Photovoltaic Power Fluctuations Research Articles

Coordinated control algorithm of hydrogen production-battery based hybrid energy storage system for suppressing fluctuation of PV power

Photovoltaic (PV) power generation has issues of volatility and intermittency. Currently, PV plants are generally equipped with 10% rated capacity lithium-ion (Li) battery energy storage systems in China, who often fail to suppress fluctuation in the output power of PV plants effectively and meet the grid-connected standard. The hybrid energy storage system (HESS) combining with hydrogen production and Li battery system can produce hydrogen by water electrolysis during the peak period of PV power generation, effectively improving PV utilization efficiency, while smoothing PV power fluctuation and improving grid connection electricity quality. Firstly, models of the solid oxide electrolysis cell (SOEC) and alkaline electrolysis cell (AEC) systems for hydrogen production, and Li battery energy storage system are established, and the transient response characteristics of each system are analyzed. Secondly, an adaptive wavelet packet decomposition (AWPD) method for PV power signal decomposition is proposed based on the wavelet packet decomposition (WPD) method. Thirdly, a capacity configuration method for HESS are proposed based on the APWD method. Fourthly, a coordinated control strategy for HESS is proposed with the transient response characteristics of different energy storage systems and the state of charge for Li battery system. Finally, the proposed method is validated through simulation experiments based on the actual power data of the PV plant. The results show that the developed methods can effectively utilize partial PV power generation to produce hydrogen, improve PV utilization, and the combined output power of PV plant and HESS can fulfill the grid-connected standard.

A multi-objective collaborative planning method for a PV-powered hybrid energy system considering source-load matching

A hybrid system including photovoltaic panels, an air-source heat pump, batteries, and a hot water tank is proposed to satisfy the building’s demands. To discover the optimal energy scheduling scheme, a multi-objective two-stage planning method is developed. In the first stage, considering the seasonal fluctuations of photovoltaic power and the loads, several operation modes are designed. Moreover, the influence of objective functions with and without the energy self-consumption (SCR) and self-sufficiency rate (SSR) on the system configuration is analyzed. The second stage aims to explore the synergetic benefits with demand response, in which the hourly loads are reconstructed accounting for the municipal tariffs. The results show that when accounting for the annual cost (AC) and primary energy (PE), the ideal operation mode is as follows: favor thermal storage during periods of energy surplus and prioritize discharging electricity in winter and heat in summer in cases of insufficient power. When considering the SCR and SSR additionally, storing heat in summer with charging batteries in winter is preferred when excess energy is available. Otherwise, give priority to thermal storage. Furthermore, based on the proposed energy scheduling scheme, the AC, PE, SCR, and SSR are improved by −31.1 %, −19.4 %, +1.5 %, and +11.3 %, respectively.

Optimization of Power Prediction of BP Network with Improved Pelican Algorithm

Abstract In view of the large fluctuation of photovoltaic output power affected by different weather, accurate prediction of photovoltaic output power is particularly important for the safe and stable operation of power system. Firstly, the pelican optimization algorithm ( POA ) is improved in the following three aspects : adding Circle chaotic map to make the population evenly distributed, introducing mutation factor to expand the search range of pelican when approaching prey, adding adaptive weight and firefly disturbance to avoid falling into local optimum in the water surface flight stage ; then, in order to improve the prediction accuracy of BP algorithm, the improved pelican algorithm ( IPOA ) is used to optimize the weights and thresholds of BP neural network, and the IPOA-BP photovoltaic power prediction model is built to improve the accuracy of power prediction. Finally, this paper tests the prediction performance of IPOA-BP, POA-BP and basic BP power prediction models in sunny, cloudy and rainy days through experiments. The experimental results demonstrate that the IPOA-BP prediction model outperforms both the POA-BP and traditional BP neural network models under various weather conditions.

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model

Accurate prediction of wind and photovoltaic (PV) power is essential to ensure the stable operation of power systems. However, the conventional power prediction methods face the challenges in capturing the stochastic fluctuations of wind and PV power. A wind and PV power prediction method fusing the multi-stage feature extraction and a particle swarm optimization (PSO)-bidirectional long and short-term memory (BiLSTM) model is developed. To illustrate the oscillation and instability of wind and PV power, the symplectic geometry model decomposition (SGMD) is presented to decompose the feature data and obtain multiple feature sub-sequences. Due to the excessive feature decomposition using the conventional methods, such as empirical modal decomposition, the kernel principal component analysis is utilized to reduce the dimensionality of the nonlinear sub-sequences of the wind and PV power. Compared to the traditional LSTM that ignores the inverse-time correlation between power and meteorological features and is sensitive to the model hyper-parameters, a power prediction model based on PSO-BiLSTM is developed. Using the power data from a power station in Xinjiang Province, China, as an example, the experimental results show that wind and PV power can be accurately forecasted using the developed method with high prediction accuracies of 97.9 % and 98.5 %, respectively.

Optimized Dual-Layer Distributed Energy Storage Configuration for Voltage Over-Limit Zoning Governance in Distribution Networks

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network’s voltage zones based on distributed photovoltaic governance resources, thereby elucidating the characteristics and governance requisites for voltages across distinct regions. Subsequently, a governance model for voltage limit exceedances, grounded in optimizing energy storage configurations, was formulated to mitigate photovoltaic power fluctuations by deploying energy storage systems. This model coordinates the reactive power output of photovoltaic installations with the active power consumption of energy storage systems, thereby augmenting voltage autonomy in the power grid. This study leveraged Karush–Kuhn–Tucker (KKT) conditions and the Big-M method to transform the dual-layer model into a single-layer linear model, thereby enhancing solution efficiency and precision. Finally, a simulation was carried out to demonstrate that the strategy proposed from this research not only achieves commendable economic efficiency, but also significantly improves the regional voltage effect by 28.7% compared to the optical storage capacity optimization model.

The total factor characteristics evaluation of photovoltaic power by coarse-fine-grained method

The study of photovoltaic(PV) power uncertainty is of great significance to promote the collaborative optimization of source-grid-load-storage. On the one hand, using a single probability distribution model to describe the fluctuation, intermittency and randomness of PV power is limited and controversial; On the other hand, qualitative analysis oriented to only one or two of them is characterized with poor interpretability and one-sidedness. So that, a evaluation framework for the total factor characteristics named "dual-DMU" is established corresponding to the discrete, mutation and unordered phenomenon of PV power, and a coarse-fine-grained evaluation method is proposed, which can realize the comprehensive description and all-sided evaluation of PV power. Combined with the historical data of a rooftop PV power plant, the analysis reveals the variability of total factor characteristics in different seasons, weather types, and typical time periods, and the methodology can provide a theoretical basis and decision-making support for the construction of PV power forecasting models, energy storage capacity optimization, and adjustable load resource allocation.

Optimal power bidding of overseas PV plants in Singapore wholesale electricity market

AbstractSingapore's power sector has set targets of net‐zero emissions by 2050. Given the limited land space of the country, a key strategy to decarbonize the power grid is to import clean power from renewable energy resources such as photovoltaic (PV) plants installed at overseas locations. The present electricity market rules require such overseas PV plants to maintain constant power generation during each bidding period. To meet such requirements, energy storage systems (ESSs) are to be deployed in the PV plants to compensate for the PV power fluctuation. This paper proposes an optimal power bidding approach for maximizing the profit of the PV plant participating in the Singapore wholesale electricity market. The problem is formulated as a stochastic programming model, which takes the short‐term PV power forecasting as the input, maximizes the expected profit considering the PV power selling revenue and the penalty cost for power shortfall during each bidding cycle (30 min), and satisfies constraints of the ESS. The proposed method can also be used for determining the optimal size of the ESS. Simulation results have verified the effectiveness of the proposed method.

Predictive power fluctuation mitigation in grid-connected PV systems with rapid response to EV charging stations

Currently, renewable energies and electric vehicle charging stations are essential for energy sustainability. However, the variable generation from renewable sources, such as photovoltaic systems, can lead to power peaks that impact the stability of the grid. This challenge is exacerbated by the increasing demand in fast-charging stations. Addressing these demand peaks is crucial to ensure the stability of the electrical grid. This paper introduces the predictive-flex smoother, an innovative method designed to mitigate power fluctuations in grid-connected photovoltaic systems while optimizing energy management in electric vehicle charging stations. The predictive-flex smoother method incorporates a hybrid energy storage system comprising supercapacitors and vanadium redox flow batteries to respond rapidly to electric vehicle charging stations demands, enhance grid electricity purchase optimization, and improve energy quality delivery. The proposed method integrates two control strategies: photovoltaic fluctuation reduction strategy and peak demand reduction strategy for electric vehicle charging stations. By leveraging prediction algorithms and machine learning techniques, the predictive-flex smoother method achieves precise power fluctuation forecasts, allowing efficient utilization of supercapacitors and vanadium redox flow batteries to smooth photovoltaic power fluctuations and reduce electrical vehicles peak demand. Comprehensive experimental investigations and simulations validate the method's performance under various operational conditions. The results demonstrate the effectiveness of the predictive-flex smoother method, significantly improving the quality of power delivered to the grid while reducing costs. The experimental platform, validates the real-time response of the proposed method, with response times under 500 ms. The experimental results further confirm the efficiency of the method in power smoothing and charging strategies with varying electrical vehicles models and connection coefficients.

Morphological control of electrodeposited iron for fluctuating photovoltaic electrical energy

With the Paris Agreement, the installed capacity of green power, such as photovoltaic (PV) and wind power, is growing rapidly at a rate of more than 10 % per year. The practical application of these green electrical energies is limited by the relative lag in the scale of energy storage systems. In response to the pressure on energy storage systems, we explored the feasibility of supplying DC power directly from a PV plant to an industrial electrolyzer by comparing the electrodeposition behavior of Fe2+ in a sulfuric acid system from stabilized DC and fluctuating PV power sources. We found that the factor that damages the stable operation of an iron electrodeposition system the most is the presence of hydrogen evolution reactions, where the generation of hydrogen bubbles reduces the current efficiency of the electrodeposition system and, in severe cases, destroys the surface morphology of the deposited products, causing devastating damage to the system. On this basis, we designed a simple scheme that uses heterogeneous bubble nucleation instead of homogeneous bubble nucleation for rapid bubble expulsion from the cathode surface. Benefiting from the rapid transfer of bubbles, the current efficiency of the electrodeposited iron system increases by 1.25%–10 % at different current densities, and the maximum roughness decreases by 129–2,396 nm, which considerably improves the stability of the electrodeposited iron system for PV power. This work provides useful experimental support for the transformation of the traditional steel smelting industry, which is characterized by high carbon emission and energy consumption, into an environmentally friendly industry.

An Automatic PI Tuning Method for Photovoltaic Irrigation Systems Based on Voltage Perturbation Using Feedforward Input

This paper proposes a new automatic tuning method for the proportional-integral (PI) controllers of photovoltaic irrigation systems (PVIS) without the need for any other power source or batteries. It enables the optimisation of the values of the PI parameters (Kp and Ki) automatically, eliminating the requirement for skilled personnel during the installation phase of PVIS. This method is based on the system’s voltage response when a disturbance signal is introduced through the feedforward input of the PI controller. To automatically assess the response properties, two indicators are proposed: the total harmonic distortion (THD), used to evaluate the sine response, and the total square distortion (TSD), used to evaluate the square response. The results indicate that the tuning changes for different irradiance and temperature conditions due to the non-linearity of the system, obtaining the most conservative values at maximum irradiance and temperature. The robustness of the results of the new automatic tuning method to abrupt photovoltaic (PV) power fluctuations due to clouds passing over the PV generator has been experimentally tested and the results show that the obtained tuning values make the PVIS stable, even when PV power drops of 66% occur abruptly.

Distributed photovoltaic power fluctuation flattening strategy based on hybrid energy storage

Aiming at mitigating the fluctuation of distributed photovoltaic power generation, a segmented compensation strategy based on the improved seagull algorithm is proposed in this paper. In this regard, a hybrid energy storage system comprising a lithium battery and supercapacitor is utilized. The internal power distribution of the hybrid energy storage system is adjusted using wavelet packet decomposition, and the state of charge is employed to adapt the primary power distribution. The start and end times for charging and discharging are determined by combining the time of use, electricity price, state-of-charge information, and load size at night to realize the economic operation of the system. The opposing search operator strategy and mutation operation are used to improve the seagull algorithm, optimize the controller parameters of the DC/DC converter, and improve its response time. Combined with the historical measured data of a distributed photovoltaic in Hubei Province, simulation results show that the proposed strategy can effectively smoothen the fluctuation of distributed photovoltaic generated power while reducing the charging and discharging frequencies of the energy storage system, hence improving its stability and service life.

The Improved Coordinated Control Method of Islanding and Grid-Connected Operations for DC Microgrids

The DC microgrid plays an essential role in realizing smart power supply systems for the DC loads. It combines various distributed generations and transmits the power to the loads or the utility grid through the DC bus. It attaches great importance to energy management because of the load and photovoltaic power fluctuations. This paper proposes an improved coordinated control based on the characteristics of various distributed generations and utility grids. The proposed method can realize the stabilization of bus voltage when the system switches between multiple operating modes in grid-connected and island mode due to the change of energy. Compared with the present ways, the proposed method provides a further optimization on the design of charging for energy storage units in the grid-connected mode and the current sharing with the backup storage units in the islanded mode which is conducted by SoC balancing droop control. Finally, the DC microgrid simulation platform is built to verify the correctness and feasibility of the proposed method.

Short-term prediction of PV power based on fusions of power series and ramp series

Photovoltaic (PV) power generation is highly volatile and intermittent, which poses a major challenge to the prediction and intra-day scheduling of PV power. In order to effectively improve the prediction accuracy of PV power, this paper proposes a short-term prediction model through the fusion of power series and ramp series based on CNN-LSTM models. In the proposed hybrid model, both the power series prediction module and the ramp series prediction module are used to simultaneously predict the PV power, and then the results of both modules are combined by fully connected layers and corrected by a residual network to obtain the final prediction results. The prediction effects of the proposed model are illustrated by a multi-level evaluation system of prediction errors, including the evaluation of overall prediction errors, local prediction errors, and ramp point prediction errors. Case studies show that the ramp series of PV power can better characterize the fluctuation of PV power and that the proposed model has a better prediction effect than other models.

Distributed Online Voltage Control With Fast PV Power Fluctuations and Imperfect Communication

Coordinated Volt-Var control methods have demonstrated their techno-economic feasibility in voltage regulation of photovoltaic (PV) rich distribution systems. However, fast fluctuating PV power and imperfect communication networks may significantly challenge the effectiveness of these methods. In this paper, a revised dynamic consensus algorithm is proposed to coordinate distributed inverters for Volt-Var control in real time. With this proposed method, Var saturation and overvoltage issues which tend to occur at downstream buses of PV rich distribution systems are significantly mitigated. To quantitatively analyse the algorithm performance in imperfect communication environments, the information delivery between agents is modelled by stochastic state transition processes among finite numbers of virtual nodes so as to quantitatively depict the random time delay and packet dropout in a discrete way. On this basis, the state transition process of the whole system is further depicted by a series of row-stochastic matrices, and the ergodic theory is used to analytically derive the algorithm tracking error in an imperfect communication environment. Our proposed method can also be extended to more complex applications, where both Var compensation and PV curtailment (or EV dispatch) are available for system voltage control. Simulation results verify the superiority of our method over traditional ones.

Model Predictive Control-Based Dual-Mode Operation of an Energy-Stored Quasi-Z-Source Photovoltaic Power System

The energy-stored quasi-Z-source inverter (ES-qZSI) has attracted much attention for photovoltaic (PV) power generations, due to its capability to stabilize the PV power fluctuations with simple structure and other advantages of Z-source inverter. The improved dual-mode (DM) ES-qZSI is able to support all-weather operation even at night or cloudy days when PV power is extremely low. Whereas, the traditional proportional-integral (PI) based control suffers from complicated controller design and poor dynamic response during mode transition, due to two sets of PI control required for the daytime and night operation modes. In order to overcome that, this paper proposes a model predictive control strategy for the DM-ES-qZSI PV power system. The system predictive models in both day and night operating modes are derived. The control strategy is disclosed to ensure high performance of the system, through calling the predictive models and defined cost functions of the two modes within a single control loop. Simulation and experiment are carried out to verify the effectiveness of proposed control strategy.

Control method to coordinate inverters and batteries for power ramp-rate control in large PV plants: Minimizing energy losses and battery charging stress

This work presents a novel control method for multi-megawatt photovoltaic (PV) plants that is able to regulate each plant inverter and the battery system to mitigate PV power fluctuations. The proposed control method makes it possible to implement different PV ramp-rate control strategies based on the use of batteries and the limitation of inverters during positive fluctuations, which have been conceptually proposed in the specialized bibliography, but have omitted how to perform the coordination between PV generators. The dynamic model and the tuning of the control parameters are presented and the method is used to correctly implement different inverter-limitation strategies using 5-second data from a real 45 MWp PV plant. Furthermore, a new control strategy is proposed. This strategy reduces curtailment losses to negligible values and takes into account and addresses the intrinsic asymmetry in the battery charging and discharging capability, an issue that has been overlooked in the specialized bibliography. The results show that the proposed control method can effectively control each of the multiple inverters in order to obtain the desired PV plant operation to regulate the battery charging power, even during highly fluctuating scenarios.

An integrated ultra short term power forecasting method for regional wind–pv–hydro

Renewable-based multi energy power system is the main trend for power system in the future. However, the randomness and fluctuation of wind and photovoltaic power, as well as the seasonality of hydropower, have an increasingly prominent impact on the stability of power system. Accurate power forecasting technology is the key to solve the above problems. At the same time, the output characteristics of heterogeneous energy sources are very different, and the existing forecasting methods are difficult to fully exploit their spatio-temporal correlation characteristics, which limits the improvement of prediction accuracy. In this paper, an integrated ultra short term power forecasting method for regional wind–pv–hydro is proposed, Firstly, it quantifies the differences and similarities among wind, pv and hydro in different scenarios based on empirical mode decomposition, and achieves the extraction of homogeneous features, on this basis, the integrated power forecasting models for wind, pv and hydro based on long short-term memory neural networks is constructed , and achieves regional-level integrated forecasting of wind, pv and hydro. The results show that the forecasting accuracy and modeling efficiency of the proposed integrated forecasting method are significantly improved compared with the traditional independent forecasting method, the forecasting accuracy is increased by 1%–3%, the modeling efficiency is increased by 6 times.

Enhancing Energy Power Quality in Low-Voltage Networks Integrating Renewable Energy Generation: A Case Study in a Microgrid Laboratory

Nowadays, energy decarbonization due to integrating renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical system networks, especially in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method for improving the low-pass filter and moving average for grid-connected photovoltaic systems. This novel method includes state-of-charge monitoring control of the supercapacitor’s energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. This research aims to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration. The results demonstrate significant power quality and stability improvements achieved through the proposed method. For instance, the power smoothing method effectively reduced power fluctuations by 16.7% with the low-pass filter, 14.05% with the ramp-rate filter, and 9.7% with the moving average filter.

A Novel Adaptive Power Smoothing Approach for PV Power Plant with Hybrid Energy Storage System

Clouds passing over solar photovoltaic (PV) power system causes power fluctuations, which contributes to power quality issues. Power fluctuations are usually compensated by an energy storage system (ESS) integrated with a filtering or smoothing controller. However, there is a great burden to tune and determine the filtering time constant (TF) that leads to better alleviation of PV power fluctuations because the irradiance variability varies from day to day. Lack of cloud information leads to incorrect selection of TF values which leads to poor smoothing performance when setting low values on cloudy days, or to unnecessary ESS operation when setting high values on clear days. This paper introduces a novel power smoothing framework to avoid the arbitrary selection of TF values and reduce the stress on ESS based on predictive and adaptive smoothing mechanisms. The proposed controller contains two layers, the first layer predicts the next day's irradiance profile and uses a clear sky model to identify the cloud class. Next, the controller sets the initial values of the filter time constants for cloudy, moderate, and mild days, and disables smoothing on clear and overcast days. The second layer adaptively adjusts the initial filter time constant based on the current power ramp rate and shares power for a hybrid energy storage system (HESS). The performance of the proposed controller is tested against the forecasting errors and compared to fixed time constant-based smoothing techniques and ramp rate control in a range of scenarios, including smoothing, ramp rate reduction, and battery degradation assessment.