Development of a day-ahead solar power forecasting model chain for a 250 MW PV park in India

Due to the steep rise in grid-connected solar Photovoltaic (PV) capacity and the intermittent nature of solar generation, accurate forecasts are becoming ever more essential for the secure and economic day-ahead scheduling of PV systems. The inherent uncertainty in Numerical Weather Prediction (NWP) forecasts and the limited availability of measured datasets for PV system modeling impacts the achievable day-ahead solar PV power forecast accuracy in regions like India. In this study, an operational day-ahead PV power forecast model chain is developed for a 250 MWp solar PV park located in Southern India using NWP-predicted Global Horizontal Irradiance (GHI) from the European Centre of Medium Range Weather Forecasts (ECMWF) and National Centre for Medium Range Weather Forecasting (NCMRWF) models. The performance of the Lorenz polynomial and a Neural Network (NN)-based bias correction method are benchmarked on a sliding window basis against ground-measured GHI for ten months. The usefulness of GHI transposition, even with uncertain monthly tilt values, is analyzed by comparing the Global Tilted Irradiance (GTI) and GHI forecasts with measured GTI for four months. A simple technique for back-calculating the virtual DC power is developed using the available aggregated AC power measurements and the inverter efficiency curve from a nearby plant with a similar rated inverter capacity. The AC power forecasts are validated against aggregated AC power measurements for six months. The ECMWF derived forecast outperforms the reference convex combination of climatology and persistence. The linear combination of ECMWF and NCMRWF derived AC forecasts showed the best result

Solmap: Project In India's Solar Resource Assessment

India launched Jawaharlal Nehru National Solar Mission in 2009, which aims to set up 20 000 MW of grid connected solar power, besides 2 000 MW equivalent of off-grid applications and cumulative growth of solar thermal collector area to 20 million m2 by 2022. Availability of reliable and accurate solar radiation data is crucial to achieve the targets. As a result of this initiative, Ministry of New and Renewable Energy (MNRE) of Government of India (GoI) has awarded a project to Centre for Wind Energy Technology (C-WET), Chennai in the year 2011 to set up 51 Solar Radiation Resource Assessment (SRRA) stations using the state-of-the-art equipment in various parts of the country, especially the sites with high potential for solar power. The GoI project has synergy with SolMap project, which is implemented by the Deutsche GesellschaftfürInternationaleZusammenarbeit (GIZ) in cooperation with the MNRE. SolMap project is contributing to SRRA project in establishing quality checks on the data obtained as per International protocols and helping data processing to generate investment grade data. The paper highlights the details of SRRA stations and an attempt has been made to present some of the important results of quality control and data analysis with respect to GHI and DNI. While our analysis of the data over one year finds that intensity and profile of the insolation are not uniform across the geographic regions, the variability in DNI is particularly high. Strong influence of monsoon is also identified. SRRA infrastructure aims to develop investment grade solar radiation resource information to assist project activities under the National Solar Mission of India.</p

Short-term PV power forecasting in India: recent developments and policy analysis

With ambitious renewable energy capacity addition targets, there is an ongoing transformation in the Indian power system. This paper discusses the various applications of variable generation forecast, state-of-the-art solar PV generation forecasting methods, latest developments in generation forecasting regulations and infrastructure, and the new challenges introduced by VRE generation. Day-ahead NWP-based GHI forecasting are validated against ground measurements from single and multiple sites in India. Recommendations for improving overall the forecasting infrastructure in India are presented