In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of n-RMSE (Normalized Root Mean Square Error), 48.04% of Max-AE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).

https://www.mdpi.com/1999-5903/15/2/46/pdf?version=1676389463

Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction

Renewable energy solutions are appropriate for on-grid and off-grid applications, acting as a supporter for the utility network or rural locations without the need to develop or extend costly and difficult grid infrastructure. As a result, hybrid renewable energy sources have become a popular option for grid-connected or standalone systems. This paper examines hybrid renewable energy power production systems with a focus on energy sustainability, reliability due to irregularities, techno-economic feasibility, and being environmentally friendly. In attaining a reliable, clean, and cost-effective system, sizing optimal hybrid renewable energy sources (HRES) is a crucial challenge. The presenters went further to outline the best sizing approach that can be used in HRES, taking into consideration the key components, parameters, methods, and data. Moreover, the goal functions, constraints from design, system components, optimization software tools, and meta-heuristic algorithm methodologies were highlighted for the available studies in this timely synopsis of the state of the art. Additionally, current issues resulting from scaling HRES were also identified and discussed. The latest trends and advances in planning problems were thoroughly addressed. Finally, this paper provides suggestions for further research into the appropriate component sizing in HRES.

/pdf/a-comprehensive-review-on-techno-economic-analysis-and-3ofwoqcb.pdf

A Comprehensive Review on Techno-Economic Analysis and Optimal Sizing of Hybrid Renewable Energy Sources with Energy Storage Systems

Grid integration impacts and control strategies for renewable based microgrid

Wasserstein metric-based two-stage distributionally robust optimization model for optimal daily peak shaving dispatch of cascade hydroplants under renewable energy uncertainties

Modeling and simulation of integrated solar PV - hydrogen systems

Optimal design of hydro-wind-PV multi-energy complementary systems considering smooth power output

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/Nm3 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. 

Modelling and capacity allocation optimization of a combined pumped storage/wind/photovoltaic/hydrogen production system based on the consumption of surplus wind and photovoltaics and reduction of hydrogen production cost

Hydrogen production using solar energy is an important way to obtain hydrogen energy. However, the inherent intermittent and random characteristics of solar energy reduce the efficiency of hydrogen production. Therefore, it is necessary to add an energy storage system to the photovoltaic power hydrogen production system. This paper establishes a model of a photovoltaic power generation hydrogen system and optimizes the capacity configuration. Firstly, the mathematical model is modeled and analyzed, and the system is modeled using Matlab/Simulink; secondly, the principle of optimal configuration of energy storage capacity is analyzed to determine the optimization strategy, we propose the storage capacity configuration algorithm based on the low-pass filtering principle, and optimal time constant selection; finally, a case study is conducted, whose photovoltaic installed capacity of 30 MW, verifying the effectiveness of the proposed algorithm, analyzing the relationship between energy storage capacity and smoothing effect. The results show that as the cut-off frequency decreases, the energy storage capacity increases and the smoothing effect is more obvious. The proposed algorithm can effectively reduce the 1 h maximum power variation of PV power generation. In which the maximum power variation of PV generation 1 h before smoothing is 4.31 MW. We set four different sets of time constants, the maximum power variation of PV generation 1 h after smoothing is reduced to 0.751, 0.389, 0.078, and 0.04 MW, respectively.

/pdf/modeling-of-hydrogen-production-system-for-photovoltaic-2klvgi6k.pdf

Modeling of hydrogen production system for photovoltaic power generation and capacity optimization of energy storage system

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Optimal design of hydro-wind-PV multi-energy complementary systems considering smooth power output

Modelling and capacity allocation optimization of a combined pumped storage/wind/photovoltaic/hydrogen production system based on the consumption of surplus wind and photovoltaics and reduction of hydrogen production cost

Modeling of hydrogen production system for photovoltaic power generation and capacity optimization of energy storage system