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Abstract: This article reports for the first time in the literature, a dye sensitized solar cells with 1.21% efficiency (Voc=0.56V, J sc=6.70mA/cm2 and F.F.=0.33) on paper substrates. The current dye sensitized solar cell technology is based on fluorine doped SnO2 (FTO) coated glass substrates. The problem with the glass substrate is its rigidity and heavy weight. Making DSSCs on paper opens the door for both photovoltaic and paper industries. The potential of using mature paper making and coating technologies will greatly reduce the current PV cost. Paper substrate based DSSCs not only offer the advantages of flexibility, portability and lightweight but also provide the opportunities for easy implantation to textile. In this study, a low temperature process is developed to coat uniform nickel on paper substrate as the metal contact to replace the traditional expensive FTO. The Ni paper showed excellent conductivity of 8–10Ω/□. It is found that the control of metal oxide electrode morphology is critical to solar cell performance. The TiO2 film has the tendency to crack on Ni coated paper, which resulted in the shunt of the device and no solar cell efficiency was obtained. ZnO film on the other hand had good morphology tolerance on Ni coated paper and yielded solar cell efficiency of 1.21% (V oc=0.56V, J sc=6.70mA/cm2 and F.F.=0.33) under AM 1.5 (activation area is 0.16cm2). The control sample of ZnO solar cell on FTO glasses has the efficiency of 2.66% (V oc=0.64V, J sc=9.97mA/cm2 and F.F.=0.42). [Copyright &y& Elsevier]

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]

In recent years, the use of cold ironing (CI) has effectively reduced the carbon emissions of ships in port areas. However, due to the limitation of port CI quantity and power supply capacity, the power demand of some ships cannot be met. To solve this problem, this paper proposes a multi-ship power sharing strategy based on two-stage robust optimization and improved Shapley value method. Firstly, considering the uncertainty of ship photovoltaic power generation, a multi-ship power sharing model based on two-stage robust optimization is established. This model is a three-level optimization model with mixed integer variables, which is a NP-hard problem. In this paper, the nested column-and-constraint generation algorithm combined with duality theory and big-M method is used to obtain the optimal scheduling strategy of ship shared power, CI power and load demand response power. Secondly, an improved Shapley value method considering berth and remaining berthing time is proposed to realize the cost allocation in the process of ship power sharing. Finally, the effectiveness of the multi-ship power sharing strategy in reducing berthing costs and carbon emissions is verified by simulation. This method is expected to provide a new idea for the development of green ports. • The energy scheduling of ships in port is optimized. • A power sharing strategy for multi-ships in port is proposed. • A cost allocation strategy considering ship mobility is designed. • A two-stage robust optimization algorithm based on NC&CG is developed. • The operation cost and carbon emission of berthing ships are reduced. [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]

• This paper designs the slicing cell as the concentrating solar cell. • The control variable experiment is carried out by the change of concentration ratio. • The changes of slicing solar cells' performance under different radiation condition are analyzed. Concentrated photovoltaic (CPV) is raised because of the high cost of solar cells. However, the commercial solar cell is designed with 1 sun. The electrical power will not have the consistent ratio with the increase of concentration ratio. This paper proposes the slicing solar cell to connect commercial cell and CPV. And the experimental research of slicing cells with variable-controlling approach is carried out with different concentration ratio. The results show that slicing design can reduce the output current and power loss caused by series resistance, and remove the upper limit of output current caused by thin grid line. The slicing cell is more suitable for concentrated light condition than normal cell. The power difference between the slicing cell and the normal cell can reach 40 W with 4 suns. Slicing crystalline silicon solar cells have great potential in concentrated photovoltaics. [ABSTRACT FROM AUTHOR]

• The solar potential in different residential blocks is simulated. • The influences of block morphological parameters on solar potential are varified. • Four parameters that impact on the block solar potential more than 70% are verified. • The facade PV capacity in high-rise and hybrid residential blocks is more than 66%. • The average PV installation rate of roof, south and west facade is 96%, 32% and 39%. Block-scale application of photovoltaic technology in city is becoming a viable solution for renewable energy utilization. Rapid urbanization process has made urban buildings have huge development potential for solar energy in China, especially in residential areas that occupy large parts of the city. However, the residential blocks in China show differences in the morphological layout and parameters, which means they have diverse potential for solar energy development. This paper studies the effect of quantified morphological parameters on solar potential for residential blocks in Wuhan, China. The distribution characteristics of solar potential in different block environments are discussed. Various strategies are evaluated in terms of the installation position and height of PV modules. The results showed that floor area ratio, building density, average building height and building interval have significant impact on solar potential for residential block, and their correlation can reach 75%, 71%, 78% and 72% respectively. The average PV installation ratio of roof, south facade and west facade can reach 96%, 32% and 39% respectively. The results of this paper not only provide a reference to the block-scale application of PV technology in urban environment, but also stimulate the implementation of photovoltaic policies on various residential blocks in China. [ABSTRACT FROM AUTHOR]

• Two-stage grid-connected PV system's operation is secured under any grid working conditions. • The safe production of arbitrary power/current profiles is enabled. • Active/reactive power production maximization and prioritization are provided. • Different hardware configurations were analyzed (centralized, decentralized and distributed). • The novel non-MPPT mode is developed to secure stable DC-link voltage during grid disturbances. The paper comprehensively addresses the control algorithm for the photovoltaic (PV) system. The system's operation during both nominal and faulty grid conditions, most notably voltage unbalances, is addressed. The control of the boost converters and the inverter that secures reliable and maximized power production is proposed. Specifically, the paper offers a novel DC-link voltage stabilization algorithm that is not based on conventional approaches, such as droop schemes. Rather, the DC-link voltage rate of change is used to realize safe primary energy sources' power management during transients caused by grid disturbances. This offers improved robustness, reliability and scalability. Moreover, the improved grid-connected converter control is proposed. It enables the production of arbitrary currents and powers profiles, during different working regimes, respecting the converter's ratings. Consequently, higher-layer control functions can be realized by utilizing the proposed scheme. Also, the flexible power production prioritization feature is integrated into the overall solution. Such an extensive set of features was not demonstrated for PV systems in the literature previously. Different hardware topologies are regarded – centralized, decentralized and distributed. The proposed control schemes were tested using the hardware-in-the-loop (HIL) environment and were validated through the analysis of the obtained results. [ABSTRACT FROM AUTHOR]

• Pilot study of solar/hybrid/storage system for vertical farming under tropical climate condition with a case study at Malaysia. • Energy utilization and management via optimized building geometry modeling integrated with energy yield estimation tool to evaluate the potential of integrating green energy into urban agriculture. • Comprehensive analysis and assessments including technological, economic and environmental aspects of the proposed work. • The proposed work is evaluated under different design configuration (grid connected, stand alone, hybrid, storage etc) considering various optimization and design requirements and real-life constraints. • Comparative studies with hypothetical model and verification with additional energy modeling tool. Human population is projected to reach 9.7 billion in 2050 and strike 11.2 billion by 2100. Residence shift from countryside to cities will occur and the human population resides in the cities is envisaged to rise from 55% to 68%. Vertical farming was proposed to address food insecurity caused by overcrowded population and farmland shortages. The compound annual growth rate (CAGR) of vertical farming in the Asia Pacific is forecasted to grow at 22.1% with a market size reaching USD 2,101.0 million by 2024. Malaysian government encourages the implementation of vertical farming as the production of major agricultural commodities has contracted by 3.4% from 2015 to 2017. However, high energy consumption is one of the shortcomings of a vertical farm as the estimated annual energy consumption for each square meter of growing area is 3500 kW h. Thus, this paper proposes an optimized building geometry modeling integrated with energy yield estimation tool to evaluate the potential of integrating green energy into urban agriculture. This research examines the load demand in the vertical farming systems and develops solar/hybrid/storage for vertical farming system with energy yield, performance ratio, economics and environmental assessments. Preliminary resources assessment by analyzing the solar radiation of the sites was carried out at stage 1. At stage 2, load consumption was calculated, and specifications of the system were justified. Stage 3 included the development of solar PV system for the vertical farms, the economic evaluation in the context of net present cost (NPC), levelized cost of energy (LCOE) and investigation of the environmental impact. Grid-connected and standalone solar PV systems were developed for comparative studies. The estimated daily energy consumption for vertical farms in Selangor and Kuching was 430.116 kW h and 1002.024 kW h. Energy generated by grid-connected solar PV systems supported 11.6% and 8.35% of the load consumption in Selangor and Sarawak. The performance ratios of grid-connected solar PV systems in both selected sites were 82.22% and 82.56%. The finding proved that proposed work has significantly reduced the dependency of the utility grid. Additionally, the LCOE with solar PV integration vertical farming system appeared to be lower than the LCOE of the grid and lower CO 2 emission. This paper serves as a reference guide to the vertical farming stakeholders, national policy-decision makers and non-profit organizations who planned for similar initiatives especially those within the tropical climate regions. [ABSTRACT FROM AUTHOR]

• This research paper deals with design and development of PV emulator for testing of MPPT algorithm and Battery Charger controller using GA optimized PI controlled DC-DC Buck converter. • Genetic Algorithm has been used to optimize the parameter of the PI controller to emulate the exact behavior of the reference PV model. • The proposed GA optimized PI controlled DC-DC Buck converter based PV emulator provides the successful operation in three region i.e., constant voltage region, variable voltage and current region and constant current region. • The proposed PV emulator has been tested with perturb and observe maximum power point tracking algorithm and constant voltage battery charging control. • The PV emulator was also tested with MPPT algorithm and solar MPPT battery charging control algorithm and it has maximum power point extraction ratio of 97.45% and charging efficiency of 93.9%. • The proposed PV emulator has been successfully realized in real time with low cost microcontroller i.e., control logic for the PV emulator was implemented using Arduino ATmega board. • The developed model was tested and IV and PV characteristics are compared with PV reference model and observed that PV emulator has an emulation efficiency of 98.2%. In this paper, PV emulator is developed using double loop PI controlled DC-DC buck converter. Ambient environmental conditions such as irradiance, temperature and wind speed are considered for creating PV reference model in the PV emulator. The simulation model of the proposed PV emulator is created and tested using MATLAB Simulink package. To validate the effectiveness of the proposed PV emulator, it is tested with different operating conditions such as varying irradiance, temperature and wind speed. The current–voltage and power-voltage characteristic of the PV emulator is compared with PV reference model. The suitability of the PV emulator is tested with MPPT algorithm and battery charging controller in addition to number of objectives. Real time testing of the proposed PV emulator is done experimentally and also corresponding results are discussed. The proposed Solar PV emulator has the following properties: (1) higher bandwidth DC-DC buck converter, (2) extremely reliable performance with lower response time, and (3) lower output voltage and current ripple. [ABSTRACT FROM AUTHOR]

This paper presents an overview of current electricity generation and consumption patterns in the Arctic. Based on published data and new data collection this paper provides an overview of the installed capacity per energy source. A more detailed view of electricity systems is also presented, showing how different types of resources can be used and combined within Arctic communities. The paper briefly discusses which aspects of transmission systems are of special interest under the harsh climatic conditions of the Arctic. A description is given of the various electricity generation technologies that have been proven to be effective for use in Arctic environment, and those that are currently in use, including diesel, hydropower, photovoltaic, and wind power, in addition to discussion of different back-up options. Furthermore, for a more holistic view concerning such energy systems the socio-economic dimension is addressed with a focus on energy security, through an investigation of energy availability and affordability in the Arctic. The availability of energy in the harsh Arctic environments can be crucial for survival and affordability, and can have a huge impact among low-income communities. • Overview of current electricity generation and consumption patterns in the Arctic. • Potential use of different energy resources in the Arctic. • Arctic Energy security. • Socio-economic dimension of energy systems in the Arctic. [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]

Building-integrated photovoltaics/thermal (BIPV/T) systems are capable of generating electricity and heat simultaneously. Several strategies have been proposed to integrate PV into a building structure to increase the efficiency of the whole system, provide indoor heating, and produce hot water. The purpose of this paper is to comprehensively review some new and efficient technologies being employed to benefit from BIPV/T systems for indoor heating. The integration of PV into the structure of buildings and the technology of thermal management have to be straightforward. Therefore, in the present work, temperature regulation of PV cells is comprehensively reviewed considering potential methods i.e. air-assisted cooling and the use of phase change material (PCM). In addition, this paper evaluates previous works on thermal management of BIPV/T—air-based and PCM based—for space heating. Special attention is given to the effects of various configurations on buildings' thermal load, and the possible methods for system operation analysis. Furthermore, related issues such as PV and building overheating are discussed and recommendations are provided for future research studies. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is increasing worldwide to achieve energy production sustainability. To reach this goal and mitigate against climate changes all energy resources must be optimally managed. In this paper, an innovative multi agent-based heuristic optimisation system is developed to address renewable energy systems' challenges associated with the management of renewables including storage devices, planning of energy distribution, and consumption flexibility. A new heuristic algorithm is developed to introduce an efficient and sustainable storage strategy of surplus energy generated by different renewable resource options. This leads to archiving optimised storage levels of energy across different located storage devices in order to face risk of production shortages due to weather conditions. In order to test the behaviour of the developed system and address its benefits, a case study in the Republic of IRAQ is developed. Twelve major cities (in order of population size) distributed in 5 main regions including North, South, East, West and Middle parts of the country are selected, and hybrid renewable power generation sources such as Wind, PV-solar and Hydro are implemented based on weather changes. The results highlight the effect of high suppliers' rate on electricity exchange and production planning from differently used renewable technologies. A sensitivity analysis is conducted to verify the behaviour of the developed model on different demand behaviour types. • This paper considers a complex renewable energy production-distribution problem. • This study proposes an agent-based model to satisfy consumers' demand of energy. • A new heuristics algorithm to provide a sustainable storage strategy of energy. • Simulation result presents optimal energy production and minimum level of shortage. [ABSTRACT FROM AUTHOR]

With the increasing proportion of renewable energy (mainly wind power and photovoltaic) connected to the grid, the fluctuation of renewable energy power brings great challenges to the safe and reliable operation of power grid. As a clean, low-carbon secondary energy, hydrogen energy is applied in renewable energy (mainly wind power and photovoltaic) grid-connected power smoothing, which opens up a new way of coupling hydrogen storage energy with renewable energy. This paper focuses on the optimization of capacity of electrolyzers and fuel cells and the analysis of system economy in the process of power output smoothing of wind/photovoltaic coupled hydrogen energy grid-connected system. Based on the complementary characteristics of particle swarm optimization (PSO) and chemical reaction optimization algorithm (CROA), a particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) are proposed. Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are constrained by wind power fluctuation, and considering environmental benefits, government subsidies and time value of funds, the objective function and its constraints are established. According to the simulation analysis, by comparing the calculated results with PSO and CROA, it shows that PSO-CROA effectively evaluates the economy of the system, and optimizes the optimal capacity of the electrolyzers and fuel cells. The conclusion of this paper is of great significance for the application of hydrogen energy storage in the evaluation of power smoothness and economy of renewable energy grid connection and the calculation of economic allocation of hydrogen energy storage capacity. • The particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) is proposed. • Based on PSO-CROA, the techno-economic of coupled renewable energy and hydrogen system is analyzed. • Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are optimized. • PSO-CROA effectively evaluates the economy of system and the optimal capacity of the electrolyzers and fuel cells. [ABSTRACT FROM AUTHOR]

Reducing building energy consumption and increasing the use of renewable energy are essential to energy conservation. Photovoltaic thermoelectric heat pumps have been considered as a sustainable measure to supply detached houses with hot water, especially those that even do not have access to the power grid. This paper investigates a stand-alone photovoltaic/thermal integrated thermoelectric water heating system (PV/T-TEWH). A dynamic model of PV/T-TEWH is developed and validated. The performance of the system in Lhasa, Beijing, Changsha and Guangzhou is simulated and analyzed respectively, and the economics of the system are compared. Results show that PV/T-TEWH can rely on the electric energy it generates without additional power supply when the current is less than or equal to 1.4 A in Lhasa and 1.0 A in Beijing, Changsha and Guangzhou. The annual total heat gain of the PV/T-TEWH system increases as the operating current increases, but the COP of the system falls with the rise of operating current. When the system is operating at the currents of 0.6 A, 1.0 A, 1.4 A, and 1.8 A, the payback period shortens with the increase of current. Lhasa has the shortest payback period among four cities and it is only 2.2 years when the working current is 1.8 A. The PV/T-TEWH system provides a new method for building hot water supply, especially for detached houses in remote areas. • This paper proposes and investigates a photovoltaic/thermal integrated thermoelectric water heating system (PV/T-TEWH). • A dynamic model suitable for simulating the PV/T-TEWH system is developed and validated. • The annual performance of the system in Lhasa, Beijing, Changsha and Guangzhou is simulated and analyzed. • The economics of the system in different cities are compared. [ABSTRACT FROM AUTHOR]

This paper proposes a grid-tied PV inverter installed at the low voltage side of a distribution grid. The architecture considers the operation of a grid-tied inverter and its robustness against the grid faults. Unlike previously proposed low-voltage-ride-through (LVRT) operation, the proposed control provides maximum-power-point-tracking (MPPT) in both normal and low-voltage fault conditions. The control strategy prevents the inverter shut-down by maintaining the DC-link. The inverter also supports the grid by reactive power injection during the voltage sags. The paper presents a prediction model of a two-stage voltage-source-inverter. The proposed predictive control can generate the appropriate references for both regular and LVRT modes based on the inverter's specification and the regulatory grid codes. The salient features of the proposed controller are: (1) decoupled power control in regular operation, (2) low-voltage-ride-through operation with reactive power support, (3) No DC-link fluctuation, and (4) MPPT in every mode. The inverter control is developed by formulating a cost function based on the prediction model of the inverter. A cost function minimization-based control eliminates the conventional cascaded-loop control, thus simplifying the controller implementation. The proposed model-predictive-control (MPC) algorithm is validated with mathematical analysis and simulations of the PV inverter in both standard and faulty grid conditions. • A low voltage ride through for a rooftop grid-connected PV system. • A battery as an energy-storage- buffer is used to compensate for the DC-link fluctuations. • A prediction model of the DC-link power and the output current of the PV inverter is derived. • The cost functions are minimized to achieve the fault-ride-through feature based on the model predictive control. • MPPT remain enabled during fault-ride-through operation. [ABSTRACT FROM AUTHOR]

In this introductory paper of this special issue, I will outline the basic principles that may govern the transformation from the conventional to the renewable photovoltaic energy supply. Based on the developed concepts, a transformation road map is proposed that describes the transformation could be realized in 40–45 years. The final issue treated in this paper is the proposed generic photovoltaic power system and its components. The scope of the published papers in this special issue is put within the frame work of this generic system. [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]