FREMONT, CA: As businesses flourish, their need for latest, agile, and cost-effective energy trading risk management (ETRM) solutions increases as well. With the digitization of processes by the forward-looking companies, leveraging from automation the manufacturing is in full swing compared to those who are still functionally manually.
Changes in technology have spread across areas like AI, ML, and Robotic Process Automation (RPA) along with digitization. Chatbots can be deployed round the clock to increase the levels of productivity, in the same way as robots that have ensured quality in manufacturing. However, before any of this can be implemented, digitization is a must, not just in the office but in the entire enterprise.
The legacy ETRM systems are usually adopted for automated front-office processes to handle entry and risk management. This is why the system falls short, as it cannot capture and configure contracts ready for settlement. Just by following digitization of ETRM, numerous cost savings and efficiency pros in the processes can be recognized. The automation of digitized processes is the next step in the framework for ETRM. Once established, most businesses can take to following levels by updating to AI and machine learning to separate the businesses away from manual work.
The energy trading market has been executing the promise of automating iterative tasks and has been contributing to creative insights. The next-gen ETRM approach is augmented with AI and thereby embedding intelligence in a variety of functional applications on a routine basis.
As an example, gas flow optimization is carried out in a linear programming simplex method that can be utilized to enable users the option of scheduling the gas flow from a receiving point to a delivery point. The supply of gas can be scheduled manually, and also by optimized flow dependent on the constraints such as location, pipelines, contracts, supply, demand side, and delivery paths. Flow optimization can automate complicated and intricate equity gas nominations with the optimization of gas as well, and the demand can be met. The maximum daily quantity (MDQ) and end positions are subjected to vary depending on the supply and order from the optimizer’s front. The scheduling or the optimization process creates nomination records that should be submitted to the pipeline.