Smart operations for reducing energy costs.
Smart energy management is an important enabler of energy efficiency, especially because it unlocks large savings with small investments, making it a very attractive investment opportunity. Using technologies such as optimization algorithms, machine learning and artificial intelligence it is possible to define control and scheduling strategies, that will minimize the costs while ensuring smooth operations. AI Optimizer is an innovative scheduling system for water sector operations which is being developed and demonstrated in 3 Portuguese Water Systems (WSS), under real conditions with promising results (20% energy cost reduction).
Water Supply Systems (WSS) are large-scale systems that extract, treat and transport water over vast geographical areas to consumers. These systems are complex because that are network composed by pumping stations, storage tanks, water pipes and delivery points and they consume energy intensively. These systems are crucial for our everyday life and a safe and efficient operation of these systems is essential.
Current control systems are designed to deliver water when needed, without any efficient control. Water networks operation and management still relies on the utilities accumulated experience, which made arise a question “if the water supply systems are most of the times inefficient, why aren’t utilities already implementing advanced approaches?”.
AI Optimizer goal is to maintain the operation requirements of the water systems while reducing energy costs of these systems through a detailed planning based on day ahead consumption predictions and taking advantage of the existing water storage tanks capacity and the different energy tariff periods.
In order to achieve this objective, different software modules are working together to create a holistic scheduling strategy for the complete water system and for the operation of pumping stations and the respective pumps. Namely, there is a predictive module that forecast the water consumption profile of the next 24 hours and hydraulic simulation module which is a digital twin of the real system. Based on the demand profiles, energy tariff profiles and the system constrain, AI Optimizer is able to identify the optimal scheduling and operational strategy to deliver requested water at the lowest cost.
About João P. Ribau, Head of the Intelligent and Digital Systems R&D:
João P. Ribau is the Head of the Intelligent and Digital Systems R&D unit of ISQ, and a Scientific Researcher. Received his PhD degree in Mechanical Engineering from IST, in 2014, with work in life cycle analysis and optimization of alternative road vehicles. He participated in several international projects (e.g. H2020, LIFE, MIT-Portugal) and is the author of several publications in scientific journals, book chapters, and conference communications, in the areas of optimization, decision, and machine learning, applied to energy systems, industrial systems, industrial symbiosis, and circular economy.
Company Description ISQ Group is a private, independent group, whose head office is in Portugal. ISQ’s mission is to provide scientific and technological support to industry and services by fostering ongoing improvement, innovation and sustainability, with an international presence and vocation.
ISQ´s support is composed by technical consultancy services, inspection, testing and training in over 20 countries in 4 continents, operating in all economic sectors, especially in energy, process and manufacturing industries, aeronautics and space, automotive, infrastructures and health. These services are enhanced by research and development activities and 16 internal accredited laboratories.