedback effects of market development on technology readiness level. The proposed approach simulates the national energy consumption and energy mix under various transition scenarios based on the Long-range Energy Alternatives Planning System (LEAP). The dynamics of market development, technology readiness level, and future renewable costs are further incorporated into a nonlinear optimization model to generate economically optimal planning solutions for wind power and photovoltaic under different policy and market scenarios. Valuable insights are obtained from three perspectives, i.e., (1) the economic and emission reduction co-benefits under different scenarios, (2) the evolution of the Levelized Cost of Energy, and (3) the impact on the transition of power sector. In particular, the simulation and optimization results reveal that appropriate acceleration of wind power and photovoltaic development can promote technology readiness level, reduce overall transition costs, and effectively reduce the peak value of emissions. The maximum peak reduction in carbon dioxide emissions is 16.06%, and the maximum cumulative reduction in carbon dioxide emissions is 14.54%. The total project cost can be reduced by a maximum of 3.23%. Thus, more active supporting policies for renewables can further enhance long-term economic and environmental co-benefits under current conditions. [Display omitted] • Quantify the market's dynamic feedback on renewable costs through learning curves. • Incorporate technology readiness level into energy long-term renewable planning. • Optimize long-term renewable planning for different policy and market conditions. • Active wind and solar power investments now offer larger co-benefits in future. • CO 2 peak down 16.06% and the cumulative CO 2 reduction 14.54%. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of existing financial incentive policies in the U.S. for integrated photovoltaic and battery energy storage (PV-BES) systems. A mathematical model of PV-BES system to evaluate annual energy performance is developed in this paper. Four types of buildings (i.e., hospital, large office, large hotel, and secondary school) located in four different states, which each has their own PV and/or BES incentives, are selected and analyzed. Based on the energy performance data for each building type, the simply payback period (PBP) for the PV-BES system in different locations is calculated according to the local incentive policies. The PBP is chosen as an indicator to evaluate the effectiveness of incentive policies for different locations and building types by comparing it to the PBP for the same PV-BES systems without incentive policies. The reduction of carbon dioxide emission (CDE) due to the PV generation is also investigated since it indicates the potential to reduce the PBP for a further step when a high carbon credit is available. Furthermore, a parametric analysis is conducted to determine the sensitivity and contribution of parameters such as the capacity of the PV-BES system, the capital cost of PV module and the battery storage on the performance of the PV-BES system. Results show that for all the evaluated buildings in California and Hawaii, the existing incentive policy could reduce the PBP effectively below 10 years. However, the PBP for most of the evaluated buildings in New Jersey and New York were high even when both the PV and BES incentive policies were taken into account (approximately from 11 to 29 years). [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) cells have attracted substantial interest recently owing to environmental concerns and the energy crisis. However, the efficiency of power generation by solar PV cells depends on various factors, such as climatic conditions and tilt angle. Therefore, maximum power point tracking (MPPT) for PV modules is important. This work proposes a novel robust design, which relies on various combinations of insolations, temperatures, and tilt angles, in a stand-alone PV system with MPPT. Orthogonal experiments are carried out using the Taguchi method, avoiding a full factorial experimental design. In the proposed Taguchi-based MPPT robust design of the PV system, insolation and temperature are regarded as an input and ‘noise’, respectively; insolation passage, tilt angle, and load resistance are factors that affect the results of the Taguchi experiments in multiple scenarios. A fuzzy logic controller, tuned by chaos particle swarm optimization (PSO) was utilized to implement MPPT. A stand-alone PV system was used to demonstrate the results that were obtained by the robust design of MPPT and applicability of the proposed method. Finally, a Digital Signal Processor-in-the-Loop simulation was performed using an OP5600 real-time digital simulator by Opal-RT to demonstrate the viability of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper formulates and solves a probabilistic optimal power flow approach (POPF) for a hybrid power system that includes plug-in electric vehicles (PEV), photovoltaic (PV) and wind energy (WE) sources. In the proposed approach, the Monte Carlo Simulation (MCS) was combined with the antithetic variates method (AVM) to determine the probability distribution function (PDF) of the power generated by the hybrid system. To reduce the computational cost of the optimal power flow calculations, we solved the POPF problem using a master–slave parallel epsilon variable multi objective genetic algorithm (Pev-MOGA). The performance of the proposed approach was assessed using the IEEE 30-bus, 57-bus and 118-bus power systems. Various scenarios incorporating several configurations of WEs, PVs and PEVs sources were considered in the evaluation. Sensitivity analysis was also performed for further assessment. The obtained results along with a comparison analysis with other optimization algorithms confirmed the effectiveness of the proposed approach in accurately providing a set of optimal solutions for the hybrid power system. [ABSTRACT FROM AUTHOR]

The emergence of Decentralized Energy Resources (DERs) and rising electricity demand are known to cause grid instability. Additionally, recent policy developments indicate a decreased tariff in the future for electricity sold to the grid by households with DERs. Energy Storage Systems (ESS) combined with Demand Side Management (DSM) can improve the self-consumption of Photovoltaic (PV) generated electricity and decrease grid imbalance between supply and demand. Household Energy Storage (HES) and Community Energy Storage (CES) are two promising storage scenarios for residential electricity prosumers. This paper aims to assess and compare the technical and economic feasibility of both HES and CES. To do that, mathematical optimization is used in both scenarios, where a Home Energy Management System (HEMS) schedules the allocation of energy from the PV system, battery and the grid in order to satisfy the power demand of households using a dynamic pricing scheme. The problem is formulated as a Mixed Integer Linear Programming (MILP) with the objective of minimizing the costs of power received from the grid. Data from real demand and PV generation profiles of 39 households in a pilot project initiated by the Distribution System Operator (DSO) ’Enexis’ in Breda, the Netherlands, is used for the numerical analysis. Results show that the self consumption of PV power is the largest contributor to the savings obtained when using ESS. The implementation of different ESS reduces annual costs by 22–30% and increases the self-consumption of PV power by 23–29%. Finally, a sensitivity analysis is performed which shows how investment costs of ESS per kWh are crucial in determining the economic feasibility of both systems. [ABSTRACT FROM AUTHOR]

• A Mixed-Integer Linear Programming (MILP) framework based energy management system model. • A rolling horizon approach to tackle with PV production uncertainties. • Ensuring a fairness among the residential end-users for PV and ESS usage. • Development of PV and ESS usage metrics for fairness evaluation. The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency. This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption. [ABSTRACT FROM AUTHOR]

Rooftop photovoltaic systems integrated with lithium-ion battery storage are a promising route for the decarbonisation of the UK’s power sector. From a consumer perspective, the financial benefits of lower utility costs and the potential of a financial return through providing grid services is a strong incentive to invest in PV-battery systems. Although battery storage is generally considered an effective means for reducing the energy mismatch between photovoltaic supply and building demand, it remains unclear when and under which conditions battery storage can be profitably operated within residential photovoltaic systems. This fact is particularly pertinent when battery degradation is considered within the decision framework. In this work, a commercially available coupled photovoltaic lithium-ion battery system is installed within a mid-sized UK family home. Photovoltaic energy generation and household electricity demand is recorded for more than one year. A comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial Lithium-ion batteries is developed. The comprehensive model accounts for all established modes of degradation including calendar ageing, capacity throughput, ambient temperature, state of charge, depth of discharge and current rate. The model is validated using cycling data and exhibited an average maximum transient error of 7.4% in capacity loss estimates and 7.3% in resistance rise estimates for over a year of cycling. The battery ageing model is used to estimate the cost of battery degradation associated with cycling the battery according to the power profile logged from the residential property. A detailed cost-benefit analysis using the data collected from the property and the battery degradation model shows that, in terms of utility savings and export revenue, the integration of a battery yields no added benefit. This result was, in-part, attributed to the relatively basic control strategy and efficiency of the system. Furthermore, when the cost of battery degradation is included, the homeowner is subject to a significant financial loss. [ABSTRACT FROM AUTHOR]

A novel micro-channel heat pipe array incorporated with crystalline silicon (c-Si) solar photovoltaic/thermal system (MHP-PV/T) was designed and constructed by the authors. The proposed design configuration combined c-Si solar cells and wide micro-channel heat pipes (MHP) that were filled with prescribed amount of acetone as refrigerant under a vacuum condition in the same insulated frame to simultaneously provide electrical and thermal energy. Heat and mass transfer characteristics of the MHP-PV/T were preliminary investigated using both numerical and experimental methods. The transient behavior and parametric heat transfer limitations of the heat pipe were also examined using MATLAB. A linear relation between the thermal instantaneous efficiency η th and the reduced temperature parameter ( T out - T in ) G T - 1 was established. The maximum instantaneous efficiency was found to be 54.0% with an electrical power output of 70 W. The results indicated that the daily thermal and electrical efficiencies were 50.7% and 7.6%, respectively. The transient behavior of the MHP shows a faster thermal response to heat input within the temperature range of 48.8–49.2 °C and slower response when the thermal diffusivity was reduced to 0.05 cm 2 /s. The results also reveal good agreements between model simulation and experimental measurement with sufficient confidence. [ABSTRACT FROM AUTHOR]

Performance limit of a solar hybrid power generation system integrating efficient photovoltaic (PV) cells and methanol thermal (T) decomposition is explored from a thermodynamic perspective within the capability of state-of-the-art technologies. This type of PVT system features potentially high “net solar-to-electric efficiency” in general, primarily resulting from a key difference in the design of the thermal part compared with conventional PVT systems, i.e. replacing heat engines by a thermochemical power generation module for thermal energy utilization. Key design parameters of the system, including PV cell type, emissivity, solar concentration ratio and solar concentrator type, are individually studied. A system combining all such optimized aspects is projected to achieve net solar-to-electric efficiencies up to 51.5%, after taking all major (e.g. optical, radiative) losses into consideration. This study reveals important insights and enriches understanding on design principles of efficient PVT systems aimed at comprehensive and effective utilization of solar energy. [ABSTRACT FROM AUTHOR]

The electrical power sector has entered an era of decarbonizing energy generation by accommodating more Renewable Energy Sources (RES), especially solar energy in the power grid. However, high penetration of such a resource has negative impacts on the power network such as power fluctuations, reverse power flows and voltage rises. Energy Storage (ES) offers an effective solution to addressing those challenges while improving the load factor as well as the utilization of network infrastructures. This paper proposes a new framework to integrate Community Energy Storage (CES) units in an existing residential community system with rooftop solar Photovoltaic (PV) units. In this framework, three analytical approaches are respectively developed to handle three important parameters of the CES integration (i.e. locations, sizes and operational characteristics) to enhance network performances. Firstly, a simple approach is developed on the basis of a Center of Gravity (COG) theory to determine the location of CES for minimizing the annual energy loss. Secondly, a new analytical formulation based on a load following control method is presented to identify the proper rated capacity of CES and its hourly dispatch strategy for achieving a desired annual load factor. Lastly, a technique to estimate the optimal operational characteristic of CES is proposed for flattening the daily demand profile and improving the voltage profile. The proposed framework is tested on a 19-bus test system while considering the probability of PV generation along with load variations. The numerical results show that the developed framework can bring the system load factor to a maximum of 0.76, at which the penetration levels of CES and PV are at 22% and 5% respectively while reducing the energy loss by 24.21% and enhancing the voltage profile significantly. The results also indicate that 22% CES penetration can reduce the annual purchased energy cost from the grid by 11.1% and the annual energy loss cost by 36.88%, where the system accommodates respective PV penetration levels of 5% and 50%. [ABSTRACT FROM AUTHOR]

A solar photovoltaic (PV) thermochemical hybrid system consisting of a point-focus Fresnel concentrator, a PV cell and a methanol thermochemical reactor is proposed. In particular, a reactor capable of operating under high solar concentration is designed, manufactured and tested. Studies on both kinetic and thermodynamic characteristics of the reactor and the system are performed. Analysis of numerical and experimental results shows that with cascaded solar energy utilization and synergy among different forms of energy, the hybrid system has the advantages of high net solar-electric efficiency (up to 41%), stable solar energy power supply, solar energy storage (via syngas) and flexibility in application scale. The hybrid system proposed in this work provides a potential solution to some key challenges of current solar energy utilization technologies. [ABSTRACT FROM AUTHOR]

The aim of our research is to explore whether the countries’ local trade pattern can reflect their preferences of selecting trade partners in different commodity’s markets. Considering of the energy and their alternative ones of daily use and power generation, we choose four energy commodities to make comparisons: crude oil, coal, natural gas and photovoltaic (PV). We use complex network and link prediction to explore local trade pattern, make projections based on 2014 data and approve them by 2015 data. We find that the international PV trade involves much denser cooperation among countries than other three commodities. Certain links, such as that between Canada and France-owned islands, are key paths for other countries to establish further trade cooperation. In addition, interestingly, high number of common trade partners promote two countries to establish fossil energy trade cooperation. However, for the global PV trade market, each country’s production capacity becomes a preferential element for further cooperation. Projections about potential trade links and core patterns are estimated. Governments can develop traditional energy with more triangle-shaped cooperation, while they can promote renewable energy cooperation by increasing the number of trade channels. Future cooperation can be estimated based on trade preferences in different markets. [ABSTRACT FROM AUTHOR]

A hybrid solar power generation system integrating a solar photovoltaic (PV) module and a solar thermochemical module is proposed based on methanol thermochemistry. Sunlight is concentrated by trough mirror collectors and partially converted to electricity by PV cells overlain on the surface of a solar thermochemical reactor. An endothermic chemical process of methanol (e.g., decomposition) within the reactor then absorbs the “waste heat” of the PV cells and simultaneously cools the cells. During this process, the low-level thermal energy from the sunlight is upgraded to high-level chemical energy in syngas, which is stored and burned to generate electricity when necessary. Analysis indicates that the theoretical net solar-electric efficiency of the hybrid system could be as high as 45% and relatively insensitive to operation temperature and pressure. Additionally, the system exhibit a good potential in solar energy storage, and capable of providing stable electricity supply around the clock. The PV–thermochemical hybrid system might suggest a promising approach for efficient and stable power generation from solar energy. [ABSTRACT FROM AUTHOR]

The paper studies grid-connected photovoltaic (PV)-hydrogen/battery systems. The storage component capacities and the rule-based operation strategy parameters are simultaneously optimized by the Genetic Algorithm. Three operation strategies for the hydrogen storage, namely conventional operation strategy, peak shaving strategy and hybrid operation strategy, are compared under two scenarios based on the pessimistic and optimistic costs. The results indicate that the hybrid operation strategy, which combines the conventional operation strategy and the peak shaving strategy, is advantageous in achieving higher Net Present Value (NPV) and Self Sufficiency Ratio (SSR). Hydrogen storage is further compared with battery storage. Under the pessimistic cost scenario, hydrogen storage results in poorer performance in both SSR and NPV. While under the optimistic cost scenario, hydrogen storage achieves higher NPV. Moreover, when taking into account the grid power fluctuation, hydrogen storage achieves better performance in all three optimization objectives, which are NPV, SSR and GI (Grid Indicator). [ABSTRACT FROM AUTHOR]

As the greenhouse effect becomes increasingly serious, cooling a vehicle cabin parked under the blazing sun without running the engine or using an electric vehicle’s power has received considerable attention. In this paper, we develop a novel portable, renewable, solar energy-powered cooling system with wireless power transfer (WPT) and supercapacitors to cool the vehicle cabin. The proposed system consists of a solar collector mechanism, an energy conduit, and a temperature control and cooling module. First, consisting of folding solar photovoltaic (PV) panels, the solar collector mechanism making the proposed system portable. Once collected, the solar energy is converted into electricity and stored in the supercapacitors through wireless power transfer without breaching the vehicle body. Automatic temperature regulation is achieved with the cooling device via the temperature control and cooling module. The experimental results indicate that a maximum output power of 2.181 W and a maximum WPT efficiency of 60.3% are achieved when the prototype loaded with 3 Ω and 5 Ω respectively. Meanwhile, the simulation shows the temperature inside the cabin is reduced by as much as 4.2 °C in average, demonstrating that the proposed solar energy-powered cooling system is effective and feasible in cooling a hot vehicle cabin. [ABSTRACT FROM AUTHOR]

This paper presents methods for assessing the statistical interdependencies between solar irradiance and the outside temperature, which are used to assess the correspondence between the operation of technologies to meet the heat demands and the PV output. It is important to be able to assess the impact of heating technology changes and increased PV penetration on power system requirements. Considering the correlation between the PV outputs and heat loads, there exist multiple effects of weather variations on the “Source-Grid-Load.” Heat load, photovoltaic (PV), electric air conditioner (EC), natural gas-fuelled combined heat and power (CHP), and central heating boiler models are established to study the influences of the solar irradiance and the temperature of the outdoors. The energy dependence impacts on the “Source-Grid-Load” are simulated in a 41-bus distribution network, and the paper's results show how the correspondence between the temperature and solar irradiance can impact the system's requirements. [ABSTRACT FROM AUTHOR]

This paper proposes a strategy for optimal allocation of multiple Community Energy Storage (CES) units in a distribution system with photovoltaic (PV) generation. The main contribution of this strategy is it considers all possible benefits accrued from and costs incurred by CES deployment to a utility. The benefits are gained from energy arbitrage, peaking power generation, energy loss reduction, system upgrade deferral, emission reduction and VAr support. A cost-benefit analysis is also conducted to identify the optimal Net Present Value (NPV), Discounted Payback Period (DPP) and Benefit-Cost ratio (BCR). Moreover, an optimal power factor approach is included in the analysis to dispatch CES units to improve load factors and voltage profiles. The probabilistic distribution of solar irradiance is also incorporated in the proposed strategy to consider the uncertainty of PV generation. Several sensitivity analyses are carried out to investigate the effects of PV penetration, load models and the number of CES units deployed on the profitability of CES investment. Numerical results show that the proposed strategy enables a power utility to identify the location, size and operational characteristic of CES units for maximizing the total NPV and enhancing load factors and voltage profiles. [ABSTRACT FROM AUTHOR]

In this paper we study the performance of 855 large (⩾10 kW) commercial rooftop photovoltaic (PV) system installations in California. Drawing on the nonparametric data envelopment analysis (DEA) method, our study takes the PV capacity, electricity generation, modules, system cost, solar irradiance, and ambient air temperature into account to provide a unified measure of the PV installation performance. This approach can overcome some of the limitations of simple output-to-input ratio analysis and allow us to assess whether solar capacity and electricity generation have been efficiently integrated into the grid in a holistic way. We find widespread inefficiencies in roughly 80–90% of the installations both within a specific year and across different years. There is a significant efficiency gain in solar installation from 2008 to 2012. Through regional analysis, we find high dispersions of performance across counties and cities, and identify locations where PV installations have achieved the best performance. The analysis also sheds lights on the magnitude of the gap between inefficient installations and efficient ones. [ABSTRACT FROM AUTHOR]