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The present paper reviews the green hydrogen uptake policies in the United States and the European Union. While the EU has a rather broad public support strategy that envisages the entire hydrogen value chain, the U.S. policies are more focused on hydrogen research hubs and subsidies having electric vehicle applications. From the experience of the solar and wind power rollout of the past three decades, we know that the subsidization schemes of the end product play an important role in the transition to renewables. These kinds of subsidization schemes are still lacking for clean hydrogen both in the EU and the U.S. This paper provides a brief overview of selected subsidization schemes from other renewables markets, which are then applied to define an exemplary subsidization path to empower large-scale growth in a market for green hydrogen. Here is proposed a long-term subsidization scheme – deemed as an adequate strategy for the expansion of clean hydrogen in an electricity mix. The suggested exemplary subsidization path recommends shifting from feed-in tariffs to direct marketing with market premiums once a certain threshold is reached, while both can be flanked by the issuance of a Guarantee of Origin for green hydrogen so that there is one market for hydrogen and another market for the certificates. • -A long-term subsidization scheme for the rollout of green hydrogen in the electricity mixes is proposed. • -An exemplary subsidization path to empower large-scale growth in a market for green hydrogen is drafted. • -U.S. and EU are used as case studies to capture lessons for subsidization of green hydrogen economy uptake. • -The U.S. has more centralized green hydrogen rollout policies, whereas the EU has a wider policy toolkit. • -Shifting from FIT to direct marketing with market premiums is key once a certain pre-defined threshold is reached. [ABSTRACT FROM AUTHOR]

• A new Free-Drift Solar Power Satellite concept is proposed. • Large amount of fuel is saved because of the free-drift orbit and attitude. • Symmetric layout is adopted to reduce attitude disturbance. • Two transmitting antennas are used to reduce the total length of cables. Solar power satellite is known as a promising source of clean energy from space in the future. One of the key problems of current solar power satellite concepts is the huge cost because of the excessively large mass and fuel consumption. The objective of this paper is to propose a new Free-Drift Solar Power Satellite concept that reduce mass and fuel consumption. Firstly, Sun-frozen orbit in geosynchronous Laplace plane and quasi-Sun-pointing attitude are designed to significantly save orbital and attitude control fuel. To this end, an orbit-attitude coupled modelling method considering space perturbations is proposed for multibody systems. Orbital and attitude dynamics and control are studied to substantiate the orbit and attitude design. Secondly, two transmitting antennas and three solar arrays are adopted and allocated elaborately to reduce structural deformations and attitude disturbance. The mass of the transmitting cables from solar arrays to transmitting antennas is also reduced by the proposed layout. Finite element analysis is performed to validate the design of supporting structures. Finally, other subsystems are briefly introduced and the system parameters are summarized. [ABSTRACT FROM AUTHOR]

Constructing an accurate and reliable solar photovoltaic (PV) power forecasting system is crucial for smart grid management and dispatch. However, due to the intermittent, non-stationary and random nature of solar energy, current methods cannot effectively capture the dynamic change patterns of PV data, resulting in a forecasting accuracy that cannot satisfy the requirement for stable operation of smart grids. To address this issue, in this paper, a new solar PV power forecasting system is proposed based on a new adaptive mode decomposition method that combines machine learning models. First an adaptive mode decomposition method is proposed for adaptively eliminating high-frequency information of PV data, mitigating the impact of high noise on the forecasting performance. Then the powerful mapping ability of machine learning model is utilized to construct a solar PV power generation combination forecasting system. The system adopts a multi-objective optimization strategy to determine the optimal weights of the combination model, which reduces the volatility of the forecasting results and enhances the stability of the forecasting system. Experimental results demonstrate that the proposed system achieves the best overall performance with online manner on 10 state-of-the-art baselines. Furthermore, the evaluation analysis by Diebold–Mariano test, improvement ratio and sensitivity analysis further show that the stability and accuracy of the proposed system outperforms the comparative baselines. [ABSTRACT FROM AUTHOR]

• A multi-microgrid energy real-time optimal control scheduling strategy is proposed. • Energy storage devices can actively participate in optimal energy scheduling. • Improved resilience and flexibility of energy dispatch for multiple microgrid. • Significantly reduce the number of microgrid connections to the distribution grid. • Maximising the full uptake of renewable energy in multi-microgrid systems. With the increasing prominence of the energy crisis and global climate issues, it has become an important cornerstone of the energy revolution to improve the utilization rate of renewable energy and gradually reduce the use of fossil energy. Nonetheless, the erratic fluctuations and instability of renewable energy sources such as wind and solar energy have presented certain obstacles to the effective utilization of renewable energy. In order to maximize the utilization of renewable energy, enhance its utilization efficiency, and reduce the carbon emission of power supply, this paper first proposes a real-time collaborative optimal energy scheduling and dispatching control model for multi-microgrids. Subsequently, it proposes a real-time optimal control and dispatching strategy for multi-microgrid energy based on storage collaborative. This model considers the energy storage device as an energy management controller, enabling it to participate in the energy collaborative dispatch of multi-microgrid. This enables the energy storage device in the microgrid not only to actively participate in the optimal scheduling of energy, but also to make the energy in the storage device be reasonably distributed in the multi-microgrid system, to maximize the full absorption of renewable energy in the multi-microgrid, thereby reducing the dependence of the microgrid on the distribution network, and significantly improving the elasticity and reliability of the multi-microgrid system. Finally, the feasibility and effectiveness of the proposed optimal control scheduling strategy are verified by simulation. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power generation forecasting is crucial for optimizing the integration of solar energy into power grids and advancing towards a cleaner, more sustainable energy future. However, the inherent variability and complexity of PV power generation data pose significant challenges for accurate forecasting. To address these challenges, this paper introduces Collaborative Directional Representation (CoDR), a novel deep learning model that extracts and represents the directional fluctuations of solar irradiance data to improve forecasting accuracy and reliability. CoDR utilizes a series of steps, including data preprocessing, fluctuation extraction, directional representation, linearization, de-extraction, mapping, and data postprocessing, to capture both the temporal and spatial dependencies within the data. CoDR leverages a unique directional representation to capture both temporal and spatial dependencies in the data, enabling superior forecasting accuracy and robustness compared to twenty-two state-of-the-art benchmark methods. We validate CoDR on a real-world dataset of PV power generation in Belgium, demonstrating its effectiveness through extensive experiments, ablation studies, and sensitivity analyses. Importantly, CoDR enhances the transparency of the forecasting process by revealing the causal relationships and directional influences among input variables and PV power output. This explainability feature provides valuable insights into the underlying drivers of PV power generation, promoting trust and informed decision-making in the transition to cleaner energy systems. • Introduces Collaborative Directional Representation (CoDR) for accurate PV forecasting. • Outperforms 22 benchmark methods, achieving significant error reductions. • Provides explainable insights into PV power generation using 3D visualizations. • Demonstrates effectiveness on a real-world Belgian dataset through extensive analyses. [ABSTRACT FROM AUTHOR]

Solar thermal energy is one of the important sources of renewable energy. The solar thermal energy gives clean energy which can be utilized for industrial applications. Industrial heaters which require heat energy for many industrial uses can be attained using these solar thermal absorbers. The solar thermal absorber is designed using a layer design that consists of material compositions of Titanium (Ti) and Stannic selenide (SnSe 2) for ground and substrate sections respectively. A graphene layer which has the good properties to be used in solar absorbers is added between the Aluminum and SnSe 2 material. A thin graphene section is inserted above the SnSe 2 substrate and below the Ti resonator. To catch the good absorption in the current design, the best four wavelengths (micrometer) have been attached such as 0.22, 0.8, 2.46, and 2.85. This design's contribution can be studied in 4-atm regimes (from UV, V to NIR, and MIR). According to the wavelength and bandwidth ranges, the current absorption explores more than 97% (97.4%) in between 2.4 and 3 μm (600 nm) bandwidth and exceeds 95% (95.2%) from 0.2 to 1 μm (800 nm) respectively. To the overall range of design, it extracts 90.3% for the 2800 nm band between 0.2 and 3 wavelength regime. The flow of the presentation of the proposed paper is (i) Introduction (ii) Design and Parameters, and (iii) Results and Discussions. The explored absorber can be effective in water heating and swimming pool heating for homes. Moreover, it can also be applied in large industries as a solar thermal absorber for heating applications. [ABSTRACT FROM AUTHOR]

Water is an essential commodity for society, and alternate resources such as seawater and wastewater are vital for the future. There are various desalination technologies that can provide sufficient and sustainable water sources. Renewable energy-based desalination technologies like solar-based interfacial evaporation are very efficient and sustainable desalination methods. Solar-based interfacial evaporation has been a focus due to its efficient and easy-to-use methods. Still, research is needed for fouling resistance, scalable and low-cost materials, and devices for solar interfacial evaporation. Recent research focuses on the materials for evaporation devices, but various other aspects of device design and fabrication methods are also necessary to improve device performance. In this article, all the evaporator device configurations and strategies for efficient evaporator devices are compiled and summarized. The evaporator devices have been classified into eight main categories: monolayer, bilayer, tree-like design, low-temperature designs, 3D-Origami-based designs, latent heat recovery design, design with storage/batch process, and contactless design. It was found that a good absorber, well-engineered air-water interface, and bottom-layer insulation are necessary for the best systems. The current research focuses on the vapor production output of the devices but not on the water production from devices. So, the focus on device-based water production and the associated cost of the water produced is essential. This article articulates the strategies and various scalable and efficient devices for evaporation-based solar-driven desalination. This article will be helpful for the researchers in improving devices output and coming up with a sustainable desalination and water treatment. [Display omitted] • The paper discusses the outlook and challenges for Solar driven interfacial evaporation devices. • The article lays the path for efficiency improvement for interfacial evaporation Devices. • Floatable devices with tree-like designs have better performance. • The absorber and condenser designs are equally essential for ISDE water production devices. [ABSTRACT FROM AUTHOR]

• Green hydrogen generation devices were studied. • Methods for annual operation evaluation were proposed. • Experimental study on transient states was conducted. • Case study for 10 MW renewable energy plant was considered. This study presents a comprehensive methodology to evaluate plants that integrate renewable energy sources and hydrogen generation devices. The paper focuses on presenting the methods for devices' operation assessment taking into account the annual operation. Multiple effectiveness indices have been presented. On the basis of experimental investigation with the hydrogen generator, the methods for assessing its operation during start-up phase and sudden change in the supply current were proposed. The results of the experiments and the provided mathematical models show that dynamics of the hydrogen generator should be taken into account when selecting the suitable device for cooperation with variable renewable energy. It is especially important for multiple start-ups throughout the day due to significant differences in the amount of hydrogen produced by devices characterized by the same efficiency, yet various time constants. Methodology for selecting the optimal nominal power for hydrogen generator to cooperate with given renewable sources was developed. It was proven the optimal power depends on the type of the renewable source and minimal load of the hydrogen generator. Several case studies, including the integration of wind and solar energy farms to yield a 10 MW renewable energy farm were considered and the minimal load of the hydrogen generator impacts the annual operation of the device has been presented. The paper provides a set of tools to contribute to the development of sustainable energy plants. The methods proposed in this paper are universal and can be used for various renewable energy sources. [ABSTRACT FROM AUTHOR]

The rapidly growing state of energy consumption and the urgency of mitigating the harmful effects of climate change have accelerated our transition to renewable energy. This paper proposes a multi-level framework for exploring the global competition to develop renewable energy technologies. Through a combination of text mining techniques and by analyzing a corpus of patents covering six renewable energy technologies from 1970 to 2019, we reveal the lifecycle of these technologies, the original sources of these technologies, their potential markets, and the different supply and demand patterns of the major countries/regions involved. The results show that solar and wind energy are currently the most widely used renewable energy sources, yet biomass is the only energy source still in its growing stage. China's patents have seen a rapid upward trend, spurred on by strong policy promotion, which runs counter to the decrease in patent applications seen in most other major countries in this area. However, China's technology imports still far outweigh its exports, indicating that there is still a sizeable gap between China and some of the more developed countries like the United States, Germany, France, and the United Kingdom in terms of high-quality, high-value patents. Accordingly, China, the United States, and Japan are currently the leading in the world's renewable energy markets. That said, the market potential of Europe, Canada, and Australia should not be underestimated. These and more themes are discussed from a political, economic, environmental, and global governance perspective. This paper provides useful insights into the global renewable energy technology landscape, particularly the competitive dynamics at play. The aim is to guide policy makers or managers on the next steps in our progress toward applying and using renewable energy at scale. [ABSTRACT FROM AUTHOR]

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

Photovoltaic systems have a reliable method to generate electrical power from direct solar irradiance. Their efficiency is only up to 20% in real operating circumstances and is affected adversely by the inevitable temperature rise in their surface. Passive, active or combined cooling methods are applied to reduce operating temperature to a suitable level, depending on specific application. A Great body of literature has been investigating different PV cooling methods and techniques to reach optimized system efficiency and cooling effect. It is critical for both researchers and practitioners to have a thorough understanding and awareness of the most recent advances and existing gaps. Although there are certain review papers in this field, it is necessary to have up-to-date reviews of the most recent progress and research since the previous ones. In the present work, the main focus was to provide a review of the most recent studies and to compare their outcomes to provide a reference for practitioners and researchers. The holistic classification and evaluation of all the possible cooling technologies were provided in order to draw a comparison between various cooling approaches. Based on the analysis obtained, cooling techniques can ensure improvement in electrical efficiency in a range from 1% to 53% with expected decrease in the photovoltaic panel's surface operating temperature from 1.8 °C to 46 °C. It was also found that spectrum filtering and jet impingement indicated highly remarkable enhancements. The other factors, such as the size of the examined photovoltaic panel, photovoltaic technology, as well as climate circumstances have a strong impact on the cooling performance besides the selection of the specific coolant. The necessity for integral evaluation (performance, economic and environmental) of cooling techniques was also identified as key issue. • Cooling technologies enhance the performance, durability, and lifetime of photovoltaic technologies. • Passive cooling techniques have less performance improvement when compared to active ones. • The size of the PV panel can strongly affect cooling performance. • The economic and environmental aspects were weakly investigated. [ABSTRACT FROM AUTHOR]

• First reviews on coupling utilisation of thermal sources in vapour compression heat pumps. • The existing thermal source coupling attempts in applied energy fields are summarised and compared. • Design features and performance enhancement principles are analysed in detail. • Recommendations for research and marketisation of energy source coupling methods are made. Vapour compression heat pump provides efficient cooling and heating through the use of various renewable energy sources and plays a significant role in the energy conservation of modern buildings. Its performance enhancement gains increasing attention under the background of conventional energy and environmental crisis. Since the efficiency of a heat pump cycle is closely related to its thermal source condition, a stable high-temperature heat source or low-temperature cold source could benefit its heating or cooling efficiency a lot. Following this improvement strategy, significant progress has been made over the past years on the use of different renewable energy sources as coupled thermal sources of heat pumps. However, a specialised review that summarises relevant studies remains to be published. This paper aims at presenting operating mechanisms, characteristics, benefits, and limitations of different energy source coupling methods for heat pump performance enhancement towards its building application. The most concerning research on solar-assisted air source heat pumps, solar-assisted ground source heat pumps, and air-ground dual source heat pumps is emphasised. The existing thermal source coupling attempts under other renewable energy fields, such as waste heat, biomass, and water sources, are also summarised. Recommendations for further research and marketisation of such technologies are made based on detailed performance characterisation and comparison. The review is expected to benefit researchers in heat pumps and relevant applied energy fields to understand the challenges and opportunities in thermal source coupling technology and to promote its research and application development. [ABSTRACT FROM AUTHOR]

• Deep attention ConvLSTM is first proposed for photovoltaic (PV) power forecast. • Attention mechanism is first introduced into PV power forecast. • Attention adaptatively fuses clear-sky prior knowledge and historical information. • Significantly improvement than conventional PV power forecast methods is achieved. Photovoltaic (PV) power is an important way to utilize solar energy. Accurate PV power forecast is crucial to the large-scale application of PV power and the stability of electricity grid. This paper proposes a novel method for short-term photovoltaic power forecast using deep attention convolutional long short-term memory (ConvLSTM) network and kernel density estimation (KDE). ConvLSTM has strong ability to automatically extract the information from multiple variables related to PV power and the temporal information from historical PV data. Considering that clear-sky solar radiation is an important physical prior knowledge in PV power generation, attention mechanism is innovatively introduced into ConvLSTM to dynamically and adaptively adjust the weights of the physical prior knowledge and the historical PV data in forecast. KDE is used for estimating the joint probabilistic density function and giving the probabilistic confidence interval. Experiments in the three-year actual photovoltaic power data of two provinces in Belgium verified the effectiveness of the proposed method. Comparison experiments were made with naive persistence and other eight conventional methods including autoregressive integrated moving average with exogenous variable (ARIMAX) model, convolutional neural network (CNN), long short-term memory (LSTM) network, multilayer perceptron (MLP), support vector regression (SVR), extreme learning machine (ELM), classification and regression tree (CART) and gradient boosting decision tree (GBDT). Experiments show that the proposed method can realize the optimal fusion of the historical data and clear-sky prior knowledge, and significantly improve the PV power forecast accuracy in all seasons of a year. [ABSTRACT FROM AUTHOR]

Over 400 colleges and universities have signed carbon reduction pledges through Second Nature. Nonetheless, a review of their greenhouse gas inventories shows that many universities continue to show limited or no progress in terms of renewable energy deployment and generation. One study from 2019 shows that it is now cheaper to build new renewable energy projects than it is to continue operating existing coal-fired power plants (for 74% of existing fleet). Meanwhile, another study shows that universities rarely achieve a positive economic net benefit when deploying solar on their campuses, despite the cost declines in solar and rising utility prices. This same study finds that these same projects accrue a positive social and environmental net benefit for the surrounding community that outweighs current economic subsidies, implying that increased subsidies for college solar deployment would, more often than not, pass cost-benefit analysis. This present study builds on the previous two studies and explores the question of whether state-level financial incentives have a meaningful impact on the deployment of renewable energy on college campuses. The thesis guiding this research is that universities in states with certain state-level financial incentives will be more successful in deploying renewable energy and in meeting their clean energy goals. The financial incentives being studied are limited to grants, tariffs, net metering (at retail rates), and solar renewable energy credits. This research paper shows that the presence of these financial incentives is positively correlated with renewable energy deployment across colleges, providing a starting map for policy-makers interested in supporting universities' renewable energy deployment in their states. [ABSTRACT FROM AUTHOR]

• Heating and cooling tests on a fully monitored large-scale pavement solar collector. • Thermal performance investigation of a controllable and configurable prototype. • Sensitivity analysis of design and operational parameters on system performance. • A great potential in reducing asphalt surface temperature to mitigate UHI effects. Solar energy harvesting as a renewable and sustainable energy source has been widely investigated in recent years across engineering fields. The use of Pavement Solar Collectors (PSC) can lead to clean energy production, an increase in road safety, prolong the service life of asphalt pavement, and can mitigate the Urban Heat Island (UHI) effect. This study describes a controllable large-scale research PSC prototype with high configuration flexibility, and full monitoring capability at the University of Antwerp, Belgium. Since small- or laboratory-scale setups do not reflect the behavior of actual projects, the present paper investigates the thermal response of a large-scale PSC in the Western European climate, including heating load, heat extraction capacity, and asphalt surface and profile temperature changes during heating and cooling experiments. The study shows that a low supply temperature compared to high (14 °C vs. 28 °C) can reduce the depletion rate of the stored thermal energy in borehole thermal energy storage up to 7 times. The sensitivity analysis indicates that an increase in flow rate from laminar to transient regime requires twice as much thermal power compared to the same flow rate changes within transient and turbulent regimes. The maximum average daily efficiency of the PSC could reach 34% with a flow rate of 4 l/min. The experimental results showed that increasing the pipe length from 50 m to 200 m reduces the cumulative power extraction capacity by up to 48%. Furthermore, the PSC system shows great potential in reducing the asphalt surface temperature (up to 12 °C) to mitigate the UHI effects. Finally, the PSC system effectively controls the temperatures of the interface zones to reduce the rutting distress in the summertime and lower the potential of cold thermal crack developments and brittle shear failure behavior in wintertime. [ABSTRACT FROM AUTHOR]

Rather than using individually distributed energy storage frameworks, shared energy storage is being exploited because of its low cost and high efficiency. However, proper sizing and operations approaches are still required to take advantage of shared energy storage in distribution networks. This paper proposes a bi-level model to optimize the size and operations of shared energy storage in hybrid renewable-resource power generation systems. The upper-level model maximizes the benefits of sharing energy storage for the involved stakeholders (transmission and distribution system operators, shared energy storage operators and the various power plant owners) and the lower-level model minimizes the hybrid system operating costs. The benefits of this system were found to be: (1) reductions in wind and solar power curtailment and coal-fired generation costs; (2) peak shaving; (3) frequency regulation; and (4) a deferral of upgrades to power transmission and distribution facilities. To fully realize the long-term planning and short-term operational interactions of shared energy storage, a bi-level nested genetic algorithm was designed to solve the proposed model. By continuously updating the solutions, a set of optimal solutions were generated. To validate the approach, numerical tests were conducted, with the results showing that by properly sizing and operating the shared energy storage in distribution networks, the wind curtailment rate was reduced by about 10.2%, the solar curtailment rate was reduced by 14.2%, and the stakeholder benefits were around 154 million dollars. Further discussion is given on the benefits of shared energy storage investments. • Shared energy storage is planned and modelled in distribution networks. • A bi-level model is established for shared energy storage sizing and operation. • An interactive bi-level nested genetic algorithm is designed. • A comparative analysis is conducted to validate the shared energy storage feasibility. [Display omitted] [ABSTRACT FROM AUTHOR]

The classical optimal power flow problem is usually formulated with only thermal generators, in which the fuel used to generate power is limited and emissions from the network system are often ignored. Due to several promising features like renewability, richness, and cleanness, renewable energy sources have been drew growing attention. As a result, more and more renewable energy sources are penetrated into the electrical grid. In this paper, the standard IEEE-30 bus system is modified by integrating renewable energy sources as the case study, where the traditional thermal generators on buses 5 and 11 are replaced by wind generators, and bus 13 is replaced by solar generators. In addition, to address the intermittence and uncertainty of renewable sources, the Weibull probability density function is used to calculate the available wind power. Meanwhile, the lognormal probability density function is employed to calculate the available solar power. The optimal power flow with stochastic wind and solar energy is formulated as a multi-objective optimization problem. A multi-objective evolutionary algorithm based on non-dominated sorting with constraint handling technique are presented to solve it. In addition, another larger test system i.e., IEEE-57 bus system is selected to further verify the performance of the proposed approach in handling large dimensional problem. Simulation results indicate that proposed approach can obtain competitive compromise solution on different optimization objectives. • OPF incorporating renewable energy sources such as wind and solar is formulated. • The intermittent and uncertain of wind and solar power is matched by different density function. • The superiority of feasible solutions (SF) is used to effectively handle constraints in OPF. • Two effective constraint multi-objective methods are proposed. [ABSTRACT FROM AUTHOR]

Averting the impending harms of climate change requires to replace fossil fuels with renewables as a primary source of energy. Non-electric renewable potential being limited, this implies extending the use of electricity generated from wind and solar beyond the power sector, either by direct electrification or synthetic fuels. Modeling the transformation towards such an energy system is challenging, because it imposes to consider fluctuations of wind and solar and the manifold ways the demand side could adjust to these fluctuations. This paper introduces a graph-based method to formulate energy system models to address these challenges. By organizing sets in rooted trees, two features to facilitate modeling high shares of renewables and sector integration are enabled. First, the method allows the level of temporal and spatial detail to be varied by energy carrier. This enables modeling with a high level of detail and a large scope, while keeping models computationally tractable. Second, the degree to which energy carriers are substitutable when converted, stored, transported, or consumed can be modeled to achieve a detailed but flexible representation of sector integration. An application of the formulation demonstrates that the variation of temporal detail achieves an average reduction in computation time of 70%. • A graph-based formulation for modeling renewable energy-systems is introduced. • The method allows the level of temporal and spatial detail to vary by energy carrier. • The degree to which energy carriers are substitutable can be modeled. [ABSTRACT FROM AUTHOR]

The growing human population in this modern society hugely depends on the energy to fulfill their day-to-day needs and activities. Renewable energy sources, especially solar energy, can satisfy the global power demand while reducing global warming caused by conventional sources. Solar irradiance is an essential component in solar power applications. The availability of solar irradiance is influenced by several factors, such as forecasting horizon, weather classification, and performance evaluation metrics, which also need consideration. The accurate forecasting of solar irradiance is of utmost importance for the power system designers and grid operators for efficient management of solar energy systems. The intermittent and non-stationary nature of solar irradiance makes many existing statistical and machine learning approaches less competent in providing accurate predictions. In this context, deep learning models have been proposed by several researchers to reduce the limitations of existing machine learning models and improve prediction accuracy. In this work, an extensive and comprehensive review of deep learning-based solar irradiance forecasting models is presented. The effectiveness and efficacy of several deep learning models, including long short-term memory, deep belief network, echo state network, convolution neural network, etc. have been reviewed. The results obtained in the reported studies proved the superiority of deep learning models in solar forecasting applications. Few researchers have proposed deep hybrid models to improve the prediction performance further. A study reported that the hybrid of CNN-LSTM can enhance the prediction accuracy by 3.62%, 25.29%, 34.66%, 37.37% and 26.20% over CNN, LSTM, GRU, RNN and DNN, respectively. Overall, this paper offers preliminary guidelines for a detailed view of deep learning techniques that researchers and engineers can use to improve the solar photovoltaic plant's modeling and planning. [Display omitted] • A review of deep learning solar irradiance forecasting models is presented. • Factors that influence the accuracy of forecast models are also studied. • A comparative analysis of several deep learning models is conducted. • Hybrid deep learning models performed well over basic deep learning models. • Hybrid deep learning models are recommended for enhanced performance. [ABSTRACT FROM AUTHOR]

Accurate generation forecasting can effectively accelerate the use of renewable energy in hybrid energy systems, contributing significantly to the delivery of the net-zero emission target. Recently, neural-network-based quantile forecast models have shown superior performance on renewable energy generation forecasting, partially because they have subtly embedded quantile forecast evaluation metrics into their loss functions. However, the non-differentiability of involved metrics has rendered their metric-embedded loss functions not everywhere-derivable, resulting in inapplicability of gradient-based training approaches. Instead, they have resorted to heuristic searches for Neural Network (NN) training, bringing low training efficiency and a rigid restriction on the size of the resultant NN. In this paper, the Indicator Gradient Descent (IGD) is proposed to overcome the non-differentiability of involved metrics, and several metric-embedded loss functions are innovatively customized combining IGD, enabling NNs to be trained efficiently in a 'gradient-descent-like' manner. Moreover, the deep Bidirectional Long Short-Term Memory (BiLSTM) is adopted to capture the periodicity of renewable generation (diurnal and seasonal patterns), and the residual technique is used to improve the training efficiency of the deep BiLSTM. Finally, a Deep Quantile Forecast Network (DQFN) based on IGD and deep residual BiLSTM is developed for wind and solar power quantile forecasting. Practical experiments in four cases have verified the effectiveness and efficiency of DQFN and IGD, where DQFN has achieved the lowest average proportion deviations (all below 1.7%) and the highest skill scores. • First study on the differentiability of quantile forecast evaluation metrics. • The obstacles of applying deep learning to quantile forecast are revealed. • IGD is proposed to overcome the nondifferentiability of quantile evaluation metrics. • The IGD is GPU-compatible and time-efficient. • A deep-learning-based non-parametric quantile forecast model is developed. [ABSTRACT FROM AUTHOR]