Short-term PV energy yield predictions within city neighborhoods for optimum grid management
Journal
Energy and Buildings
Date Issued
September 14, 2024
DOI
/10.1016/j.enbuild.2024.114773
Abstract
The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the
electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially
cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the
improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green
power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with
information needed for efficient capacity management and scheduling and ensure grid stability.
Most forecasting models provide promising results in terms of error performance; however, they need weather
variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current
work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatiotemporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used
extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the
effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model
performance. The main objective was to provide the solar PV energy sector with important information for
building a smart grid for cities or regions.
Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485
km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results
showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual
power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in
each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results
that can be compared with existing results from similar studies. Overall, the proposed method can provide
important information that can assist in decision-making for the management, optimization, and visualization of
grids across cities.
electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially
cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the
improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green
power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with
information needed for efficient capacity management and scheduling and ensure grid stability.
Most forecasting models provide promising results in terms of error performance; however, they need weather
variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current
work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatiotemporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used
extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the
effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model
performance. The main objective was to provide the solar PV energy sector with important information for
building a smart grid for cities or regions.
Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485
km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results
showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual
power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in
each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results
that can be compared with existing results from similar studies. Overall, the proposed method can provide
important information that can assist in decision-making for the management, optimization, and visualization of
grids across cities.