Load and Renewable Energy Forecasting for System Modelling, an Effort in Reducing Renewable Energy Curtailment

  • Dipam ChaudhariEmail author
  • Chaitanya Gosavi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 165)


Every day, humanity is achieving a big feat in the field of technology, and in doing so the most fundamental thing that comes to them as aid, is energy. With the invention of generators, fossil fuels have been integrated for the generation of this energy. But this process gave breeding ground for the carbon footprints. Hence, the traditional means of energy sources like wind, solar, hydro, etc. were integrated extensively all around the world to curb this deterioration of the environment. These sources have indeed helped in mitigating the problem of increasing carbon footprint. But even these Renewable Energy (RE) sources cannot be considered as the impeccable source of energy, as even these sources have got some issues related to the sustainability and the usability. The problem of curtailment of these variable sources of energy has been there since the rise in penetration of these sources in the world market. Due to uncertainty in the generation of power from renewables, various problems related to grid integration appear. Hence to maintain the real time balance between load and generation, some of the RE is wasted which is called as the RE curtailment. This paper, hence majorly focuses on reducing such power losses due to this imbalance in the power management system. At first, the literature survey was conducted on the subject, to get aware of the problem and its severity, and various system strategies earlier used to solve it. In this paper, the conception of day-ahead RE power supply and day-ahead Demand forecasting to ensure the day-ahead planning of the power demand–supply management was integrated. These forecasting models were designed using the concepts of Artificial Neural Network. These forecasted entities were used to schedule a day-ahead power demand and supply strategies. In case of system imbalance, i.e. during excess supply or excess demand the utilities can be maintained using day-ahead power transactions with electricity market. Practically, these forecasting methods may also have some modelling errors, which may affect the accuracy in the balance of the system. To mitigate this problem of system inaccuracy the concept of two-settlement strategy to balance the day-ahead market and the real time market was used.


Renewable energy curtailment Load forecasting Renewable energy forecasting Artificial neural network Grid balancing 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Government College of EngineeringAurangabadIndia
  2. 2.Technical University of BerlinBerlinGermany

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