Presented by Stéphane Velut
Session 6-6 Renewable Grid Connectivity | Wednesday, July 17th
Abstract: Environmental considerations and increasing awareness of infrastructure sensitivity have led to reconsiderations of how energy systems should be best configured. The highly centralized systems that were developed for large production units are not suitable for renewable, intermittent and distributed energy sources. This motivates the use of micro-grids, which are specifically developed for heterogeneous and local energy production. The challenge of micro-grid design and operation are now attracting considerable research interest. There now exists a wide range of tools and methods to simulate micro-grids and optimize their design and operation. The scope of the available tools is however often limited to a specific problem like system configuration, component sizing or grid operation. Due to the large variety of analyses that should be conducted for the design and the operation of a micro-grid, there is a need for a flexible framework that can consistently adapt the fidelity level of the model to the targeted analysis and to the people that will run it. A control engineer, a sales representative and electrical engineer might want to run different analyses on the same grid but using models of different scope, time-scale, parameterization and accuracy. This should be done in a way that ensures consistency of all produced data and results. In this talk, we will present a flexible platform for the design and operation of micro-grids. It is based on the open standard Modelica, which is a modelling language that allows the representation of multi-domain and complex systems in a very flexible way. One strength of Modelica is its object-oriented nature that makes it possible to represent a reconfigurable system by first defining a common architecture to all variants and then all sub-systems as replaceable components. This lets the end-user the possibility to easily configure a complex system by simply choosing the fidelity level of the components that is most adapted to the targeted analysis. The presented framework will be illustrated using three use cases. Gradient-based dynamic optimization is applied to solve for both optimal design and optimal operation. This is made possible by a high-level and flexible problem formulation that lets the user define the objective function and the optimization constraints using any model variables. The approach is illustrated on 3 use cases: 1) battery sizing formulated as a peak shaving problem, 2) economic dispatch of typical grid including photovoltaics, diesel engine and battery 3) optimal operation of a grid that also includes thermal components such as chillers and thermal storage systems.
