Towards pre-dimensioning of natural gas networks on a web-platform

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.

The MEU web platform: a tool dedicated to urban energy management

Massimiliano Capezzali, Gaëtan Cherix, Mathias Franz Pernet, Pablo Puerto

The MEU GIS-enabled web-platform [1] has been developed in close collaboration with four Swiss cities. The tool enables detailed monitoring and planning for both energy demand and supply at individual building, neighborhood and whole city scale (http://meu.epfl.ch). This web-platform acts like an interface between different tools and allows to establish detailed energy balances for entire cities comprising several thousand buildings. In its present configuration, the MEU tool does not allow yet to simulate energy networks behaviors, based on the real or projected energy demand in an urban zone. In order to meet this need from energy utilities partners, a specific data model, as well as an user-interface giving access to networks attributes and edition/simulation tools were developed, which will be then functionally integrated in the MEU platform. The idea is to create a “Natural Gas Networks” module built for energy utilities. The objectives of this project within the larger MEU endeavor were the following: 1) Create a platform gathering topological and geo-referenced data; 2) Develop a gas network pre-design/planning methodology including demand characteristics and gas supply for buildings in a selected area; 3) Interface with gas distribution system operators existing tools and add new functionalities within a single platform; 4) Include natural gas distribution system operator constraints and operational realities in the pre-design/planning process. In order to achieve those objectives, two tools and several visualization concepts have been created, along with an ad hoc data model: (i) a data model able to allow data import, storage and centralization from energy utilities databases: networks, buildings demands and specifications, as well as interface between edition, simulation and visualization tools; (ii) a network edition tool prototype (LEAFLET JavaScript based web page), which allows to display a network on a map, to add/delete or drag&drop pipes, nodes, consumption and biogas production/injection points and pressure let down stations; (iii) a network flows, and pressures simulation device (MATLAB® compressible fluids model) which computes the network behavior for each hour (pressures, flows, power equivalent and temperatures in each point); (iv) a detailed mock-up for visualization and display concept with interactive and GIS data: buildings area, networks paths, pipes characteristics, results from simulation, studied area energy balance, etc. This paper focuses more specifically on the visualization and network edition tool, as well as simulation results interactive representation on the MEU platform.

LESO-PB, EPFL2015

District Heating in the City of Tokyo: A Case Study using a new Integrated Simulation Environment.

Meinrad Bürer, Daniel Favrat, Hongtao Li

This paper aims to illustrate a new holistic modeling environment through a case study implementing a district heating system to supply four groups of users identified in the city of Tokyo. The holistic modeling environment, called DOME (Distributed Object-based Modeling Environment) is being applied by researchers in the Alliance for Global Sustainability (AGS) to develop a platform for assessing technologies and policies with the intent to reduce significantly CO2 emissions in large cities. DOME is used as an information based collaboration platform for supporting the decentralized integration of models built independently under various application tools and operating systems. It provides system engineers with the ability to access experts’ models over the Internet through user-friendly interfaces. The interfaces provide clearly identified input and output variables to support a facilitated integration. The DOME environment also provides tools for analyzing the resultant integrated systems. In this district heating simulation, a GIS (geographic information system) based land-use database manager, implemented as an object in DOME, is used to obtain the total ground area, the total building floor area, and the number and type of buildings in a specified region of Tokyo. Using the land use database manager, four groups of users are established. The heat demands of the users are derived using specific heat consumption data for the types of buildings considered. Users are either connected to the district heating system through direct heat exchangers on the supply or return line, or through local heat pumps which adapt the temperature to the needs locally, allowing a lower network temperature. Two different options are considered for the district heat supply: 1) a heat pump based superstructure comprising a gas turbine and a gas engine as cogeneration units, as well as an auxiliary boiler for peak load, and 2) a gas turbine / steam turbine combined cycle based superstructure with various option like steam injection and/or CO2 separation unit. Physical and costing models for the two alternative production unit superstructures and for the district heating system are connected with the GIS database using DOME. The environomic methodology, a simultaneous consideration of thermodynamic, economic and environmental criteria expressed as an aggregated cost, is used to assess the alternatives under different scenarios essentially characterized by the cost of natural gas, and the degree of polluion cost internalization.

2001

Kintetic modelling of natural gas auto-ignition under engine-like conditions

Daniel Favrat, Stefan Heyne, Jan-Bernhard Vos

The modelling of natural gas combustion with detailed as well as reduced kinetic mechanisms has been subject to many publications already. Most of the mechanisms are validated at higher temperatures and ambient pressures. Only recently - in parallel to a gain of interest in the homogeneous charge compression ignition (HCCI) engine concept - there are more efforts trying to reproduce the low temperature chemistry, and in particular autoignition of natural gas under engine-like conditions. This work is part of a project on stationary natural gas engines equipped with unscavanged prechambers. It has been demonstrated that it is possible to reduce the emissions below the Swiss limits both with natural gas and biogas with the spark ignition prechamber, while keeping high efficiencies of around 38 %. We are now aiming at converting the prechamber from spark ignition to autoignition leading to longer maintenance intervals in the case of stationary natural gas engines and to a probable cleaner combustion - HCCI like combustion inside the prechamber and faster combustion in the main chamber due to an earlier arrival of the flame front - compared to prechamber spark ignition. In order to be able to model the fluid dynamics and the chemistry inside the cylinder, a good chemical model is one of the most important aspects. To obtain an appropriate mechanism - by validating and/or optimising existing mechanisms - experiments have been conducted in the rapid compression machine facility at the University of Lille with different mixtures of methane, ethane and propane. The relative air-to-fuel ratio λ varied from 1 to 1.6, the temperature and pressure at the end of compression varied from 850 to 925 K and 13 to 21 bar respectively. The compression ratio was constantly kept at 9.2 for all experiments and the final temperature and pressure were defined by varying the charge and the composition of the inert gases (Ar/N2). The compression time was between 50 and 60 ms for all experiments. For modelling the ignition delay a zero-dimensional model assuming homogeneity was used, based on the measured pressure and calculated core temperature at the end of compression of each experiment. The chemical solver package used for the simulations is CHEMKIN. Different mechanisms (PRF, GRI3.0, Konnov, …) have been tested. Most of the mechanisms are mainly validated at higher temperature ranges and therefore their applicability to ignition delay prediction in our conditions is questionable. Several attempts to adjust the GRI mechanism (version 1.2) to the lower temperature regime exist, whereof we chose the UBCMech and Petersen mechanism (GRI+RAMEC) to test on our simulations. Both mechanisms initially were developed based on pure methane mixtures while the UBCMech recently was extended to model natural gas as well. The experimentally obtained ignition delays vary from 40 ms at high pressures and temperatures to more than 300 ms for the lower regime of the experimental space. For the ignition delays up to 60 ms the best fit was obtained by a reduced version of PRF accounting only for the C1 to C3 compounds. Konnov’s mechanism and UBCMech are under predicting the experimental ignition delays while the GRI3.0 mechanism (with and without RAMEC sub module) over predicts the ignition delay for the natural gas mixtures. It has to be mentioned that the GRI3.0+RAMEC mechanism as well as the initial mechanism of Petersen (GRI1.2+RAMEC) on the other hand under predict the experimental ignition delay for pure methane mixtures. This indicates that the low temperature chemistry for methane that has been added in the RAMEC sub module is enhancing ignition too strong. Replacing part of methane in pure methane mixtures by equivalent amounts of ethane and propane - know as ignition promoters - lead to longer ignition delays in the simulations. But as the GRI3.0+RAMEC mechanism (without nitrogen chemistry) is the smallest of all tested mechanisms considering number of species and reactions, it still is an interesting option for coupling it to a computational fluid dynamics code as we are planning to do. We though performed a sensitivity analysis to obtain a set of the most dominating reactions and used a multi-objective optimisation algorithm to optimize the reaction rates within their limits of uncertainty. The results show a good fit for the short ignition delays of natural gas mixtures but still an under prediction of the methane delays. For the longer ignition delays the assumption of a constant core temperature during the post compression phase becomes questionable and the under prediction of all mechanisms of the experimental delays is an obvious consequence. A heat loss model predicting the temperature evolution has to be implemented in the simulations to properly simulate the ignition delays.

2006