Management and Decision Support
The efficient use of resources requires the perfect knowledge about systems operating modes. Because of that the usage of energy in water systems should be under constant management. The management should take into account that current actions can influence the behaviour of the energy and water systems in the future. Decision making in real energy-water systems is complex, principally due to the inherent existence of trade-offs between economic, environmental and social factors. Energy and water management models and monitoring tools, described as Modelling and Decision Support Systems (MDSS) are instrumental in supporting decision making in a generic way.
- Optimize energy production/storage with water demand
Alternative solutions for producing electrical energy can be photovoltaic sources, wind, or hydroelectric. The renewable energy sources are particularly suited to the production of micro grid electricity in sunny areas and/or windy. They are then often combined with energy storage systems in the event of excess production, or to compensate for the temporary power shortage during peak consumption. The objective of this system is to ensure energy autonomy while at remaining economically and environmentally sustainable. However, photovoltaic panels and wind turbines have a significant cost and energy intensive to manufacture, thus generating an ecological debt to compensate for their life. So there's interest to maximize their production to make them best profitable.
– Energy management in water systems
The energy management strategy for an integrated system as the one describe is a concerns. Developing controls to govern the converters of the different modules, depending on the goals of maximizing power output and efficient allocation of it between storage modules adapted to application requirements while ensuring the stability of the DC bus. This is an optimal multi-objective control problem and a research challenge. Developing controls to govern the converters of the different modules, depending on the goals of maximizing power output and efficient allocation of it between storage modules adapted to application requirements while ensuring the stability of the DC bus. This is an optimal multi-objective control problem and a research challenge.
– Energy and water resources demand forecast
The Task will design models to support demand forecasting for different energy and water use sectors across different spatial and temporal scales. The selection of a demand forecasting method is a function of three main criteria: (i) planning objectives, (ii) data requirements, and (iii) the availability of resources for the data collection, model setup, development, and calibration. Depending on the specific goals and the required forecasting timeframes, different methods will be tested and the most suitable will be adopted, calibrated and validated. Methods to be assessed include statistical trend analysis models, regression models, unit-use/end-use models and more advanced pattern recognition approaches like the “similar days” model.
– Decision support system
This task will develop a decision support system (DSS) for the optimum design and operation of systems integrating water and energy resources. The assessment, management and optimization of such systems will be based on a holistic approach, covering not only the energy-water dependencies (as modelled in the previous Tasks) but also the ecological and socio-economic aspects. For the sustainability assessment, appropriate indicators will be applied and multi-criteria evaluation methods will be used to account for the combined effects of all criteria under consideration.