Optimal sizing of hybrid solar micro-CHP systems for the household sector

Accepted Manuscript Optimal sizing of hybrid solar micro-CHP systems for the household sector Caterina Brandoni, Massimiliano Renzi PII:

S1359-4311(14)00894-1

DOI:

10.1016/j.applthermaleng.2014.10.023

Reference:

ATE 6037

To appear in:

Applied Thermal Engineering

Received Date: 7 July 2014 Accepted Date: 9 October 2014

Please cite this article as: C. Brandoni, M. Renzi, Optimal sizing of hybrid solar micro-CHP systems for the household sector, Applied Thermal Engineering (2014), doi: 10.1016/j.applthermaleng.2014.10.023. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT 1

Optimal sizing of hybrid solar micro-CHP systems for the household sector

2

Caterina Brandonia, Massimiliano Renzib

3 *Corresponding author: [email protected], T:+44 (0)28 903 68166;

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F:+44 (0) 28 903 68239

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a

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Ulster, Newtownabbey, Co Antrim BT370QB, UK

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b

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39100 Bolzano, Italy

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Centre for Sustainable Technologies, School of the Built Environment, University of

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Libera Università di Bolzano, Facoltà di Scienze e Tecnologie, Piazza Università 5,

Abstract

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The present paper addresses the importance of optimal sizing hybrid microgeneration

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systems for dwelling applications. Indeed, the parameters, the constraints and the

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criteria which must be considered in the sizing phase are several: i) energy prices,

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ii) ambient conditions, iii) energy demand, iv) units’ characteristics, v) electricity grid

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constraints. The hybrid renewable system under analysis is made up of an electrical

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solar device and a micro-Combined Heat and Power, micro-CHP unit coupled to a

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cooling device. In addition to traditional PhotoVoltaic, PV, technology the work

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considers a High Concentration PhotoVoltaic, HCPV, device, with the aim of

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understanding its potential application in the countries of the Mediterranean. Results point out the importance of optimal sizing hybrid renewable energy systems, in

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particular the micro-CHP unit, in order to maximize the economic and the energy

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savings with respect to conventional generation. Furthermore results point out the

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critical nature of electricity grid constraints, which can halve the achievable energy

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savings.

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Key words: micro-generation, optimal sizing; micro-CHP; PV; CPV; energy savings. 1

ACCEPTED MANUSCRIPT Nomenclature

28

A

Area [m2]

29

c

Cost [€]

30

e

Electric Energy [kWh]

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f

Fuel Consumption [kWh]

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G

Solar Radiation [W/m2]

33

h

hours

34

I

Current [Amp]

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P

Power [W]

36

r

interest rate

37

T

Temperature [°C]

38

V

Voltage [V]

39

Greek symbols

40

α

Absorptivity

41

β

PV panel efficiency loss coefficient [1/°C]

42

η

Efficiency

43

τ

Transmisivity

44

Abbreviations

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AC

Alternating Current

AS

Alternative System

BOS

Balance of System

48

CHP

Combined Heat and Power System

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COP

Coefficient of Performance

50

CO2

Carbon Dioxide

51

CO2ER Carbon Dioxide Emission Reduction

52

DC

46 47

Direct Current 2

DNI

Direct Normal Radiation

54

EF

Emission Factor [gCO2eq/kWh]

55

EU

Europe

56

FC

Fuel cell

57

fiT

Feed in Tariff

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GTI

Global Irradiation over a surface tilted at 30°

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HCPV High Concentration Photovoltaic

60

ICE

Internal Combustion Engine

61

IEA

International Energy Agency

62

LP

Linear Programming

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MGT

Microturbine

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MJ

Multi Junction

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MOLP Multi Objective Linear Optimization

66

NOCT Nominal Operating Cell Temperature [°C]

67

NCV

68

O&M Operation and Maintenance

69

PE

Primary Usage Factor [kWhPE/kWh]

70

PES

Primary Energy Savings [%]

71

PV

Photovoltaic

SP

Separate Production

SPB

Simple Pay Back

74

STC

Standard Test Condition

75

TES

Thermal Energy Storage

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TMY

Typical Meteorological Year

77

VER

Variable Energy Resources

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Subscripts

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M AN U

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Net Calorific Value [kJ/kg]

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3

a

Ambient

80

AC

Annualized cost

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c

Cell

82

cool

Cooling

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el

Electric

84

fiT

Feed in Tariff

85

h

Hour

86

k

Day

87

µCHP

88

op

Operating

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p

peak

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n

Nominal

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sell

Sell

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th

Thermal

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Adscripts

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A

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micro-Combined Heat and Power

Annualized 1. Introduction

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The building sector is a high energy-demanding sector in both developed and new

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developing countries. Due to the increasing urbanization, the number of people living in

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cities is expected to increase by up to 70% compared to the rural population [1]. In most IEA countries the building sector accounts for the 32% of the final demand for energy

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[2], of which an important share comes from dwellings. In EU-27, residential energy

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accounts for about 26% of the total consumption, second only to the transport sector in

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terms of usage [3].

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Buildings offer great potential for savings in energy usage as revealed by an IEA study

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[4] according to which the 25% of the reduction in emissions of CO2 will come from 4

ACCEPTED MANUSCRIPT buildings by 2030. The measures identified to reach this challenging target are:

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(i) minimum energy performance standards, (ii) construction of new buildings with net-

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zero energy consumption, (iii) improvement of energy efficiency in existing buildings,

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(iv) building certificates and (v) improvement of energy performance of building

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envelope.

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The introduction of decentralised energy generation is a further measure to meet this

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goal [5], necessarily required by recent building regulations which asks for “near-zero”

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energy buildings in the coming years [6].

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Distributed generation devices can be fed by renewable or fossil fuels, and can also be

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operated in combined heat and power production [7], providing important results in

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terms of energy savings and emission reduction [8].

116

Among renewable generation, PhotoVoltaic, PV, systems are particularly suitable for

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building applications, due to: i) worldwide availability and potentiality of solar sources

118

of energy iii) their easy integration into new and existing buildings, iv) the high

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temporal correlation of solar irradiation with electricity demand.

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Over the last few years, thanks to Government funding and supporting schemes, the PV

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market has experienced a rapid expansion, with a consequently remarkable reduction in

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the capital cost of technology. For instance, the cost of a 3-10 kWp PV system, thanks to

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both improvements in research and economies of scale, has decreased from

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14,000 €/kWp in 1990 down to 1,800 €/kWp in 2014 [9]. In addition to traditional PV

technologies, High Concentration PhotoVoltaic (HCPV) systems are attracting an

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increasing interest by industry, researchers and policy-maker [10], although the

127

reduction in PV capital cost is threatening their competitiveness.

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The main characteristic of this technology is that the amount of photosensitive material

129

is reduced and it is replaced with a cheaper optical system [11]; this means that an

5

ACCEPTED MANUSCRIPT HCPV module is able to capture only the direct normal rays but with a higher efficiency

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and a lower area occupancy than traditional silicon systems [12].

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Consequently HCPV systems are really effective only in those countries where the solar

133

radiation is more intense and constant [13].

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The main problem related to the integration of solar electrical systems into the national

135

electricity grid comes from its intermittency and unpredictable nature [14], which it

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shares with wind generation [15]. Although variability and uncertainties are familiar

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features of all power systems, in order to achieve a greater impact from these sources an

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additional introduction of load will be required, following and ramping reserves in a

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time frame ranging from minutes to hours. In particular, this aspect is of great concern

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for the integration of these sources (solar and wind power) into the existing low voltage

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grid (as required by solar systems for building sector applications); in fact, in most cases

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they are not equipped with sophisticated protective relaying and control schemes such

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as a utility scale transmission line [16].

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A mid to long-term solution, widely studied in literature [17, 18], is the introduction of

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micro-grids, but currently they are at an early stage in development and most of them

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are pilot projects. The main problem is related to their higher initial cost, since they

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require power electronics and sophisticated coordination among consumers or areas.

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A promising opportunity in the short-term proposed by some of the authors in a

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previous paper [19] and studied further in the present work, is the introduction of hybrid systems, consisting of coupling solar systems with micro-CHP units fuelled by

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natural gas. Indeed developing hybrid PV systems with CHP devices enables additional

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PV deployment above what is possible with a conventional centralized electric

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generation system [20].

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The high cost in terms of investment in the technologies involved requires the

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optimization of the system size in order to be competitive with conventional generation. 6

ACCEPTED MANUSCRIPT When dealing with hybrid and, in general, poly-generation systems, identifying the

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optimal sizing of the energy conversion systems is a tough issue due to several

158

parameters that must be taken into account in the analysis, such as electricity and fuel

159

price, energy loads and weather conditions [21].

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Solutions to this problem can be achieved via different techniques: i) maximum

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rectangle methodology [22-24] ii) linear programming, which was recently applied to

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the optimal sizing of residential micro-CHP systems [25] iii) mixed integer linear

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programming, which is widely considered in the optimal sizing and operation of

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medium size CHP plants [26], iv) fuzzy logic [27] and v) genetic algorithms, used in

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particular when a multi-objective optimization is followed [28].

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The present paper addresses the optimal sizing of hybrid micro-CHP systems defined on

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the basis of linear programming techniques, with the aim of taking advantage of rapid

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calculations even in the presence of a high number of variables. The novelty of the

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paper refers, in particular, to the hybrid solution proposed for satisfying the energy

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demand in dwellings, consisting of a micro-CHP unit (chosen in a set of available

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technologies) and a solar energy device. Moreover, the analysis of the advantages and

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limitations of introducing a high concentration solar energy system compared to silicon

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PV systems is presented.

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This paper is organised as follows: section 2 describes the systems modelled; section 3

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illustrates the algorithm developed; section 4 discusses the simulation results for a

residential case study located in a country on the Mediterranean coast. Finally some

conclusions and remarks are presented.

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2. Energy systems modelling

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Figure 1 shows the conceptual lay-out of the system under analysis, which is made up

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of : i) a solar electrical system that can be either a HCPV or a PV system, ii) a micro7

ACCEPTED MANUSCRIPT CHP device (the technologies considered are ICE, Stirling, microturbine and fuel cell),

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iii) a Thermal Energy Storage (TES) and iv) a cooling device (vapour compression

184

chiller or water/LiBr absorption chiller modelled on the basis of their Coefficient of

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Performance of respectively, 0.6 and 3). The chief characteristics and performances of

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the main energy systems modelled are presented hereinafter.

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2.1 Solar systems modelling

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Figure 1. Conceptual lay-out of the system under analysis

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The solar radiation and the ambient condition required to evaluate the yield of the solar

systems, were obtained using a Typical Meteorological Year (TMY) database. For each

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of the locations studied, the hourly values of the following quantities are used: the

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Direct Normal Irradiation (DNI); the global solar irradiation over a south-oriented 30°

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tilted surface (GTI, which is the optimized tilt for the Italian latitudes); the ambient

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temperature.

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ACCEPTED MANUSCRIPT Literature is rich of formulations and procedures to model the performance of grid

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connected commercial PV systems [29, 30]. The efficiency of a PV panel is strongly

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dependent on the ambient conditions, the most influential being the available solar

202

radiation and the solar cell temperature figures: the former can be obtained

203

from specific databases representing a TMY of the location under analysis; the latter is

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defined in Celsius degrees using the correlation suggested by Mondol et al.[31] and

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Mattei et al.[32] for building integrated PV panels:

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G (NOCT − 20 )1 − η c  800  τα 

SC

TC = T A +

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(1)

Tc represents the temperature of the cell, Ta is the ambient temperature, G is the solar

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radiation, NOCT is the panel nominal operating cell temperature, ηc is the cell

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efficiency, τ and α are the transmissivity and the absorptivity of the cell. The efficiency

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of the cell is:

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

 G    1000 

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η c = η n ,c 1 − β (T − 25) + 0.12 log 

(2)

where ηn,c is the nominal efficiency of the cell under standard test conditions, β is

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efficiency loss coefficient of the solar cell with increasing temperature, expressed in

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1/°C. Using an iterative procedure it is possible to assess both the cell temperature and

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the cell efficiency. In this work, the reference PV panel is a commercial poly-cristalline

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module manufactured by Sharp [33]: it has an effective aperture area of 1.47 m2 and its

nominal cell efficiency and its performance parameters are used in the abovementioned

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formulas to evaluate the module performance under real working conditions; in

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Standard Test Conditions (STC) the efficiency of this module is 14.6%, its peak power

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production is 240 W and the efficiency temperature coefficient is 0.0044 °C-1 [24].

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The DC power produced by the solar module is:

222

PDC = η c Aeff G

(3) 9

ACCEPTED MANUSCRIPT Where Aeff is the effective net cell aperture. Besides the effect of the irradiation and the

224

cell temperature, PV systems performance is also affected by a series of other losses,

225

also referred as the Balance Of System, BOS. These losses take into account the

226

effectiveness of all the components required to run a PV plant other than the PV panels.

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These include wirings, switches, support racks, inverters, and batteries in the case of

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off-grid systems. As a consequence, the value of the BOS losses is a result of a

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combination of several parameters; for a well-designed small scale system an overall

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value of 15% of BOS losses is normally assumed.

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Finally the AC power production can be evaluated as:

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PAC = PDCη BOS

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The HCPV modelled refers to a prototype designed and developed in collaboration with

234

the authors [19]. It is characterized by an innovative design, since it uses a very compact

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and lightweight chassis, a low-encumbrance concentration optic, an accurate tracking

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mechanism and a very small triple junction solar cell. Its characteristics make it

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extremely interesting also for small-scale plants and, in particular, for building

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integration application.

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In order to achieve the goal of reducing the encumbrance of the module, a specific optic

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was developed to suite a circular triple junction solar cell having a diameter of only

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(4)

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2.3 mm: a Fresnel lens is used as the primary optic and a reflective cone as the secondary optic. The secondary optic has a double aim: firstly to improve the concentrated solar flux homogeneity on the cell (which is a requirement to achieve

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higher cell fill factor, efficiency and reliability); secondly, to reduce the optical

245

efficiency losses in case of tracking misalignment (the concentrated radiation from the

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primary optic that is not directed towards the cell is reflected by the secondary optics

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and redirected onto the photosensitive material).

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ACCEPTED MANUSCRIPT 248

The HCPV plant can be assembled according to the user needs, in terms of the

249

electrical power requirement and space availability. Table 1 shows the main

250

characteristics of a single module.

251 Table 1. Main performance parameters and efficiency data of the HCPV and PV module

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HCPV MODULE Parameter Power output (DNI 900 W/m2, ambient temp. 25°C) Cell Type Cell dimension Cell efficiency (flash test) Optics Optics efficiency (on axis) Dimensions

Value 70 W

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Monolithic Triple Junction Circular, 2.3 mm diam. 41% Fresnel lens and secondary optics 85 % 1.6x0.4x0.4 m

PV MODULE Parameter Cell Type Module model Module power (STC) Module electrical efficiency (STC) Module temperature coefficient

TE D

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Value Polycristalline silicon solar cell Sharp ND-R240A6 240W 14.6 % 0.0044 °C-1

For the simulation of the afore mentioned HCPV module, as described in previous

255

papers written by the authors [19], a simplified approach has been adopted: the profile

256

of the solar radiation spectrum is neglected and only the whole DNI flux is used as an

258 259

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input parameter. The main quantities affecting the HCPV performances that have been considered to simulate the present module are: i) the Direct Normal Irradiation (DNI) available to the module, ii) the optical system efficiency and iii) the solar cell response.

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The evaluation of the optical efficiency has been calculated using simulations made

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with a ray-tracing software. Its value is strongly influenced by the mechanical accuracy

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of the dual-axis tracking system: in fact, the optical efficiency falls if the tracking

263

accuracy is lower than the design acceptance angle of the optics. The long term tracking

264

accuracy can be evaluated by means of a probability function as reported in [34]. 11

ACCEPTED MANUSCRIPT The optics were designed to reach high levels of solar concentration: the prototype is

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equipped with a set of a primary Fresnel lens and a secondary optic that concentrate the

267

solar radiation by 1000 times (or 1000 suns). This very high value of optical

268

concentration can be achieved thanks to the adoption of small Multi Junction (MJ) solar

269

cells whose dimension and characteristics are reported in Table 1. The high

270

concentration level implies a very intense radiation flux on the solar cell receiver and,

271

therefore, a larger amount of thermal energy that must be dissipated (the energy that is

272

not converted to electric power). The use of smaller cells allows a better distribution of

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the hot spots on the module and thus the extra heat can be spread more easily.

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The design optical concentration ratio reaches up to 1000 suns in the presented

275

prototype. The use of a small MJ solar cell allows the adoption of high optical

276

concentration ratios: in fact, the hot spots on the solar cell receiver are smaller and

277

better distributed on the module thus the extra heat can be spread more easily.

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The triple junction solar cell response has been simulated by means of a semi-empirical

279

diode model. The model requires a series of parameters that have been obtained on the

280

basis of experimental measurements on the triple junction cell. A test bench with a solar

281

simulator has been used to acquire the I-V curve of the cell with varying concentration

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levels and cell temperatures. The parameters to feed the diode model are obtained using

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a regression analysis that minimizes the difference between the experimental I-V curve

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data and the I-V curve from the analytical model. As has already been mentioned, the second parameter that affects the triple junction cell performance is the working

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temperature for which a specific model has been defined. The two main figures

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affecting the cell’s temperature are the ambient temperature and the optical

288

concentration ratio (a combination of the optical efficiency and the DNI). Experimental

289

data allowed the definition of an accurate model for the evaluation of the cell

290

temperature which is described in detail in [19]. 12

ACCEPTED MANUSCRIPT Each single cell is mounted inside a module with series connections; the modules are

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then linked to an inverter that tracks the maximum power point of the HCPV string and

293

converts the DC power to AC power. Also in this case, as in the PV system, the BOS

294

losses are taken into account for the figure of 15% of the total DC power produced by

295

the HCPV module.

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In order to appreciate the performances of the solar system modelled, Figure 2 shows

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the electricity production of the HCPV system in the three selected locations (two in the

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centre and one in the south of Italy) compared with the PV performance. The graph also

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reports the available DNI and the global irradiation over a surface tilted at 30

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degrees (GTI). It is possible to see how the production of the solar power systems

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increases with higher solar radiation; another important outcome is that the HCPV

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performs better than a traditional PV system when the ratio of the DNI and the GTI is

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higher. On the basis of these results, an approximate limit for the application of an

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HCPV system should be a point where the DNI/GTI ratio is higher than 80%.

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2000

84%

HCPV

DNI

Global 30° 82%

1600

78%

1200

76%

1000

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74%

800

72%

600 400

70%

200

68%

0 PV HCPV DNI Global 30° DNI/GTI ratio

Ancona 1351 1137 923 1291 72%

Roma 1476 1391 1205 1638 74%

Palermo 1659 1705 1517 1855 82%

DNI/GTI ratio

80%

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Electric energy production [kWh/kWp]

1800

PV

66%

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Figure 2. PV and HCPV system performances for three different locations in the centre 13

ACCEPTED MANUSCRIPT 307 308

(Ancona, Roma) and south of Italy (Palermo)

2.2 Micro-CHP modelling

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All the micro-CHP units were modelled on the basis of the main characteristic

311

parameters, such as electrical efficiency and power to heat ratio.

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The electrical efficiency of the system has been considered constant in order to take

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advantage of linear programming techniques. LP has been widely used in literature in

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the optimization of the energy systems, for instance for sizing residential micro-CHP

315

systems [25] and for the system design and unit commitment of a micro-grid [35].

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Although the assumption is strong, since in off design condition and transient operation

317

the electrical efficiency decreases [36], the main aim of the paper is to highlight the

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importance of properly sizing micro-CHP systems when coupled to solar electrical

319

devices, and to assess the influence of the main design parameters on sizing, rather than

320

optimize a particular lay out.

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Micro-generation technologies are characterised by an electrical output lower than

322

50kW, as defined by the EU Cogeneration Directive 2004/8/EC [37].

323

As above-mentioned, the technologies considered in this work are four: ICE, Stirling

324

engine, microturbine and fuel cell. Table 2 shows the main parameters used in the

325

analysis which were derived from commercial units [38].

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Table 2. Main design parameters used for micro-CHP and chillers modelling Micro-CHP unit ICE

Electrical efficiency [%] Thermal efficiency [%] Power to heat ratio Specific cost [€/kWe] O&M COST Lifetime Chiller unit

Stirling

MGT

Fuel cell

24

15.8

20

40

64 0.38 3400 0.021

75 0.2 4500 0.017

60 0.33 3600 0.014 10 years

40 1 6700 0.019

14

ACCEPTED MANUSCRIPT COP Lifetime Specific cost [€/kW]

Compression chiller 3

Absorption chiller 0.7 10 year

250

300

ICEs are the most mature form of technology, taking advantage of research coming

330

from the automotive sector. For cogenerative applications, thermal power at both a low

331

and high temperature can be recovered, respectively from the engine cooling water and

332

the exhaust gas. They can be applied in dwellings and the service sector with good

333

results compared to separate energy production [39]. A 24% electrical efficiency has

334

been assumed for the analysis on the basis of parameters characterising small size

335

commercial units [40].

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Stirling engines indicate an interesting application for the household sector, thanks to

337

their features of having a simple design, producing minimal noise and vibration and

338

allowing multi-fuel use. Emissions from current Stirling engines could even be ten

339

times lower than that of reciprocating engines and comparable with modern gas burner

340

technology. For dwelling applications free-piston Stirling engines appear to be a

341

competitive technology, the advantage of this technology increases as power range

342

decreases [41]. At present units from 1 kWel to 50 kWel have been developed and

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commercialized for use in the household sector. Whispertec [42], for instance, has

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developed 1 kWel unit based on a kinematic engine with a low electric conversion

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efficiency of 12%; Microgen [43] has developed a natural gas fuelled cogeneration free piston Stirling engine unit with an electric output of 1 kWel, (electric efficiency of

15%), a thermal output of 4.5 kWth and an overall efficiency of 92-96% [44]. In the

348

present analysis an electrical efficiency of 15% has been assumed.

349

Microturbines, MGT, are a promising technology for trigenerative applications, in

350

particular for the tertiary sector, since the minimum output of a commercial available

351

unit is of 15kWel [45]. All the thermal power recovered is at a high temperature, coming 15

ACCEPTED MANUSCRIPT from the exhaust gases; these machines require low maintenance, have a long lifetime,

353

due to few moving parts and a simpler design and they are characterised by very low

354

emissions [46].

355

According to the data available in literature for a prototype of a 3kW MGT, an electrical

356

efficiency of 20% has been considered [47]. Fuel cell systems are electrochemical

357

devices that directly convert chemical energy to electricity, the most important

358

applications are in the transport and power generation sectors [48].

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They are the most promising technologies thanks to their high power to heat ratio, and

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are particularly suitable for building loads. For single-dwelling stationary applications

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they show an electrical efficiency of 40%, with an overall efficiency of about 60% [38].

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At present, the main barrier to their widespread application is the high investment cost

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and their reliability (as a matter of fact, fuel cells exhibit performance decay after

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around one thousands hours operation) [49]. For the present analysis an electrical

365

efficiency of 40% has been assumed [38].

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3. Optimal sizing of poly-generation systems

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As previously discussed, a linear program has been developed for the optimal sizing of

369

the hybrid system under analysis. The conceptual lay-out of the hybrid solar micro-CHP

370

system was designed for providing the highest flexibility (see Figure 1). Electricity

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needs can be satisfied by: i) the solar electrical system (PV/HCPV), ii) the micro-CHP unit and iii) the electricity bought from the grid, with the solar electrical system having

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the priority.

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The micro-CHP unit, the boiler or the TES can satisfy the thermal demand. Finally

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either the absorption chiller (fed by the micro-CHP unit and by the TES) or the vapour

376

compression chiller can satisfy the cooling needs. The TES that can be used in the

377

presented system is similar to commercial solar boilers. It consists of a vessel in which 16

ACCEPTED MANUSCRIPT 378

hot water is collected at the temperature required by the user. The cooling water from

379

the micro-CHP device flows in the coil installed inside the boiler and warms up the

380

vessel content.

381

The main assumptions of the algorithm are: •

the minimum time step considered is an hour;

383

•

for each hour the energy needs can be satisfied by the hybrid combined heat and

RI PT

382

power systems or by Separate Production, SP, (i.e. electrical energy bought from

385

the grid, thermal power produced by a heating boiler, cooling demand satisfied

386

by a compression vapour chiller) on the basis of the minimum cost criteria; •

a typical day for each season has been considered in the analysis and results

M AN U

387

SC

384

have been extended for the entire year. As discussed by [50] the choice of

389

typical days to simulate the entire year can influence the results of the

390

optimization. The extraction of 288h (12 typical days x 24 hours) is in line with

391

[51], although for the case under analysis, being a residential end-user, a shorter

392

time step has been chosen and week-ends have not been considered. •

394

396 397 398

a working range from zero to the nominal power, ; •

the O&M costs of the Thermal Energy Storage have been omitted

AC C

395

each micro-CHP devices, belonging to four different technologies can be used in

EP

393

TE D

388

•

for the referenced case no supporting schemes have been considered.

The formulations of the objective functions and the constraints considered in the

analysis have been reported as follows.

399 400

3.1 Objective function

401

The aim of the procedure is to minimize the annualized cost derived from the

402

implementation of hybrid micro-CHP system, C A (Eq.5), given by the sum of the

17

ACCEPTED MANUSCRIPT A A annualized capital cost, CCC , the cost of all the devices (i.e. solar system, Csolar , micro-

404

A A CHP unit, CµACHP , thermal energy storage, CTES , vapor compression chiller, Ccomp_chill , and

405

A absorption chiller, Cabs_chill ) and the yearly cost to operate the hybrid micro-CHP

406

systems, Cop .

407

RI PT

403

A A A A A min C A = C AC + Cop = C solar + C µACHP + CTES + Ccomp _ chill + C abs _ chill + C op

(5)

The annualized capital cost of each device, C AAC (Eq.6), has been calculated on the basis

409

of the capacity recovery factor, considering an interest rate of 3% and the lifetime of

410

each device.

411

C ⋅ i ⋅ (1 + r ) = device lifetime (1 + r ) −1

M AN U

(C )

A AC device

SC

408

lifetime

(6)

The yearly operating cost, Cop , is calculated according to Eq.7 which is the hourly sum

413

(assessed on the basis of 12 typical days) of: i) the fuel cost of running the micro-CHP

414

unit (given by the fuel cost, cµfuel multiplied by the micro-CHP fuel consumption in the CHP

415

specific hour, f µCHP ); ii) the fuel cost for feeding the heating boiler (given by the

416

fuel specific cost of fuel, cboiler , multiplied by the boiler fuel consumption in the specific

417

hour, f boiler ); iii) the operating and maintenance cost for the micro-CHP unit (given by

419

EP

AC C

418

TE D

412

&M the specific micro-CHP operative cost, cµOCHP ); iv) the cost for purchasing the electric

energy from the grid (given by the specific selling price, celbuy , multiplied by the electric

420

energy bought from the grid in that hour, ebuy ). It is necessary to subtract from this

421

amount: i) the revenues coming from selling the electric energy produced by the solar

422

systems and the micro-CHP unit (given by the specific selling price, celsell , multiplied by

423

the electric energy sold to the grid in that hour, esell ). In the sensitivity analysis, 18

ACCEPTED MANUSCRIPT 424

revenues derived by the feed-in mechanism have been also considered as the electricity

425

feed − in produced by the solar device, esolar , multiplied by the feed-in tariff, rsolar .

[

( ) (e ) − (c ) (e )

fuel O&M buy C op = ∑∑ c µfuel CHP ( f µCHP )h + c boiler ( f boiler )h + cµCHP (e µCHP )h + cel 12

24

k =1 h =1

426

sell el h

buy h

h

h

feed − in (esolar )h − rsolar

(7)

RI PT

427

sell

428 3.2 Constraints

430

The algorithm must respect the constraints derived from: i) hourly electricity, thermal

431

and cooling balances, ii) the operation of thermal energy storage and iii) the operation

432

of the micro-CHP devices.

433

In particular, the main constraints considered in the algorithm are:

434

•

of each typical day, k

) + (Pµ ) + (P ) ≥ (P

436

∀h,

(P

437

∀h,

(e ) + (e

438

∀h,

(P

439

•

th ,boiler h

buy h

CHP h

TES h

)

demand h

) + (e ) − (e ) − (e

µCHP h

solar h

) + (P

sell h

) ≥ (P

cool ,abs _ chill h

) ≥ (e

comp _ chill h

(8)

)

demand h

)

cool ,demand h

(9) (10)

EP

cool ,comp _ chill h

Inability of the energy produced by each device (solar system, µCHP, TES, vapour chiller and absorption chiller) to exceed its maximum ratings

AC C

440

M AN U

Electric (Eq.8), thermal (Eq.9) and cooling (Eq.10) energy balance for each hour, h,

TE D

435

SC

429

∀h,

(esolar )h ≤ Capsolar

(11)

∀h,

(e

≤ Cap µCHP

(12)

443

∀h,

(P

≤ CapTES

(13)

444

∀h,

(P

)

(14)

445

∀h,

(P

441 442

446

•

)

µCHP h

)

Th ,TES h

Cool ,Comp _ chill h

)

Cool , Abs _ chill h

≤ CapComp _ chill

≤ Cap Abs _ chill

(15)

From the second hour, the inability of the heat stored in that hour plus the heat 19

]

ACCEPTED MANUSCRIPT 447

stored in the previous hours to exceed the TES capacity

448

∀h,

449

•

(P

) + (P

Th ,TES h

)

Th ,TES h −1

≤ CapTES

(16)

From the second hour, the total amount of the heat stored at the beginning of an hour is equal to the non-dissipated heat stored in the previous hours plus the heat

451

sent to the storage device in that hour minus the heat released to meet the end-use in

452

that hour

453

∀h,

(P

) = (P

)

Th ,TES h −1

(

+ PTh ,TESin

) − (P h

)

Th ,TES out h

(17)

SC

Th ,TES h

RI PT

450

454 4. Case study

456

The algorithm developed has been applied to a residential case study in Rome, central

457

Italy, with the aim of understanding the importance of the optimal sizing of the devices

458

used in the hybrid renewable system. Figure 3 shows the thermal, electricity and cooling

459

loads of the case under study. The thermal, electricity and cooling demand has been

460

calculated according to the procedure presented in [52] where inputs are geographic

461

location, electrical peak load, maximum thermal power for heating and domestic water,

462

and the maximum cooling power in summer. Other parameters used in the analysis are

463

shown in Table 3.

AC C

EP

TE D

M AN U

455

20

ACCEPTED MANUSCRIPT Pth, Jan

Pth, June

Pth, Dec

Pel

Pcool, June

16 14

10 8 6 4 2 0 3

5

7

9

11

13 hours

15

464

21

23

Figure 3. Energy loads for the case under study

466 467

19

M AN U

465

17

SC

1

RI PT

Power [kW]

12

Table 3. Techno-economic parameters assumed for the reference case analysis Electricity purchasing price [€/kWh]

TE D

Electricity selling price [€/kWh] Natural gas price [€/kWh] PV investment cost [€/kWel] HCPV investment cost [€/kWel] PV/HCPV lifetime

20 3000

EP

TES investment costs [€/m3] [53]

Salvage value of all the technologies considered

0

Emission Factor for thermal energy produced by CHP and heating boiler fuel µCHP

, EFboiler [gCO2/kWh]

Emission Factor electricity purchase Emission Factor electricity sold,

EFelbuy

[gCO2/kWh]

PE

fuel µCHP

520 520

EFelsell [gCO2/kWh]

Primary Energy Factor for thermal energy produced by CHP and heating boiler

235

fuel

AC C

EF

off peak 0.17 peak 0.18 60% of electricity purchasing price 0.046 1800 2500

1.1

fuel

, PE boiler [kWhPE/kWh]

Primary Energy Factor electricity purchased, Primary Energy Factor electricity sold,

PE elbuy

PE elsell

[kWhPE/kWh]

[kWhPE/kWh]

468

21

2.5 2.5

ACCEPTED MANUSCRIPT 469

Besides the annualized cost of the systems and the size of each device, the achievable

470

Primary Energy Savings, PES (Eq. 18) and the CO2 Emissions Reduction, CO2ER, (Eq.

471

19) have been calculated on the basis of results coming from the algorithm developed:

PES =

Primary energySP − Primary energyhybrid _ system Primary energySP

[

(

fuel fuel O&M buy ( fboiler ) + PEelbuy (ebuy )year − ∑ ∑ PEµfuel PEboiler CHP ( f µCHP )h + PE boiler ( f boiler )h + PEµCHP (eµCHP )h + PEel 12

=

=

RI PT

472

24

k =1 h =1

fuel boiler

PE

473

( fboiler ) + PE (ebuy )year buy el

h

sell el h

buy h

sell h

(18)

CO2 ER =

CO2, SP − CO2, hybrid _ system CO2, SP

=

[

(

24

k =1 h =1

M AN U

fuel fuel O&M buy EFboiler ( fboiler ) + EFelbuy (ebuy )year − ∑ ∑ EFµfuel CHP ( f µCHP )h + EFboiler ( f boiler )h + EFµCHP (eµCHP )h + EFel 12

=

SC

474

) (e ) − (PE ) (e ) ]

fuel boiler

PE

475 476

( fboiler ) + PE (ebuy )year

) (e ) − (EF ) (e ) ] h

buy h

sell el h

sell h

buy el

(19)

where EF is the carbon dioxide Emission Factor, and PE is the Primary Energy factor,

478

which is based on the Italian electricity supply mix, shown in Table 3 [19]. The same

479

factor for both selling and purchasing electricity has been assumed.

TE D

477

480

5 Results and discussion

482

Simulation results for the case under study are shown in Table 4. A 16.7% energy

483

saving compared to conventional generation can be achieved at least, suggesting the

485

AC C

484

EP

481

possible profitability of the hybrid renewable system under analysis. The compression chiller represents the best solution to satisfy the cooling needs for all the configurations

486

analysed; as a consequence, its size depends only on the cooling peak load

487

(i.e.11.2 kWcool). The algorithm sets the size of the solar unit up to the maximum, which

488

has been defined by the authors as equal to the electrical peak load; this choice was

489

adopted in order to promote the self-consumption of the electricity produced by solar

490

technology. 22

ACCEPTED MANUSCRIPT 491 Table 4. Simulation results ICE PV

SE

HCPV

PV

MGT HCPV

PV

FC

HCPV

PV

HCPV

0.3

0.3

0.03

0.06

0.09

0.18

0

0

PV [kW]

3.3 0

3.3 0

3.3 0

3.3 0

3.3 0

3.3 0

3.3 0

3.3 0

4,060

4,269

4,062

4,273

17.4%

18.2%

17.7%

17.7%

16.7%

17.2%

18.0%

17.4%

17.5%

16.5%

HCPV [kW] Comp. chiller cooling power [kW] Abs. chiller cooling power [kW] TES [kWh] CA[€]

11.2 0 0 4,186

4,061

20.7%

20%

17.9%

CO2ER [%]

20.4%

19.7%

17.7%

5,975 € 859

493

4,273

M AN U

4,032

PES [%] Operating hours of microgeneration [hours] Savings in operating costs [€]

RI PT

Micro-CHP [kW]

SC

492

5,975

5,975

5,975

6,037

6,037

0

0

€ 703

€ 729

€ 529

€ 754

€ 575

€ 720

€ 509

This outcome derives from both the low cost of solar technologies, and the reduction in

495

the energy bill due to the avoided cost of buying the electricity produced by the

496

renewable source.

497

Due to the higher capital cost of HCPV system compared to PV technology and its

498

lower production for the case analysed (see Figure 2), the lower annual total cost is

499

always achieved with the PV system. Since HCPV electricity production depends on the

501 502

EP

AC C

500

TE D

494

DNI component available in the specific geographic area, a sensitivity analysis will be presented in the next paragraph with the aim of better understanding its potential. As shown in Table 4, the optimal size of the micro-CHP unit coupled to PV/HCPV

503

technology is very small, suggesting the need to increase the energy loads by

504

considering the demand of more dwellings. For the case analysed, a minimum of 10

505

dwellings should be considered for using commercial micro-CHP devices.

506

The number of operating hours per year is about 6000, confirming the correctness of the

507

assumption of a lifetime of 10 years attributed to the technology. 23

ACCEPTED MANUSCRIPT The assumption for micro-CHP units of working between 0% and 100% of the nominal

509

power does not affect the results. It has been found out that, on average, the micro-CHP

510

unit works at loads lower than 50% of the nominal power only for 7% of the operating

511

hours.

512

ICE performs better than other technologies in terms of primary energy savings, thanks

513

to its higher electrical efficiency and lower investment cost. In contrast, fuel cells have

514

not been chosen by this algorithm due to their higher annual capital cost; only if a lower

515

investment cost of 4800 €/kWel is assumed as input parameter, a fuel cell is introduced

516

and a TES is added by the algorithm. This allows the exploitation of both the high fuel

517

cell electrical efficiency which is 40%, thus really competitive with centralised

518

electricity generation, and the thermal energy produced by the unit. In this case, the

519

hybrid system achieves a CO2 emission reduction of 18.6%, slightly higher than the

520

case with only PV technology. Since one of the main barriers to the widespread of

521

microgeneration technologies is electricity grid constraints [14], Figure 4 shows the

522

effect of setting a value for electricity sold to the grid equal to zero.

523

In this case the savings are halved, suggesting the importance of: i) improving the

524

current electricity grid infrastructure to support the widespread use of microgeneration

525

technologies, or ii) developing possible alternatives such as: microgrids, demand side

526

management techniques and electricity storage systems.

AC C

EP

TE D

M AN U

SC

RI PT

508

24

ACCEPTED MANUSCRIPT

25%

€ 1,000

20%

€ 800

15%

€ 600

10%

€ 400

5%

Yearly savings

Savings, with grid constraints CO2 reduction, with grid constraints

RI PT

CO2 emissions reduction

Savings, without grid constraints CO2 reduction, without grid constraints

€ 200

0%

€0

SE

MGT

FC

SC

ICE 528

Figure 4. Comparison of savings and PEC reduction in case of Grid Constraints, GC,

529

M AN U

527

and no-GC

530 5.1 Sensitivity analysis

532

In order to understand the influence of the main design parameters on the optimal size

533

of hybrid renewable systems, a sensitivity analysis has been conducted taking the

534

previous results as a reference case.

535

First, it has been assumed, respectively, a 15% increase and reduction in the natural gas

536

price. Figure 5 shows that a lower NG price promotes the use of micro-CHP

537

technology, with a consequent increase in the size and CO2 emission reduction

539 540 541

EP

AC C

538

TE D

531

achievable with respect to the reference case. This effect can be observed for all the

technologies studied (i.e. ICE, Stirling and microturbines) except for fuel cells, for which a 15% reduction in the natural gas price is not enough to counterbalance the higher investment cost.

542

25

ICE, CO2 reduction

SE, CO2 reduction

size ICE

size SE

size MGT

0.6

30%

0.5

25%

0.4

20%

0.3

15%

0.2

10%

0.1

5%

0

CO2 emissions reduction

MGT, CO2 reduction

RI PT

micro-CHP [kW] TES [kWh]

ACCEPTED MANUSCRIPT

0%

-15% NG price

+15%, NG price

SC

543

NG, ref value

Figure 5. Effect of a variation in the natural gas price on the size and the operation of

545

the internal combustion engine, of the Stirling engine and of the micro gas turbine

M AN U

544

546

Figure 6 shows the effect of a variation in the electricity purchase price. As in the

548

previous case, a 15% increase and reduction in the price has been considered. Results

549

show that a reduction in the electricity price largely rules out the use of micro-CHP

550

technologies.

551

In contrast to the previous analysis, an increase in the electricity price helps fuel cell

552

technology to be chosen by the algorithm, providing a further CO2 emission reduction

553

with respect to the single application of PV technology.

554

The result suggests that the profitability of hybrid renewable systems is more

556 557

EP

AC C

555

TE D

547

significantly influenced by a variation in the electricity price than by a variation in the natural gas price. Such a result can encourage the use of Time of Use, TOU tariffs, to promote the introduction of microgeneration systems in the dwelling sector.

558

26

ACCEPTED MANUSCRIPT ICE, CO2 reduction MGT, CO2 reduction size fuel cell

FC, CO2 reduction size ICE size MGT

SE, CO2 reduction size SE 30%

0.35

25% 20%

0.25 0.2

15%

0.15 0.1 0.05 ref value

10% 5%

0% +15% electricity purchasing price

SC

559

0 -15% electricity purchasing price

RI PT

Size [kW]

0.3

CO2 emissions reduction

0.4

Figure 6. Effect of the electric energy price variation on the size and the operation of the

561

internal combustion engine, of the Stirling engine and of the micro gas turbine

M AN U

560

562

Another important parameter to be considered is the investment cost of micro-CHP

564

technologies, which, in the case of small units lower than 5 kWel, ranges from 3500€

565

(ICE) to 6700€ per kWel (fuel cell). A 25% reduction in the investment cost, as shown

566

in Figure 7, promotes the introduction of such micro-CHP systems. In particular,

567

advantages can be observed for technologies characterised by a lower investment cost

568

and higher electrical efficiency, such as ICE.

569

Focusing on the use of a TES, as shown in Table 4, a TES is identified by the optimal

EP

AC C

570

TE D

563

algorithm in none of the analysed configurations.

27

ACCEPTED MANUSCRIPT size micro-CHP, reference

size micro-CHP, -25% capital cost

1

30%

0.8

25% 20%

0.6

RI PT

15% 0.4

10%

0.2

5%

0

CO2 emissions reduction

CO2 emissions reduction, -25% capital cost

Size [kW]

CO2 emissions reduction, reference

0%

ICE

SE

MGT

SC

571

FC

Figure 7. Effect of investment cost reduction on micro-CHP size and on CO2 emission

573

reduction

M AN U

572

If a lower capital cost of the unit is assumed, as shown in Figure 8, the algorithm

576

activates the storage unit for both ICE and fuel cell, increasing the CO2 emission

577

reduction achievable.

TE D

575

578

25%

micro-CHP [kW] TES [kWh]

AC C

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

CO2, CTES 1000€/m3 size micro-CHP, CTES 1000€/m3 size TES, CTES 1000€/m3 30%

EP

CO2, CTES 3000€/m3 size micro-CHP, CTES 3000€/m3 size TES, CTES CTES 3000€/m3

579

20% 15% 10% 5% 0%

ICE

SE

MGT

FC*

580

Figure 8. Effect of TES investment cost

581

(*A reduced capital cost of 4800€/kW has been considered for the fuel cell)

28

CO2 emissions reduction

574

ACCEPTED MANUSCRIPT The result points out that only in the case of higher electrical efficiency is it convenient

583

to use a TES to decouple the fulfilment of electrical and thermal loads and take

584

advantage of the combined heat and power technology.

585

In order to show the effect of a variation in the energy loads on the size of the hybrid

586

system, a 30% increase of the thermal and electrical loads respectively, has been

587

considered. As shown in Figure 9 the size of the micro-CHP device slightly increases in

588

both cases. A reduction in the energy savings achievable for the thermal load variation

589

case can be observed, demonstrating that a simple increase in the thermal loads does not

590

imply an increase in the savings. This is mainly due the smaller share of the renewable

591

energy production, which is not compensated by a higher capacity of the micro-CHP.

M AN U

SC

RI PT

582

CO2 emissions reduction

PV size

594 595

4.0

TE D

3.0

15%

2.5 2.0

10%

1.5 1.0

5%

0.5 0.0 Ref

Thermal +30%

Electrical +30%

AC C

593

4.5 3.5

20%

0%

592

5.0

Size [kW]

25%

EP

CO2 emissions reduction

30%

micro-CHP size

Figure 9. Effect of the increase of the electrical and the thermal load

Thus, a proper study is needed in order to improve the economic profitability of the

596

solution when increasing the thermal loads, as suggested by load-sharing applications

597

[48]. In case of a higher electrical load, energy savings increase, mainly driven by

598

higher revenues from a bigger size of the PV unit.

29

ACCEPTED MANUSCRIPT Finally, in order to understand the potential for using a HCPV system, other

600

geographical areas characterised by a different value of the DNI have been analysed.

601

Figure 10 shows that the HCPV system provides higher CO2 emission reduction than

602

PV only in Palermo, which is the location characterised by the highest value of the DNI

603

component.

RI PT

599

605

20% 15% 10% 5% 0% Ancona

Roma

€ 4,000 € 3,000 € 2,000 € 1,000 €0

Palermo

Figure 10. Comparison between the PV and HCPV in locations characterised by

607

different solar irradiance

EP

TE D

606

608

Total annualised cost

HCPV, yearly savings HCPV, CO2 reduction HCPV, total annualised cost € 5,000

SC

25%

PV, yearly savings PV, CO2 reduction PV, total annualised cost

M AN U

CO2 emissions rediction [%]

604

In this city, the reduction in CO2 emissions of the hybrid system with the HCPV unit is

610

higher than the one using a traditional PV; nevertheless, since the initial investment cost

611 612 613

AC C

609

of the HCPV modules is higher, there is no economic advantage in adopting this

technology. The economic advantage over the PV technology is achieved only when the cost of the HCPV system is reduced down to 1,800 €/kW. Another possibility to

614

stimulate the use of HCPV systems, which proved to achieve higher CO2 emissions in

615

areas characterised by a high level of DNI, is the introduction of a dedicated feed-in

616

tariff. For the case under analysis, an incentive of 0.025€ for each kWh of electricity

30

ACCEPTED MANUSCRIPT 617

produced would be enough to make them more convenient than the traditional PV

618

technology.

619 6 Conclusions

621

The analysis addresses the need for optimal sizing hybrid renewable systems made up

622

of solar technology and micro-CHP units.

623

At the current investment cost of solar technology, in the case of no grid constraints,

624

both PV and HCPV units can be sized on the basis of the electricity peak demand to

625

minimise the total annual costs, independently of the parameters considered and even if

626

no feed-in tariffs are taken into account.

627

Although in those regions characterised by a high DNI component, HCPV technology

628

provides a higher reduction in CO2 emissions than PV one, better results in terms of

629

minimisation of the total annualised cost are shown by PV, due to its lower investment

630

cost that actually threatens the market penetration of the HCPV technology. For the case

631

which has been analysed, a target capital cost of 1800 €/kW or a feed in tariff of

632

0.025 €/kW would be necessary to make HCPV more convenient than PV technology.

633

In contrast to solar technologies, the size of micro-CHP units is heavily influenced by

634

several factors and parameters, such as the investment costs, energy loads and tariffs.

635

Outcomes suggest that the use of micro-CHP technology combined with a solar device

637

SC

M AN U

TE D

EP

AC C

636

RI PT

620

can further reduce the primary energy consumption of dwellings more than single PV technology, but its size must be properly identified.

638

Results also indicate that manufacturers should develop small units, specifically

639

designed for the household sector, characterised by an investment cost lower than

640

3500 €/kWe with an electrical efficiency higher than 20%. Increase in the electrical

641

efficiency can derive from management and lay-out improvements. For example, ICEs

31

ACCEPTED MANUSCRIPT can operate at variable rotational speed instead of a fixed one and Stirling engines can

643

enhance their performance by pre-heating combustion air with the exhaust gases.

644

The TES was never selected by the optimal algorithm in the analysed configurations,

645

also due to its high capital cost. Results point out that only in the case of higher

646

electrical efficiency of the CHP units, is it convenient to use a TES to decouple the

647

fulfilment of electrical and thermal loads and exploit the advantage of the CHP

648

technology.

649

Finally it has been shown that grid constraints can dangerously halve the advantages

650

achievable with hybrid renewable energy systems. In such a case, sizing the solar unit

651

on the basis of the electrical peak load, does not constitute a good strategy. Since the

652

introduction of microgeneration system is widely recognised as a strategic tool to

653

reduce the dependence of the building sector on fossil fuels, the present work suggests

654

the need to either improve the current electricity grid or implement different solutions,

655

such as the development of microgrids, and the promotion of demand side management

656

techniques.

657

Acknowledgement

658

IEA/EBC Annex 54 “Integration of Micro-Generation and Related Energy

659

Technologies in Buildings” supported the work described in this paper. The Annex 54

660

was an international research program and the authors gratefully acknowledge the

SC

M AN U

TE D

EP

AC C

661

RI PT

642

indirect and direct contributions of the other Annex participants.

32

ACCEPTED MANUSCRIPT 662 663 664

Table caption

665

module

666

Table 2. Main design parameters used for micro-CHP and chillers modelling

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Table 3. Techno-economic parameters assumed for the reference case analysis

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Table 4. Simulation results

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Table 1. Main performance parameters and efficiency data of the HCPV and PV

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Figure captions

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Figure 2. PV and HCPV system performances for three different locations in the centre

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(Ancona, Roma) and south of Italy (Palermo)

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Figure 3. Energy loads for the case under study

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Figure 4. Comparison of savings and PEC reduction in case of Grid Constraints, GD,

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and no-GC

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Figure 5. Effect of a variation in the natural gas price on the size and the operation of

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the internal combustion engine, of the Stirling engine and of the micro gas turbine

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Figure 6. . Effect of the electric energy price variation on the size and the operation of

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the internal combustion engine, of the Stirling engine and of the micro gas turbine

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Figure 7. Effect of investment cost reduction on micro-CHP size and on CO2 emission

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reduction

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Figure 8. Effect of TES investment cost

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Figure 9. Effect of the increase of the electrical and the thermal load

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Figure 10. Comparison between the PV and HCPV in locations characterised by

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different solar irradiance

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Figure 1. Conceptual lay-out of the system under analysis

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ACCEPTED MANUSCRIPT Highlights •

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• • • •

The importance of optimal sizing of renewable microgeneration systems is addressed. A hybrid system made up of solar and micro-CHP devices is considered. Both Photovoltaic and High Concentration PV technologies are analysed. Optimal sizing enhances savings in dwelling sector applications. Electricity grid constraints can halve the potential CO2 emissions reduction.