Solar Load Research Articles

Chance-constrained optimization of hybrid solar combined cooling, heating and power system considering energetic, economic, environmental, and flexible performances

Uncertain parameters and factors substantially impact the operational performances of combined cooling, heating, and power (CCHP) systems. This paper constructs a chance-constrained programming model for system design and energy dispatch management of a hybrid solar CCHP system under the uncertainties of solar energy and building loads. The uncertain scenarios with probability distributions are generated in the Latin hypercube sampling and clustering methods. The chance-constrained model transforms stochastic optimization into deterministic optimization based on these scenarios. Then, the multi-objective optimization model of CCHP system considering the performances of economic, energy, emission, and flexibility is established, and the modified ε-constraint method is employed to obtain Pareto solutions. A case study validates the proposed model. The capacities and operational strategies of components in the hybrid CCHP system are optimized, and the impacts of the confidence level of probability distributions of uncertain factors on the optimization results are discussed. The results indicate that the photovoltaic capacity in the hybrid CCHP system declines by 87.5% when the confidence level increases from 0.50 to 0.99. But other components’ capacities are raised, and the gas turbine capacity as the key component rises by 10.49%. The energetic and environmental performances of the CCHP system rise with the confidence level, and the operation safety is improved.

Solar power forecasting beneath diverse weather conditions using GD and LM-artificial neural networks

Large-scale solar energy production is still a great deal of obstruction due to the unpredictability of solar power. The intermittent, chaotic, and random quality of solar energy supply has to be dealt with by some comprehensive solar forecasting technologies. Despite forecasting for the long-term, it becomes much more essential to predict short-term forecasts in minutes or even seconds prior. Because key factors such as sudden movement of the clouds, instantaneous deviation of temperature in ambiance, the increased proportion of relative humidity and uncertainty in the wind velocities, haziness, and rains cause the undesired up and down ramping rates, thereby affecting the solar power generation to a greater extent. This paper aims to acknowledge the extended stellar forecasting algorithm using artificial neural network common sensical aspect. Three layered systems have been suggested, consisting of an input layer, hidden layer, and output layer feed-forward in conjunction with back propagation. A prior 5-min te output forecast fed to the input layer to reduce the error has been introduced to have a more precise forecast. Weather remains the most vital input for the ANN type of modeling. The forecasting errors might enhance considerably, thereby affecting the solar power supply relatively due to the variations in the solar irradiations and temperature on any forecasting day. Prior approximation of stellar radiations exhibits a small amount of qualm depending upon climatic conditions such as temperature, shading conditions, soiling effects, relative humidity, etc. All these environmental factors incorporate uncertainty regarding the prediction of the output parameter. In such a case, the approximation of PV output could be much more suitable than direct solar radiation. This paper uses Gradient Descent (GD) and Levenberg Maquarndt Artificial Neural Network (LM-ANN) techniques to apply to data obtained and recorded milliseconds from a 100 W solar panel. The essential purpose of this paper is to establish a time perspective with the greatest deal for the output forecast of small solar power utilities. It has been observed that 5 ms to 12 h time perspective gives the best short- to medium-term prediction for April. A case study has been done in the Peer Panjal region. The data collected for four months with various parameters have been applied randomly as input data using GD and LM type of artificial neural network compared to actual solar energy data. The proposed ANN based algorithm has been used for unswerving petite term forecasting. The model output has been presented in root mean square error and mean absolute percentage error. The results exhibit a improved concurrence between the forecasted and real models. The forecasting of solar energy and load variations assists in fulfilling the cost-effective aspects.

Hybrid Renewable Energy System Design: A Machine Learning Approach for Optimal Sizing with Net-Metering Costs

Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO2 emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.

Multivariate energy forecasting via metaheuristic tuned long-short term memory and gated recurrent unit neural networks

Energy forecasting plays an important role in effective power grid management. The widespread adoption of emerging technologies and the increased reliance on renewable sources of energy have created a need for a robust and accurate system for energy forecasting. This demand is becoming increasingly relevant due to the ongoing 2022 energy crisis. Modern power systems are very complex with many complicated correlations between various forecasting factors and parameters. Furthermore, renewable energy is often dependent on weather conditions, which complicates the process of forecasting. This work presents a novel artificial intelligence (AI) driven energy forecasting tuned deep learning framework. By formatting predictors as a time series, two variations of recurrent neural networks (RNN)s have been implemented: long-short-term memory (LSTM) and gated recurrent unit (GRU) neural networks. However, both approaches present several hyperparameters that require adequate tuning to attain desirable performance. Therefore, this work also proposes an improved version of a well know swarm intelligence algorithm, the sine cosine algorithm (SCA), tasked with tackling hyperparameter tuning for both approaches. To demonstrate the improvements made, three datasets have been constructed for evaluation from publicly available real-world data that contain relevant solar, wind, and power-grid load parameters alongside weather data. The proposed metaheuristic algorithm has been subjected to a comparative analysis with several contemporary metaheuristic algorithms to showcase the improvements made. The introduced metaheuristic demonstrated the best performance with a mean square error (MSE) rate for solar generation of only 0.0132 with LSTM methods and 0.0134 with GRU. Similar performance was observed for wind power generation forecasting with a MSE of 0.00292 with LSTM and 0.00287. When tackling power grid load forecasting a median MSE of 0.0162 was attained with LSTM and 0.01504 with GRU. Therefore there is great potential for tackling these tasks using the proposed approach. The best-performing models have been analyzed using SHapley Additive exPlanations (SHAP) to determine the factors that have the highest influence on energy generation and demand.

Storage periods and heating terminals types influence on the solar heating thermal storage volume in the Tibetan Plateau regions

The scale of the thermal storage system is critical to balancing system fluctuations caused by the mismatch between solar heat collection and heating load in time and space for solar heating systems. This study investigates the relationship between solar collector area and heat storage volume under the different storage periods and heating terminals types. This research simulates the heat storage volumes using a typical day, continuous overcast days, and heating period as thermal storage periods, based on solar radiation intensity, building heating load, and cloudy days in seven typical Tibetan regions. According to the results, the storage volume per unit collector area Vs/Ac increases with increasing temperature difference and storage period in the Tibetan Plateau. It is proposed that Lhoka, Chamdo, Nagqu, Lhasa, Shigatse, and Nyingchi take continuous overcast and cloudy days as thermal storage periods, and Ngari uses the heating period as thermal storage periods, corresponding to the Vs/Ac ranges of 0.27 m3/m2–0.9 m3/m2 and 1.6 m3/m2–1.8 m3/m2. Specifically, preheating the heat storage tank can improve the solar fraction fsim by 2.5 %, with the recommended preheating temperature of 50 °C. In addition, the variation equation for design solar fraction fn and Vs/Ac was obtained. These results can be utilized to develop solar heating systems on the Tibetan plateau.

Time Lag Characteristics of Building Envelop Materials on Peak Energy Demand in Typical Hot and Humid Climate of India

Abstract Using a one-dimensional model for transient heat conduction through building enclosure walls, the present research examines the effects of thermophysical building envelop parameters on transient heat exchange, peak cooling, and heating load for northern part of India. For space cooling and heating applications, the thermal performance of four distinct walling systems commonly employed in the climatic conditions of India was examined. Results demonstrate that when the thermal conductivity of the wall increases, the time lag reduces. As wall thickness rises from 230 mm to 310 mm, there is an increase in the time lag during cooling and heating modes. Additionally, the results show that the time lag between conduction and solar load increases as wall thickness increases. As wall thermal mass increased by 20% in cooling mode, the time of peak load was shifted by 2 h. When operating in cooling mode in contrast to heating mode, high thermal mass is more effective in shifting the time of occurrence of peak energy consumption.

Critical sky temperatures for passive radiative cooling

Passive radiative coolers can preserve the surface temperature below ambient by simultaneously reflecting incoming solar radiation and emitting thermal radiation to the sky. Apart from thermo-optical properties of the materials, radiative cooling performance is affected by various environmental factors which determine the atmospheric transmittance. As such, field investigations lack convergence and completion. And energy balance consideration, which aids in interpreting the field investigative results, is a deterministic cogitation on convective and radiative heat transfer by the radiative cooler that ignores the uncertainties abundant in field study. In this work, we examine the cooling performance of radiative cooling materials under different subtropical weather conditions in Hong Kong and approach the problem based on probabilistic regression modelling as an alternative. At nighttime, the response variable of surface temperature reduction can be correlated with a single predictor variable of sky temperature difference, which is a lumped parameter of ambient temperature, relative humidity, and cloud fraction. At daytime, it should be parametrized with an additional variable regarding solar intensity. The regression analysis reveals that, the higher the thermal emissivity, the larger is the temperature reduction at nighttime, especially obvious for large sky temperature difference. And heavy solar heat load is absorbed by the coolers at daytime even they feature reasonably high solar reflectivity. In this regard, further increment in solar reflectivity poses the top priority in improving daytime radiative cooling performance.

Reduced order 1 + 3D numerical model for evaluating the performance of solar borehole thermal energy storage systems

This study presents a computationally efficient numerical model for solving the fluid flow and heat transfer phenomena in co-axial boreholes heat exchangers arranged on a rectangular grid (N × N) drill hole pattern. Such systems of boreholes are an integral component of a Solar-Borehole Thermal Energy Storage System for seasonal thermal storage application. The numerical model couples a one-dimensional fluid flow and convective heat transfer model in the heat exchanger with a three-dimensional model of conductive heat transfer in the strata. In addition to solving mass and heat transfer in borehole systems, it integrates a solar thermal collector system and building dynamic thermal load to design a Solar- Borehole Thermal Energy Storage system for a 182-unit residential apartment building. Simulation also considers and evaluates the conductive, convective, and radiative thermal losses of the system. The model is validated using experimental data from a coaxial borehole heat exchanger and compared to the results of a commercial finite volume solver for multiple co-axial boreholes. The computational cost of developed numerical model is reduced by a factor of 194 compared to a commercial finite volume solver. Solar- Borehole Thermal Energy Storage system simulation is performed for a period of five years considering hourly fluctuations in solar irradiance and building dynamic thermal energy demand. Parametric study is performed on depth and spacing of boreholes, and mass flowrate in the solar thermal collectors. It is found that a system of 100 boreholes, each 100 m deep, linked to 674 solar thermal collectors can provide the heating for the building with a total thermal demand of 5600 GJ per year.

Urban integrated energy system stochastic robust optimization scheduling under multiple uncertainties

The uncertainties of the wind power generation, solar power generation, electricity load, and heat load bring difficulties to the optimal scheduling of the urban integrated energy system (UIES), which influence its secure and reliable operation. To promote the utilization of renewable energy and enhance the scheduling capacity of UIES, a two-stage stochastic robust optimization model with the day-ahead market and the real-time market is developed. The goal is to optimize the UIES’s profit in the day-ahead market and minimize the total system cost in the real-time market, accounting for the most unfavorable situations of uncertainties. To address multiple uncertainties, a four-dimensional ambiguity set considering multiple uncertainties is constructed by using the Dirichlet process mixture model (DPMM) and the variational inference algorithm. Meanwhile, an accelerated column-and-constraint generation algorithm (AC&CG) is presented for iterative solution of the proposed model, with the second stage reformulated via Lagrange dual theory. Besides, a novel acceleration strategy is adopted, which contributes the algorithm to converging faster which can also improve the iteration speed. Finally, simulation examples are used to demonstrate the effectiveness of the proposed model in comparison to stochastic programming and robust optimization. This paper may provide theoretical guidance for relevant research and application.

Research on key evaluation indexes of grid-connected characteristics of new energy based on historical data

To fully excavate the key information of the historical data of the power grid which has been sleeping for a long time, grasp the operation characteristics of the power grid, and further solve the problem of new energy generation in the power grid, the grid-connected characteristics of new energy sources in Liaoning province are studied based on the actual wind and solar power load data. Taking the actual data of Liaoning province as an example, the above analysis is carried out, the grid load of PV and wind power is classified and compared, and the typical load curve is drawn. The results show that the key evaluation index system can be easily calculated, and the results can be used as a reference for the scheduling and planning of a regional new energy system.

Symbiotic organisms search-based multi-objective optimal placement of distributed generators considering source and load uncertainty

Integration of Distributed Generation (DG) in the Distribution network will reduce the network expansion costs and increase the network reliability in terms of reduction in voltage magnitude deviations.This paper presents Symbiotic Organisms Search (SOS) based technique for finding out the best size and position of DG in Radial Distribution networks to improve the voltage profile and voltage security state of the Distribution network. The primary objectives of the paper are minimization of bus voltage variation and maximization of voltage stability index of the network as a multi objective optimization problem in presence of uncertainty of sources and loads. In this paper the uncertainty of solar power, wind power and load are modelled using 2m Point Estimate Method along with SOS algorithm.To show the effect of DG placement on voltage security state of Distribution system, the system is classified into three states based on the values of voltage stability index (VSI). Simulation results as obtained from two standard (IEEE) radial distribution networks prove the efficiency and accuracy of the proposed SOS method. The results of SOS based method are compared with some other techniques as found in recent literature, which shows that SOS algorithm outperforms other standard optimization techniques.

F-LMS Adaptive Filter Based Control Algorithm With Power Management Strategy for Grid Integrated Rooftop SPV-BES System

The power quality performance of a grid-integrated rooftop solar photovoltaic (SPV) system is significantly affected by the local nonlinear loads. Further, during peak solar insolation and load demand, the SPV system experiences overvoltage and undervoltage at its point of interconnection. Both these issues infringe the IEEE std. 519 and 1547. Therefore, this paper presents a fractional least mean square (F-LMS) based control with the power management strategy (PMS) for the SPV-battery energy storage (BES) system. This presented control uses the SPV system's power converter to compensate load currents locally. By this means, the grid voltages and currents remain distortion-free. The introduced F-LMS algorithm is used as a pre-filter for load current. It estimates the load current fundamental component more accurately and swiftly than the existing LMS method. The designed PMS supervises the SPV array generation, BES power, and grid power to cater to both over and undervoltage issues and keeps the PIC voltage amplitude within the IEEE std. 1547 limits. In addition, this work enables the autonomous operation of SPV system during grid disruption, enhancing power resilience. The efficacy of the introduced work is experimentally verified.

Assessing the influence of glass properties on cabin solar heating and range of an electric vehicle using a comprehensive system model

As consumer adoption and total energy consumption of electric vehicles continues to rapidly increase, it is important to develop comprehensive system modeling frameworks that consider the complex interactions of their mechanical, electrical, and thermal subsystems to guide component technology development. In this study, such a comprehensive system model of a generic long-range electric vehicle is developed and used specifically to assess the influence of cabin glass radiative properties on vehicle performance. The system model incorporates simplified models for all salient components in the electric traction drive, cabin HVAC, and battery subsystems, and uses a higher fidelity cabin thermal model that is able to capture the individual properties of the cabin glass used in the vehicle. The system performance is evaluated under a dynamic NEDC drive cycle which is repeated until battery depletion to determine a vehicle range. The system model is used to study five different glazing design cases, each corresponding to different transmission and reflection properties of the glass, by predicting their impact on the vehicle range. The cases span all theoretically possible glass properties while also enabling inspection of practical glass technologies that are available or under development to be adopted in modern electric vehicles. The influence of glass on vehicle range is then further compared at various locations across the United States to understand and illustrate the effects of ambient conditions and solar load. The system model predicts a vehicle range of 188.5 miles under a high solar loading scenario typical for Phoenix, AZ using traditional glass properties, which increases to a range of 221.6 miles using high-performance glass properties, representing a significant potential gain of 33.1 miles using technologies available on the market today. Under this same loading scenario, the glass properties at their extreme physical limits could theoretically affect the vehicle range by up to 92.5 miles. The influence of the glass properties is location-specific, and the model predicts that using the same glass at different locations can affect the range of vehicle by up to 100.8 miles for traditional glass properties and 73.4 miles for high-performance glass properties.

Performance Comparison Between LDR and Phototransistor Sensor for Dual-Axis Sun Tracker Sensor Based on Tetrahedron Geometry

Solar energy is optimally obtained by solar cells when the solar cells are perpendicular to the sun's position, so a sun tracker is needed to track the sun precisely. This research compares the electrical power used in two system dual-axis sun trackers with a tetrahedron geometry that uses an LDR sensor with a phototransistor sensor. The two sun trackers are built identically and the experimental data with the servo movement and the solar cell load are carried out side by side. The servo motor controls with Proportional Integral Derivative (PID) algorithm controls the movement of the dual-axis sun tracker. Data were obtained by recording the voltage and current received by the solar cells installed on the two sun trackers and comparing the results. The results showed that the phototransistor sensor performs better than the LDR sensor. This can be seen from the amount of power generated by the phototransistor sensor which is more than the power generated by the LDR sensor on the solar cell. The solar energy received by sun tracker uses a phototransistor sensor average 40% more than sun tracker uses an LDR sensor.

Numerical Analysis on the Optimisation of Thermal Comfort Levels in an Office Located inside a Historical Building

The present study examines the possibility of thermal comfort optimisation inside an office room where, due to historical heritage, it is possible to modify neither the energetic characteristic of the envelope nor the position of the inlet air vents. The distribution of global and local thermal comfort indices is evaluated in both heating and cooling conditions by establishing a computational fluid dynamics (CFD) model validated against experimental data. The obtained results demonstrate a striking asymmetry of the air velocity and temperature distribution due to the low energy efficiency of the building. In heating mode, the predicted mean vote (PMV) values were improved if the discharged air from the fan coil was at its maximal velocity. However, at the same time, the vertical air temperature gradient increased by around 0.5 °C in each working station. In the cooling condition, in the absence of the solar radiation, the minimal air-flow rate satisfied the acceptable range of the draught rate (DR), whereas in the presence of a solar load, it could not meet the required cooling load in all positions, leading to higher floor temperature. The findings of this study allow for identifying and rearranging the optimal position of working stations in terms of thermal comfort.

A cost‐effective DC microgrid system for resilient power supply to grid‐connected societies

AbstractLocal renewable energy sources have the potential to provide power security to societies. The main objective of this research is to improve electricity reliability in grid‐connected societies in case of the grid failure or during blackouts. Here, a cost‐effective method to utilize local irregular renewable energy sources for community applications has been proposed. Solar PV, pumping units, domestic loads, and pico hydro are the main parts of the proposed system. The storage water tank also performs the function of pump storage to deliver power during off‐sun periods. The pump storage generates electricity by using the unused potential of the water stored in the storage tank. In islanded mode, the system delivers power to the village to essential loads and fluctuating power to the dump load. The paper provides a case study in which the proposed DC microgrid has been demonstrated for a society to validate the system's performance for different practical scenarios. It has been validated for financial and technical feasibility through economic analysis and MATLAB Simulation performance respectively.

Evaporative cooling via panting and its metabolic and water balance costs for lizards in the American Southwest.

In lizards there is considerable variation in the ability to dissipate environmental/endogenous heat loads through evaporative cooling via panting, which effects how long lizards can spend exposed to high solar heat loads. We recently described the differing capacities of lizards to depress body temperature (Tb) through evaporative cooling via panting. Here, we link panting and Tb depression with rates of evaporative water loss and its metabolic costs under high heat loads. We used flow-through respirometry to measure evaporative water loss rates and metabolism of 17 lizard species from the American Southwest while simultaneously measuring Tb. We exposed lizards to air temperatures (Ta) ranging from 35°C to their critical thermal maximum (CTmax) while marking the onset of panting. We then estimated pre-panting Q10 values for metabolism to partition increases in metabolism associated with the van't Hoff effect from the mechanical cost of panting with increasing heat loads. We found that evaporative cooling costs substantially varied among species, with panting effort significantly affecting lizards' evaporative capacity. Lizard evaporation rates ranged from 0.32 to 1.5 g H2Oh-1, with individuals losing as much as 6%h-1 of body mass while panting. Lizards also experienced an increase of up to 7.9-fold in metabolic rate while panting, although the overall energetic costs of panting remained relatively low compared with evaporative water costs. Across species, there was a significant positive relationship between the overall rate of evaporative heat loss and the maximum Ta-Tb gradient a species could maintain. While evaporative cooling may be an effective mechanism for reducing Tb and extending activity in hot environments for many species, it has significant metabolic and water balance costs that should be considered, as habitats with high environmental heat loads can be especially costly to an animal's water budgets.

Forecast of area‐scale behaviours of behind‐the‐metre solar power and load based on smart‐metering net demand data

Forecast of area‐scale behaviours of behind‐the‐metre solar power and load based on smart‐metering net demand data

Type-2 fuzzy logic PID control for efficient power balance in an AC microgrid

This study suggests the Type-2 fuzzy logic proportional-integral-derivative (T2-FLPID) control strategy, optimally tuned using particle swarm optimization (PSO), for exigence control and efficient power management in an AC microgrid by efficient power sharing among various components and regulating DC link voltage under operational uncertainties. Proposed control strategy is assessed relative to Type-1 FLPID (T1-FLPID), and conventional PI controller, both also tuned with PSO, for better appreciation of the proposition. The varying operating conditions investigated here include variations in solar irradiance, wind speed, load demand, and grid isolation scenario. Results, analyzed and showcased both qualitatively and quantitatively, indicate that the proposed T2-FLPID controller excels the other two controllers in terms effective maintenance of power balance via seamless coordination of power among various components and providing the least values of error indices and computational time.

Bus Voltage Violations under Different Solar Radiation Profiles and Load Changes with Optimally Placed and Sized PV Systems

This study mainly discusses the implications of solar radiation profiles and changes in load with respect to base load conditions on the PV placement, size, voltage violations, and curtailment cost of PV generation in the network. The PV installation is optimized using yearly solar radiation profiles, low, medium, and high, corresponding to three different locations. The network in the study is represented as a multiphase, with provision for the installation of both single- and three-phase PV systems. For the different load changes in either one of the phases or all three phases, the optimal placement and size of PV inverters are discussed. It is indicated that with load increase in all three phases, for low solar radiation profiles, the placement and size of PVs remain non-uniform, while for medium and high solar radiation, the distribution becomes comparatively uniform throughout the network. However, with a load increase in one of the phases, for low solar radiation, optimal placement compensates with three-phase PV installation, while for medium/high solar, the corresponding load increase phase contributes to greater PV installation. The voltage rise is observed at both load-connected and non-load-connected buses. Such buses in the network are those that form the common junction with the branches connected to another set of buses having optimally placed PVs. The voltage violations are experienced at the feeder end buses with single-phase PV installation, not only in the phase having a connected load but also in one of the other phases.