Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea

Energy 189 (2019) 116132

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Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea Jeonghun Song a, *, Si-Doek Oh b, Seung Jin Song a, c a

Department of Mechanical & Aerospace Engineering, Seoul National University, Seoul, 08826, South Korea Blue Economy Strategy Institute Co. Ltd., Seocho-Dong, Seocho-Gu, Seoul, 06721, South Korea c Institute of Advanced Machines and Design, Seoul National University, Seoul, 08826, South Korea b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 July 2018 Received in revised form 5 July 2019 Accepted 13 September 2019 Available online 17 September 2019

Renewable energy sources such as solar panels with energy storage, ground-source heat pumps, and fuel cells are increasingly being integrated into buildings to improve their sustainability. This paper investigates, for an urban area, the effects of building-integrated renewable energy on the building energy portfolio and electricity & gas flows from the grid. In this study, 403 apartment buildings and 269 nonresidential buildings (with a floor area over 500 m2) in a district of Seoul, Korea, are assumed to integrate renewable energy sources to satisfy the renewable energy requirements of 10% and 30% of the total energy demand, respectively. The optimal renewable energy system for each building has been obtained via Linear Programming. The optimization has been carried out based on the hourly electricity, heating, and cooling loads of every building over a 1-year period. The energy loads, in turn, have been derived from the monthly electricity and gas usage data for every building. The renewable energy portfolio for the apartment buildings consists of photovoltaics, fuel cells, and solar water heaters. About 74% of the renewable energy for apartment buildings is provided by photovoltaics. The renewable energy portfolio for the non-residential buildings consists of ground-source heat pumps and photovoltaics. About 71% of the renewable energy is provided by the ground-source heat pumps. Consequently, total electricity and gas supply from the grid to all of the buildings in the district decrease by 17% and 3%, respectively. The photovoltaics induce a large variation in power flow from the grid during the daytime (i.e., the so-called Duck curve). The annual peak power occurs in the winter mornings because electricity powered heating by ground-source heat pumps replaces gas heating, and maximum heat demand occurs in winter mornings. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Building-integrated renewable energy Optimal energy system Linear programming Energy loads Energy grid

1. Introduction Buildings consume about 40% of the total energy in most of the developed countries [1]. Therefore, much effort has been made to save energy in buildings [2]. One option is to integrate renewable energy sources such as solar panels with energy storage, groundsource heat pumps, and small-scale fuel cells into buildings. These building-integrated renewable energy systems can provide multiple energy services (e.g., electricity, heat, and cooling [3]) onsite, facilitating decarbonization of urban areas [4], increased global energy efficiency, and decreased transmission capacity needs [5]. Therefore, many countries are promoting renewable energy in

* Corresponding author. E-mail address: fl[email protected] (J. Song). https://doi.org/10.1016/j.energy.2019.116132 0360-5442/© 2019 Elsevier Ltd. All rights reserved.

buildings [6]. In South Korea, for example, public buildings with a floor area over 1,000 m2 are obliged to satisfy 30% of their energy demands with renewable energy by 2020 [7]. The increasing integration of renewable energy into individual buildings significantly affects the energy portfolio of the building sector and flows of electricity and gas from the energy grid to the district's buildings [8]. Therefore, an accurate assessment of the effect on building energy portfolio and electricity and gas from the grid to the district is needed to guide planners for renewable energy policy, power generation and transmission [9]. Many studies have been done on the design of buildingintegrated renewable energy systems for one building [10,11]. Especially, minimization of the cost of energy capacities and hourly energy flows over a 1-year period via Linear Programming has been studied extensively [12e14]. However, relatively few studies have been done on the effects of building-integrated renewable energy

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J. Song et al. / Energy 189 (2019) 116132

Abbreviations PEMFC GSHP PV SWH

Fuel cell Ground-source heat pump Photovoltaic panel Solar water heater

Nomenclature APV Required effective roof area for 1-kW PV panel [m2] Aroof Effective roof area for solar energy in a building [m2] ASWH Required effective roof area for 1 m2 solar water heater [m2] cbatt Equivalent annual cost of batteries [$/kWh] cFC Equivalent annual cost of fuel cell [$/kW] cgas;boiler Gas rate for heating by boiler [$/MJ] cgas;FC Gas rate for operating fuel cell [$/MJ] cGSHP Equivalent annual cost of ground-source heat pump [$/kW] cPV Equivalent annual cost of photovoltaic panel [$/kW] cP;basic Basic rate for electricity in non-residential building [$] cP;basic:0 Basic rate for electricity in residential building (0 e200 kWh per household) [$] cP;basic:1 Basic rate for electricity in residential building (200 e400 kWh per household) [$] cP;basic:2 Basic rate for electricity in residential building (Over 400 kWh per household) [$] cP;use Use rate for electricity in non-residential building [$] cP;use:0 Use rate for electricity in residential building (0 e200 kWh per household) [$] cP;use:1 Use rate for electricity in residential building (200 e400 kWh per household) [$] cP;use:2 Use rate for electricity in residential building (Over 400 kWh per household) [$] cSWH Equivalent annual cost of solar water heater [$/m2] COPEHP:c COP of electric heat pump (cooling mode) COPEHP:h COP of electric heat pump (heating mode) COPGSHP:c COP of ground-source heat pump (cooling mode) COPGSHP:h COP of ground-source heat pump (heating mode) Ebatt Energy stored in the batteries [kWh] Gboiler Gas for boiler operation [MJ/h] GFC Gas for fuel cell operation [MJ/h]

increased in an urban district. Most of such studies on the effect of renewable energy systems in a district focused on the integration of large-scale renewable energy systems such as solar and wind farms, biomass plants, etc. [15e19]. Alternatively, others have focused on the effect of building-integrated renewable energy systems on a district or a region. Garshasbi et al. [20] studied how a hypothetical cluster of 10,000 residential buildings with photovoltaic (PV) panels would affect relative load variation and monthly electricity from the grid. Seljom et al. [21] studied the impact of transforming about 50% of Scandinavian buildings to zero energy buildings (including PV panels) on the Scandinavian electricity and heat generation portfolio, power dispatch, and energy cost.
lu and Yılmaz [22] studied how a district's primary energy Kalaycıog consumption and CO2 emission would be affected by the district's 42 nearly zero energy buildings (including PV panels and solar water heaters). Groppi et al. [23] studied an expected decrease in the non-renewable thermal and electricity needs of two urban areas by considering solar energy potential and the annual energy demand of each building in the two areas. In these studies, only

h M Pch Pdisch PEHP Pgrid PGSHP Pload PPV P0 P1 P2 Qdump Qc Qh Qw QSWH Qtank Sbatt SFC SGSHP SPV SSWH wFC wSWH x

hbatt hboiler hinv hFC:H hFC:P Dt g

Number of households in an apartment building A sufficiently large number Charging power [kW] Discharging power [kW] Power consumption of electric heat pump [kW] Power transmitted from the grid [kW] Power consumption of ground-source heat pump [kW] Power load [kW] Power generated by a 1-kW photovoltaic panel [kW] Total electricity use in residential building (0 e200 kWh per household) [kWh] Total electricity use in residential building (200 e400 kWh per household) [kWh] Total electricity use in residential building (Over 400 kWh per household) [kWh] Dumped heat [kWh] Cooling load [kW] Space heating load [kW] Water heating load [kW] Heat produced by a 1 m2 solar water heater [kWh] Heat stored in water tank [kWh] Capacity of batteries [kWh] Capacity of fuel cell [kW] Capacity of ground-source heat pump (cooling capacity basis) [kW] Capacity of photovoltaic panel (Number of 1-kW panel) Capacity of solar water heater (Number of 1-m2 collector) Maximum stored heat in water tank for 1-kW fuel cell [kWh] Maximum stored heat in water tank for 1 m2 solar water heater [kWh] Binary numbers Charging & discharging efficiency of batteries [%] Combustion efficiency of boiler [%] DC-AC inverter efficiency [%] Gas-to-Heat conversion efficiency of fuel cell [%] Gas-to-Power conversion efficiency of fuel cell [%] Time step (1 hour) c-rate of batteries

solar energy sources have been considered, and it is unclear whether the solar energy system configuration for each building has been optimized. In reality, every building would adopt a minimum-cost set of renewable energy systems which would include not only solar energy but also other renewable energy sources (e.g., groundsource heat pumps, fuel cells, etc.) considering the energy loads of each building. However, investigation on such buildingintegrated renewable energy systems for multiple buildings in an urban district has not yet been done. Hence, this research aims to assess the collective effect of increased building-integrated renewable energy on the building energy portfolio and energy flow considering the various optimal building-integrated renewable energy systems for each building. This study assesses a test scenario of increased building-integrated renewable energy in an actual Korean district (Seongsan-Dong of Seoul, Korea). The specific research questions are:

J. Song et al. / Energy 189 (2019) 116132

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(1) What is the district-scale building energy portfolio in an urban district when regulation requires building-integrated renewable energy? (2) How does building-integrated renewable energy affect the total electricity and gas flow from the grid to the district?

2. Methodology To optimize the energy system (including renewable energy sources) for each building (Fig. 1), the following inputs are required
cost of renewable energy sources; electricity & gas rates; performance of renewable energy sources; hourly solar irradiance; renewable energy policy; hourly energy load; roof area for solar energy. Hourly energy load and roof area vary from building to building while the rest of the parameters remain constant for every building. Given the inputs, the capacities of the renewable energy sources and the hourly energy flows in each building can be optimized. Among many optimization algorithms (Linear and Nonlinear Programming, Dynamic Programming, Heuristics like Genetic Algorithm, etc.) [10], Linear Programming has been selected for this study. Using Linear Programming, the concept of linear energy system models abundant in the previous studies [12e14] can be applied, and the global optimum is guaranteed to be found at once. Subsequently, the capacities and energy supply of each renewable energy source in all of the buildings in a district are summed up for a district-scale analysis (Fig. 2). Input data, assumptions, models, and problem formulations are described in the following subsections.

Fig. 2. Flowchart of a district-scale analysis of the increased building-integrated renewables considering the optimal renewable energy system for each building.

2.1. Input data 2.1.1. Estimation of the hourly energy load of each building The process suggested by Chung and Park ([24,25]) has been applied to obtain the hourly electricity, space heating, space cooling, and water heating loads of an actual office building, hotel, hospital, and department store over a 1-year period. The process is explained in detail below. First, the monthly electricity and gas usage data for every building are obtained. For every non-residential building, measured data are available from the Korean Ministry of Land (Fig. 3) [26]. For every residential building, the monthly electricity and gas usage can be estimated by using a model developed by the Seoul Institute [27]. The model provides a correlation between the monthly electricity & gas consumption and the floor area of a household (Fig. 4). Second, monthly electricity and gas usage data are disaggregated. The electricity usage data are then further broken down into electricity for appliances & lightning, electricity for

Fig. 3. Monthly electricity and gas uses in 4 selected Korean non-residential buildings (No gas consumption in Building C).

Fig. 4. Estimated monthly electricity and gas uses of one household in a residential building with 4 typical floor areas per household.

Fig. 1. Schematic of the energy flows in a typical building energy system which includes renewable energy sources.

space cooling, and electricity for space heating. It has been assumed that 1) most of the electricity usage in May is for appliance & lightning (Table 1), and; 2) the electricity for appliance & lightning stays relatively constant for all months (Fig. 5). The difference between the actual electricity usage and the estimated electricity for appliance & lightning is assumed to be electricity for space cooling

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J. Song et al. / Energy 189 (2019) 116132 Table 1 Cooling and heating seasons, estimated portion of electricity for space cooling in May for 5 common building types.

Office Neighborhood Department store Hospital & Welfare Hotel

Cooling season

Heating season

Electricity for cooling in May

MAY ~ SEP ~ SEP JUN APR ~ OCT ~ SEP JUN APR ~ OCT

OCT ~ APR OCT ~ APR NOV ~ MAR OCT ~ APR NOV ~ MAR

7.9% 0.0% 38.4% 0.0% 18.5%

Fig. 5. The measured total electricity use for each month and the estimated monthly electricity usage for appliances of a selected building.

during the cooling season and space heating during the heating season. Meanwhile, the gas usage data are decomposed into gas for space heating and gas for water heating. The gas usage between May and September is assumed to be entirely for water heating. The gas usage for water heating in the remaining months is assumed to be equivalent to the maximum value of the monthly gas use between May and September (Fig. 6). The difference between the actual gas usage and the estimated gas usage for water heating between October and April is then assumed to be gas for space heating. Third, the disaggregated monthly electricity and gas usage data are converted into monthly electricity demand (for appliances & lightning), monthly space cooling demand, monthly space heating demand, and monthly water heating demand. Monthly space cooling demand is equivalent to the electricity for space cooling multiplied by the Coefficient of Performance (COP) of electric heat pumps (EHPs). Monthly space heating demand is equivalent to the sum of two elements
the electricity for space heating multiplied by the COP of EHPs and the gas for space heating multiplied by the combustion efficiency of gas boilers. Monthly water heating demand is equivalent to the gas for water heating multiplied by the combustion efficiency of gas boilers. Fourth, the monthly energy demands are converted to hourly energy loads. Fig. 7 shows the hourly electricity (for appliances & lightning), space cooling, space heating, and water heating loads in a typical day for four major building types (apartment, office, neighborhood & store, and medical & welfare) in Korea. Neighborhood buildings include businesses such as grocery stores, restaurants, local clinics, barber shops etc., and public buildings such

Fig. 6. The measured total gas use for each month and the estimated monthly gas usage for water heating of a selected building.

as fire stations, post offices, libraries, etc. Y-axis shows the hourly portion of each hour in the total daily energy demand, equivalent to the monthly energy demand divided by the number of days in each month. Thus, the sum of the 24 Y values is 1. For each energy type, the hourly energy load can be obtained by multiplying the hourly portions by the estimated daily energy demand. Fig. 8 shows the estimated hourly energy loads of an apartment building and an office building on a representative weekday of each month. The electricity load in the apartment building includes the electricity for space cooling, but the electricity load in the nonresidential buildings does not.

2.1.2. Performance of renewable energy sources The candidates for building-integrated renewable energy are Photovoltaic (PV) panels, Solar Water Heaters (SWHs), GroundSource Heat Pumps (GSHPs), and Proton-Exchange Membrane Fuel Cells (PEMFCs). The assumed values of the performance parameters of PV panels, SWHs, GSHPs, EHPs, gas boilers, PEMFCs, and batteries are listed in Table 2 [28e31]. Li-ion batteries are often integrated into PV systems [32], and battery installation in buildings has been promoted in Korea since 2016. Therefore, this study considers batteries. Since EHP dominates the air-conditioning market with a 66% market share [33], the conventional space cooling source is assumed to be the EHP for all non-residential buildings. The hourly output of PV panels and SWHs are determined from the solar radiation models described in Appendix A.

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Fig. 7. Hourly energy loads of typical building types in Korea ((a) Apartment, (b) Office, (c) Neighborhood & Store, (d) Medical & Welfare).


 ið1 þ iÞn cannual ¼ cinitial 4 þ ð1 þ iÞn
1

(1)

Interest rate i has been assumed to be 5%, a common assumption for baseline scenarios in climate change studies [35]. Gas rates (from Seoul City Gas) for boilers and PEMFCs are listed in Table 4. Customers pay 110% of the gas rates due to a 10% surtax. Electricity rates (from Korea Electric Power Corporation) for a household in a residential building and a non-residential building are listed in Table 5. For a household of a residential building, the base rate and usage rate are functions of the total monthly electricity usage. For a non-residential building, the base rate is proportional to the maximum hourly power from the grid over a 1year period while the usage rate follows the time-of-use pricing. Customers pay 113.7% of the electricity rates due to a 10% surtax and a 3.7% surcharge for power industry promotion. 2.2. Optimization of building-integrated renewable energy system design

Fig. 8. Estimated hourly energy loads of two selected Korean buildings ((a) An apartment building, (b) An office building).

2.1.3. Capital and variable costs Initial investment costs, maintenance factors (4), and lifespans (n) of the renewable energy sources and batteries are listed in Table 3. The initial investment cost cinitial has been converted to the equivalent annual cost cannual by multiplying by the capital recovery factor [34] to conduct optimizations based on the data over a 1-year period:

Two kinds of Linear Programming problems are newly formulated to optimize the capacities of PV panels, SWHs, GSHPs, PEMFCs, and batteries and hourly energy flow in an apartment building and a non-residential building of Korea, respectively. Table 6 summarizes the objective function and constraints. The optimization has been carried out using MATLAB 2017a & Gurobi 7.5 (Academic licenses) on a workstation with Intel Quad Core 3.40 GHz and 16 GB memory. 2.2.1. Objective function The objective function to minimize is the sum of capital

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J. Song et al. / Energy 189 (2019) 116132 Table 2 Performance parameters of the energy sources in Korean buildings. PV

Conversion efficiency Required roof area for 1 kW of PV Constant for representing the efficiency Thermal transmittance Temperature of the water entering the collector Storable heat in water tank per 1 m2 of SWH collector Required roof area for 1 m2 of SWH collector COP (cooling mode) COP (heating mode) COP (cooling mode) COP (heating mode) Combustion efficiency Gas-to-Power conversion efficiency Gas-to-Heat conversion efficiency Storable heat in water tank per 1 kW PEMFC Charging & discharging efficiency Inverter efficiency C-rate

SWH

GSHP EHP Boiler PEMFC

Batteries

15.1% 10 m2 0.62 3.25 W/m2K 45.7  C 2.08 kWh 2 m2 4.69 3.70 3.50 3.20 85.0% 36.7% 42.3% 8.44 kWh 94.0% 95.0% 0.33

expenditure and operational expenditure. The capital expenditure consists of the equivalent annual cost of PV panels, SWHs, GSHPs, PEMFCs, and batteries:

The operational expenditure consists of the cost associated with gas and electricity purchase. The associated cost for gas is a function of gas rates for boilers and PEMFCs:

cPV SPV þ cSWH SSWH þ cGSHP SGSHP þ cFC SFC þ cbatt Sbatt

i Xh cgas;boiler Gboiler ðtÞ þ cgas;FC ðtÞGFC ðtÞ Dt; t 2 N; 1  t  8760

(2)

However, duct space construction for GSHPs in an existing apartment building is rare because it is a big disturbance to the residents [36]. Therefore, GSHPs are not considered for apartment buildings (SGSHP ¼ 0Þ: Also, the time-of-use pricing which makes batteries economically feasible [37] is applied only to nonresidential buildings. Therefore, batteries are not considered for apartment buildings (Sbatt ¼ 0Þ.

X

t

(3) The associated cost for electricity in an apartment building is a function of the base rate and the usage rate for each month m which are functions of the total electricity usage divided by the number of households h in the apartment building:

   cP;basic:0 þ cP;basic:1 x1 ðmÞ þ cP;basic:2 x2 ðmÞ þ cP;use:0 P0 ðmÞ þ cP;use:1 P1 ðmÞ þ cP;use:2 P2 ðmÞ Dt

(4)

m

Table 3 Capital costs, maintenance factors, and lifespans of renewable energy sources and batteries.

PV SWH GSHP PEMFC Battery

Cost [$]

Maintenance factor

Lifespan [year]

1640/kW 860/m2 1070/kW 29,550/kW 910/kWh

2% 5% 2% 6% 1%

25 20 25 12 10

Here, ðP0 ðmÞ þP1 ðmÞ þP2 ðmÞÞDt is equivalent to the total electricity consumption in the corresponding month m. To consider the base rate correctly, x1 ðmÞ should be 1 only if P0 ðmÞ þ P1 ðmÞ þ P2 ðmÞ  200h, and x2 ðmÞ should be 1 only if P0 ðmÞ þ P1 ðmÞ þ P2 ðmÞ  400h. Therefore, additional constraints including constraints based on big-M method [38] are required, as shown below:

 X P0 ðmÞ þ P1 ðmÞ þ P2 ðmÞÞDt ¼ Pgrid ðtÞDt

Heating Cogeneration (Winter) Cogeneration (Summer) Cogeneration (Others)

cm; ct

(5)

t2m

Table 4 Korean gas rates for buildings [Cent/MJ]. 1.50 1.41 1.25 1.19

P0 ðmÞ  200h; P1 ðmÞ  200h cm

(6)

P1 ðmÞ  Mx1 ðmÞ; P2 ðmÞ  Mx2 ðmÞ cm

(7)

Table 5 Korean electric rates for households in apartment buildings and typical non-residential buildings. Residential

Non-residential

Monthly use

Basic rate [Cent]

Usage rate [Cent/kWh]

Basic rate [$/kW]

Uagse rate [Cent/kWh]

Summer

Winter

Others

0e200 kWh 200e400 kWh Over 400 kWh

66.4 114.5 550.9

7.12 13.39 19.60

7.56

Base load Mid load Peak load

5.10 9.91 17.37

5.74 9.93 15.15

5.10 7.15 9.94

J. Song et al. / Energy 189 (2019) 116132

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Table 6 Conceptual summary of the objective function and the constraints of the optimization problem. Objective function Constraints

Sum of the equivalent annual cost of each renewable energy source and the associated costs for electricity and gas Energy balance Electricity, Heating, Cooling Limitations Roof area for solar energy, Maximum output of GSHP and PEMFC, Only space heating from heat pumps in heating mode, Limit of water tank Battery operation Temporal variation in the stored energy, Limits of the stored energy, charging, and discharging Export of electricity generated by PV in apartment buildings Renewable energy penetration

The associated cost for electricity in a non-residential building is a function of the base rate and the usage rate. The base rate is a function of the maximum value of hourly power transmitted from the grid over a 1-year period. The usage rate is a function of the total electricity usage during base-load, mid-load, and peak-load hours for each season:

Xh  i cP;use ðtÞPgrid ðtÞDt 12cP;basic max Pgrid þ

(8)

t

where maxðPgrid Þ is equivalent to an epigraph q if an additional constraint shown below is applied [39]:

Pgrid ðtÞ  q ct

(9)

temperature of water supply for space heating is 35e40  C [40]. Therefore, the sum of heat provided by EHP and GSHP is limited by the space heating demand:

COPEHP:h PEHP:h ðtÞ þ COPGSHP:h PGSHP:h ðtÞ  Qh ðtÞ ct

Heat stored in a hot water tank is limited by the capacity of the tank which is assumed to be a linear function of the capacities of SWH and PEMFC [28,31]:

Qtank ðtÞ  wSWH SSWH þ wFC SFC ct

(16)

2.2.4. Constraint: battery operation Electricity stored in a battery varies due to charging or discharging:

Ebatt ðt þ 1Þ ¼ Ebatt ðtÞ þ ðhbatt Pch ðtÞ
Pdisch ðtÞ=hbatt ÞDt ct

2.2.2. Constraint: energy balance For each hour, electricity supply should satisfy demand:

(15)

(17)

Energy stored in the batteries, and the maximum power for charging and discharging are limited by the capacity of the battery:

pload ðtÞ ¼ Pgrid ðtÞ þ hinv SPV Ppv ðtÞ þ Pdisck ðtÞ
Pch ðtÞ
PEHP ðtÞ
PGSHP ðtÞ þ hFC:P GFC ðtÞ ct

Ebatt ðtÞ  Sbatt ct; Pdisch ðtÞDt  gSbatt ct; Pch ðtÞDt  gSbatt ct (10)

(18)

Also, space cooling demand and supply should be balanced:

Qc ðtÞ ¼ COPEHP:c PEHP ðtÞ þ COPGSHP:c PGSHP ðtÞ ct

(11)

Finally, heating supply (sum of space heating and water heating) should meet heating demand:

2.2.5. Constraint: export of electricity generated by PV in residential buildings Korean residential buildings can export their excessive power generated by PV panels to reduce the net monthly electricity pur-

Qh ðtÞ þ Qw ðtÞ ¼ hboiler Gboiler ðtÞ þ SSWH QSWH ðtÞ þ COPEHP:h PEHP:h ðtÞ þCOPGSHP:h PGSHP:h ðtÞ þ hFC:H GFC ðtÞ þ Qtank ðtÞ
Qtank ðt þ 1Þ
Qdump ðtÞ ct

where Gboiler ðtÞ is forced to be zero for non-residential buildings which do not use gas. 2.2.3. Constraint: installation and operation range for renewable energy sources The sum of the area for PV and SHW is limited by the actual roof area of the building:

SPV APV þ SSWH ASWH  Aroof

(13)

Hourly electricity consumption of GSHP and gas consumption of PEMFC are limited by the capacities of GSHP and PEMFC:

COPGSHP:c PGSHP ðtÞ  SGSHP ct; hFC:P GFC ðtÞ  SFC ct

(14)

Producing domestic hot water with GSHP is challenging because the domestic hot water needs to be 55e60  C while the

(12)

chase. Therefore, the constraint shown below is imposed on the apartment buildings:

Pgrid ðtÞ 
SPV PPV ðtÞ ct

(19)

Lower bounds for all variables except for Pgrid ðtÞ of apartment buildings are zero. 2.2.6. Constraint: renewable energy requirement The total energy provided by renewables is the sum of power generated by PV panels, heat provided by SWHs, space heating and cooling provided by GSHPs, and electricity and heat provided by PEMFCs over a 1-year period. The total energy provided by the renewables must be equal to or greater than a governmentspecified fraction of the sum of electricity (including the electricity consumed by EHPs and GSHPs), space cooling, space heating,

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J. Song et al. / Energy 189 (2019) 116132

2.3. Test case

Fig. 9. Comparison between the floor area-based building type compositions of Seoul and Seongsan-Dong.

and water heating demand over a 1-year period:

Xh t

SPV PPV ðtÞ þ SSWH QSWH ðtÞ þ COPGSHP ðtÞPGSHP ðtÞ þ ðhFC:P

i þ hFC:H ÞGFC ðtÞ
Qdump ðtÞ X  a ½Pload ðtÞ þ PEHP ðtÞ þ PGSHP ðtÞ þ Qc ðtÞ þ Qh ðtÞ þ Qw ðtÞ t

(20) where a is the government-specified fraction; COPGSHP ðtÞ is COPGSHP:c at t for the cooling season, and COPGSHP:h at t for the heating season.

2.3.1. Test district In this study, Seongsan-Dong, Mapo-Gu, Seoul, has been selected as the test case because the floor area-based building type composition of Seongsan-Dong is representative of the entire city of Seoul (Fig. 9). Fig. 10 shows the geospatial topology of SeongsanDong from Google Maps. Seongsan-Dong has a population of about 60,000 people and consists of 2015 residential buildings and 592 non-residential buildings. To obtain the optimal renewable energy system in each building, the following data have been used
the monthly electricity and gas consumption data for non-residential buildings; solar irradiance in year 2015; the floor area of each building; the number of households of each apartment building from the national registered building data [41]; and the roof area of each building in the database of a Korean solar energy consulting company [42]. 2.3.2. Test scenario of renewable energy obligation Currently, Korean public buildings with a floor area over 1,000 m2 are mandated to satisfy 30% of the total energy demand with renewable energy by 2020. Also, integration of renewable energy into buildings in Seoul with a floor area over 3,000 m2 and 500 m2 are mandated and encouraged, respectively. The renewable energy requirements for apartment buildings and non-residential buildings in Seoul are 10% and 14% of their total energy demands, respectively, by 2023. To simulate a substantial increase in building-integrated renewables, a test scenario of the renewable energy requirements has been created as follows
renewable energy requirements of 10% for every apartment building with a floor area over 500 m2 and 30% for every non-residential building with a floor area over 500 m2, respectively. Under this assumption, 403 apartment buildings and 269 non-residential buildings in Seongsan-Dong are obliged to satisfy the renewable energy requirements. Table 7 shows the optimal capacities of renewable energy sources in a few buildings in Seongsan-Dong under the test scenario.

Fig. 10. Seongsan-Dong shown in Google Map.

J. Song et al. / Energy 189 (2019) 116132

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Table 7 The opitmal capacites of each renewable energy source in a few buildings in Seongsan-Dong under the test scenario. Address

Type

PV [kW]

SWH [m2]

GSHP [kW]

PEMFC [kW]

Battery [kWh]

20e12 156e7 596e0

Neighborhood Office Apartment

40 23 88

0 0 0

19 74 e

0 0 2

7 14 e

Table 8 The total capacities of the renewable energy sources and batteries in Seongsan-Dong. Type

PV [kW]

SWH [m2]

GSHP [kW]

PEMFC [kW]

Battery [kWh]

Residential Non-residential

8,397 8,028

1,680 57

e 9,990

144 1

e 1,161

Fig. 11. Energy portfolio of the buildings under the scenario of renewable energy obligation in Seongsan-Dong ((a) Apartment buildings, (b) Non-residential buildings).

renewable energy source has been also obtained to investigate the energy-based building energy portfolio. The total renewable energy supply of each renewable energy source has been represented as:

PV panels:

XX SPV ðjÞPPV ðt; jÞ j

SWHs:

XX SSWH ðjÞQSWH ðt; jÞ j

GSHPs:

(22)

t

XX COPGSHP ðtÞPGSHP ðt; jÞ j

Fig. 12. Energy supply of each renewable energy source and the relative portions ((a) Apartment buildings, (b) Non-residential buildings).

(21)

t

(23)

t

PEMFCs: ðhFC:P þ hFC:H Þ

XX XX GFC ðt; jÞ
Qdump ðt; jÞ j

t

j

t

(24) 2.3.3. Formula for the district-scale building energy portfolio The sum of the optimal capacities of each renewable energy source has been obtained to investigate the capacity-based building energy portfolio. The total renewable energy supply of each

where j is the building index. The total electricity flow from the grid P to Seongsan-Dong has been represented as Pgrid ðt;jÞ, and the total j been represented as gas flow from the grid to Seongsan-Dong has P ½Gboiler ðt; jÞ þ GFC ðt; jÞ. j

Table 9 The capacities of the renewable energy sources from simulation on Seongsan-Dong and the actual result of the renewable energy obligation for Korean public buildings in 2016.

Seongsan-dong (Renewables: 30%) Korean public buildings (Renewables: 16%)

PV [kW]

SWH [m2]

GSHP [kW]

PEMFC [kW]

8,028 235,342

57 54,589

9,990 944,472

1 486

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J. Song et al. / Energy 189 (2019) 116132

Table 10 The number of apartment buildings and households for each portfolio of the renewable energy sources.

PV PV þ PEMFC PV þ SWH PV þ PEMFC þ SWH

Number of buildings

Number of households

389 11 2 1

4,354 1,921 1,855 3,710

Fig. 14. Electricity generated by PV (X axis) and space heating & cooling supplied by GSHP (Y axis) in each non-residential building.

Fig. 13. Net monthly electricity use per household after subtraction of the electricity generated by PV of 14 apartments which integrate multiple renewable energy sources.

3. Results and discussion 3.1. Building-integrated renewable energy portfolio of SeongsanDong The capacity-based and energy-based portfolios of buildingintegrated renewable energy have been derived. Table 8 shows the total capacity of each renewable energy source in the buildings for the test scenario. The total capacities of PV panels and GSHPs are much higher than the total capacities of the other renewable energy sources. Fig. 11a shows the energy-based building energy portfolio of the 403 apartment buildings with the renewable energy requirement of 10%. Electricity generated by PV panels and PEMFCs provides about 30% of the total electricity demand. However, heat from SWHs and PEMFCs provides only about 2% of the total heating demand. Fig. 11b shows the energy-based building energy portfolio of the 269 non-residential buildings with the renewable energy requirement of 30%. Electricity generated by PV panels provides about 17% of the total electricity demand. Space heating and space cooling from GSHPs provide about 35% and 50% of the total heating and cooling demand, respectively. Fig. 12 shows

the amount and relative portion of the energy provided by each renewable energy source in the apartment buildings (Fig. 12a) and non-residential buildings (Fig. 12b), respectively. About 74% of the total energy provided by renewables for the apartment buildings is electricity from PV. About 71% of the total energy provided by renewables for the non-residential buildings is space heating and space cooling from GSHPs. PEMFCs and SWHs provide 19% and 7% of the total renewable energy supply in the apartment buildings, respectively. However, energy provided by PEMFCs and SWHs is negligible in the non-residential buildings.

3.1.1. Comparison between the simulation result and the result of the current obligation As an indirect validation, the sum of the optimal capacities of each renewable energy source in the non-residential buildings in Seongsan-Dong have been compared with the sum of the actual capacities in Korean non-residential public buildings (with a renewable energy requirement of 16% in 2016) from the 2017 Korea Energy Agency Handbook [43] (Table 9). In both cases, the capacity of GSHP is the highest and the capacity of PV is also high while the capacities of the SWH and PEMFC are much lower. The portions of PV are different because of the i) the decreasing cost of PV panels, and; ii) higher renewable energy requirement which usually results in multiple renewable energy sources. Still, the same capacitybased rankings of the renewable energy sources help validate the findings of this study.

Table 11 Formula, regression coefficients, and standardized coefficients of the multiple linear regression model for the relative portion of energy from GSHP and PV (constant term of the regression model:
0.1916). X1

X2

X3

0.899 0.750

nðftjQn ðtÞ þ Qc ðtÞ > 0gÞ 8760 0.997 0.048

hboiler $½Gas for space heating COPEHP $½Electricity for heating 0.249 0.180

Formula Regression coefficient Standardized coefficient

J. Song et al. / Energy 189 (2019) 116132

11

(125.4 kWh/m2). Fig. 13 shows the net monthly electricity usage of the 14 highrise apartment complexes. The net monthly electricity usage means the electricity usage after subtraction of the electricity generated by the PV panels. PEMFCs are integrated into the apartment buildings where the net monthly electricity usage per household is above 200 kWh. In contrast, SWHs are integrated into the apartment buildings where the net monthly electricity usage per household is below 200 kWh. The difference between the former and the latter is the associated cost for unit electricity which is 13.4 Cents/kWh for the former and 7.1 Cents/kWh for the latter, respectively (Table 5). The higher cost for unit electricity in the former group of apartment buildings makes the electricity generated by PEMFCs more valuable. Thus, the net monthly electricity usage per household is the criterion for determining the type of complementary renewable energy source (in addition to PV) in the large apartment complexes.

Fig. 15. The associated costs per 1 kWh heat supply for the space heating sources.

3.1.2. Renewable energy portfolio in the apartment buildings Most apartment buildings adopt PV as the only renewable energy source because PV is the most economical renewable energy source compared to others. Table 10 shows that 389 of the 403 apartment buildings adopt PV as the only renewable energy source. The remaining 14 apartment buildings adopt both PV and other renewable energy sources. The 14 apartment buildings are highrise apartment complexes with large numbers of households (hundreds to a thousand) while the other apartment buildings are relatively small apartment buildings with 8e30 households. The 14 high-rise apartment complexes are found to have the entire roof area covered with PV panels. This result implies that the roof area of the 14 buildings is insufficient to provide 10% of the total energy demand. Therefore, some of the 14 buildings have to adopt PEMFCs to provide additional renewable energy. Others replace some of the PV panels with SWHs to increase the renewable energy supply per roof area because the annual energy production per roof area is higher for SWHs (285.9 kWh/m2) compared to PV panels

3.1.3. Renewable energy portfolio in the non-residential buildings All of the non-residential buildings adopt both GSHPs and PV panels, and the relative portions of energy provided by GSHPs and PV panels vary from building to building. Fig. 14 shows the annual space heating & cooling demand provided by GSHPs plotted vs. the annual electricity generated by PV panels in every non-residential building under the renewable energy obligation. The relative portion of the energy supply from the two renewable energy sources in a building is expected to depend on the following aspects of the building
i) the composition of the energy demand
space heating & cooling vs. electricity for appliances & lightning; ii) availability, or operating hours of the heat pump system; and iii) the composition of the conventional sources of space heating
gas vs. electricity (due to the different energy rates). A statistical analysis is needed to compare the effects of the three aspects. In this study, a multiple linear regression model with three predictor variables representing each aspect has been conducted. The response variable is the total energy from GSHPs divided by the total energy from PV in each building. Table 11 shows the formula, coefficients of the model, and the standardized coefficients for each predictor variable. The regression model has been validated in Appendix B. The larger standardized regression coefficient of a variable indicates a greater influence of the particular variable [44]. The

Fig. 16. Monthly energy from the grid to all of the buildings in Seongsan-Dong ((a) Electricity, (b) Gas).

12

J. Song et al. / Energy 189 (2019) 116132

Fig. 17. Total electricity and gas from the grid to all of the residential and non-residential buildings in Seongsan-Dong, without and with building-integrated renewable energy.

Fig. 18. Hourly power flow from the grid to all of the buildings in Seongsan-Dong (3rd Tuesday of each month).

standard coefficient of X1 is the highest (0.750) because the portion of space heating & cooling must be high for the greater energy from GSHPs. The standardized coefficient of X3 (0.180) is higher than X2 (0.049), indicating that the composition of the sources for space heating is also important. The positive sign of X3 indicates that the portion of energy from GSHPs is relatively higher in the buildings in which gas boilers provide most of the space heating. Fig. 15 shows the hourly associated costs for 1 kWh of heat provided by gas boilers, EHPs, and GSHPs in a day. The boiler's cost for 1 kWh heat supply is the gas rate multiplied by 1 kWh divided by the boiler efficiency (0.85), and the heat pumps' cost for 1 kWh heat is the electricity rate multiplied by 1 kWh divided by COP (3.2 and 3.7 for EHPs and GSHPs, respectively). The gas boiler cost is higher than those for EHPs and GSHPs. Therefore, replacing gas boilers with GSHPs results in bigger cost savings than replacing EHPs with GSHPs.

3.2. Impact of building-integrated renewable energy on electricity and gas flows from the grid 3.2.1. Impact on reduction of energy from the grid in residential and non-residential buildings Due to the building-integrated renewables, the total amount of electricity from the grid over a 1-year period decreases by 17.1% (21.6 GWh/y) while the total amount of gas from the grid over a 1year period decreases only by 3.3% (6.3 GWh/y). Fig. 16 shows the change in the monthly electricity and gas supplied from the grid to all of the 2,607 buildings including the buildings which are not under the renewable energy obligation (e.g., single-family houses and buildings with a floor area below 500 m2). The term “without renewables” means the case without integration of renewables into any of the buildings. The electricity from the grid decreases every month while the gas from the grid decreases only during the winter. Fig. 17 shows the total electricity and gas from the grid over a 1year period to all of the residential and non-residential buildings,

with and without the integration of renewables. The electricity from the grid to the residential buildings decreases by 20.5% (11.8 GWh/y) due to the electricity generated by PV panels and PEMFCs. The electricity from the grid to the non-residential buildings decreases by 14.2% (9.8 GWh/y) due to PV panels and GSHPs. On the contrary, the gas from the grid to the residential buildings increases by 0.4% (0.6 GWh/y) because PEMFCs in apartment buildings consume gas. The gas from the grid to the nonresidential buildings decreases by 26.5% (6.9 GWh/y) only during the winter due to the introduction of GSHPs. However, such a reduction in gas usage in the non-residential buildings is only a small part of the total gas usage in the residential and nonresidential buildings. Therefore, the total gas from the grid is reduced only by 3.3%.

3.2.2. Impact on temporal power flow from the grid The building-integrated renewables make a large variation in the total hourly power load and the change in peak time. Fig. 18 shows the hourly power flow from the grid to all of the 2607 buildings for the 3rd Tuesday of each month. A large decrease in the power load (i.e. the “Duck curve” [45]) occurs during the daytime due to the power generation of the PV panels (total capacity of 16,425 kW). However, the batteries (total capacity of 1161 kWh) do not provide sufficient grid flexibility. Therefore, additional resources for grid flexibility such as demand response, thermal or water storage, and policies to promote usage of the resources will be necessary. The GSHPs change the time of annual peak power from summer middays to winter mornings. PV is not the main reason because the electricity generated by the PV panels in the summer is not higher than that in the winter (Fig. 19). However, the electricity consumed by the heat pumps decreases in the summer due to the higher COP of GSHPs compared to that of EHPs. Furthermore, the heat pumps' electricity usage increases in the winter because electricity powered heating by GSHPs replaces gas heating (Fig. 20). Especially, GSHPs increase electricity consumption between 8 a.m. and 10 a.m.

J. Song et al. / Energy 189 (2019) 116132

Fig. 19. The total electricity generated by a 1-kW PV panel in Seongsan-Dong for each month of 2015.

13

residential buildings, electricity generated by the PV panels comprises 74% of the total renewable energy supply while electricity, heat from PEMFCs, and heat from SWHs provide 9%, 10%, and 7% of the total renewable energy supply, respectively. Most apartment buildings adopt only PV panels. However, some high-rise apartment complexes adopt both PV and either PEMFC or SWH, depending on the net monthly electricity usage per household. In the non-residential buildings, space heating and space cooling from GSHPs provide 47% and 29% of the total renewable energy supply, respectively and electricity from PV panels provide 24% of the total renewable energy supply. All of the non-residential buildings under the obligation adopt both GSHPs and PV panels, and the relative portion of energy provided by each of the two renewable energy sources vary from building to building. The total amount of electricity and gas from the grid decrease by 17.1% and 3.3%, respectively. PV panels and PEMFCs reduce the amount of electricity from the grid to the buildings. GSHPs reduce the amount of gas from the grid to the non-residential buildings. However, the reduction in gas from the grid to the non-residential buildings is much smaller than the total gas usage in residential buildings. The total capacity of PV panels in the district is sufficient to create the so-called “Duck curve” in the hourly power flow from the grid to all of the buildings. GSHPs reduce the peak power of summer daytime because of its COP which is higher than EHPs'. In contrast, GSHPs increase the peak power of winter mornings because some of the gas heating is replaced with electricity powered heating by GSHPs. Therefore, the annual peak power is expected to appear in winter mornings. The actual future building-integrated renewable energy portfolio can show some differences because there are some aspects which have not been considered in this studye lower output of PV in some buildings due to spatial relationship with other buildings; limited available capacity of GSHP in some buildings; different hourly energy load profiles of buildings with the same building type; further decrease in the capital cost of renewables. Nevertheless, considering the representativeness of the inputs and use of the measured monthly energy usages of each building, the results of this study can be used as quantitative input to design policies to improve building-integrated renewables, energy grid planning, and operation. Also, this study's framework enables evaluation of the expected outcomes (city-scale energy portfolio, potential CO2 reduction, cost-effectiveness, etc.) of various urban energy policies.

Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Fig. 20. The total electricity consumed by the heat pump systems (EHPs and GSHPs) of the non-residential buildings in Seongsan-Dong for each month.

Acknowledgement because the space heating load is high during this period (Fig. 7b and c). Thus, the annual peak power occurs in the winter mornings. 4. Conclusions The renewable energy portfolio of buildings in a representative urban district in Seoul under a test scenario of building-integrated renewable energy obligation has been investigated. The candidates for renewable energy are Photovoltaic (PV) panels, Solar Water Heaters (SWHs), Ground-source Heat Pumps (GSHPs), and Fuel Cells (PEMFCs). PV panels and GSHPs turn out to take the greater part of building-integrated renewable energy portfolio. In the

The authors gratefully acknowledge financial support for the authors from the BK21 Program of Korean Government, Institute of Advanced Machines and Design of Seoul National University, and the National Research Foundation of Korea (Project No. 042020180014).

Appendix A. Models for solar irradiance, power generation of PV panels, and heat production of solar water heaters Energy produced by PV panels and SWHs are functions of hourly irradiation on a tilted surface IT , which can be obtained as [46]:

14

J. Song et al. / Energy 189 (2019) 116132

 



I I cos q I 1 þ cos b IT ¼ Ib þ d b þ Id 1
b Io cosqz Io 2 " rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi # 

 Ib 3 b 1
cos b sin  1þ þ I rg I 2 2

(A.1)

Where rg is the ground reflectance, b is the surface tilt angle from the horizon, q is the angle of incidence on the panel, qz is the zenith angle, Io is extraterrestrial irradiance on a horizontal surface, Ib is beam radiation, Id is diffuse radiation, and I is the hourly global horizontal irradiance (GHI) from the Korean Meteorological Administration [47]. Ib or Id is needed to obtain IT since I ¼ Id þ Ib . By using clearness index KT ¼ I=Io , Id =I can be obtained by a correlation [48]. Then, Ib can be estimated since I ¼ Id þ Ib , and IT can be obtained. PV panel has been assumed to convert IT to power PPV ðtÞ with a constant efficiency. Heat produced by a 1 m2 SWH can be calculated as [49]:

QSWH ¼ f ½IT
UðTSWH
Tamb Þ

(A.2)

Where f is a constant which represents the efficiency of SWHs, U is the thermal transmittance, and TSWH is the temperature of the water entering the collector of SWH approximated by a constant [12]. The above procedure for obtaining IT has been validated by comparison of a measured power generation of PV panels at a water station in Korea and the estimated PV power generation at the site predicted by the procedure (Fig. A1).

Appendix B. Validation of the multiple linear regression model for the ratio between energy from PV and energy from GSHP non-residential building Three conditions must be met for the statistical validity of the regression model. First, the regression should fit the data well. Second, the relevance of the predictor variables for the response should be statistically significant. Third, the predictor variables should not be strongly correlated themselves. The first condition (goodness of fit) can be evaluated with the adjusted coefficient of

predictor variable. All the calculated p-values are much less than 10
4 , indicating that all the predictor variables are relevant for the response. The third condition (nonexistence of multicollinearity) can be evaluated with the Variance Inflation Factor (VIF), which becomes about over 5 if there is a multicollinearity problem [44]. The calculated VIFs for the three predictor variables are 1.4327, 1.0506, and 1.3787, respectively, which are all lower than 5. Therefore, the regression model is statistically valid.

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