1-s2.0-S135943111300673X-main

8/18/2019 1-s2.0-S135943111300673X-main

1/12

Simulation of energy use in buildings with multiple micro generators

S. Karmacharya a,*, G. Putrus a, C.P. Underwood a, K. Mahkamov a, S. McDonald b,A. Alexakis a

a Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE18ST, UK b National Renewable Energy Centre (NAREC), Blyth, Northumberland, UK

h i g h l i g h t s

Dynamic modelling of a building along with its space heating and hot water systems. Dynamic modelling of mCHP including its start-up and shut down characteristics.

Integration of micro generations with energy demands in a dwelling in real time.

Fuel utilisation and energy ef ciency in a dwelling is analysed in real scenario.

Overall ef ciency of a mCHP is largely inuenced by the number of switching.

a r t i c l e i n f o

Article history:

Received 8 May 2013

Accepted 21 September 2013

Available online 23 October 2013

Keywords:

Micro combined heat and power

Matlab/Simulink

Dynamic thermal modelling

Domestic electrical demand

Renewable energy sources

a b s t r a c t

This paper focuses on the detailed modelling of micro combined heat and power (mCHP) modules and

their interaction with other renewable micro generators in domestic applications based on an integrated

modular modelling approach. The simulation model has been developed using Matlab/Simulink and

incorporates a Stirling engine mCHP module embedded in a lumped-parameter domestic energy model,

together with contributions from micro wind and photovoltaic modules. The Stirling cycle component

model is based on experimental identication of a domestic-scale system which includes start up and

shut down characteristics. The integrated model is used to explore the interactions between the variousenergy supply technologies and results are presented showing the most favourable operating conditions

that can be used to inform the design of advanced energy control strategies in building. The integrated

model offers an improvement on previous models of this kind in that a fully-dynamic approach is

adopted for the equipment and plant enabling fast changing load events such as switching on/off do-

mestic loads and hot water, to be accurately captured at a minimum interval of 1 min. The model is

applied to two typical 3- and 4-bedroom UK house types equipped with a mCHP module and two other

renewable energy technologies for a whole year. Results of the two cases show that the electrical

contribution of a Stirling engine type mCHP heavily depends on the thermal demand of the building and

that up to 19% of the locally-generated electricity is exported whilst meeting a similar percentage of the

overall annual electricity demand. Results also show that the increased number of switching of mCHP

module has an impact on seasonal module ef ciency and overall fuel utilisation. The results demonstrate

the need for the analysis of equipment design and optimal sizing of thermal and electrical energy

storage.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

The market for micro combined heat and power (mCHP) mod-

ules is expected to grow, as a viable option for domestic boiler

replacement [1]. Options include fuel cells, Stirling engine, organic

Rankine cycle and conventional reciprocating engine based mCHP.

One of the most promising of these options is the Stirling cycle due

to its relatively low noise emission and low maintenance [2].

Having considered the energy and economic performance of a

range of domestic scale technologies, Barbieri et al. [3] concluded

that Stirling engine based mCHP is the best current option for a

range of domestic operating scenarios. However challenges exist

around: a) how to integrate these units into an unfamiliar appli-

cation in which both heating demand and power demand vary* Corresponding author.

E-mail address: [email protected] (S. Karmacharya).

Contents lists available at ScienceDirect

Applied Thermal Engineering

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m/ l o c a t e / a p t h e r m e n g

1359-4311/$ e see front matter 2013 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.applthermaleng.2013.09.039

Applied Thermal Engineering 62 (2014) 581e592

mailto:[email protected]://www.sciencedirect.com/science/journal/13594311http://www.elsevier.com/locate/apthermenghttp://dx.doi.org/10.1016/j.applthermaleng.2013.09.039http://dx.doi.org/10.1016/j.applthermaleng.2013.09.039http://dx.doi.org/10.1016/j.applthermaleng.2013.09.039http://dx.doi.org/10.1016/j.applthermaleng.2013.09.039http://dx.doi.org/10.1016/j.applthermaleng.2013.09.039http://dx.doi.org/10.1016/j.applthermaleng.2013.09.039http://www.elsevier.com/locate/apthermenghttp://www.sciencedirect.com/science/journal/13594311http://crossmark.crossref.org/dialog/?doi=10.1016/j.applthermaleng.2013.09.039&domain=pdfmailto:[email protected]

8/18/2019 1-s2.0-S135943111300673X-main

2/12

throughout the season often with a signicant random component;

b) unusual module response characteristics (particularly the Stir-ling cycle) involving relatively long run-uptimes; c) a wide range of

electrical and fuel tariff possibilities and, d) the possible existence

of other competing embedded renewable energy supply

technologies.

Much of the progress concerning the behaviour of mCHP plant

in domestic applications has focused on comparing the seasonal

economics of alternative module types [1,3,4] and on the mea-

surement of the individual and comparative performances of these

systems [5e7]. Peacock and Newborough [8] studied the impact of

heat and power ows from Stirling engine and fuel cell modules

using constant effective module data and 1-min recordings of do-

mestic energy demand concluding that these systems could supply

25e46% of a single dwelling’s annual electricity demand. However

this is at odds with De Paepe et al. [9] who concluded that only 10%of the electricity generated by 5 different types of mCHP modules

can be used by the host dwelling based on energy demands from

the (relatively simple) simulation program DOE-2.5 applied to

typical Belgian family houses.

Though the modelling of mCHP modules has received attention,

most of this is related to solid oxide and proton-exchange mem-

brane fuel cells [10e12] and very little work has been done on the

dynamic simulation of these plants when including the detailed

dynamic response of the local heating and electrical demand en-

vironments. Lombardi et al. [13] present a detailed semi-empirical

dynamic model of a domestic-scale Stirling engine mCHP module

which demonstrate improvements over an existing model as well

as a reduction in the number of experimental parameters needed.

However, no attention is given in this model to the balance of system components and sub-systems (i.e. building, heating, elec-

trical connections, etc). Dorer and Weber [14] used a simulation

modelling approach to compare two types of fuel cell, Stirling en-

gine and internal combustion engine based mCHP options at do-

mestic scale using the modular simulation program TRNSYS.

However most of their simulations were conducted at time in-

tervals of 15-min which is adequate for capturing daily and sea-

sonal energy proles and mean performances but can fail to

capture many of the shorter term dynamics particularly in relation

to local loading changes and electrical grid interaction.

The aim of the work presented in this paper is to address the

design and operational issues mentioned earlier, by proposing a

comprehensive dynamic system simulation in which not only the

mCHP module participates, but also the building, its heating and

other thermal demand systems, other local renewable energy in-

puts, and the interaction with the local electricity grid. The objec-tives of the development of such a simulation model therefore are

as follows:

Todevelop a detailed dynamic thermal model of a domestic type

building together with its heating and domestic hot water sys-

tems, control and thermal storage

To incorporate a dynamic thermal model of a Stirling engine

micro-CHP module with suf cient detail to capture its pro-

tracted run-up transients

To incorporate other domestic-scale embedded renewable

electricity systems (specically photovoltaic systems and micro-

wind turbines)

To incorporate a local area electrical grid model to enable the

impact of the typically randomly-varying demand and renew-able supply patterns to be reconciled with the mCHP module

and prevailing grid behaviour

To apply the simulation model to a range of operating scenarios

applicable to typical UK housing

To use the results obtained to identify key operational, design

and sizing issues necessary for analysing and optimising energy

use in building.

The paper is organised as follows: Section 2 gives the principles

of system modelling and Section 3 gives details of the model

development. Section 4 gives the model implementation and Sec-

tion 5 gives results obtained and discussion. Finally, conclusions

and suggestions for further work are given in Section 6.

2. Modelling philosophy

Approaches to the dynamic modelling of distributed thermo-

dynamic systems broadly fall into three categories:

Generic system models; in which pre-dened components and

sub-systems are dened and solved sequentially. Parameters

can be changed at the component level by the user but in all

other senses these types of models are rigid and inexible in

that new components cannot be added (or existing components

changed in character).

Modular component-based modelling in which recognisable

system components (a heat exchanger, a pump, a valve for

example) are treated in a self-contained ‘

black box’ manner

Nomenclature

AU area-integrated thermal transmittance value (heat

emitter, W K1)

C thermal capacitance (J m2 K1)

C wm heat emitter combined water and material thermal

capacitance (J K1)

C w,tank water tank thermal capacity (J K1)

G(s) transfer function of either power or heat in response to

switched event

K gain (kW)

N total number of heat emitter modelling zones

R thermal resistance (m2 K W1)

T i temperature, internal (C)

T m temperature, middle (C)

T o temperature, external (C)

T s temperature, surface (C)

T w temperature, water (C)

T w,i temperature, water at inlet (C)

T w,o temperature, water at outlet (C)

c pw specic heat capacity of water (J kg1 K1)

mw mass ow rate, water (kg s1)

mw,io mass ow rate, water at both inlet and outlet (kg s1)

n integer reference to heat emitter modelling zone

qloss heat loss from the tank (W)

qrad heat input due to radiation (W m2)

qsupplied Heat supplied to the tank by mCHP (W)

t d delay time (s)

xi inner material resistance ration

xm middle zone material resistance ration

xo outer material resistance ration

yi inner material capacitance ration

yo outer material capacitance ration

s time constant (s)

3(s) uniformly distributed error term

S. Karmacharya et al. / Applied Thermal Engineering 62 (2014) 581e592582

8/18/2019 1-s2.0-S135943111300673X-main

3/12

with dened input/output information ows such that each

individual component forms an object. Components can be

selected and inter-connected by the user to form sub-systems

from which complete systems can be made. This approach

gives high exibility as well as being intuitive for practice-

focused users.

Equation-based modelling using differential algebraic equa-

tions (DAEs). DAEs form sets of differential equations, in

which time is the independent variable, with their input/

output information ows such that each equation forms an

object. This approach offers innite exibility but additional

tools are needed to (a), sort and transform the user-supplied

equations into an algorithm and, (b) process the algorithm

to execution.

Sahlin et al. [15] concluded that component-based approaches

are too rigid in structure to accommodate the improvements and

exibility in use that are likely to be increasingly needed in future.

They argue that equation-based methods offer greater exibility.

However, though equation-based methods form a exible and

convenient approach when it is possible to express a closed set of

equations to describe a problem, they can often lead to longer

computation times than is the case with conventional component-based simulation programs. Bertagnolio and Lebrun [16] found this

when they developed detailed steady-state component models of

HVAC plant with a low-order single zone building and applied the

model to problems in benchmarking [16] and auditing [17].

Furthermore, certain users (practitioners and some applied re-

searchers) are not comfortable with these methods which limit

their use to those with requisite scientic knowledge and skills. On

balance, with an appropriate level of component ‘granularity’ it is

possible to alleviate the exibility issue to a large extent and, thus,

it is clear that for most applications a component-based approach

offers the widest range of advantages.

The multi domain graphical simulation environment Simulink

[18] integrated within Matlab was used in this work. Components

and sub-systems were arranged into 5 groups:

The building envelope

Space heating and domestic hot water systems

The mCHP module

Locally-embedded renewable energy systems

The electrical distribution network.

3. Model development

3.1. The building envelope model

A high-order lumped parameter method was used for the

building envelope having the advantage of relative simplicity (i.e.computational ef ciency) whilst also providing suf cient accuracy

and rigour to capture a wide range of transient effects. The method

adopted was developed by Gouda et al. [19] which envisages a

second-order description of each signicant thermal capacity

pathway (i.e. exterior wall, internal partition, oor, roof) together

with algebraicheat balances at lowcapacity pathways(i.e.windows

and ventilation air transfer) completed with a rst-order treatment

of the enclosing room air. Thus a 9th order model for each space is

arrived at, as shown in Fig. 1. This method retains the simplicity of

the lumped parameter method whilstofferinggreateraccuracy than

the more common lower order models of this kind [20].

Energy balance about each element can be written as second-

order matrix differential equations. For example, for the external

wall element (Eq. (1)) [21]:

_T s_T m

¼

1=½R$C $ð xi yi þ xm yiÞ 1=ð xm yiR$C Þ

1=ð xm yoR$C Þ 1=½R$C $ð xm yo þ xo yoÞ

T sT m

þ ::::::

1=ðR$C $ xi yiÞ 1=ð yiC Þ 0

0 0 1=ðR$C $ xo yoÞ

24 T iqrad

T o

35 (1)

where the resistance (R), capacitance (C ) and ‘rations’ ( xi,m,o; y i,o)

are obtained using the method described by Gouda et al. [19]. For

the thermal resistance through each construction element (i.e. wall,

partition, etc), these ‘rations’ are fractions of the total elementresistance allocated to the notional inside resistor ( xi), middle

resistor ( xm) and outside resistor ( xo) of each construction element

as illustrated in Fig. 1 (and, xi þ xm þ xo ¼ 1). For the thermal

capacitance the yi and yo rations allocate the overall element

thermal capacity to the inside and outside capacitors respectively

(and, yi þ yo ¼ 1). Gouda et al. [19] used the term ‘ration’ for these

fractions to imply that they remain constant (in effect, a quasi

‘property’ of the construction element). They require to be tted

using a suitable rigorousreference model and an appropriatetting

methodsuch as optimisation. In this work, values recommended by

Gouda et al. [19] typical of UK house construction were used. Eq. (1)

can be readily and ef ciently represented in Simulink using a state-

space block. Further details of the application of the method can be

found in Ref. [22].Here, the room model was completed by adding a simple

transmittance factor glazing model with an associated solar radi-

ation algorithm to read-in measured hourly global horizontal ir-

radiances from a weather le and resolve the data into in-plane

direct and diffuse radiation. The window model does not include

any blinds, curtains or internal shading devices. All radiant sources

(both short wave and long wave) are assumed to be distributed

uniformly to all opaque room surfaces with the exception of the

direct component of solar radiation which is assumed to be

absorbed by the oor surface only. The solar radiation algorithm

was based on Liu and Jordan’s [23] method. A simple air change

ventilation model was incorporated. Other rooms or clusters of

rooms forming heating zones could easily be added to form a multi-

zone building by copying and pasting in Simulink and making

Fig. 1. Representation of the lumped-parameter building envelope model.

S. Karmacharya et al. / Applied Thermal Engineering 62 (2014) 581e592 583

8/18/2019 1-s2.0-S135943111300673X-main

4/12

appropriate connections where appropriate via the oor, ceiling

and partitions paths (see Fig. 1).

3.2. Space heating and hot water systems modelling

For the space heating, a distributed low pressure hot water

heating system model was rst spatially-discretised resulting in a

set of ordinary differential equations (Eq. (2)) [24]:

C wmðnÞ d

dt T wðnÞ ¼ mwc pwðT wðn 1Þ T wðnÞÞ AUðT wðnÞ T iÞ

(2)

Where ‘n’ represents the nth zone of a heat emitter having a total of

N zones of equal volume. Underwood and Yik [25] and Fong et al.

[24] suggest that a satisfactory trade-off between accuracy and

computational cost for most heat exchangers of this type can be

obtained with N ¼ 3 and this was used in the present work.

Thoughthe model developedin this work has the potential to be

used for advanced control strategies, in the work reported here it is

applied to conventional heating controls as would be expected in

most existing UK houses. All new and refurbished domestic hot

water heating systems in the UK require energy saving controls and

‘thermostatic’ radiator valves are mandatory. Despite their name,

‘thermostatic’ radiator valves contain a direct-acting sensing bulb

which acts to provide a variable waterow rate in response to room

temperature e they in fact modulate hot water ow with a rela-

tively wide proportional control band. They also tend to exhibit

quite long time constants (5 min or more). Space heating control

was therefore represented by a single on/off thermostat in one

space of the house (usually the ground oor lobby) together with

an upper limit thermostat on the water discharge side of the mCHP

module; either of which can switch the mCHP module off, but both

of which must be on to permit the heating to operate. Both ther-

mostats adopted a hysteresis switching pattern with a switching

dead-band of 2 K (room thermostat) and 5 K (mCHP module ow

water temperature). At the emitter level, the ‘thermostatic’ radiatorvalve was modelled as an equal-percentage modulating valve with

an actuator and sensing bulb with a 10-min time constant; details

of which can be found in Gouda et al. [21].

The domestic hot water system model consisted of a stored hot

water tank for two principal reasons. Firstly, it is well-established

that stored hot water provides capacity to smooth peaks in hot

water demand and, secondly, the stored capacity can act as a heat

sink for the mCHP module during periods of light heating demand

e an important consideration in order to restrict the number of

module starts over a given time period. A simple single-zone tank

from the Simulink extended thermodynamic systems component

library was used for this purpose which is based on the following

energy balance (Eq. (3)) [26]:

C w;tankd

dt T w;o ¼ mw;ioc pw

T w;i T w;o

þ qsupplied qloss (3)

Where T w,i is the cold water in-feed temperature to the tank (C),

T w,o is the outow water temperature to the draw-off points (C),

qsupplied is the heat input to the tank from the mCHP module (W)

and qloss is the heat loss from the tank (W). The water demandow

rate (mw,io, kg s1) forms a model input variable which is read from

a le of demand data obtained from eld-monitoring surveys.

3.3. Micro-CHP modelling

The modelling of mCHP based on Stirling engines has received

relatively little attention. For dynamic simulations it is necessary to

treat the problem rigorously because these systems tend to exhibit

quite long run-up times. Whilst having the advantage of generic

applicability, a rigorous theoretical approach will lead to a complex

model requiring high computational power for its solution. It

would also most likely require at least some parameterisation from

empirical data (e.g. Lombardi [13]). In situations where experi-

mental transient response data are available it is suggested that an

empirical model tted to such data is a preferred choice on the

grounds of computational cost (i.e. relative model simplicity) and

accuracy. This is the approach adopted in the present work. A

transient response test was carried out on a natural gas-red do-

mestic scale mCHP module (specications are given in Appendix A)

discharging into hot water panel radiators and a local electrical

distribution bus in laboratory conditions. The laboratory heating

system test rig was congured using conventional panel radiators

with a combined capacity similar to what might be found in a

typical family house. The test mCHP module (Fig. 2) had a nominal

rated capacity of 6 kW (heat output) and 1 kW (single phase power

output). The heating system held the mCHP module at a moder-

ately steady load for several hours before being shut down.

Inspection of the test data suggested that a transfer function of

the following form could be tted (Eq. (4)):

GðsÞ ¼ Ketds

ðss þ 1Þ þ 3ðsÞ (4)

Where G(s) is the response in power output orheatoutput of the

mCHP module, K is the gain, t d is a pure time delay, s is a time

constant and 3(s) is a uniformly distributed error term. Fitted pa-

rameters to Eq. (4) for the test module are given in Table 1.

The error term reects a random pattern of variance about a

nominal mean and mainly arises from variations in the heating

water ow rate. Such variations are evident in most ow mea-

surements of this kind due to small variations in pump operating

condition and the presence of small bubbles of air, etc, and cannot

generally be individually explained in detail. This was modelled in

the time domain as a Gaussian random number sequence with a

mean of zero and a variance equal to the standard deviation of theheat output value or power output value with respect to the their

respective mean values during the steady load period of the

response.

Results of the test including the tted model response data are

given in Fig. 3. The data to which the model was tted reects a

cold-start situation in which the laboratory air (and heating emitter

water contents) was at an initial steady temperature of approxi-

mately 18 C.

It is noted here that exibility arises when constructing system

simulation models in Simulink in that many alternative model

formats may be used without extensive mathematical manipula-

tion by the user. Eq. (4) represents the third (of three) different

types of differential equation format used in the present work e

that of a linear transfer function. It is conveniently built into thesimulation information ow diagram using a generic transfer

function block which can then be appropriately decomposed by the

numerical engine without further involvement by the user. The

other types used here are given by Eq. (2) which is a nonlinear

ordinary differential equation (nonlinear on account of the product

of a variable water mass ow rate, mw, and variable water tem-

peratures, T w(n 1) and T w(n)) and Eq. (1) which is a linear matrix

differential equation. The former was built using a user-dened

equation function (for the derivative) and a generic integrator

block whereas the latter was dened using a generic state-space

block. This sort of exibility is not normally available in other

simulation modelling environments (such as equation-based

methods) which are usually restricted to tight formatting condi-

tions on the equations used.


8/18/2019 1-s2.0-S135943111300673X-main

5/12

3.4. Modelling of micro renewable energy systems

Besides incentivising mCHP, theFeed-inTariff (FiT) in theUK [27]

also provides subsidies for other electrical renewable energy tech-

nologies. At the domestic level, the two applicable technologies are

photovoltaic (PV) systems and micro-wind turbines. Because of the

likelihoodof interaction between one (or both)of theseoptionswith

the mCHP module (for energy management), it was essential to

incorporate themin the simulationmodel.The PV system model wasbased on the two-diode model of Ishaque et al. [28] combined with

the maximum power point tracking (MPPT) controller model pro-

posedby Jianget al.[29].Asimple xedinverterloss assumptionwas

made. The micro-wind turbine was modelled using a classical

power-law model with power coef cients taken from typical man-

ufacturer’s literature andentered into theSimulinkmodelby means

of a look-up table. Also included is a permanent-magnet generator

model with a voltage regulator based on a buck-boost converter.

Details of the latter can be found in Mohan et al. [30].

3.5. Modelling of the electrical network

The local power grid connection was modelled as one balanced

three-phase (400 V) power supply including resistive and inductiveimpedance of the feeder based on part of the distribution network

model developed by Barbier et al. [31]. The intention is to capture

the impact of a simultaneous penetration from several micro gen-

erators to the distribution network. The ‘dynamic load’ block

(Fig. 4) will either import or export power from/to the local three-

phase grid connection taking into account any capacity from local

embedded renewable generators and the prevailing local demand

by the house. Although the model is focused on one subject house,

the impact of uctuating power ow from a small number of

several neighbouring houses can be explored, as the three phase

network is assumed to be balanced.

Electrical demand in domestic applications has considerable

randomness due to switched loads by occupants. Some attention

has been given to the modelling of these demands by considering

rst the power usage characteristics of electrical appliances andthen introducing a probabilistic model to describe how and when

they are likely to be used by the house occupants [32,33]. In this

work, the power demand patterns measured by Richardson and

Thompson [34] were used instead as they were considered to be

more representative and typical and, in addition, the appliances in

use were precisely stipulated.

4. The integrated system model

The completed simulation model expressed as a Simulink block

diagram is shown in Fig. 4. Note that the ‘space heating’ block

masks the multi-zone house model (Section 2.2) as well as the

heating system and its thermostatic controls (Section 2.3). Time

series input data are read from les as follows:

Domestic hot water demand

Weather data (external air temperature, wind speed, global

horizontal solar radiation)

Occupancy activity schedule

Electrical power demand.

4.1. Application

The model was applied to two typical UK family house types; a

semi-detached house (Fig. 5) and a larger detached house (Fig. 6).

As a simplifying assumption, the houses were divided into 3

heating zones according to internal temperature standard; the

Living Room zone (nominally 21

C); the bedrooms zone

Fig. 2. mCHP test module.

Table 1

Model parameters for the test case mCHP module.

State K (kW) s (s) t d (s)

Switch on e heat 6 91.4 60

Switch on e power 1 131.4 120

Switch off e heat 4 161.4 0

Switch off e power 0 e e


8/18/2019 1-s2.0-S135943111300673X-main

6/12

(nominally 16 C) and a balance zone comprising all other spaces

(nominally 18 C). This is a common assumption for domestic en-

ergy modelling in the UK [32]. The house constructions were

assumed to be in accordance with typical recent construction

standards as set out in the UK’s National Calculation Methodology

simplied building energy modelling database [35] and the UK

Building Regulations, Part L, 2006 for the semi-detached house and

1996 for the detached house. The occupancy patterns assumed two

working parents with children of school age (i.e. weekdays 06:00e

08:00 h and 17:00e23:00 h; weekend days 07:00e23:00 h). In-

ternal casual heat gain assumptions were based on those used by

Anderson et al. [32] in the development of the UK’s leading do-

mestic energy model ‘BREDEM’. A typical International Weather

form Energy Calculations (IWEC) weather data le for the north of

Fig. 4. Integrated simulation model.

Fig. 3. Fitted model to mCHP data.


8/18/2019 1-s2.0-S135943111300673X-main

7/12

England (Finningley) [36] was used. The houses were assumed to

be ventilated at an average rate of 0.5 air change per hour

(including external air inltration) with respect to the whole house

volumes in winter, and allowed to rise to 3 air changes per hour

when the heating systems were inoperative (i.e. in summer) in an

attempt to limit high summertime temperatures. This average rate

was assumed to apply during conditions of average site wind speed

with a correction applied at other wind speeds.In both applications of the model, the mCHP module was

congured for heat-led control. Thus when there is a demand for

heat, the module is active and simultaneously servicing either po-

wer demand or exporting power to the grid (or both). As the de-

mand for heat falls, power importing will become predominant.

A domestic-scale wind turbine with a rated capacity of 420 W

(specications of the wind turbine are given in Appendix B) was

applied to both house types. Four roof-mounted mono-crystalline

PV modules were applied each with a rated power output of 185 W

(specications at standard test conditions are given in Appendix C)

were applied to the semi-detached house. For the detached house

with a much larger roof area, the PV module provision was

increased to 14 modules (with the same module specication). The

mCHP module applied to both houses was a natural gas-red ‘un-

der-bench’ Stirling engine based module with a rated active power

output of 1 kW and corresponding heat output of 6 kW at a hotwater ow temperature of 75 C.

In this application, the electrical demands were based on

measured demands [34] for two houses with similar characteristics

to those depicted in Figs. 5 and 6. The 1-min resolution data in both

cases reect the appliances in the monitoredhouses as summarised

in Table 2. The weekday and weekend demand patterns are given in

Figs. 7 and 8 and it was assumed that the daily patterns formed a

repeating sequence throughout one complete year. Similar pattern

Fig. 6. Detached house type used in model application.

Fig. 5. Semi-detached house type used in model application.


8/18/2019 1-s2.0-S135943111300673X-main

8/12

was repeated for the case of domestic hot water demands. The

monitored hot water demand at an interval of 5 min in UK houses is

show in Fig. 12.This is one of the main advantage of this model

where the demand pattern at resolution of 1 min can be given as

input, whereas in other models the interval used is 15 min [14] or

higher.

5. Results and discussion

A full annual simulation was carried out using a variable step

trapezoidal solver. This solver was selected to provide an ef cient

computation (i.e. variable step) and the use of the trapezoidal rule

was considered to give some limited capacity to handle stiffness in

the model (mainly arising due to control devices within the space

heating model). The computational requirement was noted to be

3.6s of computer time per hour of simulation time in winter

simulation periods when the space heating is active, falling by

about half in summer simulation periods based on a typical modern

desktop PC. The time step limits were set at 5 s (minimum) and

1800 s (maximum e though this was never reached). Samples of

results were extracted for a typical winter week (Figs. 9e11), a

typical warm summer week (Figs.12 and 13) for the semi-detached

house only, and summaries of annual energy ows are given inTables 3 and 4 for both house types.

Zone temperatures for one typical winter week (starting with

the weekend) are shown in Fig. 9 for the semi-detached house (also

plotted are the corresponding external air dry bulb temperature

and global (horizontal) solar radiation). In the application given

here, the house is occupied intermittently during the day. Thus two

characteristic peaks in zone internal temperatures can be seen for

each day for the morning and evening periods during which the

house is occupied often with a free-oat mid-day peak which can

be traced in Fig. 9 to a corresponding increase in solar radiation.

Note that the heating is controlled from a thermostat mounted in

the ground oor entrance lobby together with local ‘trim’ by

‘thermostatic’ radiator valves. This is normal practice in UK do-

mestic heating control. As a consequence, room temperature con-trol involves wide variations among zones (in applications where

the lobby space contains signicant heat gains, serious under-

heating can occur in other spaces). In practice, the comfort-

critical living room space in UK houses is commonly equipped

with a further method of heating (e.g. a ‘focal-point’ heater such as

a wood burning stove,etc). Note that the energyassociated with the

focal point heating has not been included in the present study on

practical grounds. The fuel used for this varies and, nowadays, solid

fuel appliances such as log-burning stoves are gaining in popularity.

Therefore, the total energy demands given in Tables 3 and 4 shouldbe interpreted accordingly.

Electrical energy ows for a typical winter week are plotted in

Fig. 10 for the semi-detached house. This shows power generated

by the three sources (mCHP, wind turbine and PV modules) as well

as imported (shown negative) and exported (shown positive) po-

wer ows. Between two and three ‘bursts’ of daily power from the

mCHP module can be seen as the module is called to meet space

heating and domestic water heating demands. It is encouraging

that more frequent switching of the mCHP module is avoided as

this might lead to module wear and early breakdown. Renewable

energy activity in this typical week is low with the exception of a

windy day (day 2) giving rise to the wind turbine operating at its

rated capacity for a signicant part of the day. Most of the power

generated is used by the host even during unoccupied periodsduring which standby loads absorb the available renewable power,

though there are small contributions to export from the mCHP

module when active.

The thermal energy ows in a typical winter week and plotted

in Fig. 11 and, in a typical summer week, in Fig. 12, for the semi-

detached house. In winter, the mCHP module is switched typi-

cally once only in the morning and once or twice to meet the

evening demand. Usually, just one or two relatively short charging Table 2

Electrical appliances in use [34].

Appli anc e type De tac hed house Semi -detach ed ho use

Electric shower Yes No

Occasional use of electric

heating

Yes No

Economy-t tariff Yes No

Use of timer controls Yes No

Energy saving lighting

usage (%)

25 25

Halogen lamp usage 12 4

Outdoor oodlight No Yes

Refrigerator 2 Fridge/freezer 1

Freezer No 1

Television 1 (tube) 1 (tube), 2 (plasma), 1 (LCD)

Computer 6 3

Electric oven 1 1

Microwave 1 1

Kettle 1 1

Toaster/sandwich toaster 1 2

Dish washer 1 1

Washing machine 1 1

Tumble drier 1 1

Fig. 7. Daily power demand pattern (semi-detached house type).

Fig. 8. Daily power demand pattern (detached house type).


8/18/2019 1-s2.0-S135943111300673X-main

9/12

cycles per day are needed for the hot water tank. When just one is

needed, this tends to take place during the evening when the mCHP

module is also servicing space heating loads. In the summer, when

the mCHP module is called to meet domestic hot water demands

only, just one or two short daily bursts of mCHP activity are needed

for hot water tank charging and this usually occurs when there is

high coincident demand by the host for electricity (Fig. 13). In all

cases, the high degree of damping (due tothe space heating and the

hot water tank) is such that frequent switching of the mCHP

module is avoided which is good for module maintenance and

operation.

Electrical energy ows during a typical warm summer week are

plotted in Fig.13 for the semi-detached house. One most days, onlyone burst of mCHP activity per day is evident in summer and this

will be due to entirely domestic hot water tank charging. In sum-

mer, a stronger pattern of electrical export is evident and this is

mainly due to daytime power generated by the PV modules (in this

typical summer week, the contribution from the wind turbine is

very small). However, power imported is high during this week due

mainly to the restricted use of the mCHP module since the heating

demand is now restricted to domestic hot water only and the PV

modules are generating at periods outside the main occupied

hours. The use of battery technology could help to reduce import at

the expense of export (but only if the economics were favourable)

and this will be added to the model in future. Annual totals for

electrical and thermal energy are given in Tables 3 and 4 for both

house types. It is clear that the heat-led strategy for the mCHP

module restricts its ability to contribute electrical power e indeed

the power generated by the wind turbine and PV modules both

exceed that produced by the mCHP module (bya substantial degree

in the case of the detached house type due to a much larger PV

capacity). However this is desirable in that the zero-carbon tech-

nologies dominate power in this application whereas the low car-

bon technology contributes a smaller share of the power but all of

the heating. Combining technologies in this way, almost 20% of allpower generated is exported by the semi-detached house but this

falls to approximately 11% for the detached house due to its much

higher power demand. However, against the overall electrical de-

mand for the house, the mCHP module and renewable technologies

met a similar proportion of around 20% of the demand of both

house types (i.e. around 80% of the annual electricity demand

needed to be imported). It is clear that the electrical contribution

Fig. 9. Typical winter week zone temperatures (semi-detached house type).

Fig. 10. Typical winter week electrical power

ows (semi-detached house type). Fig. 11. Typical winter week thermal power

ows (semi-detached house type).


8/18/2019 1-s2.0-S135943111300673X-main

10/12

from the mCHP module is heavily dependent on coincident heating

and howwaterdemand. To illustrate this point, consider the winter

months November through February when very little renewable

electricity is available (Fig. 10). If all of the base demand for elec-

tricity (averaging approximately 250 W for the semi-detached

house (Fig. 7) and 800 W for the detached house (Fig. 8)) was

met by the mCHP module without regard to heating, then at least

720 kWh of electricity would have been offered by the mCHP

module to the semi-detached house (whereas the module gener-

ated just 425.9 kWh in the entire operating year e Table 3) and

2304 kWh offered to the detached house (as against 719 k W h

generated in the entire year e

Table 3).As to heating, the mCHP module met all of the domestic hot

water demand and all of the space heating demand of the back-

ground (radiator) heating system collectively amounting to

2820 kWh for the semi-detached house and 5114 kWh for the de-

tached house (Table 4). As was pointed out earlier, however, this is

for background space heating only and excludes energy consumed

by local ‘focal point’ heating in the living room (which can be sig-

nicant). There is clearly a need to examine the design capacities of

the various technologies together with both electrical and a longer-

term form of thermal storage than a simple water tank (e.g. phase

change storage) in relation to available import and export tariff

structures so that an optimum economic balance can be arrived at.

On this last point for instance, a 300 L water tank coupled with the

mCHP module with a thermostat switching differential of 5 K

would meet the design heating demand of 6 kW for approximately

30 min between switching intervals. A phase change store

comprising 150 L of phase change material with a transition

enthalpy of 200 kJ/L plus 150 L of water would meet the 6 kW

design heating demand for 1.5 h e three times as long.

The fuel consumption of the mCHP module was 4137 kWh for

the semi-detached house and 7949 kWh for the detached house(Table 4) giving an ef ciency (electrical power-to-fuel energy) of

10.30% and 9.05% respectively. The overall fuel utilisations (elec-

trical and thermal-to-fuel energy) are 78.5% and 73.4% respectively.

The reason for the lower ef ciency and fuel utilisation in the case of

the larger detached house is due to a greater number of cold

module starts. The initial warm-up phase during a cold module

start results in a short period of rated fuel use during which the

power and heat outputs are below their rated values.

6. Conclusions

An integrated simulation model for analysing micro combined

heat and power (mCHP) systems and embedded renewable sys-

tems in domestic applications has been developed and presented inthis paper. The model differs from most previous models of this

kind in that a detailed dynamic treatment of the plant, equipment

and building envelope has been considered in the present work

whereas previous models have tended to use simplied ‘quasi-

steady-state’ methods. Thus the present model is able to predict

plant response at very small time intervals allowing it to capture

high frequently changing electrical and hot water demands and

accurately simulate the run-up and shut down characteristics of

domestic scale mCHP modules.

The model has been applied to two typical UK domestic appli-

cations consisting of a 3-bedroom semi-detached house and a 4-

bedroom detached house. Both houses are equipped with a mCHP

module, a wind turbine and a photovoltaic array. The simulation

model is used for analysis of typical winter and summer operatingweeks as well as the overall annual energy use during the whole

year. Results obtained show that the electrical contribution by the

mCHP module is heavily dependent on the space heating and hot

water demand of the building. The results also show that the

contribution from the mCHP supplemented with embedded wind

and solar power (for export or use within the building) depends on

Fig. 13. Typical summer week electrical power

ows (semi-detached house type).

Table 3

Annual energy account (power).

Energy stream Semi-detached kWh Detached kWh

Generated by mCHP 425.9 719

Generated by wind turbine 279.4 279.4

Generated by PV modules 539.2 1752.7

Exported 233.6 (18.8%) 305.5 (11.1%)

Overall demand 5298 11,542

Imported 4287 (80.9%) 9119 (79%)

Table 4

Annual energy account (heat).

Energy stream Semi-detached kWh Detached kWh

Demand due to space heating 1728 3995

Demand due to hot water service 1092 1119

Annual fuel demand (natural gas) 4137 7949

Fig. 12. Typical summer week thermal power ows (semi-detached house type).


8/18/2019 1-s2.0-S135943111300673X-main

11/12

the electrical demand of the house and can contribute to around

20% of the overall electrical demand of both house types whilst

exporting almost 19% of their combined electrical outputs for the

semi-detached house falling to around 11% for the larger detached

house. However the increased number of mCHP module starts in

the case of the detached house was found to result in a reduction in

the electrical ef ciency and overall fuel utilisation of this module

compared with thesemi-detached house type where the numberof

module starts was lower. The results obtained demonstrate the

need for the analysis of tariff structures and the impact they have

on equipment design as well as the potential for thermal and

electrical energy storage.

Appendix A. Specications of Stirling engine based micro-

CHP (Whispergen)

Appendix B. Specications of the wind turbine

Appendix C. Specications of the PV module

References

[1] A.D. Hawkes, M.A. Leach, Cost-effective operating strategy for residentialmicro-combined heat and power, Energy 32 (2007) 711e723.

[2] H.I. Onovwiona, V.I. Ugursal, Residential cogeneration systems: review of thecurrent technology, Renewable Sustainable Energy Rev. 10 (2006) 389 e431.

[3] E.S. Barbieri, P.R. Spina, M. Venturini, Analysis of innovative micro-CHP sys-tems to meet household energy demands, Appl. Energy 97 (2012) 723 e733.

[4] O.A. Shaneb, G. Coates, P.C. Taylor, Sizing of residential mCHP systems, EnergyBuild. 43 (2011) 1991e2001.

[5] M. Dentice d’Accadia, M. Sasso, S. Sibilio, L. Vanoli, Micro-combined heat andpower in residential and light commercial applications, Appl. Therm. Eng. 23(2003) 1247e1259.

[6] M.A. Smith, P.C. Few, Domestic-scale combined heat-and-power systemincorporating a heat pump: analysis of a prototype plant, Appl. Energy 70(2001) 215e232.

[7] D.C.G. Veitch, K. Mahkamov, Assessment of economical and ecological benetsfrom deployment of a domestic combined heat and power unit based on itsexperimental performance, Proc. Inst. Mech. Eng. Part A J. Power Energy 223(2009) 783e798.

[8] A.D. Peacock, M. Newborough, Impact of micro-combined heat-and-powersystems on energy ows in the UK electricity supply industry, Energy 31(2006) 1804e1818.

[9] M.D. Paepe, P.D. Herdt, D. Mertens, Micro-CHP systems for residential appli-cations, Energy Convers. Manage. 47 (2006) 3435e3446.

[10] L. Barelli, G. Bidini, F. Gallorini, A. Ottaviano, Dynamic analysis of PEMFC-based CHP systems for domestic application, Appl. Energy 91 (2012) 13e28.

[11] A. Arsalis, M.P. Nielsen, S.K. Kær, Modeling and off-design performance of a

1kWe HT-PEMFC (high temperature-proton exchange membrane fuel cell)-based residential micro-CHP (combined-heat-and-power) system for Danishsingle-family households, Energy 36 (2011) 993e1002.

[12] H. Xu, Z. Dang, B.-F. Bai, Analysis of a 1 kW residential combined heating andpower system based on solid oxide fuel cell, Appl. Therm. Eng. 50 (2013)1101e1110.

[13] K. Lombardi, V.I. Ugursal, I. Beausoleil-Morrison, Proposed improvements to amodel for characterizing the electrical and thermal energy performance of Stirling engine micro-cogeneration devices based upon experimental obser-vations, Appl. Energy 87 (2010) 3271e3282.

[14] V. Dorer, A. Weber, Energy and CO2 emissions performance assessment of residential micro-cogeneration systems with dynamic whole-building simu-lation programs, Energy Convers. Manage. 50 (2009) 648e657.

[15] P. Sahlin, L. Eriksson, P. Grozman, H. Johnsson, A. Shapovalov, M. Vuolle,Whole-building simulation with symbolic DAE equations and general purposesolvers, Building Environ. 39 (2004) 949e958.

[16] S. Bertagnolio, J. Lebrun, Simulation of a building and its HVAC system with anequation solver: application to benchmarking, Build. Simul. 1 (2008) 234 e250.

[17] S. Bertagnolio, P. Andre, V. Lemort, Simulation of a building and its HVACsystem with an equation solver: application to audit, Build. Simul. 3 (2010)139e152.

[18] Mathworks, Simulink e Simulation and Model-based Design. Available at:http://www.mathworks.co.uk/products/simulink/ (accessed 08.08.12).

[19] M.M. Gouda, S. Danaher, C.P. Underwood, Building thermal model reductionusing nonlinear constrained optimization, Build. Environ. 37 (2002) 1255e1265.

[20] L. Laret, Use of general models with a small number of parameters: part 1 etheoretical analysis, in: Proc. Seventh International Congress of Heating andAir Conditioning e CLIMA 2000, Budapest, 1980.

[21] M.M. Gouda, C.P. Underwood, S. Danaher, Modelling the robustness proper-ties of HVAC plant under feedback control, Building Serv. Eng. Res. Technol. 24(2003) 271e280.

[22] G. Henze, C. Neumann, Building simulation in building automation systems,in: J.L.M. Hensen, R. Lamberts (Eds.), Building Performance Simulation forDesign and Operation, Spon Press, London, 2011, pp. 436e440.

[23] B.Y.H. Liu, R.C. Jordan, The interrelationship and characteristic distribution of direct, diffuse and total solar radiation, Solar Energy 4 (1960) 1e19.

[24] J. Fong, C.P. Underwood, J.S. Edge, A. Tindale, S.E. Potter, Modelling of heatemitters embedded within third-order lumped-parameter building envelopemodel, in: Proceedings of the IBPSA England First National Conference 2012,Loughborough, 2012.

[25] C.P. Underwood, F.W.H. Yik, Modelling Methods for Energy in Buildings,Wiley-Blackwell, 2004.

[26] Eutech Scientic Engineering GmbH, Thermolib User Manual: Thermody-namic Systems Library, Release 5.1 Simulation Toolbox for the Design andDevelopment of Thermodynamic Systems in MATLAB/Simulink, 2012.

[27] Government of U.K., Feed-in Tariffs: Get Money for Generating your ownElectricity. Available at: http://www.decc.gov.uk/en/content/cms/meeting_energy/Renewable_ener/feedin_ tariff/feedin_tariff.aspx (accessed 01.08.12).

[28] K. Ishaque, Z. Salam, H. Taheri, Simple, fast and accurate two-diode model forphotovoltaic modules, Sol. Energy Mater. Sol. Cells 95 (2011) 586e594.

[29] T. Jiang, G. Putrus, S. McDonald, M. Conti, B. Li, D. Johnston, Generic photo-voltaic system emulator based on Lambert omega function. Universities’ Po-wer Engineering Conference (UPEC), in: Proceedings of 2011 46thInternational, 2011, pp. 1e5.

General:

Engine 4 Cylinders double acting Stirling cycle

Generator 4 Pole single phase induction motor

Electrical output:

Electricity supply 230 Vac, 50 Hz

Nominal mode Up to 1 kW

Thermal output:

Nominal mode Up to 7 kW

Maximum Up to 12 kW (including auxiliary burner)

Fuel:

Type 2H-2nd family natural gas

Supply pressure 17e25 mbar (20 mbar nominal)

Fuel consumption:

Maximum burner ring rate 1.55 m3/h

Wind rotor characteristics

Radius of the wind rotor 2.25 m

Number of blades 2

Moment of inertia of the wind rotor and rotating parts

of the generator

9.77 kg m2

Cut-off wind speed 12 m/s

Permanent magnet generator

No of pole pairs ( p) 6

Stator phase resistance (Rph) 1.25 U

k0 (¼ pFm) 1.5

Specication Value

Short circuit current 5.69 A (dc)

Open circuit voltage 44.4 V (dc)

Current at maximum power 5.03 A (dc)

Voltage at maximum power 36.8 V (dc)

Short circuit current coef cient 3.1 105 A (dc)/K

Open circuit voltage coef cient 3.84 104 V (dc)/K


http://refhub.elsevier.com/S1359-4311(13)00673-X/sref1http://refhub.elsevier.com/S1359-4311(13)00673-X/sref1http://refhub.elsevier.com/S1359-4311(13)00673-X/sref1http://refhub.elsevier.com/S1359-4311(13)00673-X/sref1http://refhub.elsevier.com/S1359-4311(13)00673-X/sref2http://refhub.elsevier.com/S1359-4311(13)00673-X/sref2http://refhub.elsevier.com/S1359-4311(13)00673-X/sref2http://refhub.elsevier.com/S1359-4311(13)00673-X/sref3http://refhub.elsevier.com/S1359-4311(13)00673-X/sref3http://refhub.elsevier.com/S1359-4311(13)00673-X/sref3http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref9http://refhub.elsevier.com/S1359-4311(13)00673-X/sref9http://refhub.elsevier.com/S1359-4311(13)00673-X/sref9http://refhub.elsevier.com/S1359-4311(13)00673-X/sref10http://refhub.elsevier.com/S1359-4311(13)00673-X/sref10http://refhub.elsevier.com/S1359-4311(13)00673-X/sref10http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref16http://refhub.elsevier.com/S1359-4311(13)00673-X/sref16http://refhub.elsevier.com/S1359-4311(13)00673-X/sref16http://refhub.elsevier.com/S1359-4311(13)00673-X/sref16http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://www.mathworks.co.uk/products/simulink/http://refhub.elsevier.com/S1359-4311(13)00673-X/sref18http://refhub.elsevier.com/S1359-4311(13)00673-X/sref18http://refhub.elsevier.com/S1359-4311(13)00673-X/sref18http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref22http://refhub.elsevier.com/S1359-4311(13)00673-X/sref22http://refhub.elsevier.com/S1359-4311(13)00673-X/sref22http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref24http://refhub.elsevier.com/S1359-4311(13)00673-X/sref24http://refhub.elsevier.com/S1359-4311(13)00673-X/sref24http://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://www.decc.gov.uk/en/content/cms/meeting_energy/Renewable_ener/feedin_%20tariff/feedin_tariff.aspxhttp://www.decc.gov.uk/en/content/cms/meeting_energy/Renewable_ener/feedin_%20tariff/feedin_tariff.aspxhttp://refhub.elsevier.com/S1359-4311(13)00673-X/sref26http://refhub.elsevier.com/S1359-4311(13)00673-X/sref26http://refhub.elsevier.com/S1359-4311(13)00673-X/sref26http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref27http://refhub.elsevier.com/S1359-4311(13)00673-X/sref26http://refhub.elsevier.com/S1359-4311(13)00673-X/sref26http://refhub.elsevier.com/S1359-4311(13)00673-X/sref26http://www.decc.gov.uk/en/content/cms/meeting_energy/Renewable_ener/feedin_%20tariff/feedin_tariff.aspxhttp://www.decc.gov.uk/en/content/cms/meeting_energy/Renewable_ener/feedin_%20tariff/feedin_tariff.aspxhttp://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://refhub.elsevier.com/S1359-4311(13)00673-X/sref25http://refhub.elsevier.com/S1359-4311(13)00673-X/sref24http://refhub.elsevier.com/S1359-4311(13)00673-X/sref24http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref23http://refhub.elsevier.com/S1359-4311(13)00673-X/sref22http://refhub.elsevier.com/S1359-4311(13)00673-X/sref22http://refhub.elsevier.com/S1359-4311(13)00673-X/sref22http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref21http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref20http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref19http://refhub.elsevier.com/S1359-4311(13)00673-X/sref18http://refhub.elsevier.com/S1359-4311(13)00673-X/sref18http://refhub.elsevier.com/S1359-4311(13)00673-X/sref18http://www.mathworks.co.uk/products/simulink/http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://refhub.elsevier.com/S1359-4311(13)00673-X/sref17http://refhub.elsevier.com/S1359-4311(13)00673-X/sref16http://refhub.elsevier.com/S1359-4311(13)00673-X/sref16http://refhub.elsevier.com/S1359-4311(13)00673-X/sref16http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref15http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref14http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref13http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref12http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref11http://refhub.elsevier.com/S1359-4311(13)00673-X/sref10http://refhub.elsevier.com/S1359-4311(13)00673-X/sref10http://refhub.elsevier.com/S1359-4311(13)00673-X/sref10http://refhub.elsevier.com/S1359-4311(13)00673-X/sref9http://refhub.elsevier.com/S1359-4311(13)00673-X/sref9http://refhub.elsevier.com/S1359-4311(13)00673-X/sref9http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref8http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref7http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref6http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref5http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref4http://refhub.elsevier.com/S1359-4311(13)00673-X/sref3http://refhub.elsevier.com/S1359-4311(13)00673-X/sref3http://refhub.elsevier.com/S1359-4311(13)00673-X/sref3http://refhub.elsevier.com/S1359-4311(13)00673-X/sref2http://refhub.elsevier.com/S1359-4311(13)00673-X/sref2http://refhub.elsevier.com/S1359-4311(13)00673-X/sref2http://refhub.elsevier.com/S1359-4311(13)00673-X/sref1http://refhub.elsevier.com/S1359-4311(13)00673-X/sref1http://refhub.elsevier.com/S1359-4311(13)00673-X/sref1

8/18/2019 1-s2.0-S135943111300673X-main

12/12

[30] Ned Mohan, Tore M. Undeland, W.P. Robbins, Power Electronics Converters eApplications and Design, third ed., John Wiley, New York, 2003 .

[31] C. Barbier, A. Maloyd, G. Putrus, Embedded Controller for LV Network withDistributed Generation, Econnect Ventures Ltd., Department of Trade andIndustry, May 2007.

[32] B.R. Anderson, P.F. Chapman, N.G. Cutland, C.M. Dickson, G. Henderson, J.H. Henderson, P.J. Iles, L. Kosmina, L.D. Shorrock, BREDEM-12 ModelDescription: 2001 Update, 2002. BRE Press, Watford.

[33] R. Yao, K. Steemers, A method of formulating energy load prole for domesticbuildings in the UK, Energy Build. 37 (2005) 663e671.

[34] I. Richardson, M. Thomson, One-minute Resolution Domestic Electricity UseData, 2008e2009, UK Data Archive Colchester, Essex, October 2010.

[35] National Calculation Method, NMC: National Calculation Method e Intro-duction to SBEM. Available at: http://www.ncm.bre.co.uk/ (accessed08.08.12).

[36] U.S. Department of Energy, EnergyPlus Energy Simulation Software: WeatherData. Available at: http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region ¼6_europe_ wmo_region_6/country¼GBR/cname¼United%20Kingdom (accessed 08.08.12).

http://refhub.elsevier.com/S1359-4311(13)00673-X/sref28http://refhub.elsevier.com/S1359-4311(13)00673-X/sref28http://refhub.elsevier.com/S1359-4311(13)00673-X/sref28http://refhub.elsevier.com/S1359-4311(13)00673-X/sref29http://refhub.elsevier.com/S1359-4311(13)00673-X/sref29http://refhub.elsevier.com/S1359-4311(13)00673-X/sref29http://refhub.elsevier.com/S1359-4311(13)00673-X/sref30http://refhub.elsevier.com/S1359-4311(13)00673-X/sref30http://refhub.elsevier.com/S1359-4311(13)00673-X/sref30http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref32http://refhub.elsevier.com/S1359-4311(13)00673-X/sref32http://refhub.elsevier.com/S1359-4311(13)00673-X/sref32http://www.ncm.bre.co.uk/http://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://apps1.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=6_europe_%20wmo_region_6/country=GBR/cname=United%20Kingdomhttp://www.ncm.bre.co.uk/http://refhub.elsevier.com/S1359-4311(13)00673-X/sref32http://refhub.elsevier.com/S1359-4311(13)00673-X/sref32http://refhub.elsevier.com/S1359-4311(13)00673-X/sref32http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref31http://refhub.elsevier.com/S1359-4311(13)00673-X/sref30http://refhub.elsevier.com/S1359-4311(13)00673-X/sref30http://refhub.elsevier.com/S1359-4311(13)00673-X/sref30http://refhub.elsevier.com/S1359-4311(13)00673-X/sref29http://refhub.elsevier.com/S1359-4311(13)00673-X/sref29http://refhub.elsevier.com/S1359-4311(13)00673-X/sref29http://refhub.elsevier.com/S1359-4311(13)00673-X/sref28http://refhub.elsevier.com/S1359-4311(13)00673-X/sref28

1-s2.0-S135943111300673X-main

Documents

Transcript of 1-s2.0-S135943111300673X-main