Present-day power conversion and conditioning systems focus on transferring energy from a single type of power source into a single type of load or energy storage system (ESS). While these systems can be optimized wit...Present-day power conversion and conditioning systems focus on transferring energy from a single type of power source into a single type of load or energy storage system (ESS). While these systems can be optimized within their specific topology (e.g. MPPT for solar applications and BMS for batteries), the topologies are not easily adapted to accept a wide range of power flow operating conditions. With a hybrid approach to energy storage and power flow, a system can be designed to operate at its most advantageous point, given the operating conditions. Based on the load demand, the system can select the optimal power source and ESS. This paper will investigate the feasibility of combining two types of power sources (main utility grid and photovoltaics (PV)) along with two types of ESS (ultra-capacitors and batteries). The simulation results will show the impact of a hybrid ESS on a grid-tied residential microgrid system performance under various operating scenarios.展开更多
Due to the variability and unpredictability of solar power, which relies heavily on weather variables such as solar irradiance and temperature, precise forecasting of photovoltaic (PV) energy production is crucial for...Due to the variability and unpredictability of solar power, which relies heavily on weather variables such as solar irradiance and temperature, precise forecasting of photovoltaic (PV) energy production is crucial for effectively planning and operating power systems incorporating solar technology. Several machine learning algorithms (MLAs) have recently been developed for PV energy forecasting. This paper discusses various machine learning (ML) techniques for predicting the power output of a PV plant connected to the grid. Multiple algorithms, including linear regression (LR), neural networks (NNs), deep learning (DL), and k-nearest neighbors (k-NNs), are evaluated. The models use real-time data collected from various weather sensors and electrical output over a year, including solar irradiance, ambient temperature, wind speed, and cell temperature, to forecast PV power generation. Over a medium-term horizon, forecasting accuracy is assessed using datasets covering an entire week. The models are analyzed based on multiple performance metrics, such as absolute error (AE), root mean square error (RMSE), normalized absolute error (NAE), relative error (RE), relative root square error (RRSE), and correlation coefficient (R). The results indicate that the deep learning algorithm achieves the highest accuracy, with an RMSE of 0.026, an AE of 0.014, an NAE of 0.064, and an R of 99.7% for the weekly forecast validation. These precise forecasts produced in this research could assist grid operators in managing the variability of PV power output and planning to integrate fluctuating PV energy into the grid.展开更多
文摘Present-day power conversion and conditioning systems focus on transferring energy from a single type of power source into a single type of load or energy storage system (ESS). While these systems can be optimized within their specific topology (e.g. MPPT for solar applications and BMS for batteries), the topologies are not easily adapted to accept a wide range of power flow operating conditions. With a hybrid approach to energy storage and power flow, a system can be designed to operate at its most advantageous point, given the operating conditions. Based on the load demand, the system can select the optimal power source and ESS. This paper will investigate the feasibility of combining two types of power sources (main utility grid and photovoltaics (PV)) along with two types of ESS (ultra-capacitors and batteries). The simulation results will show the impact of a hybrid ESS on a grid-tied residential microgrid system performance under various operating scenarios.
文摘Due to the variability and unpredictability of solar power, which relies heavily on weather variables such as solar irradiance and temperature, precise forecasting of photovoltaic (PV) energy production is crucial for effectively planning and operating power systems incorporating solar technology. Several machine learning algorithms (MLAs) have recently been developed for PV energy forecasting. This paper discusses various machine learning (ML) techniques for predicting the power output of a PV plant connected to the grid. Multiple algorithms, including linear regression (LR), neural networks (NNs), deep learning (DL), and k-nearest neighbors (k-NNs), are evaluated. The models use real-time data collected from various weather sensors and electrical output over a year, including solar irradiance, ambient temperature, wind speed, and cell temperature, to forecast PV power generation. Over a medium-term horizon, forecasting accuracy is assessed using datasets covering an entire week. The models are analyzed based on multiple performance metrics, such as absolute error (AE), root mean square error (RMSE), normalized absolute error (NAE), relative error (RE), relative root square error (RRSE), and correlation coefficient (R). The results indicate that the deep learning algorithm achieves the highest accuracy, with an RMSE of 0.026, an AE of 0.014, an NAE of 0.064, and an R of 99.7% for the weekly forecast validation. These precise forecasts produced in this research could assist grid operators in managing the variability of PV power output and planning to integrate fluctuating PV energy into the grid.
