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Hydrogen is a relatively new source of sustainable energy resources; presently, its usage ranges from direct energy generation in mixed-cycle thermal power plants to its usage in the internal combustion engine. If the generation of hydrogen is coupled with renewable energy resources, the use of hydrocarbons can be substantially reduced in both energy generation and transportation. In this paper, we conducted a national-level assessment to identify the hydrogen generation capacity of Pakistan. The methodology used in this study is a combination of spatial multi-criteria analysis and density-based clustering in a geographical information systems environment. Based on the analysis, the national clusters capable of producing green hydrogen were identified. The results shows that the hydrogen generation capacity for Pakistan is approximately 7 million tons per year using solar photovoltaic energy. The Surab-Gwadar alignment, N-25 national highway, and Quetta-to-D-I-Khan alignment offer potential hydrogen generation sites with water resources. Most of the identified sites for the production of hydrogen in the country were found to be near the national electricity transmission networks. The Best-Worst Method was further employed to provide policymakers with a road map for prioritizing the development of these capacities based on their proximity to the national networks, their terrain suitability, and their generation capacities. • This paper assesses the suitable sites for solar-based green hydrogen production in Pakistan. • The methodology used involves a combination of spatial multicriteria analysis, clustering, and the best-worst method. • Pakistan's theoretical annual hydrogen generation capacity is around 7 million tons annually. • The Surab-Gwadar alignment presents promising hydrogen generation opportunities. [ABSTRACT FROM AUTHOR]

Meeting global energy demands has been a major challenge due to the shortage of primary energy resources. Adding renewable energy resources to the grid has become increasingly popular. During peak hours of energy demand, the sun's irradiance is strong enough to generate a significant amount of energy. This paper will design a photovoltaic energy generating system that harnesses solar energy using the latest technological advancements available. A system like this one is an ideal solution to addressing rising energy demands and reducing carbon footprint. Renewable energy sources can minimize carbon emissions, operating costs, etc., but their intermittency and uncertainty make their reliability a serious concern. The objective of this paper is to improve the reliability of the power grid through the use of solar photovoltaic (PV), battery energy storage systems (BESS), and distributed generation systems (DG). [ABSTRACT FROM AUTHOR]

Hydrogen energy storage systems (HESS) play an essential role in Microgrid (MG) systems to address the inherent generation characteristics of renewable energy sources. Also, the integration of Demand Response (DR) into the Energy Management System (EMS) of a renewable-based Multi-Microgrid (MMG) can lead to substantial technical and economic benefits. This paper proposes a modified optimization algorithm for optimizing MMG Energy Management (EM). The proposed algorithm is a modified version of the Student Psychology-Based Optimization (SPBO) technique called Modified Student Psychology-Based Optimization (MSPBO). This modification aims to improve issues such as slow convergence, low solution accuracy, lack of diversity, and getting stuck in local optima. The proposed MSPBO method incorporates a local escape operator and a collaborative student class to achieve a better balance between exploiting known solutions and exploring new possibilities. The MSPBO algorithm is applied to address the EM challenge within a MMG context. Considering the integration of renewable sources as Wind turbines and solar photovoltaic and the HESS, the EM problem is formulated as a two-stage multi-objective optimization: minimizing the operating cost of conventional generators and power transactions cost in addition to the cost of HESS, while maximizing operator benefits and peak load reduction. This multi-objective problem is tackled using a hybrid ε-lexicography–weighted-sum approach that avoids the need for normalization. The performance of the proposed MSPBO is evaluated using CEC 2017 benchmark test functions, utilizing various statistical measures such as best, average, worst, rank, and standard deviation (SD) of fitness values, along with Wilcoxon's rank-sum test. The MSPBO technique is compared with other optimization algorithms for these test functions, highlighting its efficiency and adeptness in achieving a harmonious trade-off between exploiting known solutions and exploring new ones. Furthermore, the MSPBO method is applied to solve two case studies. In Case 1, which involves a single stage with conventional demand response optimization, the results achieved using MSPBO are benchmarked against other optimization techniques, revealing its superior efficacy in addressing the EM challenge. In Case 2, a more complex two-stage multi-objective problem is tackled using MSPBO, and the assessment of the integration of HESS in the MMG is evaluated. In the second stage, there is a notable enhancement in peak load reduction percentage (PRP), from 13.9% to 16.13% without the HESS and from 12.68% to 16.46% with the integration of HESS. [ABSTRACT FROM AUTHOR]

With the large-scale integration of renewable energy systems, the power inertia and voltage support of the grid are low. Using the inverter with temporary storage capabilities cope with shortages and power fluctuations. This paper presents a new sensorless control of a single-phase grid-connected PV system based on a multilevel flying capacitor inverter MFCI) able to smooth out the power produced. The designed controller involves two cascade loops, an outer loop control extracts the maximum power despite climate change, and an inner loop consists of a model predictive control based on the Lyapunov theory for power factor correction and flying capacitor voltage balancing. Furthermore, an interconnected Kalman-like high-gain observer is combined with the predictive control algorithm to estimate the flying capacitor voltages accurately based on the actual grid current. The stability study of the suggested observer in the closed loop has been described. An algorithm that generates reference signals to manage power flows is also developed. The performance of the proposed controller has been verified on the MATLAB/Simulink platform, and the simulation results confirm the effectiveness of the proposed controller over different grid operating modes. • the proposed PV system is based on MFC inverter able to smooth out the grid-injected power. • The designed controller combines a Lyapunov-based model predictive control with a Kalman-like high-gain observer. • The PV conversion system optimizes energy extraction, injecting it smoothly into the grid. • The inverter, with temporary storage, copes with power fluctuations without external energy storage during grid faults. • The closed-loop stability of the controller is rigorously proved during the normal mode and grid fault conditions. [ABSTRACT FROM AUTHOR]

• Metaheuristic Multi-Start algorithm for the demand consumption planning. • Determining the optimum PV installation sizing. • Nearly Zero Energy Building using photovoltaic energy. The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %. [ABSTRACT FROM AUTHOR]

The ongoing transition from fossil fuel-based electricity generation toward green energy relying on the intermittent renewable energy sources indispensably requires development of novel energy conversion and storage methods. Depending on a particular location and local resources, pumped storage, lithium- or sodium-ion batteries, compressed air energy storage, as well as the so called power-to-gas (P2G) technologies may be applied. Taking into account the abundant solar energy resources available in the northwestern China, such as Ningxia hui autonomous region, and local water resources coming from the Yellow River, a power-to-hydrogen (P2H2) distributed energy conversion system is proposed in this paper, which is based on a high-efficiency reversible Solid Oxide Cell (rSOC). The conducted studies comprise tests with simulated current based on the actual photovoltaic power station data, and with usage of the as-obtained Yellow River water for the electrolysis mode without any pretreatment. It is documented that the reversible operation is beneficial, and the tested cell can operate for a prolonged time. Of importance, Na, Ca, Mg, and Si elements remain in an evaporator, and do not contaminate the cell, while higher than the initial boron concentration can be found in the condensed exhaust gases. The respective operational parameters of the system correspond to an electrolysis current density loading up 433.3 mA cm−2 for 4 h, as well as fuel cell operation mode with a current density of 8.3 mA cm−2 for 12 h. It is found that the average voltage losses in the electrolysis cell and fuel cell mode are 0.27 %/cycle and 0.886 %/cycle, respectively, indicating that the lifetime of the cells can be more than 800 h. The reported studies demonstrate validity of applying rSOC-based P2H2 system, especially if abundant photovoltaic power and water resources are available. [Display omitted] • The Yellow River water was utilized by rSOC for the first time. • The designed test current was modeled with local photovoltaics. • The P2H2 nearby consumption system can reversibly operate for over 800 h. • The degradation rates in SOFC mode are lower than those in SOEC mode. • The Yellow River water has no significant effect on the degradation rate. [ABSTRACT FROM AUTHOR]

Renewable energy is regarded as an effective method to alleviate environmental pollution and accelerate the green transition in China. This paper systematically investigates the impact of financial inclusion on renewable energy demand by using S-GMM model based on provincial data in the green finance era. This paper also analyzes the asymmetry, heterogeneity, and mediating mechanism in the nexus between financial inclusion and renewable energy demand, and highlights the following findings: (1) Improved financial inclusion can promote renewable energy development in China and present the characteristics of green finance; (2) the impact of financial inclusion on renewable energy demand is asymmetric and heterogeneous; specifically, this impact is much higher in northern China and the regions with higher renewable energy development; and (3) wind and photovoltaic power generation are effective mediators between financial inclusion and renewable energy industry, while the mediation effects of hydropower and nuclear power are not significant. New evidence in this study is presented for the achievement of green transition in China, as well as practical policy implications for improving financial inclusion and increasing renewable energy consumption. For instance, fiscal and tax incentives and green investment subsidies in the renewable energy sector need to be further improved. • This study explores the impact of financial inclusion (FI) on renewable energy demand (RED) in China. • Asymmetry, heterogeneity, and the mediating mechanism are further discussed. • Increased FI can promote RED , which presents the characteristics of green finance. • The impact of FI on RED is asymmetric and heterogeneous. • Wind and photovoltaic power are effective mediators between FI and RED. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for predicting the irradiance of PV greenhouse in many cases was proposed. • Four models were discussed at variable time, data size and climatic conditions. • The instantaneous irradiance prediction under 9 weather conditions was provided. • Transformer model had the best prediction effect for dataset at one year of usage. • Pyraformer model had the best prediction effect on winter and summer. Photovoltaic power generation can provide energy for greenhouses and achieve high quality and high yield of crops. In reality, solar irradiance is fluctuating and intermittent. Thus, the key to ensure efficient photovoltaic power use under greenhouse environmental conditions is to provide an accurate prediction of solar irradiance. Yet, currently studies on irradiance prediction in terms of the variable time lengths, multi-parameter and full climate conditions are rather limited. To improve the comprehensive performance of the prediction model, this paper proposes models of irradiance time series prediction such as Pyraformer, Informer, Transformer and TimesNet. Those models were tested based on the synergistic combination of weather conditions (WC), sunshine time accumulation (STA/h), instantaneous total irradiance (ITI/(W/m2)) and irradiance daily accumulation (IDA/(MJ/m2)). The model performance was rigorously evaluated with 9 prediction lengths, 5 training days, 4 seasons and 5 days for 9 weather conditions. The results showed that the Transformer model had the best overall prediction performance for STA, ITI, IDA and WC at different time steps of 10 min to 24 h. All models were suitable for predicting time series within 1 h. However, TimesNet model was not suitable for predicting time series with steps outside 1 h. On the other hand, by using the sun combination dataset, the Transformer model had the best performance at a time step of 10 min. The mean absolute error (MAE), mean-square error (MSE), root mean squared error (RMSE) and coefficient of determination (R2) of ITI were 0.118 W/m2, 0.059 W/m2, 0.243 W/m2 and 93.9 %, respectively. When exploring the minimum dataset, with the increase of data samples, the prediction effect of TimesNet showed an increasing trend. While, Transformer had the best prediction effect for the dataset with one year of use. When exploring seasonality, Pyraformer model had the best prediction effect on winter and summer, and TimesNet had the best prediction effect on autumn and spring. Local prediction of 9 climate conditions showed that the effects of snow and dust storm were not ideal. The research results showed that the characterization factors that were closely linked to irradiance. The prediction scheme proposed in this paper combined the advantages of different time steps, different factor combination datasets, different data volumes and seasonality, which greatly improves the generalization ability of the model. This study can provide a reference for irradiance prediction and more refined management of photovoltaic (PV) greenhouse. [ABSTRACT FROM AUTHOR]

The increasing expansion of photovoltaic power generation leads to unpredictable fluctuations in electricity supply, which can potentially jeopardize the stability of the power grid and escalate the costs associated with grid imbalances. As a result, precise forecasts of photovoltaic power generation play a vital role in optimizing capacity deployment, enhancing consumption levels, improving planning strategies, and maintaining grid balance within systems characterized by significant penetration of solar energy. This paper proposes a structural adaptive grey seasonal model based on data reorganization. Solar photovoltaic power generation data typically exhibit seasonal fluctuations, which pose a challenge to existing prediction techniques. Therefore, this paper adopts the idea of data reorganization to eliminate the seasonal fluctuations of observations, and the adaptive accumulation operator can accurately simulate the change trend of the original data in different periods, overcoming the defect of insufficient adaptability of the traditional accumulation operator. Subsequently, the time trend items are incorporated into the model structure to identify the trend characteristics of system development, which can effectively explain the power generation trend of photovoltaic systems at different time periods and improve the prediction accuracy of the model. In addition, the compatibility and unbiased nature of the proposed model have been demonstrated to help us better perceive the model. The Grey Wolf Optimizer (GWO) is used to optimize the adaptive parameters of the model, endowing it with higher flexibility and stronger adaptability. In order to verify the effectiveness of the model, three practical cases (namely quarterly solar power generation in the United States, Japan, and Germany) were compared with existing econometric techniques, artificial neural networks, and grey prediction methods. The experimental results show that the new model outperforms other benchmark models in both simulation and prediction performance, and enjoys high robustness. • A novel grey seasonal model with data reorganization is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]

• Proposed a spatial–temporal prediction model for PV power generation that combines data-driven and deep learning. • The ABCGRU network based on BiConvGRU, self-attention mechanism and encoder-decoder structure is established. • Pearson correlation coefficient, the normalized Euclidean distance and the Shape-based distance (SBD) analysis based on cross-correlation are used to analyze spatial-temporal characteristics. • The model is verified by four regional and urban datasets. Effective utilization of spatial–temporal information can improve the accuracy of ultra-short-term prediction of power generation from distributed photovoltaic (PV) stations in the region. This paper introduces an ultra-short-term spatial–temporal prediction model for distributed PV power generation, blending data-driven methodology with deep learning technique. The model integrates a self-attention mechanism (SA), a Bi-directional Convolutional Gated Recurrent Unit (BiConvGRU), and an encoder-decoder structure, called ABCGRU. The spatial–temporal attributes of PV power generation can be effectively utilized to accurately predict the output of PV power stations at different locations. Firstly, this paper proposes a 2D distributed PV measurement frame approach considering the spatial–temporal properties of PV power. The combination of Pearson correlation coefficient, the normalized Euclidean distance, the Shape-based distance (SBD) analysis based on cross-correlation and geographic distance reduces the input dimensionality. Secondly, to better capture the spatial–temporal patterns within the 2D distributed PV measurement frame, this paper proposes the ABCGRU model. Finally, the predictive performance of the model is verified through experiments. On the Birmingham dataset, the relative absolute error (RAE) for single-step (15 min) prediction is 0.13, and the average RAE for multi-step (30–60 min) prediction is about four times higher than ConvGRU. The single-step prediction RAE of Little Rock and New Orleans datasets is about 3–4 times higher than ConvGRU. In the comparison between the same series of models, the 4-layer ABCGRU has the highest accuracy. Moreover, the effectiveness of data dimensionality reduction was verified through experimental comparison. The RAE for single-step prediction on the Datong dataset is 0.0048. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid distribution transformer integrated with photovoltaic (HDT–PV) including a three-phase four-leg inverter, as a promising electrical device applied in the unbalanced active distribution network (ADN). The HDT–PV has the ability to access distributed PV system, balance three-phase power and provide reactive power compensation. In order to take advantage of these capabilities of the HDT–PV, collaborative optimization control strategy of HDT–PVs in the unbalanced ADN is presented. The optimization aims at minimizing network power losses and improving voltage profiles of the entire ADN. To do so, the HDT–PV's model in the case of unequal three-phase loads is established, and the corresponding simulation is conducted to verify the correctness of the model. Finally, case studies of collaborative optimization control of HDT–PVs are carried out in various scenarios using the modified unbalanced IEEE 33-bus system. The results verify the effectiveness of the proposed collaborative control scheme on reducing network power losses and suppressing three-phase voltage unbalance. • A novel topology of the hybrid distribution transformer integrated with photovoltaic (HDT–PV) including a three-phase four-leg inverter is proposed for the first time in this paper, to access the distributed PV system, suppress unbalanced loads and regulate reactive power of the distribution node. • Power flow of the HDT–PV with unbalanced loads is analyzed to establish dynamic reactive power compensation model. • Collaborative control strategy of HDT–PVs based on SOCP is proposed to reduce losses and improve voltage profile in the unbalanced ADN. [ABSTRACT FROM AUTHOR]

With the increasing influence of new energy power system, the prediction of Photovoltaic (PV) output power becomes more and more important In this paper, it is the first time to put forward a hybrid modeling method combining long–short term memory recurrent neural network (LSTM) and stochastic differential equation (SDE). This method realizes the prediction of PV output power in different seasons and overcomes the uncertainty of PV power generation. Wavelet analysis and automatic encoder are used to decompose data and extract important features. According to the detailed signal sequence and the approximate signal sequence, the LSTM prediction model is established. Meanwhile, the mathematical model of SDE is established according to the detailed signal sequence. Finally, the output sequences of the two models are reconstructed by wavelet transform. This hybrid model can not only realize the point prediction of PV output power according to the predicted mean value, but also achieve the interval prediction under different confidence levels according to the randomness. In this paper, the proposed method is applied to predict the PV output power of CHINT photovoltaic power generation system with installed capacity of 10MW in different seasons, and the weather forecast data with errors of ± 10%, ± 20% and ± 30% are used. Experimental results prove the effectiveness of the method. In the summer model considering forecast errors within ± 20% of weather forecast data, the RMSEs of BP neural network, LSTM and convolutional neural network (CNN) are 5.9468, 5.6762 and 5.8004 respectively. However, the RMSE of the mean prediction with the confidence level of 90% under the proposed method is 4.4647. With this method, the results of interval prediction and point prediction of PV output power can provide better decision support for the stable and safe operation of PV grid connection. They have higher reference value for energy dispatching departments. • Combining LSTM neural network with stochastic differential equation for the first time by wavelet analysis. • The features of weather data are strongly extracted by autoencoder. • Application of hybrid modeling method to photovoltaic power prediction. • Obtaining different confidence intervals by changing random factors. [ABSTRACT FROM AUTHOR]

The price of photovoltaics (PV) has been steadily decreasing over the last decade, and many reports suggest that PV has become considerably cheaper than conventional electricity sources. In this paper, we critically evaluate the PV grid parity and use China as a case study. China is an interesting case study due to the wealth of data combined with the recent decrease in financial subsidies. Electricity costs are commonly compared in the literature using levelized costs of electricity (LCOE). However traditional LCOE analyses neglect important cost factors that are specific to PV, in particular the cost of grid integration. Here, we demonstrate that system LCOE calculation more accurately estimates the grid parity of PV. We find that the integration costs account for 15% of the total system costs, which cannot be neglected with the higher penetration of PV in the electricity system. Further, provincial grid parity indexes are greater than 1, indicating that the grid parity from a system LCOE perspective has not yet been achieved. The learning rate of Chinese PV is still relatively small, and we estimate that grid parity for PV in China will be achieved between 2020 and 2032, depending on the region. These findings shed new light on assessing grid parity of PV systems by considering relevant local parameters and thereby offer an assessment method framework and prediction modeling for assessing grid parity of PV installations. • This paper assesses PV's commercial feasibility in the current electricity market. • Grid parity is estimated using a new approach of system LCOE and learning curve. • The impacts of system LCOE and electricity price on grid parity are investigated. • The additional grid integration costs amount for 15% of total PV system costs. • Grid parity from a system LCOE perspective will be achieved between 2020 and 2032. [ABSTRACT FROM AUTHOR]

This paper studies the energy storage and generation characteristics of the photovoltaic power generation coupling compressed air energy storage system for the 5 kW base station, and analyzes the photovoltaic power generation characteristics within 24 h and its influence on the flow characteristics of the compressed air energy storage system. The results show that, affected by precipitation and sunlight, the compressed air energy storage power reaches highest value in May and August, and the compressed air flow and cooling water flow in a single day increase with the increase of light intensity. The power generation time reaches longest value in June, shortest in December, the energy storage time reaches longest in June and shortest in January. Considering the combined demand of cold energy, thermal energy and electric energy in actual production and life, the setting scheme of the compressed air energy storage system for combined cold heat power supply is obtained, and the parameter values such as compression/expansion stage, compression/expansion pressure ratio, temperature and pressure at the inlet and outlet of the compressor/expander/heat exchanger are defined. Adding solar thermal energy storage to the photovoltaic power generation compressed air energy storage system, it is found that it can effectively increase the air temperature and thus reduce the expansion stages required at a specific time. [ABSTRACT FROM AUTHOR]

The emergence of bifacial photovoltaics as a serious option for large-scale power generation makes the accuracy of the in-situ estimation of ground surface albedo increasingly critical. Recent studies have evaluated the performance of different models but an investigation of how these models should be calibrated is missing. This paper proposes geographically generalised parametrisations for time-variant albedo models of vegetated and desert surfaces based on a worldwide database and reports their performance compared to site-specific, unbiased parametrisations. These geographically generalised formulations can be used as an alternative for lengthy calibration campaigns as their only parameters can be easily measured. The paper also presents a simple method for deriving white-sky albedo without the need for diffuse irradiance data. The method is founded both theoretically and empirically. If on-site measurements can be collected to calibrate a model, the duration of the campaign is usually a few weeks at the maximum. This paper first shows evidence of the strong impact of the timing on calibration performance and accordingly gives practical guidelines on the most suitable measurement seasons and weather conditions. In general, early-summer months appear to be the most appropriate time for calibrating most models. Atmospheric turbidity during the campaign period, however, should be either minimised or maximised depending on the site and the model. Finally, in case no flexibility exists in timing the calibration, simple models with few parameters should be deployed. Geometric mean shows the most robust performance at most sites. • General parametrisations derived for time-variant surface albedo models. • Diffuse-fraction-free method developed and tested for estimating white-sky albedo. • Comprehensive guidelines proposed for calibrating surface albedo models. • Geometric mean the most robust against seasonal calibration performance variation. [ABSTRACT FROM AUTHOR]

With the high proportion of renewable energy connected to the grid, the problem of insufficient flexibility in the power system has emerged. Renewable energy and controllable distributed resources can be aggregated and managed through virtual power plants, reducing the need for flexibility to a certain extent. Although building new energy storage systems can compensate for the lack of flexibility, it requires high initial investment costs. To address this, this paper proposes a lease mechanism for coal-fired units based on the combination of carbon credits and prices, providing the right to use coal-fired units for virtual power plants. Subsequently, different demand response strategies are utilized to control the controllable loads of various users, thereby providing certain controllable resources for the virtual power plant. Additionally, to ensure the optimal decision-making of the virtual power plant operator, a cost model accurately describing the capacity degradation state of the energy storage system is adopted. Moreover, a multi-time scale scheduling strategy of virtual power plant is implemented to fully utilize controllable resources of different time scales, effectively dealing with the power imbalance caused by multiple uncertainties. The results show that utilizing the leasing mechanism of coal-fired units and employing demand response strategies from different users can provide flexibility to the virtual power plant. The accuracy of the capacity degradation model used by the operator significantly impacts the optimality of the scheduling plan. • The scheduling model of VPP consider capacity degradation of ESSs, carbon trading, and DR strategies. • A lease mechanism for coal-fired units based on the combination of carbon credits and prices is proposed. • This paper proposes ISBDR strategies for reduceable loads. • The ESSs capacity degradation model adopted takes into account DOD and SOC. • A multi-time scale scheduling strategy of VPP is adopted to deal with multiple uncertainties. [ABSTRACT FROM AUTHOR]

The main challenge of integrating high photovoltaic (PV) systems in power grids is managing their associated uncertainty which poses significant risks on the system stability and reliability. A probabilistic approach is well-suited to explore and analyse the impact of these intermittencies and uncertainties on power system operation. This paper proposes a probabilistic risk-based assessment (PRBA) framework which is suitable for power systems with high penetration of renewables such as PVs. Conventional analytical security assessment approaches typically examine the behaviour of electromechanical modes under varying operation scenarios in the power grid and often focus only on a few specific modes of interest. However, this may impose a small-disturbance stability risk of missing out critical information from other unobserved modes. The methodology proposed in this paper aims to capture the behaviours of all critical modes through modal analysis and risk matrices in order to quantify the stability risks associated with high PV-integrated power networks. Feasibility of the proposed framework is tested on a modified version of the 39-bus New England test network. Probabilistic risk-based assessment is performed and the severities measured are assessed using a sensitivity matrix to evaluate the overall small-signal instability risks associated with various PV penetration levels. • Probabilistic stability assessment of power systems with PVs is performed. • Stability assessment is carried out considering PV variability and uncertainty. • Historical data sets are used for stochastic PV modelling and Monte-Carlo for scenario generation. • A risk matrix based on probability and severity of an event is developed. [ABSTRACT FROM AUTHOR]

Integration of solar photovoltaic systems to low-voltage distribution networks is witnessing an unprecedented growth in many parts of the world. Although solar photovoltaic generation is of significant benefit from a number of angles, exceedance of hosting capacity levels by such installations in low-voltage distribution networks continue to cause significant technical challenges in network operation, especially to the management of network voltage. Modern smart inverters are equipped with Volt-VAr and Volt-Watt control capabilities, which can assist in the management of network voltage levels. This paper provides a detailed analysis of the influence of different connection standards which cover these strategies on solar photovoltaic hosting capacity and their applicability in low-voltage distribution networks. Smart inverters with differing Volt-VAr and Volt-Watt control functions are modelled in the DIgSILENT PowerFactory platform. Influence of different connection standards on solar photovoltaic hosting capacity is analysed to investigate the most beneficial connection approach/es to address the issue of voltage violations. Furthermore, the work presented in this paper provides a greater understanding on the hosting capacity improvement by employing advanced inverter control functions where such improvements are subjected to locational aspects of inverters in low-voltage distribution systems. • Control strategies: Volt-VAr, Volt-Watt and combined Volt-VAr & Volt-Watt were assessed. • Combined Volt-VAr & Volt-Watt control is effective in enhancing hosting capacity. • Hawaii Rule 14 and IEEE 1547 are effective to increase solar PV hosting capacity. • Volt-VAr control is the most effective with bare conductors. • Volt-Watt control is the most effective with bundle cable type feeders. [ABSTRACT FROM AUTHOR]

Systems involving more than one energy source, namely hybrid systems, can be arranged in different configurations and layouts depending on the available or used energy source, application and scale of the system. Despite an increasing awareness and penetration of renewable energy sources at different scales in many countries over the world, the adoption of hybrid and novel renewable systems is relatively scarce, especially at a small scale. This scenario is also presented from the point of view of the research of such systems. Therefore, the scope of the paper is to improve the knowledge regarding small-scale hybrid renewable energy systems performance and operation by investigating a novel trigeneration system based on a biomass fired steam cycle, wind turbine, photovoltaic panels and adsorption chiller. In the proposed system, the thermal energy recovered by the steam cycle is used to match the user thermal demand by means of a storage tank supplying directly heat to the user during winter, and driving the adsorption unit during summer. The electrical energy produced by the steam turbine, photovoltaic field and wind turbine is managed by a control system that integrates a bidirectional connection with the electric grid. The grid allows one to virtually store the electrical energy produced in excess and to recover it in part when needed by the user. In order to assess the system performance, a farm and residential buildings are considered as case study. The proposed system is modelled and dynamically simulated by means of Transient System Simulation software. In the paper, the dynamic operation of the system and the energy and economic performance of the system is assessed. The operation of the system is presented in a daily and monthly basis, while the global performance parameters of the system are reported considering a one-year period. The primary energy saving of the system is above 70% when a reference system based on natural gas, electric chiller and grid is considered. For this scenario, the Simple Pay Back of about 10 years is achieved. The results of the analysis show that the system is feasible from the technical and energy point of view, whereas its economic profitability is significantly affected by its cost and the reference system that is adopted by the user. • Modeling and dynamic simulation was performed with TRNSYS software. • Energy and economic performance of the system was assessed. • Different reference systems are considered for heating. • Primary Energy Saving ratio was between 57.4 and 73.3%. • The minimum Simple Pay Back is about 10 years. [ABSTRACT FROM AUTHOR]

Developing new technologies is a key to achieve renewable energy utilization. The near-field thermo-photovoltaics (NFTPV), an emerging power generation device, is investigated from the energy perspective in this paper. A novel oxy-enrich NFTPV system is proposed, and a thermo-physical model is established for sustainable fuel gas based on energy balance. The effects of parameters on system performance are numerically explored, including the furnace size, oxygen ratio, voltage and vacuum gap, etc. Moreover, special attention is paid to the effects of oxygen ratio on system performance under 2 atm of O 2 /N 2 and O 2 /CO 2. Besides, exergy analysis is conducted. The results show that it is more appropriate to control the power density of furnace at 50 kW/m2 in system design. The efficiency of NFTPV system is about twice greater than that of far-field TPV system. The power density of NFTPV system increases by more than 2.5 times when the oxygen ratio increases; the TPV volume can increase by 2–3 times and the efficiency is also improved. The results indicate that oxy-enrich combustion with higher oxygen ratio matches a larger NFTPV system, which reduces the manufacturing difficulty and is significant in engineering. This study provides new ideas and references for NFTPV practical application. [ABSTRACT FROM AUTHOR]

In this paper, the authors present a two-step model to obtain quasi-optimal solutions for the storage and transportation of renewable energy as its installation becomes more widespread in remote areas. The model is formulated as a total cost minimization linear programming problem and considers both renewable energy facilities and the energy supply and demand of the entire country. The first step of the model optimizes the entire country, while the second step determines the marginal capacity of energy storage and transportation facilities when the excess production capacity of remote renewable energy is added. This approximation for reducing computational complexity is shown to be sufficiently accurate under realistic conditions. The authors apply the model to analyze energy storage and hydrogen production at renewable energy power plants in Japan in 2050. The study shows that photovoltaic power generation uses storage batteries attached to the power plant for electricity transmission. In contrast, wind power generation transmits most electricity directly without storage batteries and converts some into hydrogen for consumers. The proposed lightweight model allows for numeral sensitivity analyses, which provide valuable insight into optimizing energy storage and transport of renewable energy. • We developed a 2-step model for quasi-optimization of equipment at VRE site. • This model optimizes energy storage and transport with VRE deployment. • If additional VRE is sufficiently small, the model division retains sufficient accuracy. • Energy supply costs minimized by combining power transmission and H 2 transportation. • PV has large LIB, while WT has direct transmission and H 2 production. [ABSTRACT FROM AUTHOR]

Steam is an important medium for energy transmission in industrial parks. For providing steam to multiple users, it is key to determine the optimal number of steam levels in the design of steam system. With the increasing number of steam levels, the energy demand and supply can be better matched but the investment of steam pipeline network increases. In this paper, this trade-off is solved by a two-stage method. In the first step, a mixed-integer nonlinear programming (MINLP) model is established to minimize the total cost of the steam system of the industrial park, while a simple pipeline network optimization model is involved. The optimal scheme of steam production including the number of steam levels can be obtained automatically. In the second step, GeoSteiner algorithm is used for solving Rectilinear Steiner Minimum Tree (RSMT) model to further optimize the pipeline network separately. In a case study, the optimal number of steam levels is 3. The total annual cost (TAC) of the optimal steam system is 11.4%, 5.28%, and 1.43% lower than the schemes with the lowest pipeline network cost (1 level of steam), the best matching between heat supply and heat demand (6 levels of steam), and the largest electricity generation capacity (4 levels of steam), respectively. The sensitivity analysis indicates that the overall heat load required by consumers and the distance between the energy station and consumers can make a difference to the optimal number of steam levels. • Steam production and steam supply in industrial park are optimized simultaneously. • Trade-off between electricity generation and pipeline cost is considered. • Effects of the load and distance of consumers and economic parameters are discussed. [ABSTRACT FROM AUTHOR]

• Three status indicators are built to solve the uncertain influence of solar irradiance to photovoltaic arrays outputs. • Non-parametric kernel density estimation method is used to establish the probability density model of status indicators without any assumption regarding the real data. • Dynamic upper and lower bounds of electrical parameters can be obtained through setting the confidence level in the operation process of photovoltaic system. • Fault diagnosis method based on non-parametric kernel density estimation method can identify different anomalies in photovoltaic arrays. Fault diagnosis is crucial for photovoltaic (PV) power generation, but the output uncertainty characteristics of PV arrays caused by different failures and their fluctuations make them facing new challenges. The parameter estimation (PE) method is widely used for uncertainty analysis, but there exist big differences between the PE results and the real output distributions. To solve this problem, this paper proposes a method for acquiring the fault diagnosis threshold based on non-parametric kernel density estimation (NKDE). First, the distribution characteristics of PV arrays output are statistically analysed, it is found that current and power are mainly affected by the solar irradiance, resulting in strong volatility and uncertainty, which makes it hard to get the threshold for fault diagnosis, thus we propose new three status indicators to finish it. Then, the probability models of the three indicators are built based on the kernel density estimation (KDE) method, by assigning the confidence values of the models, the fault diagnosis threshold intervals can be obtained. Verification shows that NKDE method performed better than traditional PE method in fitting the distribution of the PV arrays output, it does not need prior knowledge of the probability density function. And the proposed fault diagnosis method proves it is feasible to apply the uncertainty analysis method to PV array fault diagnosis. The paper provides a new idea for setting the dynamic threshold for PVarray fault diagnosis. [ABSTRACT FROM AUTHOR]

With the implementation of energy saving and carbon reduction, the quantitative analysis of solar energy spectral characteristics has been paid more and more attention. Currently, the international test standards provide a spectral distribution with a large span between different wavebands, which makes it difficult to meet the needs of special spectral wavebands. In this paper, based on the principle of spectral splitting, the spectral distribution of solar radiation models (SDSR models) is proposed, and the differences in the spectral distributions of natural outdoor and artificial indoor light sources are evaluated. Firstly, the differences in PV module output power between the outdoor light sources and indoor artificial light sources are assessed in terms of inclination angle and solar radiation, respectively. Secondly, based on that difference, the influencing factors of the spectral distribution of solar radiation are investigated, and the SDSR models of different wavelengths is established based on spectral analysis results. Finally, based on the established SDSR models, the spectral distribution modified models (SDM models) for different inclination angles under artificial light sources is proposed. By comparing the calculated values of the SDM models with the measured data, the calculated values of the model are close to the measured data at different incidence angles. The SDM models proposed in this paper shortens the gap between the spectral distribution of the artificial light source and the outdoor natural light source, and reveal the influence mechanism of the difference in the output power of indoor and outdoor PV modules from the perspective of the spectral distribution. The results can provide guidance for the spectral distribution of solar radiation and the quantitative modification of the artificial light source. • The difference in the output power of PV modules between solar radiation and artificial light sources is evaluated. • The effect of inclination angle and orientation on the spectral distribution of solar radiation is investigated. • The spectral distribution of solar radiation model (SDSR model) at different wavelengths is developed. • The spectral modification models for different wavelengths of of artificial light sources are developed. [ABSTRACT FROM AUTHOR]

Urban building layout has an important influence on the formation of shadows on building façades, and photovoltaic (PV) power generation performance is greatly affected by shadows. However, there is limited research on detailed modeling of the power generation of photovoltaic systems on facades with different building layouts. In this paper, according to different urban building layouts, an annual dynamic shadow model of the building and a mathematical model of PV array power generation were established and validated. Then the model was used to calculate the annual power generation hour by hour, optimize the PV array topology, and analyze the shadow shading ratio and PV power generation performance of different building layouts. The results show that 95 % of the best topologies of all partially shaded façades in the four layouts are Total-Cross-Tied (Vertical and Horizontal); the highest annual shading ratio is layout 4 with a value of 60.161 %, the largest annual power generation is layout 3, but layout 4 has the highest broad generation efficiency, with layouts 1, 2, and 3 lower than it by 4.29 %, 0.20 %, and 0.11 %, respectively. This paper provides new ideas for the early design of building layouts and the performance research of PV systems. • Yearly dynamic shadow and detailed PV models developed for urban layouts. • Optimized PV topologies under year-round dynamic shading. • 95 % optimal topologies are Total-Cross-Tied (Vertical and Horizontal). • Compared annual PV output and broad efficiency across building layouts. • Layout 4 excels in broad generation efficiency, leading by 4.29 %. [ABSTRACT FROM AUTHOR]

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP)，Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing. • Multiple neural network is used to estimate the generation of solar radiation data using other meteorological parameters. • An advanced neural network is used to provide an effective TMY generation method in missing hourly solar radiation data. • The accuracy of the experimental results is verified by energy consumption simulation and photovoltaic power generation. [ABSTRACT FROM AUTHOR]

Under the goal of global carbon reduction, hydropower-wind-photovoltaic complementary operation (HWPCO) in the clean energy base (CEB) has become the key to achieving a high-quality clean energy supply. However, stochastic wind power and photovoltaic need the help of adjustable hydropower to get onto the grid in the HWPCO process, which makes the conflict between different power generation entities more and more complex. This paper takes Yalong River CEB as the research object and sets up the separate operation scenario and complementary operation scenario of hydropower stations and wind-photovoltaic plants. Meanwhile, the corresponding day-ahead schedule model considering the stability of the power system is established. On this basis, this paper analyzes changes in profit and loss indexes of each power generation entity and clarifies the profit and loss relationship of different power generation entities in the CEB. Further, based on the model group for quantifying contributions and the compensation electricity contribution value, this paper proposes the benefit compensation mechanism between the cascade hydropower station and wind-photovoltaic plants and the benefit compensation mechanism between each hydropower station within the cascade hydropower station. The main results of the research are as follows: (1) after HWPCO, the total revenue of wind-photovoltaic plants increases by 61.48 million RMB (8.61 million dollars), and the total revenue of the cascade hydropower station decreases by 10.77 million RMB (1.51 million dollars), which means wind-photovoltaic plants are the beneficiary and the cascade hydropower station is the loss. (2) after the benefit compensation, the total revenue of wind-photovoltaic plants increases by 20.97 million RMB (2.94 million dollars), and the total revenue of the cascade hydropower station increases by 29.74 million RMB (4.16 million dollars) compared with the hydropower-wind-photovoltaic separate operation (HWPSO). By the benefit compensation mechanism proposed in this paper, the benefit of each power generation entity can reach a relative balance in the Yalong River clean energy base. • Quantify the profit and loss relationship between different power generation entities in the HWPCO process. • Establish the model group for quantifying contributions between different power sources. • The incremental benefit of HWPHS is reasonably distributed. [ABSTRACT FROM AUTHOR]

• Study of PV power plant with quasi-Z-source cascaded H-bridge multilevel inverter under failure. • Control system to provide a suitable DC voltage at the input of the VSI and ensure the grid power delivery. • Control tested from simulation results under different operating conditions and HIL experimental results. • Correct response of the developed control strategy with failure in qZSI module. To ensure the reliability of microgrids (MGs), this paper presents a multi-fault tolerant control for a three-phase energy storage quasi-impedance multilevel-cascaded H-bridge inverter (ES-qZS-CHBMLI) with a photovoltaic (PV) power generation-based MG. In this paper, a battery energy storage system (BESS) is implemented to smooth out the PV generation fluctuations. In the event of a fault, most studies propose the injection of a fundamental zero sequence (FZS) to balance the system after a fault. However, the FZS based-method increases the modulation and it is limited by the converter operation range. The state-of-charge (SOC) unbalanced problem has traditionally been solved by balancing the BESSs as a single unit. This paper introduces an energy management system (EMS) based on the SOC proportional power distribution to balance the power injected into the grid. When one bridge faults, it is bypassed, and the remaining bridges in that phase change their operating conditions according to the references set by the EMS. Individual phase control allows multi-fault issues to be addressed. The simulation results of a three-phase grid-connected ES-qZS-CHBMLI PV-based MG implemented in MATLAB/Simulink validate the proposed multi-fault control and EMS. In addition, an experimental validation (based on OPAL RT4520 and dSPACE MicroLabBox units) confirm these results. [ABSTRACT FROM AUTHOR]

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and large-scale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively. 3) A large penalty factor is conducive to reducing the amount of abandoned hydropower, but in general, the penalty factor and ecological flow level have no obvious influence on the dispatching results, and the values of both can be reasonably determined according to the actual operation requirements. • An improved interval optimization method is proposed to deal with the uncertainty of inflow runoff and PV power output. • A MILP model for the medium- and long-term operation of a cascade hydro-PV complementary system based on extreme scenarios is established. • The resulting scheduling scheme achieves balance between economy and robustness. • The influence of penalty factor and ecological flow on the dispatching results is not obvious. [ABSTRACT FROM AUTHOR]

The integration of converter-based renewable energy resources in a power system leads to reduced inertia, creating a multitude of challenges relating to stability and reliability. On the positive side, the faster response of converter-based renewable energy resources to frequency disturbances, compared to the conventional synchronous-based generators, reduces the required minimum level of inertia in future low-inertia power systems. Accordingly, grid support from distributed photovoltaic (DPV) systems is one of the emerging solutions to overcome the challenges of these systems. This paper demonstrates how adaptive power system frequency support, which modifies the dynamic of frequency support in DPV systems according to the available level of power system inertia, improves overall system operation. In this way, for lower values of power system inertia, a higher amount of power reserve is maintained across DPV systems. Additionally, a faster frequency–Watt response is implemented to provide a larger amount of grid support under frequency disturbances. Simulation results on a composite load model of distribution feeder show that applying for adaptive frequency support from DPV systems improves the frequency nadir during under-frequency events. Experimental results show the effectiveness of the proposed approach in providing a faster dynamic response and higher amount of power support from DPV systems under low inertia conditions. • Proposing an adaptive approach for frequency support with distributed photovoltaic systems. • Obtaining faster frequency response with injection of higher amount of power to grid during under-frequency. • Demonstration of improved frequency response using the composite load model of a distribution feeder. [ABSTRACT FROM AUTHOR]

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed. [ABSTRACT FROM AUTHOR]

Solar cells lose efficiency as the operating temperature increases under normal operating conditions, resulting in a very significant power output reduction. This issue is most relevant in areas with the highest photovoltaic energy harvesting potential, where irradiance and ambient temperature are particularly high. A novel cooling system for commercial photovoltaic modules based on low-enthalpy geothermal cooling is proposed in this paper. Excess heat is evacuated from the solar module in a clean and efficient manner by a single-phase and close-loop cooling circuit that uses the underground as a natural heatsink. In addition, a thermoelectrical theoretical model is presented, that allows the optimization of the flowrate and the maximization of the extra power generation of the cooled module. A prototype was manufactured and tested in outdoor conditions in September 2021 in Spain. Test results are in good agreement with the theoretical model, which has been used for prediction of the performance of the system for a typical meteorological year in the region. A peak efficiency improvement of 10.7% is calculated for the month of July, with an optimal flowrate of 1.8 l/min per square meter of solar module. A yearly weighted average power gain of up to 4.9% is also calculated. [ABSTRACT FROM AUTHOR]

[Display omitted] • Difference in working principle of Solar Thermoelectric Generators (STEGs) and PV-TEG Hybrid Generators is discussed. • Design parameter optimization of TEG only system is not same as that for STEG and PV-TEG hybrid system. • Enhancement in the concentration ratio improves TEGs efficiency, leading to increased conversion efficiency of the hybrid system. • Micro-channel heat pipe significantly reduces the number of TEG used, thus, reducing the overall cost of the system. With the continuously increasing demand for energy, reduction in greenhouse gas emission for daily energy usage is a challenging task. Solar energy based technologies possess the potential to address this challenge since sun is a renewable and abundant source that does not produce any emissions. It would be additional benefit if in the process of using such technologies, wasted heat energy is also converted into electrical energy. Thus, integration of thermoelectric and photovoltaic hybrid systems offers the flexibility to exploit solar spectrum across full wavelength. Thermoelectric Generator (TEG) when integrated with solar electricity conversion technologies result in fabrication of (i) solar thermoelectric generators (STEGs) and (ii) photovoltaic-thermoelectric (PV-TEG) hybrid devices with enhanced efficiency. Improvements in the conversion efficiencies of these technologies would aid in making solar energy at par with conventional forms of energy generation. This paper reviews the prospect of integrating TEG with solar electricity conversion technologies by examining the recent efforts in the field. In particular, the difference in the working of these two type of devices have been focussed on. This review presents in-depth analysis of the state-of-the-art methods used to achieve optimum performance. Moreover, various applications are stated and PV efficiency and losses are discussed along with possible resolutions. Furthermore, recent advancements in these device technologies have been compiled and cost and commercialization aspects have been focussed on. The review aids as a guide to select appropriate procedure of optimizing the performance in these devices as per requirements and specific application. [ABSTRACT FROM AUTHOR]

The present paper applies the thermodynamic analysis with the determining the efficiency of a combined cycle power plant with a chemically recuperated gas turbine. Thermochemical recuperation of exhaust heat after a gas turbine is realized via the steam methane reforming process. The main concept of combined cycle power plant (CCPP) with chemically recuperated gas turbine (CRGT) is based on the use of exhaust heat for endothermic reforming of the original hydrocarbon fuel in a reformer and for steam generation for a steam cycle. To understand the effect of operating variables such as temperature, pressure, and steam-to-methane ratio on the overall efficiency, the energy and mass balances were compiled. The energy flows were represented by a Sankey diagram. The results of the thermodynamic analysis show that efficiency of CCPP with CRGT is significantly higher (4–7%) than efficiency of CCPP with a conventional gas turbine without TCR. Maximum efficiency of CCPP with CRGT of 0.6412 is observed at inlet temperature of working gas of 1600 °C, pressure of 23 bar for a steam-to-methane ratio of 3.0. In the temperature of inlet working gas below 1200 °C the increase in the efficiency of CCPP with TCR is less than 2%. [Display omitted] • Concept of a combined cycle power plant with chemically recuperated gas turbine. • Effect of pressure, temperature and steam-to-methane ratio on efficiency is studied. • Thermochemical recuperation leads to an increase in efficiency on 4–7%. • Maximum efficiency is observed at T in =1600°C in the pressure rage of 20–25 bar. [ABSTRACT FROM AUTHOR]

With the proposal of the "double carbon" goal, the large-scale development of new energy has spawned the development of green card market. It is urgent to study and explore the formation mechanism of on grid electricity price suitable for new energy power generation under the "double carbon" goal. Therefore, this paper studies the on grid price mechanism of new energy power stations considering the market environment. Firstly, the cost structure of photovoltaic power generation and wind power generation is analyzed, and the least squares support vector mechanism (LS-SVM) of quantum particle swarm optimization (QPSO) is used to build the cost prediction model of new energy power generation. Then, considering the coupling relationship of carbon market, electricity market and green card market, the income model of new energy power stations is constructed. Furthermore, the price mechanism under incomplete competition based on Bayesian game and the price mechanism under perfect competition based on bargaining game are designed. Then, the price of the designed mechanism is adjusted combined with the cost sharing of ancillary services. Finally, an example is given to verify the effectiveness of the model. [Display omitted] [ABSTRACT FROM AUTHOR]

In the context of increasing integration and relevance of distributed energy resources, this paper proposes an advanced public policy to maximize their long-term benefits through optimal regulated electricity market management. More specifically, the focus is on renewable distributed generation and energy storage systems. The proposed policy arises from three models or techniques, i.e., the optimized tariff model (regulated electricity market model), Bass diffusion model (long-term forecasting of the integration of distributed energy resources), and random walk (risk assessment technique), along with a stochastic optimization problem. The policy leads to socioeconomic welfare maximization while ensuring fairness for all the market players and enhanced control by the regulatory agency on the integration of distributed energy resources. By applying the policy to a concession area located in northeastern Brazil, where photovoltaic distributed generation systems dominate the market share, results demonstrate that it is highly valuable as a 156 (MR$) mean socioeconomic welfare gain was achieved when optimizing the design variables (compared to a non-optimal solution). [Display omitted] [ABSTRACT FROM AUTHOR]

Reliable estimates and forecasts of Photovoltaic (PV) power output form a fundamental basis to support its large-scale integration. This is recognized in literature, where a growing amount of studies deal with the development of PV power estimation and forecasting models. In particular, machine learning techniques received significant attention in the past decade. Yet, the importance of predictor variables are consistently ignored in such developments and as a result those models fail to acknowledge the value of including physics-based models. In this study we quantify the value of predictor variables for PV power estimation and forecasting, assess deficiencies in estimation and forecasting models, and introduce a number of pre-processing steps to improve the overall estimation or forecasting performance. To this end, we use common physical models to create so-called expert variables and test their impact on the performance of single-point and probabilistic models. In addition, we investigate the optimal selection of predictor variables for PV power estimation and forecasting. By means of a sensitivity analysis, the paper shows how the value of expert variables is affected by the tilt angle of the PV system. To allow for a deeper insight into the importance of predictor variables, two case studies in different climate regions are considered in the numerical evaluation. • The value of predictor variables in solar estimation and forecasting is quantified. • Single-point and probabilistic regression and machine learning models are tested. • Physical-based models are used to improve the model performance and explainability. • The transposition model holds most value to the estimation and forecasting models. • A sensitivity analysis shows the dependence of the model improvement to the tilt angle. [ABSTRACT FROM AUTHOR]

In recent works by Yang et al. (2017a, 2017b), and Yang et al. (2019), geographical, temporal, and sequential deterministic reconciliation of hierarchical photovoltaic (PV) power generation have been considered for a simulated PV dataset in California. In the first two cases, the reconciliations were carried out in spatial and temporal domains separately. To further improve forecasting accuracy, in the third case these two reconciliation approaches were applied sequentially. During the replication of the forecasting experiment, some issues emerged about non-negativity and coherency (in space and/or in time) of the sequentially reconciled forecasts. Furthermore, while the accuracy improvement of the considered approaches over the benchmark persistence forecasts is clearly visible at any data granularity, we argue that an even better performance may be obtained by a thorough exploitation of spatio-temporal hierarchies. To this end, in this paper the spatio-temporal point forecast reconciliation approach is applied to generate non-negative, fully coherent (both in space and time) forecasts. New spatio-temporal reconciliation approaches are adopted, exploiting for the first time some relationships between two-step, iterative and simultaneous spatio-temporal reconciliation procedures. Non-negativity issues of the final reconciled forecasts are discussed and correctly dealt with in a simple and effective way. The spatio-temporal reconciliation procedures are applied to the base forecasts with forecast horizon of 1 day, of PV generated power at different time granularities (1 h to 1 day), of a geographical hierarchy consisting of 324 series along 3 levels. The normalized Root Mean Square Error (nRMSE) and the normalized Mean Bias Error are used to measure forecasting accuracy, and a statistical multiple comparison procedure is performed to rank the approaches. In addition to assuring full coherence and non-negativity of the reconciled forecasts, the results show that for the considered dataset, spatio-temporal forecast reconciliation significantly improves on the sequential procedures proposed by Yang et al. (2019), at any level of the spatial hierarchy and for any temporal granularity. For example, the forecasted hourly PV generated power by the new spatio-temporal forecast reconciliation approaches improve on the NWP 3TIER forecasts in a range from 4.7% to 18.4% in terms of nRMSE. • New approach to produce fully coherent solar forecasts both in space and time. • Complete previous discussions on deterministic solar forecast reconciliation. • Avoid negative forecasts and preserve full spatio-temporal coherence. • Significant improvement of the forecast accuracy through hierarchical forecasting. • Forecast skills over NWP hourly forecasts between 4.7% and 18.4%. [ABSTRACT FROM AUTHOR]

The rapid fall of photovoltaic generation and battery storage costs can pave the way for future distributed power systems. However, transitioning from centralized to distributed systems has economic implications for different power sector stakeholders: incumbent energy firms, new distributed generation investors, and consumers. Consumers can significantly increase demand by adopting electric transport, a crucial component of decarbonization. In turn, investors and firms will need to provide the capital and operating capability to supply more power, even for self-consumption. In this paper, we develop a methodology to assess the future average price of electricity for two fundamentally different systems: one based on utility-scale projects and another based on distributed generation. We also evaluate how prices affect the financial benefits of energy sector investors and transport users that result from transport electrification. We apply the methodology to Costa Rica's transport electrification objectives, a middle-income country with vast renewable generation capacity with pledges to reach net-zero emissions by 2050. We find that the future unit costs of solar and wind generation with energy storage infrastructure affect electricity prices more than other uncertainties. In turn, prices have a high correlation with transport benefits. We also find that low discount rates produce high benefits and low electricity prices. [ABSTRACT FROM AUTHOR]

The complementary operation of cascade hydropower (CHP) and photovoltaic (PV) can increase the integration of PV power and has become a trend in modern power systems. However, the randomness and variability of PV power output might cause challenges in precisely tracking the submitted generation plan of the CHP-PV complementary system. The safety and stability of the receiving-end power grid might deteriorate. In this paper, a multi-time scale dispatch framework with day-ahead and intra-day dispatch is established for the CHP-PV complementary system to address the PV uncertainties. The day-ahead dispatch is employed to provide reliable reference results and sufficient adjustment reservations for the intra-day dispatch. An Itô-theory-based stochastic optimization (ITB-SO) approach is proposed for the intra-day dispatch by modeling the PV prediction error with stochastic differential equations (SDE). The SDE model can be embedded into the ITB-SO approach without scenario generation, and thus computational burden can be significantly reduced compared with scenario-based methods. A complementary system in southwest China is chosen as a detailed case study. The simulation results demonstrate that real-time random PV fluctuations could be compensated by adjusting CHPs. Meanwhile, the target water level of cascade reservoirs can be tracked under the proposed approach. [ABSTRACT FROM AUTHOR]

• The efficiency of dust prevention reaches 93% when the tilt angle is 75°. • The density of dust deposition increases with wind speed. • The average efficiency of dust prevention is about 25% within 3 m/s. • The density of dust deposition decreases with the wind direction. • The efficiency of dust prevention reaches 65.85% when the wind direction is 0°. Currently, solar photovoltaic power generation technology is developing rapidly around the world to achieve the objective of carbon neutrality. The deposition of dust on solar photovoltaic modules is one of the main reasons for the decline in power generation efficiency. To identify the factors affecting dust deposition, scholars have conducted numerous outdoor experiments or numerical simulations. However, there are still few studies conducted in an indoor setting. Also, there is a lack of investigation into the effect of wind direction on dust deposition. In this paper, three indoor wind tunnel experiments are designed. Considering the characteristics of wind speed, tilt angle and wind direction, the performance of super-hydrophobic coatings is evaluated. The results show that the density of dust deposition on the photovoltaic module decreases with the increase of tile angle when it faces the wind, with the dust-proof efficiency of the super-hydrophobic coating reaching a maximum of 94.43% at 75°. At the same time, the density of dust deposition increases with wind speed, and the dust-proof efficiency is 28.81% at maximum when the wind speed is 1.2 m/s. In addition, the density of dust deposition gradually declines with wind direction, and the dust-proof efficiency reaches 65.85% at maximum when the photovoltaic module faces the wind. Therefore, the use of super-hydrophobic coatings can significantly reduce the dust deposition on photovoltaic modules and improve the efficiency of photoelectric conversion. [ABSTRACT FROM AUTHOR]

• This paper summarizes the performance of multi-crystalline, mono-crystalline and silicon heterojunction photovoltaic (PV) arrays operating in desert climate. • Indoor characterization of all PV modules from seven arrays was performed to determine the degradation rate. • Visual and Electroluminescence (EL) inspections were performed to gain more insight on the module failure. • Encapsulant yellowing, back sheet cracking and cell cracking were the most prominent failure modes observed after 5-years operation in the desert climate. Performance and reliability of photovoltaic (PV) systems are important for the deployment of PV in desert climate. In this study, we investigate the performance and reliability of multi-crystalline, mono-crystalline and silicon heterojunction PV arrays operating in the field for 5-years from 2014 to 2018 under desert climate. From data monitoring and performing visual inspection at the field, a drop in the energy yield and the presence of various visual defects were observed and therefore further in-depth analysis was required to identify the potential of main cause of the performance losses. For the (Multi_D), (Multi_E), (Multi_F), (Mono_G), and (SHJ_H) arrays a degradation rate of −2.65 %/year, −1.91 %/year, − 0.14 %/year, − 1.59 %/year and − 0.62 %/year, respectively, was calculated using the RdTool. From each array, all modules returned from the field to perform indoor current–voltage (IV) measurements at Standard Testing Condition (STC) and at different irradiance and temperature operating conditions. The electrical parameters of these modules were analyzed and the degradation rate was estimated by comparing the STC values provided by the manufacturer with the STC values measured after 5-years exposure in desert climate. From the seven difference manufacturers, multi-crystalline silicon (Multi_A), (Multi_B) and (Multi_E) showed the lowest annual degradation rate below 1 %/year. Multi-crystalline silicon (Multi_D) and mono-crystalline silicon (Mono_G) showed the highest PV module power degradation of −3.0%/year and 6.3 %/year, respectively. Further, Encapsulant yellowing, back sheet cracking and cell cracking were the most prominent failure modes observed during the first 5-years of operation in the field. [ABSTRACT FROM AUTHOR]

Existing renewable power generation forecasting methods mainly focus on a single energy source and fail to effectively capture the spatio-temporal correlation between different power generation resources. Furthermore, the current single-site power forecasting no longer fulfills the demands of grid dispatch. This paper introduces an innovative framework for multi-task learning and uses it to achieve regional wind-photovoltaic combined power generation forecasting. First, this paper employs Maximum Information Coefficient (MIC) to identify the crucial meteorological features affecting power generation and analyze the complementarity and correlation between wind and photovoltaic power generation. Then, an innovative multi-task learning framework is proposed that separates task-specific components and shared components, allowing each task to select adaptive information that benefits itself. Besides, this paper proposes a loss optimization strategy to balance the loss magnitude and training velocity of different tasks. In order to effectively share the coupling information among the two kinds of power generation, the proposed framework is adopted to construct the regional wind-photovoltaic combined power generation forecasting model based on Temporal Pattern Attention LSTM (TPA-LSTM) algorithm. Finally, the efficiency and superiority of the proposed method are validated through several verification and comparison case studies. • A combined power forecasting model is proposed based on multi-task learning. • A novel multi-task learning framework is constructed. • A comprehensive joint loss optimization strategy is proposed. • Extensive experiments demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

• A new multi-energy combined system utilizes abundant wind/PV to produce hydrogen. • The refined coupling model of the system is simulated and realized. • The judgment matrix method is used to evaluate the system performance. • The optimal capacity of pumped storage achieves the system stability and economy. To achieve "carbon neutrality", clean energy such as wind and solar energy is being developed, but due to the random and intermittent characteristics of wind energy and photovoltaics, the instability of grid connections and the abandoning wind and photovoltaics need to be addressed urgently. This paper proposes a method to solve the problems of grid connection and abandonment in wind and photovoltaic generation systems, that is, to establish a combined pumped storage/wind/photovoltaic/hydrogen production system through the pumped storage and hydrogen production system. The system consists of a pumped storage/wind/photovoltaic complementary subsystem and a hydrogen production subsystem. First, different models in the system are modelled using Simulink and the characteristics of the models are analysed. Subsequently, the wind turbine model and the PV model are simulated to derive the wind-PV complementary characteristic curves, and it is found that the load demand cannot be met by relying on wind-PV complementary power generation alone. To achieve system stability and economy, pumped storage is configured to smooth the output of wind power and PV. After comprehensive consideration, the optimal pumped storage capacity is determined to be 1000 MW. Then, through the simulation of the pumped storage/wind/photovoltaic system, it is found that even though pumped storage can absorb the wind and photovoltaic outputs, there is still a surplus when the wind and photovoltaic outputs are in excess. Therefore, this surplus energy is used for hydrogen production and the configuration of the hydrogen production system is optimized using a particle swarm algorithm to minimize the cost per unit of hydrogen production. After particle swarm optimization, it was determined that the unit cost of hydrogen production was minimized to 3.54 CNY/ Nm 3 for the configuration of 55 electrolysis tanks of 4 MW and 219 lead-acid tanks of 1 MW/2 MWh. Meanwhile, the electrolysis tank simulation model and the lead-acid battery simulation model were used to obtain maps of their operating conditions, hydrogen production and hydrogen storage tank pressure. In view of the addition of an energy storage system to the wind and photovoltaic generation system, this paper comprehensively considers the two energy storage modes of pumped storage and hydrogen production, and proposes a corresponding capacity optimization configuration scheme, which has reference value for improving the consumption and stability of wind and photovoltaics and realizing the sustainable utilization of clean energy. [ABSTRACT FROM AUTHOR]

• The coupled heat transfer process of rooftop photovoltaic shading units and indoor heat gain are analyzed. • The energy-saving potential of photovoltaic rooftops compared to traditional rooftops is revealed. • The energy-saving performance of photovoltaic and traditional rooftops under different roof reflectivity are summarized. • The impact of the optimal tilt angle on the power generation of the photovoltaic rooftop are discussed. • An energy-saving scheme for applying rooftop photovoltaic systems in hot summer areas is proposed. Rooftop photovoltaic panels can serve as external shading devices on buildings, effectively reducing indoor heat gain caused by sunlight. This paper uses a numerical model to analyze rooftop photovoltaic panels' thermal conduction, convection, and radiation in hot summer areas as shading devices. The researcher builds an experimental platform to verify the model, exploring the potential for energy savings of photovoltaic rooftop units in the Wuhan area. The results show that after installing photovoltaic panels, the delay performance of the roof increases by 0.5 h, the roof heat flux is reduced by 41.7%, the peak temperature of the roof is reduced by 22.9 °C, and the daily heat gain is reduced by 74.84%. Finally, this paper discusses the impact of high-reflectance and low-reflectance roofs on the shading effect. The study finds that low-reflectance roofs are more energy-efficient in the hot summer, as high-reflectance roofs lead to a 10.8% increase in indoor heat gain when the photovoltaic panel is installed. Finally, a quantitative method for evaluating the comprehensive potential for energy savings is proposed, considering the electricity generation gain of photovoltaic panels and the comprehensive energy-saving efficiency of photovoltaic roofs, which generates a total potential for energy savings rate of 61.06%. The model presented in this paper provides theoretical guidance for analyzing the comprehensive energy-saving effects of photovoltaic rooftop systems and reveals the potential for energy savings of rooftop photovoltaic panels as external shading devices. [ABSTRACT FROM AUTHOR]

Solar energy has been rapidly utilized in urban environments owing to its significant potential to fulfill the energy demand. The precise forecasting of solar energy, including solar radiation and photovoltaic power forecasting, is crucial for effective energy utilization in cities. Currently, artificial intelligence algorithms, including machine learning (ML) and deep learning (DL) algorithms, have been widely utilized in the research of solar energy forecasting and have shown excellent performance. The different research scales lead to variations in the influencing factors and forecasting approaches used in solar energy prediction. Therefore, this paper provides a comprehensive review of solar radiation and photovoltaic power forecasting research using ML and DL algorithms from a multi-scale perspective, including the meso-scale, micro-scale, and building-scale forecasting in urban environments. The aim is to summarize the state-of-the-art progress and evaluate the solar forecasting effectiveness in diverse research scenarios. The characteristic analyses of ML- and DL-based solar forecasting approaches, prediction models, forecasting horizons, inputs and outputs were conducted for different research scales. This paper discusses the existing issues and future research directions for multiscale solar energy forecasting in cities. The analyses can help researchers and engineers in identifying suitable prediction algorithms and approaches. Consequently, this exploration can contribute to the efficient utilization of solar radiation and photovoltaic power generation in multi-scale urban environments. [Display omitted] • Machine learning and deep learning-based solar radiation and photovoltaic power forecasting research in urban environment are classified from a multi-scale perspective. • The characteristics and key points of solar radiation and photovoltaic power forecasting at the meso-scale, micro-scale, and building-scale in cities are analyzed. • Solar radiation and photovoltaic power forecasting approaches, prediction models, input and output values, and forecasting horizons are discussed. • Future research directions of solar radiation and photovoltaic power forecasting in urban environment are presented. [ABSTRACT FROM AUTHOR]

• A novel estimation method for maximum photovoltaic hosting capacity (PHC) is proposed in this paper, featuring fast and easy implementation due to simple algorithms. • The conservative estimation for PHC is favorable for practical applications. • Estimation error is very small (less than 6%) and can be further reduced to be within 1% by considering loss change. • Estimation can be carried out with limited parameters that are easy to obtain by power system operators. Large-scale photovoltaics (PV) generation at the distribution network causes overvoltage problems. These large PV systems are generally connected to the distribution network in three-phase configurations to ensure that power is balanced across all phases. At the same time, radial distribution networks are a common configuration of power systems worldwide as it is simpler and more cost-effective than meshed networks. In such a context, this paper proposes a novel method to estimate PV hosting capacity (PHC) for connection points in a radial and three-phase balanced distribution network. Different from existing methods that normally require power flow solutions of the network, the proposed method only needs line parameters and the historical voltage at the connection point. In addition, the proposed method features conservative estimation for PHC and thus can ensure the safety of network voltage compared with existing methods. The accuracy is tested on the IEEE 33 bus system and the estimation error is within 6%. Furthermore, the proposed method can be easily extended to consider branch losses that can reduce the estimation error to be within 1%. The obtained results have confirmed the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A cloud-edge collaborated dust deposition degree monitoring scheme for distributed PV systems. • Classification using temporal and interactive information of PV arrays. • Improved scheme adaptivity by training model with randomly grouped data. • The classification accuracy reaches 98% verified by 201,600 sets of real data. After installation, the long-term efficient operation dominates the profitability of photovoltaic (PV) generation systems. Unlike large-scale PV plants, distributed PV systems are relatively dispersed, and belong to different owners, whose main obstacle for maximizing the profit is the lack of professional maintenance. This paper therefore proposes a cloud-edge collaborated dust deposition degree monitoring scheme for distributed PV systems without employing additional equipment, sensors or meteorological data, which just assumes the operational and historical data of all distributed PV systems in the same area to give the guidance whether the distributed PV system needs cleaning. In implementation, temporal and interactive models are established based on the operation characteristics of distributed PV systems, whose overall classification accuracy could reach more than 98%. In addition, in order to improve the adaptivity of monitoring scheme, this paper proposes a data-based random grouping method, which avoids the model mismatch caused by the addition or withdrawal of PV arrays. Finally, experimental verifications are carried out to prove the effectiveness of the presented scheme. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper presents a new dynamic 2D fusion model and analysis method for analyzing the dynamic characteristics of floating photovoltaic modules under continuous changes in motion, irradiance, and temperature caused by actual wind and waves conditions. • The coupling relationship between the motion state and the environmental factors is established to achieve accurate measurement of floating photovoltaic modules. • The complementary filter is used to eliminate the low-frequency interference signals of the gyroscope and the high-frequency interference signals of the accelerometer. • A new dynamic 2D fusion model of floating photovoltaic modules is set up to obtain the dynamic output characteristics, which integrates mechanical motion state information with the electrical parameters. • The experimental results show that proposed model and analysis method have high performance, and power generation efficiency can be increased by 2.5%. As a new application form, floating photovoltaic power generation is a green and sustainable energy source with immense development potential. However, built on marine or riverine surface, the floating photovoltaic system not only faces challenges from environment factors that land-based photovoltaic system meets, but also faces challenges from the motion of the floating bodies which are caused by the continuous external forces of wind and waves. Modelling and analyzing the output characteristics of floating photovoltaic modules is desirable and useful to improve power generation efficiency. This paper presents a new dynamic 2D fusion model and analysis method for analyzing the dynamic characteristics of floating photovoltaic modules under continuous changes in motion, irradiance, and temperature caused by actual wind and waves conditions. To achieve accurate measurement of floating photovoltaic modules, the coupling relationship between the motion state and the environmental factors is established. Then the motion state modeling of floating photovoltaic modules based on multi-sensors information fusion is established to acquire the absolute attitude angle with the continuous superposition of high and low frequency motion in the geographical coordinate system. On this basis, a new dynamic 2D fusion model of floating photovoltaic modules is set up to obtain the dynamic output characteristics, which integrates mechanical motion state information with the electrical parameters. Finally, the proposed modelling and analyzing method is evaluated with experimental data. The experimental and analytical results show that the accuracy of the 2D fusion model is higher than 98.7%, the output efficiency of floating photovoltaic modules is only 94.2% compared to the static state, and power generation efficiency can be increased by 2.5% by using the proposed method. The new modelling method proposed in the paper can accurately obtain the output characteristics of the floating photovoltaic system under actual operating conditions, which can effectively improve the efficiency of power generation. [ABSTRACT FROM AUTHOR]