Modelling of Low Carbon Energy Systems In LEBD

Overview

  why use modelling?

different modelling approaches to modelling LCES   simple (example for LCES) detailed  quick review of modelling tools   component models (+example) systems modelling and approaches   example LCES (fuel cell + PV) pros and cons of detailed modelling for LCES

Low Carbon Energy System?

 what do we mean by a low carbon energy system 

those supply and/or demand side systems which, through their implementation, bring about a reduction in global carbon dioxide emissions

 applied to both active systems and passive systems at all scales   can apply to a simple well insulated wall to a complex hydrogen energy system in this talk we’ll concentrate on small scale active systems

Why Modelling?

 appropriate modelling yields information on the operational characteristics and impacts of LCES  supplements and expands upon results from field trials and experimentation  modelling can be used to provide the data needed to back up decisions: from policy to detailed design  design: hopefully lead to better performance and/or reduced energy consumption/emissions  strategic: or provide technical evidence for better policy formulation

Appropriate Complexity

     modelling in general can be an incredibly simple process or it can be (tediously) detailed the complexity of a model to be developed depends on:   the issues that need to be addressed available resources: time, finance, manpower, the available information and data  the skill of the modeller a simple or complex model used in inappropriate circumstances can produce misleading results ditto for a model based on poor data ditto for a model used by a modeller without the prerequisite knowledge and experience

Simple Modelling Example: DHPS

 use of a simple model to address a strategic issue  will new DHPS bring about tangible carbon savings?

 modelling elements:  simple model of electricity supply make-up  demand profiles hot water, space heating and for water for characteristic buildings  simple spreadsheet models of DCHP components and control

Simple Modelling Example:

supply mix

DHPS

electricity carbon coefficient heating demand profile – hourly, 1 year hot water demand profile – hourly, 1 year electricity demand profile – hourly, 1 year simple efficiency based models of Boiler, SOFC ICE CHP Stirling-CHP ASHP fuel data

annual CO 2 emissions

Limitations

         assumed operational efficiencies limited interaction between supply and demand no thermal/electrical storage ideal controller SOFC standby losses not accounted for time averaged heat, hot water and electrical profiles constant carbon coefficient for electricity etc, etc need to take all of this into account when analysing results … however info is useful in making broad strategic decisions – e.g. deciding in which technologies to invest R&D time

Domain Specific Simulation Environment Example - CFD

CFD Modelling



In CFD the real world is made into a discrete solution space



solution space is defined by a ‘grid’



properties of one or more fluids are calculated as they flow through the grid – Eulerian solution



solution dependent upon boundary conditions



effectively a CFD solution is the extrapolation of the boundary conditions to the interior of the grid



generally imposing steady state solution on transient phenomena!

CFD Modelling

 Where can CFD be deployed in the design process …  external flows (air flows around buildings):  wind loadings on external surfaces  contaminant dispersal from flue stacks  ventilation opening placement;  pedestrian comfort

External Flows

External Flows

External Flows

CFD Modelling



Internal flows flows (air flows inside buildings):



natural and mechanical ventilation system design;



local comfort assessment;



contaminant distribution;



heating cooling system design;



component design*.

Internal Flows

Internal Flows

Internal Flows

CFD Modelling



To achieve the types of solutions shown we need to solve a set of equations for each grid ‘cell’ …

CFD Modelling

CFD Modelling



Previous equations hold for non-turbulent flow



The influence of turbulence further complicates matters!



Need to add a turbulence model

K-e Model

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Most common example is the k epsilon model



Effect of turbulence is “represented” rather than explicitly modelled



Two extra equations need to be solved …

CFD Modelling

Challenges for Effective Use

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CFD tools were not developed for flow conditions found in the built environment!

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k-e model developed for high Re flow



buildings generally have low Re (partially turbulent ) flows

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lots of buoyancy effects

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lots of fluid/surface interactions

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need to properly define boundary conditions

Challenges for Effective Use



Close to wall surfaces viscous effects dominate and flow becomes less turbulent

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explicit modelling of boundary layer prohibitively computationally expensive

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approximation of boundary layer is usually used (log-law wall function)

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not really well suited for low Re applications

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other correlations available (low-Re, with buoyancy)

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other boundary treatments are available e.g. Robin boundary condition

k dT dy



h

(

T w



T

 )

Challenges for Effective Use



Boundary conditions need to be accurately defined



usually “prescribed” e.g.



wall temperatures



ventilation inlet flow (velocity turbulence levels)

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wind speed, direction turbulence and profiles

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accuracy of solution dependent upon those prescribed conditions



can use other tools to determine boundary conditions (e.g. building simulation for wall temperatures and ventilation inlet details)

Challenges for Effective Use



Quality of the grid is also very important



ideally a solution should be “grid independent”



difficult to achieve in practice due to time constraints!

Detailed Modelling

 previous example described impact of LCES without modelling operational performance of the LCES system in detail: complexity was hidden behind an average operational efficiency  detailed modelling is appropriate when specific issues associated with the LCES performance are being addressed:  impact of thermal storage  power quality  impact of different control strategies  different systems configurations  output from detailed models can feed simpler models (i.e. derive seasonal efficiency for components)

Modelling Tools

 there are many options for detailed modelling and can be applied to many ‘domains’   domain specific physical simulation [1] FLUENT, PHOENICS, WAMIT …. ‘customised’ simulation environment [2] - ESP r, TRNSYS …  general purpose modelling environments [3] - MATLAB (SIMULINK), EES, FEMLAB, SPREADSHEET  try and get over the basic elements behind 2&3 when applied to systems simulation

Customised Simulation Environment Example – Low Carbon Energy Systems Modelling

Components

   components are the fundamental building blocks of all detailed energy modelling applications basically a component is a self contained mathematical model of a physical process:  energy conversion  transport of working fluid       pressurization heating or cooling phase change control device data recording etc, etc.

can either be used individually or connected together in a systems model (often called a network)

Example: DWT

  model of small building integrated wind turbine “stand alone” or can be used in a network  uses climate and building-related geometrical information to calculate electrical power output  basis:

W T

max  3

C v

3 

A

 3 2

U

3  +ve pressure PV spoiler -ve pressure rotor casing generator

Example: DWT

Power Output by Orientation

300 200 100 0 800 700 600 500 400 South West East

Orientation

North averaged 5% turbulence 10% turbulence 20% turbulence 30% turbulence 10000 1000 100 10 1 0

Power Output Frequency of Occurrence

500 1000 1500

Total Power Output (W)

2000 averaged 5% turbulence 10% turbulence 20% turbulence 30% turbulence

LCES Components

    a word of warning …. LCES is a (relatively) new field the emergence of publicly available robust components lags behind the evolution of the technology  real lack of models for some newer technologies:   fuel cells, ICE CHP, Stirling Engine CHP (IEA annex42) demand side controllers reasonable coverage of models:   PV, Solar thermal, battery storage, power conditioning demand side reduction/management (e.g. lighting control)

Systems Models

     systems are modelled by linking together a group of component models – network LCES model mixture of ‘sexy’ low carbon component models (e.g. hydrogen electrolyser) and mundane BOP – pumps, fans, pipes, etc. results in a set of consistent or mixed equations describing the LCES lots of solution options    sequential simultaneous mixed (pragmatic!)

objective of solution: determine system performance in user defined sets of circumstances

Systems Models

    systems sometimes describe a particular physical ‘domain’ (ESP-r):   electrical system fluid flow specific domain models can be linked together to form an integrated model (ESP-r) sometimes one system model can be used to describe a multi domain system (TRNSYS)   HVAC integrated hydrogen system above philosophies require different solution approaches

Sequential Solution

 solution is achieved by sequentially solving each component model  output of one model is input to the next  good for systems featuring very different model types – ability to mix and match different models  problems with feedback of variables (requires iteration), solution control, stability  can model systems with mixed inputs/outputs thermal/electrical/control signals

Sequential Solution

Comp 1 Comp 2 Comp. 4 comp. 5 Comp 6

Simultaneous Solution

  similar modelling approach for each component simultaneous (matrix) solution of system of equations  stable solution mechanism no problems in dealing with feedback of variables  less flexibility in describing and modelling specific components – models need to be specifically developed for simultaneous solution  systems model usually describes one type of system (e.g. flow, electrical) but possible to combine systems models – integrated systems model

Simultaneous Solution

general equation form for component control volume (energy balance)

Systems Modelling

  ESP-r   customised simulation environment lots of ‘domains’ employing same basic modelling approach – finite volume flux balance    systems (fluid flows, plant, electrical), building fabric, moisture all physical elements of model can be described using FVs simultaneous solution of individual domains boundary conditions for solution from:     control criteria climate occupant interaction demand schedules

O 2 H 2 O

Example: Fuel Cell CHP

warm air to building hot water storage

Sample Output

Further Additions

 what about the impact on building environment?

 we can couple systems model to building model  what about adding some other heat power sources?

 can change model and add more components e.g. PV  what about electrical power output?

 we can add more detail (e.g. electrical systems model)

Building Model

warm air to building hot water storage

PV Model

 fully integrated model (uses building model to provide boundary conditions)  multi-domain  building-integrated   electrical component flow (ventilated façade)  basis:

Electrical Systems Model

hall lights other lights and SPL grid fuel cells pumps PV fans

More Output …

Multi-domain Solution Approach

 previous model include multiple domains describing the LCES  plant (+flow)  electrical  ESP-r employs mixed solution approach for domain coupling  simultaneous solution of each domain  passing linking variables between domains (e.g. flow rates, electrical outputs)  sequential solution of set of domains with iteration

Detailed Modelling of LCES Some Pros and Cons

  Pros:     very rich quantity of data available to the modeller detailed performance information available better representation of ‘real’ performance information available to answer very specific design questions (storage tank volume, best control algorithm) Cons:  sheer quantity and diffuse nature of output (really need to know what you are looking for)  data input and knowledge burden on user (data input increases exponentially with complexity and the more detailed the model the more problem-specific knowledge required)  increased scope for error!

 time penalty to gather data and develop models

Summary

        important to select appropriate modelling level for the task in hand – diving into a detailed model is not always a good idea!

detailed modelling useful for answering very specific design questions often the ONLY way to answer these questions used correctly can improve system performance; reduce energy consumption; used incorrectly … lots of detailed modelling tools and approaches available not necessarily geared up to modelling of LCES!!

need to weigh advantage of improved results resolution from detailed model against increased burden of data and knowledge on user!

FINALLY important to recognise limitations of model in interpretation of results ….