Photovoltaic Output Research Articles

Application of Satellite Data for Estimating Rooftop Solar Photovoltaic Potential

Rooftop solar photovoltaics can significantly contribute to global energy transitions by providing clean, decentralized energy without the need for new land, thereby avoiding land-use conflicts. It serves as a valuable complement to other renewable-energy sources and is expected to play a crucial role in future electricity systems. Due to the spatiotemporal variability in the solar radiation on roof surfaces, it is essential to determine the potentials of the rooftop photovoltaics and its variations in specific regions. In Germany, this potential was assessed in 5 km × 5 km zones, as well as at the federal-state and national levels. High-quality satellite and reanalysis data were used to determine the power output of the solar photovoltaics. Additionally, high-resolution (2 m × 2 m) European Settlement Map data, calibrated with solar cadastre data, were utilized to evaluate different scenarios. The results show that the potential is concentrated in larger urban areas and the southwestern part of Germany due to the availability of rooftop space and solar radiation. Overall, the national rooftop areas are substantial across all scenarios, ranging from 2100 to 4500 km2. The applied methods and scenarios provide a straightforward way to reveal the spatiotemporal variability and define realistic ranges of the solar photovoltaic potential without requiring detailed information about each building. However, assessing the rooftop photovoltaic potential remains challenging and uncertain due to the lack of large-scale, high-resolution data on building characteristics and the complexity of the solar radiation distribution in urban environments.

Design and investigation of high power quality PV fed DC-DC boost converter

This study presents a new improved voltage gain dc-dc converter architecture to maximize solar photovoltaic (PV) power output. The maximum power point tracking (MPPT) method utilizes particle swarm optimization (PSO)–based artificial neural networks (ANN) to reduce the oscillations of output electrical performance at the maximum power point (MPP). For any solar cell temperature and irradiance, the ANN delivers the electrical current and voltage outputs at the MPP and thus could reach the MPP in minimum instances. A DC-DC converter needs specific characteristics to work with photovoltaic systems. These include higher voltage development to meet increased DC link voltage requirements, the continuous input current to extend the PV system life, common grounding to prevent electromagnetic interference, and reduced electrical stress with fewer components. This paper combines a quasi-switched switch inductor network and extendable diode capacitor modules to achieve the quality mentioned earlier. The converter offers constant input current, high efficiency (95 %) with considerable gain (12 times higher than input), lower voltage stress (almost one-fifth of output voltage), and a common grounding feature. The effectiveness of the proposed MPPT technique is analyzed in terms of higher efficiency, MPP tracking time, gain, and the normalized voltage stress on diodes. The experimental step is developed to validate the simulation (Simulink and Psim) results of the present research work.

The Influence of Environmental Factors on a Photovoltaic Panel–PVsyst Simulation

Renewable energy sources and the CO2 emissions in the atmosphere are predicted to play a very important role in attaining the climate neutrality by the half of this century. The rapid depletion of fossil fuel reserves and the looming threat of climate change have intensified the global pursuit of renewable energy sources as a viable alternative. The positive aspect of using renewable energy sources is that this kind of resources can easily supply the worlds energy needs. Despite the fact that we cannot capture all this energy, harnessing less than 0.02 % would be enough to cover all the necessary energy needs. Obstacles to expanding solar power generation involve the substantial cost of manufacturing solar cells and the dependency on weather conditions. The efficiency and performance of photovoltaic panels are inherently tied to environmental variables, prominently including climate factors. This article investigates the intricate relationship between climatic conditions and photovoltaic panel yields, offering crucial insights for optimizing the solar energy generations. It has been analysed the impact of environmental factors such as ambient temperature, wind speed and air humidity above the yield of photovoltaic panels. For each environmental factor, the variable conditions were simulated to highlight their correlation with positive or negative effect they have on the yield of a photovoltaic panel. By perusing this material, readers can develop a comprehensive grasp of how the environment conditions can influence the production of electricity using the photoelectric effect. Leveraging empirical data and simulation models using the PVsyst software, the analysis discerns regional variations and seasonal fluctuations in photovoltaic panels yields according to diverse climatic factors regimes.

Decentralised control strategy for effective utilisation of distributed community energy storage in PV-rich LV distribution network

Integrating a high-capacity residential-scale photovoltaic (PV) system into a low-voltage (LV) network results in voltage rise. Conversely, voltage drops occur during periods of high demand. Furthermore, PV generation characteristics vary with seasonality and daily weather conditions, complicating voltage rise and drop management. On the other hand, community energy storage (CES) emerges as a solution for future community energy management. This study presents an application of distributed CES to effectively manage voltage rise and drop, addressing a prominent challenge accompanying the widespread adoption of PV. However, upon introducing the CES and its accompanying energy capacity, the control strategy must effectively incorporate the utilisation of the CES’s available capacity year-round. In brief, CES control should adapt to the characteristics of PV by charging the CES during high PV generation. It should also release active power to meet high demand by discharging the CES. This will enable both voltage management and effective CES capacity utilisation in response to PV variations due to seasonality and daily weather conditions. The goal mentioned above can be effectively accomplished by forecasting PV output and using the data to adapt to PV characteristics in a centralised manner. However, due to the challenges associated with centralised control, the present paper proposes a decentralised control algorithm that determines charging and discharging power rates for distributed CES, aligning with the PV and load characteristics in an LV distribution network. The proposed control framework consists of two stages: the first stage regularly updates power references based on clear-sky PV profiles, active power measurements at the LV transformer, and CES active power and energy level. The second stage is activated to mitigate voltage transient caused by fast-moving clouds and small increments in solar irradiance, load between consecutive updates of the first stage. To illustrate and validate the concept, a 13-bus LV distribution network is used, and the proposed control algorithm is implemented on a co-simulation platform that combines MATLAB with a real-time digital simulator.

Performance enhancement and techno-economic analysis of photovoltaic modules under dynamic weather conditions using dual exponential sawtooth method

The maximum output power extracted from the Photovoltaic (PV) modules is mainly dependent on ambient temperature and solar irradiance. The photovoltaic modules performance is hugely influenced by the Partial Shading (PS) effect, which results in the reduction of output power. It is caused by shadows of buildings, trees, moving clouds and towers. Under PS conditions, shaded modules receive less irradiance as compared to the unshaded modules, and this leads to overheating of PV array. The present work has been developed by a technique based on Dual Exponential Sawtooth (DES) pattern to reconfigure the PV modules so as to increase the PV output power under PS conditions along with the energy savings and economical aspects. In this technique, the PV modules of Total Cross Tied (TCT) technique and their physical locations are arranged as per DES pattern to distribute the shading effect over the entire array without altering the electrical connections of the PV modules. The DES arrangement gives the enhanced power of 41.48% by reducing the effect on any row of the shaded modules. Further, it provides the details about power sharing percentage as the number of shaded modules increase and also the affect of dynamic shade variation over the entire PV array. The proposed method validation with the existing methods for the analysis, simulation and hardware experimentation shows better performance in terms of mismatch losses and fill factor. Further, the analysis has been extended for units generated and energy savings with each module rating of 1.5KW on 4×4 PV modules.

Comparative Analysis of Single Stage and Dual Stage PV Based Generation System for Pollution Reduction in Asian Nations

The growing need for sustainable and clean energy solutions has elevated photovoltaic (PV) systems to the forefront of competitive options. In order to evaluate the performance and viability of single-stage and dual-stage single-phase PV voltage source inverter systems within the context of renewable energy, this study undertakes a thorough comparative analysis. MATLAB Simulink has been used to run the simulations, and the results produced show that the selected configurations are robust and closely match theoretical expectations. The study explores the subtle distinctions, benefits, and drawbacks that come with single- and dual-stage systems. By extensively evaluating the consequences on system stability, grid synchronisation, and power quality, useful insights emerge. The simplicity and efficiency of the single-stage system are demonstrated by the direct connectivity between the PV MPPT output and the inverter. In contrast, the dual-stage system addresses the issue of low PV output voltage by incorporating a boost converter with an MPPT algorithm, albeit at the expense of more parts and complexity. The research described here adds a great deal to the current discussion on PV system optimization. The study notably clarifies methods for improving dependability and efficiency in applications involving renewable energy. With the increasing worldwide trend towards sustainable energy, it is critical to comprehend the subtle dynamics of various PV setups. In addition to offering a comprehensive grasp of the complexities involved in single- and dual-stage single-phase PV voltage source inverter systems, this research paves the way for future developments in the planning and execution of effective renewable energy solutions especially for Asian nations that still rely on coal-based power plants. Increased contribution of solar based electricity generation will lead to a significant drop in pollution contributed by the ash and gases released by thermal power plants that employ fossil fuels. The ultimate goal of this study is to provide information to stakeholders in the renewable energy industry so that they may make more informed decisions and create PV systems that are both sustainable for the environment and efficient for the utility.

Optimizing the deep learning parameters using metaheuristic algorithms for PV-battery-based DC nanogrids

This paper presents an implementation of metaheuristic algorithms to optimise deep learning neural networks (DNNs) for sensor-less control of photovoltaic (PV) Converters in DC nanogrids. Using a Fixed Forward Neural Network (FFNN), it estimates PV output current (IPV ) based on three days of real data. Given the vulnerability of current sensors in DC system measurements, accurately replicating current sensor data is vital. The data exhibits dynamic nonlinear relationships with inputs like solar irradiance, temperature, and voltage. The study assesses the effectiveness of Evolutionary Mating Algorithm (EMA) and Sand Cat Swarm Optimisation (SCSO), comparing them with Adaptive Moment Estimation (ADAM), Genetic Algorithm (GA), and Particle Swarm Optimisation (PSO). The research leverages these metaheuristic algorithms to optimise machine learning integration, addressing regression estimation problems, and enhancing system reliability by eliminating sensors. Key findings indicate that the EMA-DNN combination achieved remarkable performance, with the lowest Mean Squared Error (MSE) of 0.5906, the lowest Mean Absolute Error (MAE) of 0.4680, and the lowest Mean Absolute Percentage Error (MAPE) of 12.1780%. These results offer valuable insights into how the metaheuristic-DNN approach can solve regression estimation problems and reduce the number of sensors in PV battery-based nanogrids’ control layers.

Design and Performance Analysis of Photovoltaic Power Generation Light Emitting Diode Device

This research focuses on an independent photovoltaic power generation system with supercapacitor energy storage as the study subject. The model is simplified, and the photovoltaic power generation light emitting diode (LED) device is designed based on the given parameters. This system selects a Boost-type step-up converter for the supercapacitor charging circuit, capable of achieving Maximum Power Point Tracking (MPPT). The supercapacitor discharge circuit employs an LM2596 series regulator to power the rated load LED (12 V, 1 W), providing good linear regulation capability. In designing other hardware components of the device, the characteristics of a photoresistor are utilized to implement an automatic load switch circuit. An efficient single-chip integrated circuit LM2575 series is used as a regulator to convert the voltage across the supercapacitor terminals to +15 V and +5 V. Considering the need to collect the output voltage of the photovoltaic cell array and the voltage across the supercapacitor terminals, a resistor divider method is used to sample these two voltages. The sampling circuit includes a resistor divider circuit and a linear optocoupler isolation circuit. An HNC-25LTS series Hall current sensor is used for current sampling to measure AC, DC, and pulse signals under electrical isolation conditions. The supercapacitor, solar photovoltaic panel, control unit, and the main circuit for supercapacitor charging and discharging have been assembled in the experiment. Connecting the charging and discharging circuit with the photovoltaic panel and LED, the system provides power to the LED during the night. Under varying light intensity and temperature conditions, the photovoltaic output voltage waveform, PWM waveform, and Boost circuit output voltage waveform remain stable when reaching the MPPT point. The output power with added MPPT control at different times is compared. The results indicate that the designed system has effectively achieved the functionality of MPPT for solar energy.

Analysis of the impact of irradiance, temperature and tilt angle on the performance of grid-connected solar power plant

In order to maximize the solar radiations falling on a Photo-voltaic (PV) panel and hence, to maximize the solar power generation, an optimum tilt angle of the PV panels for a specific geographic location plays a critical role. This paper exploits the tilt angle and establishes an empirical relation among optimum tilt angle, module temperature and ambient temperature. Moreover, estimating accurate solar photovoltaic power output depends on the correct modelling of the PV module. Temperature and irradiance dependent modelling need statistical support for their behaviour and pattern. This work also examines and institutes the relationship between Ambient temperature and Module temperature throughout the year. Furthermore, in order to determine the impact of irradiance, ambient temperature and module temperature on the solar power generation of a grid-connected solar power plant, this paper evaluates Karl Pearson correlation coefficients for each of the following three pairs (1) generation and irradiance (2) generation and ambient temperature and (3) generation and module temperature, for all the 12 months of a year. The results obtained shall help to better understand, manage, plan, forecast and stabilize the solar power output. Earlier researchers usually used weather data for their study, which are not location-specific therefore, accuracy is questionable. Hence, the data used in this research is recorded from a 3.3 MWp grid-tied ground-installed solar power plant and a 119 ​KW grid-tied rooftop-installed solar power plant, both located at Aligarh Muslim University, Aligarh, India. This paper presents an exhaustive analysis of the two grid-tied solar power plants as there is very little work with actual data of generation, irradiance, temperature and tilt angle, all measured on the spot with high accuracy; results obtained are realistic with a novel approach.

A markov chain based photovoltaic power simulation method

Photovoltaic power generation has volatility and randomness, which affects the safety and reliability of the power system. In order to achieve accurate simulation of photovoltaic output, this paper proposes a photovoltaic power timing simulation method based on rolling sampled Markov chain model. Firstly, establish a photovoltaic output model and analyze the actual output characteristics; Then, based on the first-order Markov chain model, the relationship between adjacent days is considered, and a multistate transition probability matrix is established to construct an annual time series output model; Finally, based on the annual output data of a photovoltaic power station and historical meteorological monitoring data, an example is simulated to verify the effectiveness of the proposed method.

Validation of Vertical Bifacial Agrivoltaic and Other Systems Modelling

In agrivoltaic systems combining solar photovoltaic and agricultural activities, ground albedo is mainly characterized by the crop and its seasonal variations. This study examines the effects of using fixed, satellite-derived, and hourly measured albedo on the performance of a vertical bifacial system and a 1-axis tracking system using a bifacial photovoltaic model (AgriOptiCE). The model is developed with Matlab® and partially based on the open-source package pvlib. AgriOptiCE is firstly validated by comparing estimated front and rear irradiances with on-site measurements for specific periods from a 1-axis tracker site in Golden, USA and a vertical agrivoltaic system in Västerås, Sweden. Furthermore, photovoltaic system power output estimations using AgriOptiCE are also validated for the vertical agrivoltaic system and the conventional ground-mounted fixed-tilt system at the same location. The validations demonstrate the high accuracy of the proposed model in estimating front and rear irradiances and power output, obtaining R2 > 0.85 for all the studied cases. The study results indicate that measured albedo provides the highest accuracy, while satellite-derived albedo has poorer results due to the broader spatial, temporal, and spectral resolution. Fixed albedo is not recommended for yearly assessment of bifacial PV systems because it cannot account for snow events and daily variations, resulting in lower overall accuracy.

Distributed power conditioning unit of large-scale space solar power station

In this paper, a multi-bus distributed Power Conditioning Unit (PCU) is proposed for the Space Solar Power Station with large scale photovoltaic (PV) array and power levels reaching MW level. In this unit, there are multiple independent PV arrays. In each PV array, there are multiple independent PV subarrays. In this paper, a V-P droop control method with adaptive droop coefficient is proposed, which modifies the droop intercept based on the bus voltage deviation and the power per unit value of the PV array. This method ensures the accuracy of bus voltage and achieves proportional distribution of power between PV arrays based on the proposed topology structure in this paper. When the load changes or the output power of the PV array fluctuates, this method can ensure that power is distributed proportionally. The principle and control method of the proposed droop control method is analyzed in this paper. The effectiveness of the method is verified through MATLAB/Simulink simulation and experiment. Simulation and experimental results show that the proposed method can achieve power distributed proportionally when load changes and PV output power fluctuates, reduce bus voltage error caused by line impedance and differences in rated power of different PV arrays, and improve the performance of PV power generation system applied to space.

Agrivoltaics Over Berries in Chile: Potential for Clean Energy Generation and Climate Change Adaption

Agrivoltaics (AV), the concept of installing photovoltaic (PV) panels on agricultural land, enabling a dual use of the surface, has the potential to foster renewable energy expansion without land use conflict and to protect water from evapotranspiration. Although there is growing interest in AV, there has been no structured analysis of its potential for clean energy generation and climate change adaptation in Chile. In this paper, we provide the first national-level estimate of the AV potential over blueberries, using a combination of filtered geo-datasets and meteorological data to quantify PV yields and impact on evapotranspiration. We find a theoretical potential of 13.4 GWp for AV over blueberries, predominantly in the central and southern regions. The derived potential for AV could provide 22% of the current national electricity generation while lowering irrigation demand by nearly 18 million m³ per year. Finally, we identify about 8,000 GWh of current annual conventional electricity generation that could be regionally replaced by AV, showing the potential to contribute significantly to the decentralization and decarbonization of the Chilean electricity mix. Further research on the agronomic and economic aspects of AV implementation should be carried out to enable synergetic development.

Study on Master-Slave Game Optimization Operation of Integrated Energy Microgrid Considering PV Output Uncertainty and Shared Energy Storage

Integrated energy microgrids and shared energy storage have significant benefits in improving the energy utilization of the system, which is gradually becoming the current research hotspot. And the uncertainty of new energy output also significantly affects the stable and economic operation of integrated energy microgrid. So how to establish a set of integrated energy microgrids optimization operation model considering photovoltaic (PV) output uncertainty and shared energy storage is an urgent problem to be solved nowadays. Firstly, this paper introduces the framework of an integrated energy system microgrid containing a shared energy storage operator (ESO), and analyzes the scheduling method of the upper tier operator within the system as well as the economic benefits at the lower tier user end. Secondly, to address the randomness of PV output, Monte Carlo method is used to generate the scenarios, and then the scenarios are cut down by using the fast antecedent elimination technique. Then, an optimal operation model is established for micro grid operator (MGO) and user aggregator (UA), respectively, and based on the master-slave game relationship, so that the MGO is the leader and the UA is the follower, a Stackelberg game model is proposed to consider the integrated demand response of electricity and heat between the MGO and UA in the context of the participation of ESO in the auxiliary service of the UA. Finally, the proposed model is brought into a typical residential building community for simulation verification, and the results show that the model proposed in this paper can effectively balance the interests of MGOs and UAs, and realize win-win benefits for UA and ESO.

Study on the optimal layout of roof vents and rooftop photovoltaic of the industrial workshop

Roof vents are crucial for facilitating natural ventilation within industrial workshops. However, their substantial size and height can limit available roof space and create extensive shadow areas, thereby impeding the installation of photovoltaic (PV) systems on industrial roofs. This study quantitatively assesses the influence of roof vent positioning, morphology, and roof architecture on the natural ventilation efficiency of workshops with varying indoor heat sources. Subsequently, the study evaluates how different vent configurations affect the installable area for rooftop solar panels. Findings suggest that shifting from traditional roof ridge vents directly above a heat source to north-side vents inclined towards the roof can marginally decrease the ventilation flow rate by approximately 5 %, translating to a minor temperature rise of around 0.46 °C within the occupied zone. Nonetheless, the Predicted Mean Vote (PMV) thermal comfort indices remain consistent across scenarios. Notably, adopting north-side vents, either flush with the roof or inclined, can enhance the annual energy yield of the rooftop PV system by 15.49 % and 22.67 %, respectively, under equivalent solar irradiation. Thus, strategic adjustments to vent positioning, form, and roof design can substantially boost the photovoltaic system's power output without compromising the workshop's natural ventilation performance.

PV based Systems with Advanced Control Strategies for Load Balancing in Multilevel Inverter

In an era driven by sustainable energy solutions, the synergy of photovoltaic (PV) system stands as a beacon of hope for meeting the world's growing energy demands while minimizing environmental impact. This research ventures into the domain of renewable energy integration by seamlessly including a PV system, ingeniously controlled by Chaotic Flower Pollination Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT (Maximum Power Point Tracking) controller capable of optimizing the efficiency in the face of ever-changing weather dynamics. The PV system's quest for optimal efficiency receives a substantial boost through the implementation of the High Gain Modified Luo Converter. Designed to achieve an optimal PV output voltage, this converter's prowess finds its true calling in grid applications, where precision and efficiency are paramount. Furthermore, this research extends its purview to incorporate a bidirectional converter linked to an energy storage solution, such as a battery, through a common DC link. The output power is then passed to the Flyback Converter, seamlessly connected to a 31 level Cascaded H Bridge Multi-Level Inverter (31-level CHB MLI) controlled by PI controller. This formidable inverter architecture facilitates the efficient delivery of power to the grid, ensuring a smooth and controlled integration of renewable energy resources. This strategic integration bolsters the system's adaptability, enabling the seamless management of energy flows and grid interactions along with load balancing in MLI. The MATLAB simulation platform is used for confirming the system's overall performance. According to the simulation results, the proposed approach achieves the maximum efficiency with the lowest THD value of 94.5% and 2.5%, respectively.

Edge passivation: Considerable improvement in photovoltaic performance of perovskite/silicon tandem solar cells

Edge recombination is considered hard to avoid entirely in silicon (Si) solar cells as well as Si-base solar devices, hindering their future commercialization. However, such an important issue in perovskite/silicon (PK/Si) tandem solar cells has not attracted much attention. Herein, a low-temperature, non-vacuum liquid-based edge passivation strategy (LEPS) to improve the power conversion efficiency (PCE) of PK/Si tandem solar cells is proposed. The minority carrier lifetime (τeff) of the PK/Si tandem sample with 495.8 μs significantly enhances to 739.7 μs after passivating the Si sub-cell edge recombination. The open circuit voltage (VOC) of the PK/Si tandem solar cell increases by up to +3.8%abs from the initial state after LEPS treatment due to edge passivation, leading to the PCE of the PK/Si tandem solar cell increases by up to +1.2%abs. Finally, a monolithic PK/Si tandem cell with a PCE of 29.48% was achieved by further utilizing the LEPS, which opened up a simple and effective avenue for enhancing the PCE of PK/Si tandem solar cells and further promoting a higher photovoltaic output power of tandem modules.

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

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.