optimal energy management of water-energy nexus in multi-carrier systems integrated with renewable sources

This paper proposes a plenary structure of an energy hub system to combine electrical, thermal, cooling, and water infrastructures to promote modern distribution systems and increase flexibility and efficiency. the lstm neural network predicts electrical loads with controlling variables such as air temperature, wind speed, humidity, holidays, and weekends. it is assumed that the system operator likes to use groundwater sources better. however, climate change, land subsidence, droughts, destruction of glaciers and lakes, dust storms, and inadequate water and food sources for future generations are the main challenges of unsustainable use of groundwater sources. a multi-objective optimization is proposed to simultaneously keep down the total operating costs and use of groundwater sources in the system. renewable energy resources, electric vehicles, responsive electrical and thermal load programs, and energy storage systems have been developed to increase flexibility in the proposed system and are tried out in a standard case study. the simulation results show that the proposed approach reduces groundwater extraction by 26.88%.