Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.
Estimation of solar energy reaching the earth’s surface is essential for solar potential assessment. Solar radiation data based on satellites provide higher spatial and temporal coverage of regions compared to surface based measurements. Solar potential of the Indian hill state of Himachal Pradesh has been assessed using reliable satellite based global horizontal insolation (GHI) datasets validated based on its complex terrain. Solar maps representing regional and temporal resource availability in the state have been generated using geographical information systems (GIS). Spatial analyses show that the state receives annual average GHI above 4.5 kWh/m2/day and a total of 99530395 million kWh (or million units, MU). The regional availability of GHI in Himachal Pradesh is influenced by its eclectic topography, seasons as well as microclimate. The lower and middle elevation zone (<3500 m) with tropical to wet-temperate climate receives higher GHI (>5 kWh/m2/day) for a major part of the year compared to the higher elevation zone (>3500 m) with dry-temperate to alpine climate (4–4.5 kWh/m2/day). Results show that Himachal Pradesh receives an average insolation of 5.86 ± 1.02–5.99 ± 0.91 kWh/m2/day in the warm summer months; 5.69 ± 0.65–5.89 ± 0.65 kWh/m2/day in the wet monsoon months; 3.73 ± 0.91–3.94 ± 0.78 kWh/m2/day in the colder winter months.
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