Massimiliano Capezzali, Gaëtan Cherix, Mathias Franz Pernet, Pablo Puerto
The MEU GIS-enabled web-platform has been developed in close collaboration with four Swiss cities: it enables detailed monitoring and planning for both energy demand and supply at individual building and neighborhood level (http://meu.epfl.ch). This web-platform gives access to entire cities comprising several thousands of buildings. In its actual configuration, this tool does not allow to pre-design networks depending on the energy demand of existing or futures infrastructures. Indeed, in MEU, gas networks or districts heating networks are handled in a simple steady-state way. This would in particular mean that, in scenarios, buildings can be arbitrary connected to energy networks without any territorial, power or technologic compatibility verification. Data about the amount of energy supplied by the gas or heat network in different parts of the studied cities are available but some data like the maximum power available, pipe’s diameters, parameters of pressure or heat loss and levels of temperatures, speed or flow rate are not known or reachable. In another hand, data about cooling and heating needs for a building are already known or estimated for each hour of the year by some modules of the platform. The precision already exist and we will use it to simulate the behavior of a gas network, and calculate all the flow rates, injections and consumptions for an urban zone. The idea is to create a new module allowing to test different pre-designed scenarios, to simulate the behavior of each scenario in order to calculate and display dynamics data for the considered network. We are targeting to create new functionalities for city energy manager in order to help them to design new network and expand present network or for system diagnostic purpose. Our aim is to allow users to create new buildings, add or delete nodes and pipes, modify installed heating power for an existing building and add feed-in of biogas. The resulting new network will be then tested and its behavior simulated to see if the means of production matches the demand and if the pressure is adequate in reducing stations to pipe the gas to consumers. The network tool will be useful to determine if a new district can be connected to the existent network or if the actual network need to be densified. It will also help to estimate the effect of a missing pipe due to road maintenance. The first step for the creation of those new functionalities consists in developing an operational module prototype for gas network management. The first phase of a test for a new scenario is the creation of the new state for the considered network. The user will be able to create his gas network based on the actual installation, pipes and structures. Then come the simulation phase. The user can choose the step, the duration and the period of the simulation. The simulation is based on a perfect and compressible gas model. The composition of the gas is considered as pure methane. The model include linear pressure loss equation and is built on a conservation of the molar flow rate through each node. Heating power are calculated for each building using a MEU module building physics software which simulates the energy demand by correlating real annual data from utilities and hourly simulated data. Feed-in gas flow rates are so determined. The studied system is limited to medium pressure (< 5bar) and low pressure networks (< 0.050 bar). Distribution pipelines between 40 bar and 50 bar are not include in the model. The simulation calculates pressure, molar flow rate and density of gas for every node of the network. The resolution of the equations system for each node of the network is made by a MATLAB® function (fsolve) using the Levenberg-Marquardt algorithm. The raw results of the simulation are then exploited and converted to graphics. The user can choose nodes and the associate variable (pressure, speed, flow rate, etc…) that catch his attention and require to be added to graphics. We are currently testing the model using a comparison between the simulation of our model results and the results of a NEPLAN® simulation for the same neighborhood.
2015
