Selection of micro-cogeneration for net zero energy buildings (NZEB) using weighted energy matching index

Accepted Manuscript Title: Selection of micro-cogeneration for Net Zero Energy Buildings (NZEB) using weighted energy matching index Author: Ayman Mohamed Sunliang Cao Ala Hasan Kai Sir´en PII: DOI: Reference:

S0378-7788(14)00480-0 http://dx.doi.org/doi:10.1016/j.enbuild.2014.05.055 ENB 5092

To appear in:

ENB

Received date: Revised date: Accepted date:

17-2-2014 12-4-2014 30-5-2014

Please cite this article as: A. Mohamed, S. Cao, A. Hasan, K. Sir´en, Selection of micro-cogeneration for Net Zero Energy Buildings (NZEB) using weighted energy matching index, Energy and Buildings (2014), http://dx.doi.org/10.1016/j.enbuild.2014.05.055 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.

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Ayman Mohamed a*, Sunliang Cao a, Ala Hasan b, Kai Sirén a

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Aalto University School of Engineering, Department of Energy Technology, P.O. Box 14400, FI-00076 Aalto, Finland VTT Technical Research Centre of Finland, Espoo P.O. Box 1000, FI-02044 VTT, Finland * Corresponding author. Tel.: +358 50 431 5774; fax: +358 9470 23418. E-mail address: [email protected] (A. Mohamed).

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Abstract

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Recently, the extended matching indices for electrical and thermal energy were defined for different types of energy use and

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conversion based on two basic matching indices: on-site energy fraction (OEF), which is the load proportion covered by the

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on-site generated energy, and on-site energy matching (OEM), which is the on-site generated energy proportion utilized by

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the load rather than being dumped or exported. Additionally, the overall weighted matching index (WMI) was proposed,

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combining the extended indices by multiplying them by certain weighting factors expressing the preferences of each. This

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study presents a new model calculating the weighting factors of the WMI based on the non-renewable primary energy

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factors of different energy carriers involving the imported fuel, for micro-cogeneration (µ-CHP) under thermal and

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electrical tracking strategies, with electrical and thermal heat grid feed-in schemes reflecting two opposite extreme matching

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situations in the net zero energy building: load-matching priority and energy export priority strategies. The model is generic

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and can be used for hybrid micro-generation options. As a case, a single family house served by a µ-CHP is analyzed under

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a wide range of electrical outputs and power-to-heat ratios. The µ-CHPs’ characteristics are selected according to the

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highest WMI.

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Keywords

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Energy matching analysis; Mismatch; Net zero energy buildings (NZEB); Micro-cogeneration heat and power (µ-CHP);

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Thermal tracing strategy; Electrical tracking strategy; Electrical grid feed-in; Thermal heat grid feed-in.

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Nomenclature

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Variables:

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dt: The time-step used in the research [h] Eoff-h: Off-site part of electrical power sent to electrical driven heating machines [kWe]

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Selection of micro-cogeneration for Net Zero Energy Buildings (NZEB) using weighted energy matching index

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Eon-h: On-site part of electrical power sent to electrical driven heating machines [kWe] ESoff: Net off-site part of the electrical power sent to the electrical storage [kWe] ESon: Net on-site part of the electrical power sent to the electrical storage [kWe] fgrid,el, fgrid,h: Non-renewable primary energy factor of electrical grid, heat grid, respectively [kWhpr/kWhsite] fCHP,el, fCHP,h: Allocated non-renewable primary energy factor of generated electricity and heat by the µ-CHP [kWhpr/kWhsite] fF: Non-renewable primary energy factor of the fuel supplied to the µ-CHP [kWhpr/kWhsite] fon-site,overall,el, fon-site,overall,el : The on-site overall non-renewable primary energy factors of the generated electricity and heat by on-site hybrid systems [kWhpr/kWhsite] Feg: Interacted electrical power with the electrical grid [kWe] Fdh: Interacted thermal heat power with the thermal heat grid [kWh] FSOC: Fractional state of charge [-] G(t): Temporal on-site generated power [kW] GCHP,el : On-site electrical power generated by µ-CHP [kWe] GPV,el: On-site electrical power generated by PV modules [kWe] Gtot,el: Total on-site electrical power generated by µ-CHP and PV modules [kWe] GCHP,h: On-site thermal heat power generated µ-CHP [kWth] GSTC,h: On-site thermal heat power generated STC modules [kWth] Heoff-h: Generated heat power by the electrical driven heating machines by off-site part of the electricity [kWe] Heon-h: Generated heat power by the electrical driven heating machines by on-site part of the electricity [kWe] HSoff: Net off-site part of the thermal heat power sent to the thermal heat storage [kWth] HSon: Net on-site part of the thermal heat power sent to thermal heat storage [kWth] L(t): Temporal load power [kW] Lel: Electrical load power excluding the electrical load from the electrical driven heating and cooling machines [kWe] Lh: Thermal heat load power excluding the thermal heat load from the thermal driven cooling machines [kWth] lel: Electrical losses of on-site electrical power during distribution process [kWe] lh: Thermal heat losses of on-site thermal heat power during distribution process [kWth] LHV: Lower heating value [kWh/kg] n50: Number of air change per hour at 50 Pa indoor/outdoor differences [1/Pa] OEF: On-site energy fraction [-] OEFe: On-site electrical energy fraction [-] OEFh: On-site thermal heat energy fraction [-] OEFc: On-site thermal cooling energy fraction [-] OEM: On-site energy matching [-] OEMe: On-site electrical energy matching [-] OEMh: On-site thermal heat energy matching [-] OEMc: On-site thermal cooling energy matching [-] PEimport, PEexport: The annual imported and exported primary energy of an energy carrier, respectively P/H: Electrical power-to-heat ratio [-] QF: Total lower heating value [kWh/kg] T: Temperature [Co] t: Time [h] t1: Starting point of the time span. t2: Ending point of the time span. wi: Weighting factor of the detailed matching indices [-] w1, w2, w3, w4: Weighting factor of OEFe, OEMe, OEFh, and OEMh, respectively [-] WMI: Weighted matching index [-] Xe, Xh: The proportions of the associated non-renewable primary energy with the generated electricity and heat, respectively [-] ηCHP,el , ηCHP,h, ηCHP,overall : The electric, thermal and overall efficiencies of the µ-CHP, respectively [-]

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Abbreviations:

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AHU: Air handling unit bio-SNG: Substitute natural gas from biomass DH: District heating

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DHW: Domestic hot water HWST: Hot water storage tank ICE: Internal combustion engine LPG: Liquefied petroleum gas NRPE: Non-renewable primary energy NZEB: Net zero energy building NG: Natural gas ORC: Organic Rankine cycle PV: Photovoltaic panel PEMFC: Proton exchange membrane fuel cell STC: Solar thermal collector SE: Stirling engine SOFC: Solid oxide fuel cell µ-CHP: Micro-cogeneration heat and power

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1.

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Micro-cogeneration heat and power (µ-CHP) in household applications generates on-site electrical and thermal power

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simultaneously for the building, with the potential to provide energy efficiency and environmental benefits by reducing the

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Introduction

primary energy consumption and associated greenhouse gases (GHG) emissions [1]. Over the last decade, many studies

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were carried out to assess energy, environmental, and economic implications due to use of the µ-CHP in building sectors via

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either integrated building simulation tools, [2,3,4,5,6], or experimental work [7,8]. Most of the µ-CHP technologies covered

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by these studies are based on Stirling engine (SE), internal combustion engine (ICE), organic Rankin cycle (ORC), proton

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exchange membrane fuel cell (PEMFC), and solid oxide fuel cell (SOFC). The supply fuel was mostly natural gas (NG) or

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liquefied petroleum gas (LPG).

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The NZEB is defined as a building that has high energy-efficient performance and its energy needs have to be balanced by

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on-site renewable technologies or nearby [9]. Limited research focused on implementing the µ-CHP to be a main on-site

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energy supply option, achieving the Net Zero Energy Building (NZEB) balance. The NG-fueled µ-CHP was used as a main

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on-site supply option in [10], and it was implemented in real cases in [11], where the surplus electricity produced by the µ-

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CHP is being exported. The biomass-fueled µ-CHP was investigated in [12,13], as well as the hydrogen-fueled µ-CHP and

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small CHP in [Error! Bookmark not defined.,14]. The bio-syngas produced from biomass, such as wood chips or forest

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residue, is considered a renewable fuel source, which can be fed all µ-CHP technologies. However, technical modifications

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are necessary. For example, the SE and the ORC are external combustion cycles, thus, they can easily be bio-syngas fueled.

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Also, the bio-syngas-fueled ICE was tested in [15]. Regarding the fuel cell technology, the bio-syngas-fueled SOFC and

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PEMFC are studied in [16,17], respectively. It should be mentioned that the centralized gasification plants gasify the wood

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chips or forest residue producing the bio-syngas; it can then be injected into the regional gas grid, replacing the NG [18,19].

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Because the NZEB is grid connected, matching capability between the on-site generation and the energy needs is one of the

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criteria suggested to be identified whenever the NZEB has to be in a consistent definition [20,21]. Many factors and

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indicators were suggested to quantify matching in the NZEB, for example, the mismatch compensation factor [22], and

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load-matching and grid interaction indicators [23,24] with respect to imported and exported energies. These indicators are

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applied for the electricity, because the electric grid feed-in is currently the common scheme where the exported electricity

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has to compensate the imported energies fulfilling the NZEB balance. Moreover, the exported thermal energy to the thermal

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grid was analyzed to examine the influences of the excess heat production from NZEBs on Danish DH systems [25] and to

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optimize the ratio between the solar thermal collector (STC) and the photovoltaic (PV) from matching points of view for the

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Swedish NZEB [26].

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Recently, in [27,28], the basic indices of both the on-site energy fraction (OEF), which identifies the proportion of the load

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covered by the on-site generated energy, and the on-site energy matching (OEM), which identifies the proportion of the on-

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site generated energy that is used in the load rather than being dumped or exported, are extended to electrical, heating, and

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cooling matching indices, taking into account the energy conversion, storage, and hybrid grid connections. Depending not

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only on the electric grid feed-in scheme but also the thermal grid feed-in scheme, which has been developing in some EU

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countries, the µ-CHP as the on-site generation option was analyzed based on extended OEF and OEM factors for both the

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electricity and thermal energy in our previous study [29]. The µ-CHP was analyzed under two different control strategies:

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thermal tracking with the electrical grid feed-in scheme, and electrical tracking with the thermal grid feed-in scheme.

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Additionally, a weighted matching index (WMI) is evolved to show the matching situations instead of the specified

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extended indices by summing the detailed index multiplied by certain weighting factors, expressing the preferences of each.

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As mentioned in [Error! Bookmark not defined.], the selection of the weighting factors can be based on various criteria,

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such as the environmental impact, economic benefit, and political decisions.

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The contribution of this research is an answer for the following question: how can the weighting factors be defined

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physically and mathematically in the case of µ-CHP operated under thermal and electrical tracking, with and without

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installing PV and/or STC modules fulfilling the NZEB balance? A new model is suggested to calculate the weighting

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factors of the WMI for the previous situations, reflecting the two opposite extreme matching situations in the NZEB load-

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matching priority and energy export priority strategies as given in [Error! Bookmark not defined.]. The load-matching

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priority strategy means the self-consumption of the on-site generated energy has to be maximized, while energy export

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priority strategy means that the on-site generated energy has to be exported regardless the building's load or storage

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possibilities, aiming to fulfill the balance with the imported energy. Depending on the variation of the crediting factors such

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as non-renewable primary energy (NRPE), CO2 equivalent emission factors and costs of the interacted energies with the

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building, the weighting factors of the WMI are calculated. In this study, the NRPE factors of different energy carriers

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crossing the building boundary is used to calculate the weighting factors of the WMI, as long as the primary energy is the

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metric balance of NZEB. However, other crediting factors, such as CO2 equivalent emission factors and costs, can be used

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according to the NZEB metric balance. The model is generic, and it can be used for different micro-generation options. This

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study updates the matching topology given in the previous study [Error! Bookmark not defined.], dealing with µ-CHP,

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controlled under thermal and electrical tracking strategies, with and without installing PV and STC modules, with the

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capability of electrical/thermal grid feed-in schemes, fulfilling nearly and net ZEB. Therefore, this paper continues our

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research published in the previous paper [Error! Bookmark not defined.]. A lot of details are not reported in this paper

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because they are same as presented previously in [Error! Bookmark not defined.] and the reader is invited to read it. The

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importance of calculating the WMI based on the new model of calculating its weighting factors is the ability of using the

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WMI not only through design phase in new buildings but also through retrofitting the existing buildings. Moreover, it can

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be used as a quantitative parameter, evaluating the energy matching situation in multi-objective optimization calculation for

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nearly and net ZEB.

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In Section 2, the NZEB and its aspects are identified. In Section 3, the µ-CHP operation strategies are introduced. Extended

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and weighted matching indices is presented in Section 4. The physical and mathematical model of calculating the weighting

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factors of the WMI are proposed in Section 5. Thereafter, Section 6 shows a case study of a single-family house served by

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µ-CHP, operated based on parametric analysis for µ-CHP with different nominal electrical capacities and a wide range of

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the power-to-heat (P/H) ratio. Two different fuels are fed to the µ-CHP, NG, and bio-syngas. Based on the highest weighted

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matching index (WMI) value, the µ-CHP characteristics are obtained. The results and discussion are then presented in

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Section 7. Finally, conclusions are drawn in Section 8.

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2.

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The Net Zero Energy Building (NZEB) means that the building has a high-energy efficient performance, and its energy

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needs have to be balanced by on-site renewable energy supply options or nearby [Error! Bookmark not defined.]. The

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NZEB needs some aspects which must be defined in consistent framework [Error! Bookmark not defined.,Error!

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Bookmark not defined.].This study focuses on the µ-CHP as the main on-site energy supply option. The argument of the

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unambiguous definition for “on-site” and “off-site” renewable energy supply options for NZEB was presented in [Error!

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Bookmark not defined.]. The distinction is mainly based on whether the meaning focuses on the fuel origin or on the

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actual generation system location. In this study, the µ-CHPs are considered as on-site supply options, which is in

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accordance with most of the investigated studies of the small and µ-CHPs as on-site supply options for NZEB [Error!

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Bookmark not defined.,Error! Bookmark not defined.,Error! Bookmark not defined.], with respect to the location and

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physical building boundary. The supply fuel can be either NG or bio-syngas produced from forest residue. The NG as a

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fossil fuel is accepted to be a supply fuel for NZEB as long as its non-renewable primary energy (NRPE) factor is lower

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than that of the grid electricity as given in [Error! Bookmark not defined.,Error! Bookmark not defined.]. The solar

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energy and the bio-syngas produced from the forest residue at a centralized plant and transported to the house through a gas

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grid are renewable energy sources. However, the imported fuel has to be taken into account in the NZEB balance. The µ-

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CHP is investigated under thermal and electrical tracking strategies with possibilities of exporting electricity and heat to the

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grids. The primary energy is the metric balance. The NRPE factors of the grid electricity, district heating, NG, bio-syngas,

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and the solar energy are given in Table 1. The NRPE factors of the district heating, NG, and the solar energy are obtained

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from [30], while the NRPE factor of bio-syngas produced from forest residue and the electricity is obtained from [Error!

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Bookmark not defined.].The typical operating energy uses are considered for the balance, including heating, ventilation,

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domestic hot water, lighting, HVAC equipment, and appliances. The import/export is the balancing type. Symmetrical

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NRPE factors are used for importing and exporting the electricity or the heat. Typically, the balance period is a year. The

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NZEB balance is fulfilled when the net primary energy is equal or less than zero as shown by Eq. (1).

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NZEB definition and its balance

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(1)

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where PEimport and PEexport are the annual imported and exported primary energy of the energy carrier, respectively. i and j

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refer to one imported and exported energy carrier, respectively. n and m are the number of the imported and exported energy

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carriers crossing the building boundary.

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Firstly, the analysis is carried out for the µ-CHPs without installing any solar energy systems under thermal and electrical

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tracking strategies. The results of the matching analysis will be shown and discussed to find whether the NZEB balance is

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achieved or not. Secondly, under the thermal tracking strategy, the electricity required to fulfill the NZEB balance, and its

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corresponding PV area, is determined. Similarly, under the electrical tracking strategy, the exported heat and its

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corresponding STC area are calculated.

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Table 1.

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3.

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The matching analysis of the µ-CHP is carried out under two different control strategies: thermal and electrical tracking.

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Fig. 1 presents the schematic diagram of the control strategy, thermal connections of the µ-CHP, and electrical connections

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of both the µ-CHP and the PV modules (when used) under the thermal tracking strategy. Fig. 2 presents the schematic

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diagram of the control strategy, electrical connections of µ-CHP, and thermal connections of both the µ-CHP and the STC

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modules (when used) under the electrical tracking strategy. The µ-CHP operates with its nominal capacity (full load), which

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is in accordance with the previous studies [Error! Bookmark not defined.,31].

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µ-CHP operational strategies

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3.1. Thermal tracking strategy

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Under thermal tracking strategy with the electrical grid feed-in scheme, the µ-CHP operates as given in [Error! Bookmark

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not defined.]. As shown in Fig. 1, the PV modules as on-site supplementary system are installed to fulfil the NZEB balance

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when the balance is not fulfilled by the µ-CHP alone. The hourly total generated electricity (Gtot,el) summing of the

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electricity generated by the µ-CHP (GCHP,el) and the PV modules (GPV,el) is compared with the hourly electric demands (Lel),

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which includes appliances, lighting, HVAC equipment, circulated pumps, and auxiliary electrical heaters demands. If the

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Gtot,el is higher than the Lel, the surplus electrical power will be exported. If the Gtot,el is lower than the Lel, the shortage will

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be compensated by electricity imported from the electrical grid. As long as this study focuses on NZEB with availability to

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import the shortage of the electrical demand and export the surplus generated electricity to and from the electrical grid,

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adding electrical storage will not be analyzed under the thermal tracking strategy. The effect of adding electrical storage 7 Page 7 of 36

under the thermal tracking strategy on matching analysis was conducted in the previous study [Error! Bookmark not

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defined.]. It should be noted that the building is not connected with thermal grid in case the µ-CHP operates under the

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thermal tracking strategy. This means that the µ-CHP thermal capacity and the auxiliary heaters should be able to cover all

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thermal loads.

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Fig. 1.

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3.2. Electrical tracking strategy

Under electrical tracking strategy with the thermal grid feed-in scheme, the µ-CHP operates as given in [Error! Bookmark

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not defined.]. As shown in Fig. 2, the STC modules as on-site supplementary system are installed to fulfil the NZEB

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balance when the balance is not fulfilled by the µ-CHP alone. It should be noted that the electrical consumption of the

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circulating pump of STC system is added to the electrical demand. Also, the auxiliary heat exchanger is supplied by the DH,

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because the thermal grid is already available in the electrical tracking strategy.

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Fig. 2.

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4.

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Based on [Error! Bookmark not defined., Error! Bookmark not defined.], the matching capability for on-site generation

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in buildings is analyzed using two basic matching indices: on-site energy fraction (OEF) and on-site energy matching

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(OEM) as shown by Eqs. (2) and (3).

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Extended and weighted matching indices

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(3)

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where G (t) and L (t) are temporal on-site generation power and load power, respectively, as shown in Fig. 3. OEF is equal

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to the ratio of the area of section III to the overall areas of sections I and III, whereas OEM is equal to the ration of the area

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of section III to the overall areas of sections II and III. A better matching is represented by the higher values of OEF and

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OEM simultaneously.

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Fig. 3.

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The OEF indicates the energy demand proportion covered by on-site energy production, while OEM indicates the on-site

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generated energy proportion being consumed in the building and system rather than being exported or dumped. A topology

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was created to extend these two basic matching indices OEF and OEM to electrical OEFe and OEMe, thermal heating

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OEFh and OEMh, and thermal cooling OEFc and OEMc matching indices taking into consideration the energy form, energy

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conversions, thermal and electrical storage, and hybrid grids connection. This topology and the detailed equations which are

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developed and formulated for all extended matching indices are discussed in [Error! Bookmark not defined.].

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Furthermore, the extended matching indices OEFe, OEMe, OEFh, and OEMh equations were simplified for the µ-CHP as

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the sole on-site energy supply option in [Error! Bookmark not defined.].

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In the current study, the matching topology, as shown in Fig. 4, and the extended matching indices are updated to be in line

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with the aim of this study, dealing with the µ-CHP with photovoltaic (PV) and/or flat plat solar thermal collector (STC)

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modules, fulfilling the NZEB balance. The following Eqs. (4) - (7) are the mathematical formulas for the extended matching

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indices.

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(4)

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(5)

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(6)

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Fig. 4 shows a simplified topology for extended matching specified for µ-CHP coupled with PV and STC modules. B1

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refers to the building boundary including the on-site generation options µ-CHP, PV modules, and STC modules. All energy

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carriers crossing this B1 should be taken into account in the NZEB balance. B2 refers to an imaginary boundary where all

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energies in electrical or thermal forms are categorized as on-site or off-site. It is obvious that the matching analysis does not

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deal with fuel crossing the building boundary B1 (e.g. the fuel of the µ-CHP), but it deals with the electrical and thermal

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heating energies (flow across boundary B2) as long as the main goal is to evaluate the interaction between the building

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needs, on-site generation, and/or the electrical and thermal grids with possibilities of exportation (or damping the surplus)

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[Error! Bookmark not defined.]. Also, for the conversion process as shown by the electrical driven heating, the analysis

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follows the energy carrier, since it is electrical, and thermal heating energies within the B2.

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Recently, an overall matching index is evolved to show the matching situations instead of the four comprehensive indices,

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Eqs (4) - (7) [Error! Bookmark not defined.]. This overall matching index is the Weighted Matching Index (WMI) as

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presented by Eq. (8). The WMI is the summation of matching indices multiplied by certain weighting factors wi, while the

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sum of weighting factors is 1.0. As extended matching indices, the highest value of WMI is 1.0, which presents the ideal

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matching case.

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278 (8)

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The weighting factors wi can be selected to reflect various criteria such as economic benefits, environmental impacts, and

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political decisions [Error! Bookmark not defined.]. In the current study, the question concerns how the weighting factors

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can be defined physically and mathematically in case of µ-CHP operated under thermal and electrical tracking with and

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without installing PV and/or STC modules, fulfilling the NZEB balance without imposing a priori- preference. The answer

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to this question is indicated in Section 5, followed by a case study.

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Fig. 4.

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Due to the availability of the hourly based weather data, internal gain profiles, and occupied profiles, the time step (dt) is

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one hour; however, it is well known that one hour is quite rough for a matching analysis, and finer time resolution leads to

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more accurate matching results. Additionally, the matching analysis is calculated for one year. Accordingly, the WMI is

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annually valued using a one-hour time step.

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5.

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As indicated, the weighting factors in the WMI definition are preferences, reflecting the importance of each extended

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matching index from the matching analysis point of view. To assess the building grid interaction, especially for nearly and

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net ZEB, two extreme situations have to be quantified using the suitable indicator (s) as given in [Error! Bookmark not

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defined.]. The two opposite extreme situations are (i) a load-matching priority strategy (maximizing the load matching), (ii)

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an energy export priority strategy (maximizing the energy export). These two opposite extreme situations will be defined by

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NZEB matching situations. The load-matching priority strategy means that the self-consumption of the on-site generated

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energy has to be maximized. This means maximizing (w1 OEFe + w3 OEFh) in Eq. (8). The energy export priority strategy

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means that the on-site generated energy has to be exported regardless of the building's load or storage possibilities, aiming

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to fulfill the balance with the imported energy. This means minimizing (w2 OEMe + w4 OEMh) in Eq. (8).

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By taking a look at the crediting factors of different energies such as the NRPE factors, CO2 equivalent emission factors, or

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costs [Error! Bookmark not defined.], it is found that in most cases, the grid electricity has the highest factors, then the

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Weighting factors of WMI and their mathematical models

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fossil fuels, then district heating, then the renewable energies whether it is renewable fuel (i.e. biogas or biomass) or solar

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energy. Thus, the NZEB factors, CO2 equivalent emission factors, and costs of the fuel supply and the imported and

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exported energies from/to the grids can be used to calculate the weighting factors of the WMI. The physical and

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mathematical explanation are described in Subsection 5.1and 5.2. Because the NZEB balance metric is the primary energy,

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the calculation method of the weighting factors are formulated depending on the NRPE factors of the energy carriers of the

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fuel supply and the imported and exported grids.

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As illustrated in Section 4, the matching analysis will be conducted with respect to the boundary B2. In a case of analyzing

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the µ-CHP without any PV or STC modules, let's first allocate the NRPE factor of the fuel supply of the µ-CHP to the

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generated electricity GCHP,el and heat GCHP,h. The used allocation method is “energy content of the product” [32]. In this

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study, the energy content refers to the lower heating value (LHV) of the fuel supply. The NRPE factors of the generated

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electricity fCHP,e and heat fCHP,h can be obtained as shown by Eq. (9).

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(9)

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(10)

317

Xe and Xh are the proportions of the associated non-renewable primary energy with the generated electricity and heat,

318

respectively. QF is the amount of the energy content of the fuel fed to the µ-CHP. The electric, thermal, and overall

319

efficiencies of the µ-CHP are ηCHP,el , ηCHP,h, ηCHP,overall, respectively.

320

Eq. (11) and (12) shows how Xe and Xh are calculated based on the energy content allocation method.

321 12 Page 12 of 36

322

(11)

ip t

323 (12)

325

Therefore, excluding the electricity and heat produced by the PV and STC modules, four NRPE factors fgrid,el, fgrid,h, fCHP,el,

326

fCHP,h are identified corresponding to four energies crossing the boundary B2: grid electricity, district heating, generated

327

electricity from the µ-CHP, and generated heat from the µ-CHP, respectively.

us

328

cr

324

an

5.1. Weighting factors of WMI for µ-CHP

The calculation method can be physically explained based on the topology shown in Fig. 4. The weighting factor (w1) of the

330

on-site electrical energy fraction OEFe is calculated based on the grid electricity NRPE factor, because the OEFe identifies

331

the proportion of energy demand covered by the on-site electrical energy generation instead of the grid electricity. Similarly,

332

the on-site generated heat replaces the thermal heating grid to cover the thermal demand, partially or totally, based on the

333

control strategy and the characteristics of the µ-CHP. Therefore, the weighting factor (w3) of the on-site thermal energy

334

fraction OEFh can be correspondingly calculated based on the thermal heat grid NRPE factor. The OEM identifies the on-

335

site generated energy proportion being consumed in the building and system rather than being exported or dumped.

336

Therefore, the weighting factors (w2) and (w4) of OEMe and OEMh can be reasonably calculated to reflect the NRPE factors

337

of the on-site electrical and thermal energy generated by the µ-CHP, respectively. The (w2) and (w4) always have the same

338

values due to the use of the simple energy content allocation method.

339

Mathematically, each weighting factor wi is expressed as a ratio of its corresponding NRPE factor fi as physically illustrated

340

by the sum of all NRPE factors of energies crossing the boundary B2 shown in Fig. 4, as given by Eq. (13).

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341 342

(13)

13 Page 13 of 36

The weighting factor of OEFe, i.e. w1, is calculated with respect to the proportion of the NRPE factor of grid electricity

344

fel,grid with respect to the sum of the four NRPE factors fgrid,el, fgrid,h, fCHP,e, fCHP,h. The weighting factor of OEFh, i.e. w3, is

345

similarly calculated as a ratio of fgrid,h to the sum of four NRPE factors. The weighting factors of OEMe and OEMh, i.e w2

346

and w4, respectively, are calculated as a ratio of fCHP,e and fCHP,h to the sum of four NRPE factors, respectively. The

347

mathematical formulas of calculating the weighting factors are given by Eq. (14).

cr

ip t

343

an

us

348

d

M

349

351

Ac ce p

te

350

352

(14)

353

Table 2 shows the weighting factors wi of the WMI for the µ-CHP fed by NG and bio-syngas with 90% ηCHP,overall when the

354

NRPE factors in Table 1 are used.

355

Table 2.

356

As a result of the high NRPE factor of the grid electricity (or other crediting factors like CO2 emission factors or costs, due

357

to the fact that the electricity has the highest energy quality), w1 will always be the highest among the weighting factors as

358

shown in Table 2. This reflects the NZEB matching situations from the electrical matching analysis (i.e. high w1 and low w2)

359

regardless of the fuel source of the µ-CHP. It also reflects the energy quality by putting more preferences on the electricity 14 Page 14 of 36

than thermal energy (i.e. w1 is higher than w3). From the thermal heat matching analysis (i.e. comparing between values of

361

w3 and w4), two opposite results are obtained regarding the fuel source of the µ-CHP as shown in Table 2. For bio-syngas,

362

w3 is higher than w4, reflecting the NZEB matching situations, while w4 is higher than w3 for NG as an opposite result. For

363

NG, the results indicate that it does not make sense to import NG with a NRPE factor of 1.14 and generate on-site thermal

364

energy from the µ-CHP with NRPE factor of 1.27 (which is equal to 1.14 divided by ηCHP,overall of 0.9) to export to and /or

365

replace the thermal heating grid from DH, which already has a NRPE factor of 0.77. It can be concluded that calculating the

366

weighting factors based on the NRPE factors (or any other crediting factors like CO2 emission factor or cost) defines

367

situations either in-line with or reverse to NZEB matching situations, taking into account the energy quality and energy

368

conversion as well.

369

us

cr

ip t

360

an

5.2. Weighting factors of WMI for hybrid on-site generation systems

To deal with hybrid on-site generation systems, i.e. µ-CHP with PV and/or STC modules, the on-site overall NRPE factors

371

fon-site,overall,el and fon-site,overall,h are suggested for the on-site generation electricity and heat, respectively. The overall NRPE

372

factor is calculated proportionally for each of the electrical and thermal generated energies via all on-site supply systems as

373

shown in Eq. (15). For example, as shown in Fig. 4, the electricity and heat are produced on-site by the µ-CHP and the PV

374

module, and the µ-CHP and the PV module, respectively. Therefore, the overall on-site electricity and heat generated NRPE

375

factors can be calculated as shown in Eq. (16).

Ac ce p

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370

376

377 378

(15)

379

Accordingly,

15 Page 15 of 36

381 (16)

383

The weighting factors of the WMI are calculated using Eq. (17) as following,

an

us

382

cr

ip t

380

d

M

384

Ac ce p

te

385

386

387 388

(17)

389

The weighting factors wi will change from case to case when this method is applied for µ-CHP with PV and/or STC

390

modules, however the NRPE factor of the solar energy is considered equal to zero, due to the fact that varying PV and STC

391

modules as supplementary systems fulfilling the annual NZEB balance with the µ-CHP as the main on-site energy supply

392

system. 16 Page 16 of 36

393 394

6.

Case study

6.1. Simulated building The simulated building is located in Helsinki, Finland (60.2°N, 24.9°E). This house was used and described in [Error!

396

Bookmark not defined.,Error! Bookmark not defined.]. The building and the supply options are simulated by Trnsys 17

397

software [33]. The annual thermal heat demand, including AHU, space, and DHW heating demands, is 103.3 kWh/m2 a.

398

The electrical demand, including ventilation fan, lighting, and equipment demands, and excluding the circulated pump of

399

the µ-CHP and auxiliary electric heaters, is 29.9 kWh/m2 a. The peak thermal and electrical powers are 6.0 and 5.6 kW,

400

respectively.

cr

us

6.2. On-site µ-CHP description

an

401

ip t

395

Similar to the parametric analysis which was carried out for the µ-CHP in the previous study [Error! Bookmark not

403

defined.], The P/H ratio of the analyzed µ-CHP varies between 0.05 and 0.8 with 0.05 step. Under thermal tracking

404

strategies, the nominal electrical power varies between 0.5 kWe and 3.0 kWe with 0.5 kWe step, while under electrical

405

tracking strategy, the nominal electrical power varies between 0.5 kWe and 2.0 kWe with 0.5 kWe step. The analysis is

406

carried out under the assumption of a constant overall efficiency of 90%.

d

te

6.3. Photovoltaic (PV) and Flat plate solar thermal collectors (STC) modules

Ac ce p

407

M

402

408

The PV and STC modules are oriented facing south with a 45o tilt based on [34].The electricity production of one square

409

meter of the PV as a module after the inverter is 93.0 kWh/a, which is equivalent to 0.62 kWh/a per floor area of the house.

410

The flat plate solar thermal collector (STC) module has a gross area of 2.874 m2, where it is used and simulated by [35].The

411

STC has the priority over any alternative system when the solar energy is sufficient. The STC is controlled according to the

412

temperature difference across the STC (∆Tsc). The circulating pump of the solar system turns on under a condition of 2 ºC

413

≤∆Tsc≤ 10 ºC. The electrical consumption of the circulating pump of the solar system is taken into account through

414

calculating the total electrical demand. It should be mentioned that under the thermal tracking strategy, the PV area is

415

determined directly by dividing the required supplementary electricity by the amount of electricity produced by one PV

416

module, where the produced electricity from PV is depending on the solar energy availability. Under the electrical tracking

17 Page 17 of 36

417

strategy, the STC area is determined by an iteration process, because the produced heat is dependent on the thermal demand.

418

The PV and STC area for thermal and electrical tracking strategies is rounded up to get an integer number of modules.

419

7.

420

The obtained results are classified based on the µ-CHP control strategies: thermal and electrical tracking. For each control

421

strategy, the net primary energy and matching analysis were calculated firstly for the bio-syngas and NG-fueled µ-CHPs

422

alone as an on-site energy supply system. Thereafter, if the NZEB balance is not fulfilled, the required area of the PV

423

modules or STC modules is determined and installed under thermal or electrical tracking strategies, respectively.

425

ip t

cr

us

7.1. Thermal tracking strategy

an

424

Results and discussion

7.1.1. On-site µ-CHP energy option

The annual net primary energy consumptions of bio-syngas- and NG-fueled µ-CHPs as the on-site energy system are shown

427

in Fig. 5-a and -b, respectively. In Fig. 5-a, the bio-syngas-fueled µ-CHPs with electrical capacities equal to or higher than

428

2.0 kWe have zero and negative net primary consumption with P/H ratio of 0.55 or higher. The reason is that the µ-CHP,

429

which has a high electrical capacity and high P/H ratio, can firstly cover a larger portion of the thermal needs directly by the

430

on-site generated heat and indirectly by converting the on-site generated electricity to heat through the auxiliary heaters.

431

Secondly, by covering part of the electrical load and exporting the surplus electricity, the exported primary energy can

432

compensate the imported energies as long as the NRPE factor of the exported electricity of 2.23 is higher than that of the

433

imported bio-syngas of 0.17. In Fig. 5-b, the NZEB balance is not fulfilled due to a high NRPE factor of 1.14 of NG. The

434

minimum net primary energy is 142 kWh/m2a, corresponding to 3.0 kWe NG-fueled µ-CHP and 0.8P/H ratio. Also, it can

435

be observed that, for the bio-syngas fueled µ-CHPs with electrical capacities of 0.5, 1.0, and 1.5 kWe, the minimum net

436

primary energy of 84 kWh/m2a, 55 kWh/m2a, and 24 kWh/m2a happen at P/H ratios of 0.15, 0.35, and 0.55 respectively,

437

then by increasing the P/H ratio, the net primary energy increases. The same behavior is observed for the NG-fueled µ-CHP,

438

as shown in Fig. 5-b. The reason for this is that the µ-CHP with a low electrical capacity and low P/H ratio has high on-site

439

generated heat. This heat can cover approximately all of the thermal needs (the OEFh higher than 0.95), where the

440

associated produced electricity can be utilized by the electrical needs and/or exported to the grid, reducing the net primary

441

energy accordingly. For the cases have P/H ratio higher than the P/H ratios of the minimum net primary energy, the on-site

442

generated heat is reduced thus; the OEFh reduces at P/H ratio of 0.8 to 0.48 and 0.74 in cases of 0.5 kWe and 1.0 kWe,

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M

426

18 Page 18 of 36

respectively. In this case, the electrical needs of the electrical heater increase (Eon-h and Eoff-h) to cover the shortage of

444

thermal needs. Since the electrical capacities are lower than the needs, the shortage, which includes a high portion as Eoff-h,

445

is already covered by imported electricity from the electrical grid. Accordingly, the net primary energy increases, and the

446

OEFe decreases as well.

447

Fig. 5.

448

The extended matching indices calculation depends only on the electrical and thermal energies as shown by Eqs. (4) - (7);

449

thus, they have same values for both bio-syngas- and NG-fueled µ-CHPs. The weighting factors of the extended matching

450

indices are shown in Table 2. The WMIs of the bio-syngas- and NG-fueled µ-CHPs as the on-site energy system are shown

451

in Fig. 6-a and -b, respectively, where the variation between the bio-syngas and NG µ-CHPs is only due to a variation of the

452

weighting factors. In Fig. 6-a, the highest WMI is 0.75, corresponding to 2.0 kWe bio-syngas-fueled µ-CHP and 0.8 P/H

453

ratios. Moreover, for the cases fulfilling the NZEB balance, the bio-syngas-fueled µ-CHP with a nominal capacity of 2.0

454

kWe and P/H ratio range of 0.6 to 0.8, has a high range of WMI of 0.69 to 0.75. It can be observed that, for the bio-syngas-

455

fueled µ-CHP of 0.5 kWe and 1.0 kWe electrical capacities, the P/H ratios are 0.15 and 0.35 for the highest WMI values,

456

respectively; the same P/H ratios of the minimum net primary energy consumptions. For the NG-fueled µ-CHPs in Fig. 6-b,

457

the highest WMI is 0.77, corresponding to 1.5 kWe µ-CHP and 0.8 P/H ratios. When the ratio of w1/w3 is large, as in the

458

bio-syngas-fueled µ-CHP, the highest WMI is obtained by the µ-CHP which has a minimum nominal electrical capacity,

459

achieving the NZEB balance as shown by 2.0 kWe. On the contrary, when the ratio of w1/w3 is small, as in the NG-fueled µ-

460

CHP, the highest WMI is obtained by the µ-CHP, which reaches a minimum net primary energy consumption and starts to

461

rise again within the range of the P/H ratio. For bio-syngas-fueled µ-CHP , it can be observed that the WMI values of the µ-

462

CHP of 2.0 kWe is always better than those of 2.5 kWe and 3.0 kWe µ-CHPs; however, the µ-CHPs of 2.5 kWe and 3.0

463

kWe have the lowest primary energy consumption. The reason for this is that, under the thermal tracking strategy,

464

increasing the thermal heat capacity by increasing the electrical capacity under a constant P/H ratio of the µ-CHP (i.e. using

465

2.5 kWe and 3.0 kWe µ-CHP instead of 2.0 kWe), the annual operating hours of the µ-CHP are reduced, resulting in an

466

increase in the opportunities of covering the hourly electrical needs by importing electricity from the grid. Accordingly, the

467

OEFe reduces, which has the highest weighting factor w1 of 0.66 as shown in Table 2.

468

Fig. 6.

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443

19 Page 19 of 36

Fig. 7 shows the imported and exported primary energy of bio-syngas- and NG-fueled µ-CHPs under the thermal tracking

470

strategy. This figure was suggested to present an overall view of the matching situation [Error! Bookmark not defined.],

471

but without detailed quantitative values as given by the WMI and the extended matching indices, taking into account all

472

energy imported and exported carriers, including the fuel supply (i.e. energies crossing boundary B1). Each analyzed case is

473

presented by a single point, which has imported and exported primary energy values on the X and Y axes. Any point lies on

474

or above the zero energy balance line, which fulfills the NZEB balance between the imported and exported primary

475

energies. The lowest imported primary energy represents the best matching situation regarding the covering demand by the

476

on-site generated energies. The lowest exported primary energy represents the best matching situation regarding the on-site

477

generations utilized by the building demands. For the bio-syngas-fueled µ-CHP in Fig. 7-a, the case of 2.0 kWe bio-syngas-

478

fueled µ-CHP and 0.8 P/H ratios, which have the minimum imported primary energy fulfilling the NZEB balance, is the

479

same as that which has been obtained by the WMI. Additionally, the same influences of an increasing P/H ratio are shown

480

for each µ-CHP capacity as shown previously in Fig. 5; for example, the 0.5 kWe, 1.0 kWe, and 1.5 kWe µ-CHP have

481

minimum imported primary energy at P/H ratios of 0.15, 0.35, and 0.55 respectively. For the NG-fueled µ-CHP in Fig. 7-b,

482

the import/export primary energy figure does not refer to the same conclusions as the WMI. The reason is that the extended

483

matching indices deal with electrical and thermal energies, not like the import/export primary energy balance, which does

484

not distinguish between the different energy carriers including the fuel supply. Accordingly, the NRPE factors (i.e.

485

associated imported primary energies) of both bio-syngas and NG fuels are the main reason to change the obtained results

486

by the import/export figures.

487

Fig. 7.

cr

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Ac ce p

488

ip t

469

7.1.2. On-site µ-CHP and photovoltaic modules

489

Aiming to achieve the NZEB balance, the required area of PV modules are calculated based on how much annual electricity

490

is required to be exported to fulfill the balance and how much generated electricity is produced by one PV module as given

491

in Section 6.3.

492

The PV area required for all studied cases are shown in Fig. 8. The largest PV areas are 127 m2 and 178 m2 for the smallest

493

µ-CHP of 0.5 kWe with P/H ratio of 0.8 for the bio-syngas- and NG-fueled µ-CHPs, respectively. The reason, as

494

mentioned, is the high electricity amount imported from the grid to cover both electrical and thermal demands due to small

495

electrical and thermal capacities of this µ-CHP. 20 Page 20 of 36

The WMI for all cases fulfilling the NZEB balance are shown in Fig. 9. It should be noted that the weighting factors used to

497

calculate the WMI vary from case to case depending on the PV area and illustrated methodology in Section 5.2. For

498

instance, the variations of w1, w3, and w4 are between +5% and 0%, while the variation of w2 is between 0% and -89%. The

499

main reason is related to the zero NRPE factor of the solar energy. For the bio-syngas-fueled µ-CHP in Fig. 9-a, the highest

500

WMI has a value of 0.775, corresponding to1.5 kWe µ-CHP at P/H ratio of 0.7 and PV area of 23 m2. The reason for

501

changing the highest matching case from 2.0 kWe µ-CHP without installing any PV (Fig. 6-a) to 1.5 kWe µ-CHP with 23

502

m2 PV area is that the OEFe, which has higher weighting factors w1, is enhanced due to that 42% of the produced electricity

503

from the PV modules is utilized by the electricity needs. For the NG-fueled µ-CHP in Fig. 9-b, the highest WMI has a value

504

of 0.775, corresponding to 2.0 kWe µ-CHP at P/H ratio of 0.8 and PV area of 116 m2. The fluctuations of the WMI values

505

in Fig. 9 are related to the fluctuations of the OEFe and OEMe, which are affected by coupling the PV system (especially

506

for 3.0 kWe µ-CHP, which has the lowest operating hours and highest on/off cycling compared to others at each P/H ratio).

507

Fig. 8.

508

Fig. 9.

cr

us

an

M

7.2. Electrical tracking strategy

510

te

7.2.1. On-site µ-CHP energy option

d

509

ip t

496

For electrical tracking strategy, the net primary energy of analyzed cases is shown in Fig. 10. As observed, the net primary

512

energy is quite similar for all µ-CHP with electrical capacities equal to or higher than 1.0 kWe, with 1.0 kWe µ-CHP and

513

higher. The reason is that using one hour as the simulation time step, if the µ-CHP electrical output will be higher than the

514

free capacity of the battery, the µ-CHP electrical output will be equal to the free capacity of the battery to prevent the

515

fraction from reaching more than 0.95 (i.e. the µ-CHP can be switched off before the end of the simulation time step). For

516

the bio-syngas-fueled µ-CHP in Fig. 10-a, the NZEB can be fulfilled due to high thermal generated energy where the P/H

517

ratio is low. This high thermal generated energy is utilized by the thermal needs, and the surplus is exported to the thermal

518

grid. Of course, it is beneficial to replace the imported thermal energy from the thermal heat grid, which has a high NRPE

519

factor by on-site generated thermal energy from fuel which has a low NRPE factor, like bio-syngas, and vice versa for the

520

NG where the NZEB balance cannot be achieved as shown in in Fig. 10-b.

521

Fig. 10.

Ac ce p

511

21 Page 21 of 36

The WMI of the µ-CHP under the electrical tracking strategy is shown in Fig. 11. Since the WMI of µ-CHPs over 1.0 kWe

523

electrical capacity are quite similar (the 1.5 and 2.0 kWe µ-CHP have a WMI slightly higher than the 1.0 µ-CHP) as shown

524

in Fig. 11-a and -b, the 1.0 kWe bio-syngas-fueled µ-CHP is selected to represent the highest WMI of 0.92 with P/H ratio of

525

0.2, and the 1.0 kWe NG-fueled µ-CHP represents the highest WMI of 0.89 with P/H ratio of 0.8. It can be noticed that the

526

WMI values of the electrical tracking strategy are higher than that of the thermal tracking strategy, because the OEFe has

527

the highest weighting factor (equal to 0.73 for the µ-CHP of 0.5 kWe and 0.94 for µ-CHP which has electrical capacity

528

equal to or more than 1.0 kWe). Regarding the NG-fueled µ-CHP, as shown in Fig. 11-b, the OEMh weighting factor w4 of

529

0.229, as given in Table 2, is higher than the OEFh weighting factor w3 of 0.139, reflecting reverse NZEB matching

530

situations by giving priority to utilizing the on-site generated thermal energy over covering the local demand by on-site

531

generated thermal energy. The WMI is approximately constant for all NG-fueled µ-CHP which have P/H ratio higher than

532

0.2 because that multiplying the OEFh and OEMh by w3 of 0.229 and w4 of 0.139, respectively, neglects the influence of

533

decreasing OEFh (e.g. for 1.0 kWe µ-CHP, OEFh decreases from 0.95 at 0.2 P/H ratio to 0.44 at 0.8 P/H ratio) and

534

increasing OEMh (e.g. for 1.0 kWe µ-CHP, OEFh increases from 0.54 at 0.2 P/H ratio to 0.97 at 0.8 P/H ratio) with

535

increasing the P/H ratio.

536

Fig. 11.

537

The imported and exported primary energy for the µ-CHPs under the electrical tracking strategy are shown in Fig. 12. As

538

shown in Fig. 12, for both bio-syngas- and NG-fueled µ-CHPs, the import/export primary energy figures indicate

539

approximately the same highest matching cases as given by the WMI. The reason for obtaining the same results by the WMI

540

and the import/export figures for bio-syngas-fueled µ-CHPs is the very low NRPE factor of bio-syngas fuel. While for NG-

541

fueled µ-CHPs, the reasons are relate to the high NG NRPE factor and the control strategy used. Because of this, the NG

542

fuel NPEF factor is higher than the thermal heat grid NRPE factor; the increasing P/H ratio of the µ-CHPs reduces the total

543

imported primary energy, and the required exported thermal heat fulfilling the NZEB as given in Fig. 12-b.

544

Fig. 12.

545

Ac ce p

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522

7.2.2. On-site µ-CHP and solar thermal collector modules

546

To achieve the NZEB balance under the electrical tracking strategy, the STC modules are installed to produce thermal

547

energy, which can be utilized on-site, reducing the imported thermal energy needed for auxiliary heat exchangers and

548

exported to compensate the imported energies. Fig. 13-a and -b show the number of installed STC modules for all studied 22 Page 22 of 36

cases of bio-syngas- and NG-fueled µ-CHPs, respectively, obtained by a trial and error approach. Remember, the STC

550

module gross area is 2.874 m2. For bio-syngas-fueled µ-CHP, 0.5 kWe µ-CHP with P/H ratio of 0.8 has the maximum

551

installed STC modules of 30, while for NG-fueled µ-CHP, 2.0 kWe with P/H ratio of 0.05 has the maximum installed STC

552

modules of 236. However, the range of the STC modules required for NG-fueled µ-CHP is not practical for installation due

553

to its very large area and limited building roof area. Fig. 14 shows the WMI for all cases after fulfilling the NZEB under the

554

electrical tracking strategy. For the bio-syngas-fueled µ-CHP in Fig. 14-a, it can be observed that the 1.0 kWe µ-CHP with

555

P/H ratio of 0.2 still has the highest WMI. Also, for all cases with STC, the WMI is slightly decreased (with 0.03 for 0.5

556

kWe µ-CHP and 0.01 for others) compared with cases without STC. The reason is that OEMh is decreased with increasing

557

the exported thermal energy due to installing the STC modules. For the NG-fueled µ-CHP in Fig. 14-b, the relations

558

between the weighting factors come in line with the NZEB matching situations, i.e. w1 > w2, and w3 > w4. The weighting

559

factors w1, w2, w3, and w4 vary between (0.454 - 0.502), (0.258 - 0.285), (0.157- 0.173), and (0.123 - 0.040), respectively.

560

Therefore, the 1.0 kWe µ-CHP with P/H ratio of 0.2 with 82 STC modules has the highest WMI of 0.88. The characteristics

561

of µ-CHP with STC modules varied from the µ-CHP alone due to the OEMh. The OEMh increases from 0.05 to 0.18 within

562

range of the P/H ratio of 0.05 to 0.2 multiplied by relatively high w4 (0.13 > w4 > 0.1) getting the highest WMI, then it

563

becomes constant with value of 0.2 within the range of P/H ratio of 0.25 to 0.8 multiplied by relatively low w4 (0.1 > w4 >

564

0.05) reducing the WMI slightly within the range of P/H ratio of 0.25 to 0.8.

565

Fig. 13.

566

Fig. 14.

cr

us

an

M

d

te Ac ce p

567

ip t

549

7.3. Remarks

568

The aim of this study is to specify a physical and mathematical model, which could be used to calculate the weighting

569

factors of the extended matching indices through calculating the WMI reflecting the NZEB matching situations. As

570

aforementioned, the NZEB has two opposite matching extreme situations: load-matching strategy and exported energy

571

strategy. Generally, these strategies were reflected by keeping the weighting factor w1 of the OEFe higher than the

572

weighting factor w2 of the OEMe under the thermal tracking strategy with electrical grid feed-in scheme, and the weighting

573

factor w3 of the OEFh higher than the weighting factor w4 of the OEMh under the electrical tracking strategy with thermal

574

grid feed-in scheme. Whichever NZEB metric is used, the crediting system (such as NRPE factors, CO2 equivalent emission

575

factors and costs of different energy carriers) has to fulfill the previous conditions. In more detail, as shown in the case

23 Page 23 of 36

study, through the parametric analysis of the NG-fueled µ-CHP under the thermal tracking strategy with electrical grid feed-

577

in scheme, the weighting factor w1 of OEFe was higher than the w2 of OEMe; however, the w4 of OEMh was higher than w3

578

of OEFh. Because of this, there is no heat exported at all; the OEMh has a constant value of 1.0 for all studied cases. Once

579

the OEMh and its weighting factor w4 are constant, they do not affect the comparative results. Moreover, through the

580

parametric analysis of the NG-fueled µ-CHP with PV modules as hybrid on-site generation systems, the weighting factors

581

changed slightly with negligible influence on the results. Under the electrical tracking strategy with thermal grid feed-in

582

scheme, there is no electricity exported at all. This means that the OEMe has a constant value of 1.0 for all studied cases.

583

The opposite NZEB matching situation is obtained by the NG-fueled µ-CHP, where the weighting factor w4 of the OEMh

584

was higher than w3 of the OEFh. Consequently, the highest matching case obtained by the WMI reflected the situation that

585

utilizing the on-site generated thermal energy has the priority over covering the local demand by on-site generated thermal

586

energy. However, after installing the STC, aiming to fulfill the NZEB balance, the relation between weighting factors

587

reflects the NZEB matching situation, i.e. w3 > w4, for all cases. Therefore, the WMI reflected the NZEB matching

588

situations.

589

The suggested model is verified for the µ-CHP as on-site energy system using the extended matching indices, calculated

590

based on its matching topology. However, more investigations would be required to check the ability of applying it with

591

other on-site systems and matching topologies. The authors believe that the suggested model for the µ-CHP is more

592

challengeable than other systems which produce a single energy form as on-site thermal heat, for instance.

593

8.

594

Recently, the extended matching indices for electrical and thermal energies were defined based on two basic matching

595

indices: on-site energy fraction (OEF), which indicates the covered load proportion by the on-site generated energy, and on-

596

site energy matching (OEM), which indicates the on-site generated energy proportion, utilized by the load rather than being

597

dumped or exported. The extended matching indices for electricity and thermal heat were defined as OEFe, OEMe, OEFh,

598

and OEMh. Also, an overall weighted matching index (WMI) was proposed, combining the extended matching indices by

599

multiplying them by certain weighting factors (w1 to w4), expressing the preferences of each. These weighting factors can be

600

defined based on various criteria, such as environmental impact, economic benefit, and political decisions. This study

601

updates the matching topology presented in the previous study to calculate the extended matching indices when micro

602

cogeneration (µ-CHP) is supplemented by photovoltaic (PV) and/or solar thermal collector (STC) modules as on-site supply

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Conclusion

24 Page 24 of 36

options, when they are used to achieve the NZEB balance. Moreover, a new model is suggested physically and

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mathematically to show how the weighting factors of the WMI can be calculated under thermal and electrical tracking

605

strategies of µ-CHP with electricity and thermal heat grid feed-in schemes, reflecting the two opposite extreme matching

606

situations in net zero energy building (NZEB): load-matching priority and energy export priority strategies. A single family

607

house located in Helsinki, Finland, served by a µ-CHP, is analyzed as a case study under a wide range of electrical outputs

608

and power-to-heat (P/H) ratios. The following findings can be highlighted:

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1) The model shows how the weighting factors w1, w2, w3, and w4 can be calculated based on the non-renewable primary energy (NRPE) factors of the different energy carriers crossing the building boundary. Moreover, other

611

NZEB metrics could be used, such as CO2 equivalent emission factors and costs.

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2) To reflect the two opposite extreme matching situations in NZEB, the weighting factor w1 of the OEFe has to be

613

higher than the weighting factor w2 of the OEMe under the thermal tracking strategy with electrical grid feed-in

614

scheme, and the weighting factor w3 of the OEFh has to be higher than the weighting factor w4 of the OEMh under

615

the electrical tracking strategy with thermal grid feed-in scheme. (e.g. w1= 0.66 > w2= 0.056 and w1= 0.403 > w2=

616

0.229 under the thermal tracking strategy for bio-syngas- and NG-fueled µ-CHPs, respectively, and w3= 0.228 >

617

w4= 0.056 under the electrical strategy for bio-syngas-fueled µ-CHP).

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3) For NG-fueled µ-CHP under the electrical tracking, where NZEB balance is not fulfilled at all as an example,

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opposite matching situations of the NZEB are obtained by the model, where w3 = 0.139 > w4 = 0.229. This

620

indicates that it does not make sense to import NG with a NRPE factor of 1.14 and generate on-site thermal energy

621

from the µ-CHP with a NRPE factor of 1.27 to export to and/or replace the thermal heating grid, which already has

622

a NRPE factor 0.77. (i.e. dependence on the off-site thermal generation is better than the on-site thermal generation

623

environmentally).

624 625

4) The model of calculating weighting factors is generic, because it takes into account the energy quality and energy conversion by using NRPE factors (or any other crediting factors like CO2 emission factor).

626

5) This study also shows that the best matching cases from an electrical and thermal energies point of view could not

627

be obtained by the import/export figure of the NZEB balance, when other energy carriers (e.g. fuels) are imported

628

beside the electricity and thermal heat. 25 Page 25 of 36

629

6) Installing the required PV areas to the analyzed µ-CHPs under the thermal tracking strategy to fulfill the NZEB balance changes the best matching case of the µ-CHP, fulfilling the NZEB alone due to enhancing the detailed

631

matching indices, especially OEFe, which has the highest weighting factor w1 (e.g. 1.5 kWe bio-syngas-fueled µ-

632

CHP with P/H ratio of 0.7, and PV area of 23 m2 has WMI of 0.78, which is higher than the WMI of 0.75 of 2.0

633

bio-syngas-fueled kWe µ-CHP alone with 0.8 P/H ratio).

636

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7) The WMI, as well as the detailed matching indices, can be used not only through design of new buildings but also through retrofitting the existing buildings, showing the energy matching situation.

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8) Because the WMI is a quantitative parameter, indicating the overall energy matching situation, the possibility of including the energy matching capability to be an objective in a multi-objective optimization calculation for nearly

638

and net ZEB arises.

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Acknowledgement

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The authors would like to acknowledge the Finnish Academy, Aalto University, AEF program, SAGA project, as well as

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Tekes RYM-Indoor Environment project for partly funding this research.

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26 Page 26 of 36

643 References

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28 Page 28 of 36

Highlights

754

Energy matching analysis of µ-CHPs coupling with PV and/or STC is presented.

755

A new model calculating the weighting factors of overall matching index is proposed.

756

The weighting factors calculation is based on energy carriers’ NRPE factors.

757

Matching analyses of µ-CHPs under thermal and electrical tracking strategies.

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Two opposite extreme matching situations in the NZEB are reflected.

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31 Page 29 of 36

List of Figure Captions

Figure Captions Fig. 1. The control principle of thermal tracking strategy, thermal connections of the µ-CHP, and electric connections of the µ-CHP and the PV modules.

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Fig. 2. The control principle of electrical tracking strategy, electrical connections of the µ-CHP, and thermal connections of the µ-CHP and the STC modules. Fig. 3. The main principle for the two basic indices OEF and OEM [27].

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Fig. 5. Net primary energy of µ-CHPs under the thermal tracking strategy.

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Fig. 4. Simplified topology for extended matching specified for µ-CHP, coupled with PV and STC modules. Note that the electrical demand is excluding the electrical needs of the electrical-driven heating machines.

Fig. 6. The overall annual weighting matching index WMI for µ-CHPs under the thermal tracking strategy.

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Fig. 7. Imported and exported primary energy of µ-CHPs under the thermal tracking strategy. Fig. 8. The PV area required to fulfill the NZEB balance under the thermal tracking strategy.

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Fig. 9. The overall annual weighting matching index for µ-CHP and PV modules under the thermal tracking strategy. Fig. 10. Net primary energy of µ-CHPs under the electrical tracking strategy.

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Fig. 11. The overall annual weighting matching index for µ-CHPs under the electrical tracking strategy.

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Fig. 12. Imported and exported primary energy of µ-CHPs under the electrical tracking strategy. Fig. 13. The STC area required to fulfill the NZEB balance under the electrical tracking strategy.

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Fig. 14. The overall annual weighting matching index for µ-CHP and STC modules under the electrical tracking strategy.

1

Page 30 of 36

Figure(s)

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Figures

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Fig. 1. The control principle of thermal tracking strategy, thermal connections of the µ-CHP, and electric connections of the µ-CHP and the PV modules.

Fig. 2. The control principle of electrical tracking strategy, electrical connections of the µ-CHP, and thermal connections of the µ-CHP and the STC modules.

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Page 31 of 36

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Fig. 3. The main principle for the two basic indices OEF and OEM [27].

Fig. 4. Simplified topology for extended matching specified for µ-CHP, coupled with PV and STC modules. Note that the electrical demand is excluding the electrical needs of the electrical-driven heating machines.

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Page 32 of 36

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Fig. 5. Net primary energy of µ-CHPs under the thermal tracking strategy.

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Fig. 6. The overall annual weighting matching index WMI for µ-CHPs under the thermal tracking strategy.

Fig. 7. Imported and exported primary energy of µ-CHPs under the thermal tracking strategy.

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Page 33 of 36

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Fig. 8. The PV area required to fulfill the NZEB balance under the thermal tracking strategy.

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Fig. 9. The overall annual weighting matching index for µ-CHP and PV modules under the thermal tracking strategy.

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Fig. 10. Net primary energy of µ-CHPs under the electrical tracking strategy.

Fig. 11. The overall annual weighting matching index for µ-CHPs under the electrical tracking strategy.

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Page 34 of 36

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Fig. 12. Imported and exported primary energy of µ-CHPs under the electrical tracking strategy.

Fig. 13. The STC area required to fulfill the NZEB balance under the electrical tracking strategy.

Fig. 14. The overall annual weighting matching index for µ-CHP and STC modules under the electrical tracking strategy. 5

Page 35 of 36

Table(s) with Caption(s) revised

Table 1. The non-renewable primary energy (NRPE) factors of the energy carriers NRPE factors Unit

Primary energy

kWhpr/kWhsite

Electricity

District heating

Natural gas

Bio-syngas

Solar energy

2.23

0.77

1.14

0.17

0.00

Ref. [30], [19]

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Weighting system

w2 of OEMe 0.056 0.229

w3 of OEFh 0.228 0.139

w4 of OEMh 0.056 0.229

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w1 of OEFe 0.660 0.403

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Fuel supply of µ-CHP Bio-syngas Natural gas

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Table 2. The weighting factors of the WMI for µ-CHP fed by bio-syngas and natural gas (NG).

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Page 36 of 36