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Prognosis-Based Operating Strategies for Smart Homes with Power-to-Heat Applications
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Energy (2018) 000–000 136–148 EnergyProcedia Procedia155 00 (2017)
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12th International Renewable Energy Storage Conference, IRES 2018 12th International Renewable Energy Storage Conference, IRES 2018 Prognosis-Based Operating Strategies for Smart Homes with PowerThe 15th International Symposium on District Heating and Cooling to-Heat Applications Prognosis-Based Operating Strategies for Smart Homes with Powera,b,c the feasibility of using a,b,c a,b,c Assessing the heat demand-outdoor Georg Angenendt *, Sebastian Zurmühlen , Hendrik Axelsen , Dirk Uwe Sauera,b,c to-Heat Applications temperaturea,b,c function for a long-term district heata,b,c demand forecast a Chair of Electrochemical Energy Conversion and Storage Systems,a,b,c Institute for Power Electronics and Electrical Drives (ISEA), RWTH a,b,c
Georg Angenendt *, Sebastian Zurmühlen Hendrik Axelsen , Dirk Uwe Sauer Aachen University, ,Germany a,b,c a Aachen Research a Alliance, JARA-Energy, b b Juelich I. Andrić *, A. Pina , P. Ferrão , J. Fournier ., B.Germany Lacarrièrec, O. Le Correc a Chair of Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH a
c Institute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University, Germany Aachen University, GermanyTécnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal IN+ Center for Innovation, Technology and Policy Research - Instituto Superior b Juelich&Aachen Research Alliance, JARA-Energy, b Veolia Recherche Innovation, 291 Avenue Dreyfous Daniel,Germany 78520 Limay, France c c Institute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University, Germany Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
The number of PV battery energy storage systems (PV BESS) as well as the number of heat pumps in domestic households in Abstract Abstractis continuously increasing. Heat pumps enable the use of electricity for both electrical and thermal appliances. Germany Therefore, they can play a major role to enhance the decarbonisation of the heat sector. Heat pumps are operated in combination The number of PVnetworks battery energy storage systems (PV BESS) as well as as one the numbermost of heat pumpssolutions in domestic households in District heating are to commonly the literature effective decreasing the with a thermal storage in order reduce theaddressed switchingincycles of the heat pump.ofAthe combination of a heat pump for system and a PV Germany is gas continuously increasing. Heat pumps enable the use require of electricity for both electrical and thermal appliances. greenhouse emissions from the building sector. These systems high investments which are returned through the heat BESS could enhance the flexibility of such a system. The flexibility of the battery storage is combined with the flexibility of the Therefore, they can play a major role to enhance the of the policies, heat sector. Heat pumpsinarethe operated in combination sales. Due to by thethe changed conditions anddecarbonisation buildingitself renovation heat demand heating system thermal climate storage capacity of the building and the thermal storage unit of the heat future pump. could decrease, with a thermal storage in order to reduce the switching cycles of the heat pump. A combination of a heat pump system and a PV prolongingoperation the investment return Numerous strategies forperiod. PV BESS as well as for heat pump systems already exist. The combination of these two BESS could enhance the flexibility of such a system. The flexibility of the battery storage is combined with the flexibility of the The main scope of paper is to assess the feasibility using heat demand – outdoor temperature function for heat demand systems demands forthis intelligent operation strategies thatofuse thethe flexibility of both components and could enhance the overall heating system by the thermal storage located capacityinofLisbon the building itself and the thermal storage unit ofThe the district heat pump. forecast. The district of Alvalade, (Portugal), was used as a case study. is consisted of 665 energy efficiency within the household. Numerous operation strategies for PV BESS as and well typology. as for heat pump systems already (low, exist. medium, The combination thesedistrict two buildings vary the in both construction Three weather scenarios andofthree This paper that analyses different operationperiod strategies for both electrical and thermal storage systems andhigh) examines the gain in systems demands for intelligent operation strategies that use the flexibility of both components and could enhance the overall renovationbyscenarios developed (shallow, intermediate, deep).battery To estimate theoferror, demandto values were efficiency combinedwere strategies. Operation strategies that enhance lifetime a PVobtained BESS areheat extended efficiently energy efficiency within the household. compared with results a dynamic heat demand model, previously developed and validated by the authors. fulfil the demand of the from heat system additionally. This paper analyses the operation strategies both electrical and thermal storage systems and examines the gain in Theinfluence results showed thatdifferent when strategies only weather change isfor considered, thethe margin of error could be acceptable for some applications The of the operation is investigated by the use of levelized costs of energy (LCOEnergy). Additionally, efficiency by combined strategies. Operation strategies that enhance battery lifetime of a PV BESS are extended to efficiently (thelevelized error in costs annualofdemand was lower for levelized all weather scenarios considered). However, introducing renovation the heat (LCOH), as than well 20% as the costs of electricity (LCOEle), are after calculated to compare the fulfil the demand of the heatincreased system additionally. scenarios, the error value to 59.5%and (depending on the weather and renovation scenarios combination considered). investigated system with conventionalup electricity heat systems. The the operation strategies is on investigated by the the use range of theoflevelized of energy (LCOEnergy). Additionally, Theinfluence value show of ofslope averageapplications within 3.8% upcosts to 8% per decade, corresponds to the The results thatcoefficient PV BESSincreased with power-to-heat enhance the self-consumption and thethat self-sufficiency rate. the levelized costs of heat (LCOH),hours as well as the levelized costs of season electricity (LCOEle), are calculated toofcompare the decrease in the number of heating of 22-139h during the heating (depending on the combination weather and Prognosis based operation strategies are suitable to meet the requirements of both systems. Last but not least the results indicate investigated system with conventional and heat systems. renovation Onelectricity the are other hand, function interceptwith increased for 7.8-12.7% that domesticscenarios power to considered). heat (P2H) systems economically competitive fossil heating systems.per decade (depending on the The results show that PV BESS with power-to-heat applications enhance the self-consumption the self-sufficiency coupled scenarios). The values suggested could be used to modify the function parameters for and the scenarios considered,rate. and Prognosis based operation strategies are suitable to meet the requirements of both systems. Last but not least the results indicate ©improve 2018 The Authors. Published by Elsevier Ltd. the accuracy of heat demand estimations. Thisdomestic is an open access article under the CCare BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) that power to heat (P2H) systems economically competitive with fossil heating systems. Selection and peer-review under responsibility of the scientific committee of the 12th International Renewable Energy Storage © 2017 The Authors. Published by Elsevier Ltd. Conference. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +49 241 80 49373; fax: +49 241 80 92203. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change * Corresponding Tel.: +49 241 80 by 49373; fax: Ltd. +49 241 80 92203. 1876-6102 © 2018 author. The Authors. Published Elsevier address: [email protected] ThisE-mail is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Selection and peer-review under responsibility of the scientific committee of the 12th International Renewable Energy Storage Conference. 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. 1876-6102 © 2017 The Authors. by Elsevierlicense Ltd. This is an open access article underPublished the CC BY-NC-ND 1876-6102 © 2018 The Authors. Published by Elsevier Ltd.(https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of The 15thof International SymposiumRenewable on DistrictEnergy Heating and Cooling. Selection peer-review under under responsibility the scientificlicense committee the 12th International Storage Conference. This is anand open access article the CCofBY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 12th International Renewable Energy Storage Conference. 10.1016/j.egypro.2018.11.061
© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) of the etscientific committee of (2018) the 12th International Renewable Energy Selection and peer-review under responsibility Georg Angenendt al. / Energy Procedia 155 136–148 137 Storage Conference. Keywords: heat pump; photovoltaics (PV); battery energy storage system (BESS); thermal storage; heat and power coupling; power to heat (P2H)
1.
Introduction
For environmental protection a central goal is to reduce CO2 emissions. Therefore, the German government aims to reduce CO2 emissions in the electricity sector by 61-62 % and in the building sector by 66-67 % until 2030 in comparison to 1990 [1]. Most of the energy demand in residual households occurs for heating, especially for space or water heating. A typical two-person household in Germany has an electricity consumption between 1,500 to 3,000 kWh/a for electricity and a heating demand of 8,000-18,000 kWh/a [2]. In the heating sector, the share of renewable energy was 17.1 % and in the electricity sector 33.7 % in 2016. In Germany, we are facing a growing share of renewable energy. From 2010 to 2016 the share of renewable energy in the electricity sector doubled, but the share in the heating sector only increased by about 50 % [3]. This is why a change in the heating policy is required [4]. An option for decarbonisation of the heating sector is the coupling of the heating sector with the electrical power generation from PV systems in residential households. The coupling of the sectors heat and electricity in prosumer households could lead to a growing share of renewable energy in the heating sector and therefore lead to a further decarbonisation. If electricity is used in the heating sector, a gas connection could become obsolete. One of the major technologies for the sector interconnection in residential households are heat pumps [4]. Heat pumps could reduce the feed-in of PV systems, which could lead to grid-relieving effects [5], [6]. Therefore, the interconnection of the sectors in prosumer households could provide further flexibility to the grid, especially under the consideration of storage technologies. The number of PV battery energy storage systems (PV BESS) is steadily increasing in Germany [7], and so is the number of heat pumps in residential households [8]. Numerous publications deal with the topic of operation strategies for PV BESS, for example [9]. Further publications investigate different operation strategies for the PV-battery-heat-storage systems. In [10] the heat storage system is charged in priority of the battery system. The approaches in [11] and [12] investigate a priority charge of the battery storage system. In [13] the authors examined different topologies and sizing of PV-heat systems. This publication compares the different approaches for the operation of the heat system and combines these approaches with forecast-based operation strategies for PV BESS presented in [14]. The forecast-based operation strategies aim to enhance the battery lifetime in order to increase the economy of such systems. An economical evaluation over system lifetime is used to compare the different operation strategies. To evaluate the economics, a separated observation of the levelized costs of energy (LCOEnergy), the levelized costs of electricity (LCOEle) and the levelized costs of heat (LCOH) is presented. The discussed operation strategies are investigated using simulations of a DC-coupled PV BESS with power-to-heat application, based on real data measurements.
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3
Nomenclature BESS DC P2H FOS DHW PV SC SoC Tmin Tmax LCOEnergy LCOEle LCOH CO2 EMS BMS HMS 2.
battery energy storage system direct current power to heat forecast-based operation strategies domestic hot water photovoltaics self-consumption state of charge minimum temperature maximum temperature levelized costs of energy levelized costs of electricity levelized costs of heat carbon dioxide energy management system battery management system heat management system
Methodology
The different operation strategies are evaluated based on a power-to-heat model of a smart home. This smart home consists of a PV BESS and a heat system to provide heating power and domestic hot water (DHW). A heat pump couples the electric PV BESS system with the heat system. The following figure gives an overview of the model components. PV
PV converter
Battery
Battery converter
Grid inverter
Grid
Electrical Load
Thermal Load and Storage Fig. 1: Model of the grid-connected DC-coupled PV BESS [15].
2.1
Electrical Model
The electrical model, containing the battery model, is based on the model presented in [16] and [17]. This battery model represents a DC-coupled PV BESS with a lithium-ion battery. The model of the lithium-ion battery is parametrized with the results of aging tests of a 2.15 Ah 18650 cylindrical lithium-nickel-manganese-cobalt-oxide (NMC) battery cell from LG-Chem (LG ICR18650MF1). A separate calendar and cyclic aging model is used based
4
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on [15] and [18]. Input data for the load [19] and PV radiation of the electrical model are based on real data measurement. The modelled BESS has a storage capacity of 10 kWh. The battery converter has a maximum power of 10kW as well as the PV converter and the grid inverter. The PV system has a rated power of 10 kWp. 2.2
Thermal Model
A heat pump couples the thermal model and the electrical model. This heat pump model is based on a Vitocal 200-S from Vissmann [20]. It supplies a heat system with buffer storage and a DHW (domestic hot water) storage. The DHW storage is in parallel to the buffer storage [21]. Both storage units have been modeled according to commercially available systems with 300 l volume each [22], [23]. Figure 2 shows a representation of the heating system.
electric heater
domestic hot water DHW storage
buffer storage
electric heater
heat pump
heating
Fig. 2: Overview of the heating system.
2.3
Operation strategies
The operation of the domestic power to heat system is based on a master-slave-architecture. The energy management system (EMS) receives information from the heat management system (HMS) and the battery management system (BMS). The main task of the EMS is to satisfy the electric energy demand of the household and the heat demand. To satisfy the heat demand, the HMS requests an electric demand for the heat pump. Based on this information the EMS calculates the residual load. The operation of the heat storages and the battery storage depends on the residual load, which is calculated with the following formula:
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PRL = PLH − PPV
5
(1)
PRL = Residual load [W] PLH = Load household with heat pump [W] PPV = PV generation [W] If the residual load is negative, which means that more PV generation is available than needed for the actual demand, the EMS charges the storages. Therefore, the EMS sends a charging order to the different storage technologies in dependency of the applied operation strategy. 2.3.1
Battery system operation
Two different types of battery operation strategies for PV BESS are investigated: the forecast-based operation strategy (FOS) and the maximized self-consumption (max SC) strategy. Both strategies are based on [14] and [16]. 2.3.1.1 Maximized self-consumption The first operation strategy is the maximized self-consumption operation strategy. If the residual load is negative, the PV panel produces more energy than immediately consumed, the battery storage is charged. If the residual load is positive, the battery storage is discharged. If the battery storage is empty, the grid covers the remaining load. If the battery storage is fully charged and there is still surplus energy, this energy is fed into the grid. 2.3.1.2 Forecast-based operation strategy The forecast-based operation strategy (FOS) aims to reduce the battery aging by reducing the average SoC as described in [14]. Therefore, only the amount of energy that will be consumed in the following night is stored in the battery. If the forecast-based operation strategy (FOS) is applied, not all surplus energy from PV will be stored. The BESS is only charged until a maximum SoC limit that stores enough energy to satisfy the energy demand of the following night. In this paper a persistence prognosis is used. The persistence prognosis uses the assumption that the weather changes very slowly. It is assumed that today's solar radiation is similar to the solar radiation yesterday and therefore the PV power will be the same as the day before.
PPV , forecast (t ) = PPV ,measured (t − 1d )
(2)
For the load forecast the assumption is made that the load will be similar to the load last week on the same day [24], which is presented in the following equation.
Pload , forecast (t ) = Pload ,measured (t − 7 d )
(3)
The forecast-based operation strategies are only applied in the summer months, to use the full potential of the storage system during winter. During winter the battery is always fully discharged overnight, because of the lower energy yield from PV in comparison to the consumption. 2.3.2
Heat system operation
The operation strategy of the heat system is not influenced by the operation strategies applied to the battery system. In dependency of the applied operation strategies of the heat system, the EMS charges the battery or the heat storages.
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2.3.2.1 Heat as a passive element The simplest strategy is that the heat system works only as a passive element. In this case, the heat system is used as an additional load. The storage units are only used to reduce the number of cycles of the heat pump and to make sure that the heat pump is only used in an efficient operation mode. The heat storage units are not used to increase the self-consumption of the PV system by storing surplus energy. 2.3.2.2 Priority battery charging The operation strategy “priority battery charging” charges the battery storage system first, because electric energy is a more versatile form of energy than heat. Only if the battery storage is fully charged, in the case of the maximized self-consumption strategy or the BESS is charged until the max SoC limit in case of the FOS and there is still negative residual load available, the thermal storages are charged. The battery is employed if the EMS sends a charging or discharging order to the BMS. Before the EMS sends a charge command to the HMS, the EMS checks if the available negative residual power is enough to run the heat pump. The modeled heat pump is only switched on or off, therefore there has to be enough surplus power to run the heat pump. The algorithm does not check if the surplus will be high enough to run the heat pump for the minimum runtime. 2.3.2.3 Priority heat storage charging The second operation strategy for heat systems charges the heat storage with highest priority. Therefore, the EMS sends a charging order to the HMS if residual power is available. This operation strategy aims to avoid conversion losses of the BESS. If energy is stored in the battery during daytime and used for heating after sunset, conversion losses due to charging and discharging of the battery occur. Therefore, the heat storages are charged in priority, in order to avoid these conversion losses. To avoid that too much energy is stored in the heat system, a persistence prognosis is applied. The assumption is made, that the heat demand will be the same as the heat demand yesterday. Therefore, the residual energy is only charged to the heat storage as long as the thermal energy stored today is less or equal to the total thermal energy of yesterday. 2.4
Levelized costs of energy
To compare the impact of the different operation strategies the levelized costs of energy (LCOEnergy) are used for an economic evaluation. The levelized costs of energy (LCOEnergy) are the weighted sum of the levelized cost of electricity (LCOEle) and the levelized costs of heat (LCOH). Due to the separate observation of LCOEle a comparison with a system without a BESS is possible. Furthermore, the comparison with alternative heating systems, due to the separate observation of the LCOH, is possible. The calculations of the LCOEle are based on previous work [12]. The LCOEle are the net present value of the unit-cost of electricity over the lifetime of a generating asset [25]. Therefore, the LCOEle incorporate all costs over the system’s lifetime including initial investment, cost of operation and maintenance, and cost of capital. This costs cover all investment costs of the electric system, include the cost for the battery, as well as the cost for the three converters and the PV system. Cost for reinvest, as well as savings because of residual values are taken into account accordingly. Falling prices of the BESS due to scale effects in case of reinforcements are minded [26]. The variable costs consist of the cost for the grid exchange, concerning the net present value of the electricity costs and savings due to the PV feed-in tariff. The changes of the electricity price are evaluated by using an electricity price increasing factor. Maintenance costs are considered as fix costs. The following equation (4) depicts the formula to calculate the LCOEle.
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∑ LCOEle =
n t =1 t
I (r , d I ) + Vt (r , dV ) + Ft (r )
∑
n
t =1
7
(4)
Et ( r , d E )
I t : Net present value of the investment in t Vt : Net present value of the variable costs in t Ft : Net present value of the investment in t
Et : Net present value of the investment in t r : Interest Rate d : Discount rate The LCOH are the present value of the unit-cost of heat over the lifetime of a generating asset and therefore calculated with the same method as the LCOEle. The investment costs of the heat system consist of the costs for the heat, the buffer storage, the DHW storage as well as the cost for the pumps. Maintenance costs are considered as fix costs. The variable costs are calculated differently, because they are influenced by the changed behaviour of the electric system. These costs consist mainly of the costs for the additional load required by the heat pump. This load can be either covered by the PV panel or the grid. But these costs could also contain additional battery costs or savings if the lifetime of the BESS is influenced by the heat system. To take this into consideration the calculation of the LCOH is done in two steps. First, the annuity value of the overall balance of costs and revenues (Asumtot) are calculated with the operation of the heat system. In this case the investment costs include the cost for the electric system as well as the cost for the heat system. Furthermore, the variable costs include the electricity consumption by the heat system and the electric system. In a second step the annuity value of the overall balance of costs and revenues of the electricity system (Asumele) without the heat system is calculated. The annuity value of the overall balance of costs and revenues of the heat system (Asumheat) is the difference between this two costs and calculated with the following formula.
Asumheat = Asumtot − Asumele
(5)
The LCOH are the calculated annuity costs divided by the total heat consumption.
LCOH =
∑
(6)
Asumheat
n
t =1
H t (r , d E )
H t : Heat consumption in t The LCOEnergy are the weighted sum of the LCOH and the LCOEle. With this approach an inter sectorial evaluation is possible.
LCOEnergy = LCOG ⋅
Qheat E + LCOE ⋅ elect Etotal Etotal
(7)
With
Etotal = Eelect + Qheat
(8)
8
3.
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Results
In this chapter the PV BESS with power-to-heat application is compared to common concepts of the energy and heat supply. A comparison on the electrical side with a PV generator is examined. Heating power from gas and oil is used as the benchmark of the thermal model. The annual electrical load is roundabout 4700 kWhel and the thermal load is roundabout 13500 kWhth. 3.1
Input parameters
The following tables provide an overview on the economic input data of the model. This data is used to calculate the LCOEnergy. Table 1. Input data for LCOEle calculation Parameter
tcalc icalc cbat ibat icbat ;kfW
cconv Lconv cPVgen LPVgen c feed −in celectricity ielectricity ima int enance
Value
Unit
Description
15
a
calculation period for invest assessment
1.3
%/a
interest rate
400
€/kWh
specific battery cost
7
%/a
annual battery cost degression
13
%
subsidy rate on battery investment cost from KfW funding Q3 and Q4 2017 [27]
173
€/kW
specific converter cost for one converter
20
a
converter lifetime
1170
€/kWp
specific converter cost for one converter
20
a
converter lifetime
0.122
€/kWh
feed-in tariff (Sep. 2017)
0.292
€/kWh
electricity cost (Sep. 2017)
1.85
%/a
annual electricity price increase
1.5
%/a
annual maintenance cost relative to investment cost
Table 2. Input data for LCOH calculation Parameter
cheatpump cbuffer cDHWstorage c pumpDHW c pumpheat ima int enance, heat
3.2
Value
Unit
Description
6795
€
heat pump costs
449
€
buffer storage costs
619
€
DHW storage cost
150
€
Costs for the DHW pump
150
€
Costs for the pump supplying the heat system
50
€/a
annual maintenance costs for the heat system
Impact of system topology on self-consumption and self-sufficiency rate
This chapter compares the different scenarios described above. Therefore, the influence of different operation strategies and different topologies are analysed. The self-sufficiency and the self-consumption rate are shown. Influence on self-consumption rate and self-sufficiency rate are examined on monthly basis. The self-consumption rated is defined as the energy used from the PV generation divided by the energy produced by the PV system.
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self consumptionrate =
9
(9)
PVused PVgen
100 self-consumption rate [%]
90 80 70 60 50 40 30 20 10 0
Jan
Feb
Mar
PV-BESS without P2H
Apr
May
Jun
Jul
PV-BESS with P2H
Aug
Sep
PV only
Oct
Nov
Dec
PV with P2H
Fig. 3. Self-consumption rate of different systems: 10 kWp PV system; PV BESS with 10 kWp PV system and 10 kWh BESS; 10kW heat pump
If a BESS is installed, the self-consumption rate can be increased by around 30 % on annual basis. This value is highly depended on the battery system size. The use of a heat pump for heat-electricity-coupling can increase the self-consumption rate on annual basis by around 10 % per year. The increase due to power-to-heat application is rather small, because of the seasonal effects especially in summer. In the following, the self-sufficiency rate is analysed. The self-sufficiency rate is defined as the energy from the PV System used to cover the load divided by the total load.
self sufficencyrate =
PVused loadtotal
(10)
The influence of different topologies on the self-sufficiency rate is shown in figure 4. If PV energy is used for the heating sector as well, the total load of the electrical system is increased. Therefore, the self-sufficiency rate of systems with power-to-heat application is rather low during winter. The self-sufficiency rate is dominated by the demand of the heating system. Nevertheless, during summer the thermal demand is relatively low and therefore the influence on the self-sufficiency rate of the system is smaller. The application of a BESS has a high influence on the self-sufficiency rate. This is why seasonal effects influence the self-sufficiency rate.
10
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100
self-sufficency rate [%]
90 80 70 60 50 40 30 20 10 0
Jan
Feb
Mar
PV-BESS without P2H
Apr
May
Jun
Jul
PV-BESS with P2H
Aug
Sep
PV only
Oct
Nov
Dec
PV with P2H
Fig. 4. Self-sufficiency rate of different systems: 10 kWp PV-system, PV BESS with a 10 kWp PV system and a 10 kWh BESS; 10 kW heat pump
3.3
Comparison of the LCOEle
The influence on the electricity costs are examined in the following, therefore the PV BESS is compared to a PV system without a BESS and a grid-only supply. It is important to mind that this analysis is based on a non-optimized PV BESS. Therefore, the cost for the PV BESS system can be reduced significantly if a system is used, which is optimized regarding the size of the components. An optimization of the component sizes is done in [17]. Figure 5 shows the LCOE depending on the system topology. It can be seen that a PV only system without a BESS has the lowest LCOEle. The application of a forecast-based operation strategy can reduce the LCOEle of a PV BESS system, as shown in [16]. 35 30
€ct/kWh
25 20 15 10 5 0
BESS FOS
BESS max SC
PV only
grid
Fig. 5. Levelized costs of electricity (LCOEle) of different systems: 10 kWp PV-system, PV BESS with a 10 kWp PV system and a 10 kWh BESS
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3.4
11
Comparison of the LCOH
The influence of the different operation strategies presented in chapter 2.3.2 is exanimated in the following and compared to a fossil heating system. Figure 6 illustrates that the battery priority charging of the power-to-heat application leads to the lowest LCOH. Furthermore, the influence of the forecast-based operation strategy is shown. Using a FOS leads to reduced electricity costs, because the battery is operated in a more economical way. When the PV BESS is used in combination with a heat system, the load of the system is higher and this leads to a higher average SoC. This means that the FOS stores more energy in the battery to cover the higher load at night. The resulting higher average SoC reduces the battery lifetime. Therefore, the LCOH are slightly higher when the FOS is applied, because the additional battery aging costs are added to the LCOH as described in chapter 2.4. The resulting LCOH of the PV BESS system with sector coupling is compared to the LCOH of a conventional heating system. The LCOH for the gas and oil system are based on [28] and [29]. 30 25
€ct/kWh
20 15 10 5 0
passiv
battery prior LCOH max SC
heat prior
oil/gas
LCOH FOS
Fig. 6. Levelized costs of heat (LCOH) of a PV BESS system with power-to-heat application (10 kWp PV system; 10 kWh BESS; 10 kW heat pump) and different operation strategies in comparison to fossil heating systems ([28]; [29])
3.5
Comparison of the LCOEnergy
Figure 7 shows the comparison of the LCOEnergy for the aforementioned system topologies and operation strategies. The analysis combines the results of the previous chapters. The LCOEnergy are calculated based on equation (7). In the analysed scenario 74 % of the energy demand is thermal energy and 26 % of the demand is electrical energy. The results implicate that the use of FOS for BESS leads to reduced LCOEnergy. The influence of the presented operation strategies of the power-to-heat application is rather small. Advanced operation strategies of the domestic power to heat system might have a higher influence. Furthermore, the results show that a PV system has a positive influence on the LCOEnergy.
12
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30
€ct/kWh
25 20 15 10 5 0
passiv
battery prior
heat prior
LCOEnergy max SC
grid+oil/gas
PV+oil/gas
LCOEnergy FOS
Fig. 7. Levelized costs of energy (LCOEnergy) of PV BESS system with power-to-heat application (10 kWp PV system; 10 kWh BESS; 10 kW heat pump) and different operation strategies in comparison to fossil heating systems ([28]; [29]) and a PV system (10 kWp).
4.
Conclusion and Discussion
Power-to-heat applications can play a major role in the decarbonisation process. Therefore, this paper investigates a domestic PV BESS system in combination with a heat pump for heat and power coupling. Different operation strategies of the heat and power system are examined. The results show that a battery priority charging is the most economical case. Nevertheless, the influences of the examined strategies for the heat and power system are rather low. Advanced strategies could have a higher impact and help further reduce cost. The resulting LCOEnergy show that power-to-heat applications are economically competitive compared to fossil heating systems, even though a non-optimized heat and power system is investigated. An optimization of component sizes of the heat and power system could lead to further reduction of energy costs. References [1] BMUB Bundesministerium, "Klimaschutzplan 2050 - Klimaschutzpolitische Grundsätze und Ziele der Bundesregierung," Publikation der Bundesregierung. [2] M. Frondel et al., "Erhebung des Energieverbrauchs der privaten Haushalte für die Jahre 2011-2013," März 2015. [3] Umweltbundesamt, "Erneuerbare Energien in Zahlen," 21 Mai 2017. [Online]. Available: http://www.umweltbundesamt.de. [4] Fraunhofer IWES/IBP, "Wärmewende 2030: Schlüsseltechnologien zur Erreichung der mittel- und langfristigen Klimaschutzziele im Gebäudesektor," 2017. [5] dena Deutsche Energie-Agentur GmbH, "dena-NETZFLEXSTUDIE: Optimierter Einsatz von Speichern für Netz- und Marktanwendungen in der Stromversorgung," Berlin, 2017. [6] S. Zurmühlen et al., "Grid-relieving effects of PV battery energy storage systems with optimized operation strategies," in 33rd European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam, 2017. [7] K.-P. Kairies, D. Magnor and D. U. Sauer, "Scientific Measuring and Evaluation Program for Photovoltaic Battery Systems," 2015. [Online]. Available: [8] Bundesverband Wärmepumpe e.V., "waermepumpe.de," 21 January 2017. https://www.waermepumpe.de/presse/pressemitteilungen/details/17-prozent-marktwachstum-machen-2016-zum-waermepumpenrekordjahr/. [Accessed 30 November 2017]. [9] J. Weniger, J. Bergner and V. Quaschning, "Integration of PV power and load forecasts into the operation of residential PV battery systems," Berlin. [10] M. Battaglia et al., "Increased self-consumption and grid flexibility of PV and heat pump systems with thermal and electrical storage," Energy Procedia 135, pp. 358-366, 2017.
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[11] T. Tjaden et al., "Einsatz von PV-Systemen mit Wärmepumpen und Batteriespeichern zur Erhöhung des Autarkiegrades in Einfamilienhaushalten," in 30. Symposium Photovoltaische Solarenergie, Kloster Banz, Bad Staffelstein, 2015. [12] G. Angenendt et al., "Modellierung eines Hauses mit PV-Batteriespeichersystem und einer Strom-Wärme-Kopplung," in Batterieforum Deutschland, Berlin, 2017. [13] T. Beck et. al., "Optimal operation, configuration and sizing of generation and storage technologies for residential heat pump systems in the spotlight of self-consumption of photovoltaic electricity," Applied Energy 188, pp. 604-619, 2017. [14] G. Angenendt, S. Zurmühlen , R. Mir-Montazeri, D. Magnor and D. U. Sauer, "Enhancing Battery Lifetime in PV Battery Home Storage Systems using forecast based operating strategies," Energy Procedia, 2016. [15] D. Magnor, J. B. Gerschler, M. Ecker, P. Merk and D. U. Sauer, "Concept of a battery aging model for lithium-ion batteries considering the lifetime dependency on the operation strategy," 24th European Photovoltaic Solar Energy Conference, 2009. [16] G. Angenendt, S. Zurmühlen, C. Chen, D. Magnor and D. U. Sauer, "Reducing Cut-Off Energy of PV Home Storage Systems by Using Forecast Based Operation Strategies," in 11. Internationale Konferenz zur Speicherung Erneuerbarer Energien (IRES 2017), Düsseldorf, 2017. [17] D. Magnor and D. U. Sauer, "Optimization of PV Battery Systems Using Genetic Algorithms," Energy Procedia, no. 99, pp. 332-340, 2016. [18] M. Braun, K. Büdenbender, D. Magnor and A. Jossen, "Photovoltaic self-Cconsumption in Germany using Lithium-Ion Storage to increase self-consumed photovoltaic energy," in Proceeedings of the 24th PVSEC, 2009. [19] M. Bost, B. Hirschl and A. Aretz, "Effekte von Eigenverbrauch und Netzparität bei der Photovoltaik," 2011. [20] Viessmann Werke GmbH & Co. KG, Datenblatt Vitocal 200-S. [21] K. Schwamberger, "Modellbildung," in Fortschrittberichte VDI, VDI Verlag, 1991, pp. 5-66. [22] Schindler und Hofmann, 2016. [Online]. Available: https://www.kamdi24.de/Pufferspeicher-300-L.html. [23] Schindler und Hofmann, 2016. [Online]. Available: https://www.kamdi24.de/Brauchwasserspeicher-300-L-mit-1x-Waermetauscher.html. [24] J. Struth, K.-P. Kairies, D. Magnor and D. U. Sauer, "Analysis of the effects of different operation strategies of PV-storage-systems for grid stabilization," 2013. [25] K. Branker, M. Pathak and J. Pearce, "A Review of Solar Photovoltaic Levelized Cost of Electricity," Renewable and Sustainable Energy Reviews 15, p. 4470–4482, 2011. [26] A. Gitis, G. Merei, M. Leuthold and e. al., "ETG-Studie: Batteriespeicher in der Nieder- und Mittelspannungsebene," 2015. [27] KfW, "Merkblatt Erneuerbare Energien KfW-Programm Erneuerbare Energien "Speicher"," https://www.kfw.de/Download-Center/F%C3%B6rderprogramme-(Inlandsf%C3%B6rderung)/PDFDokumente/6000002700_M_275_Speicher.pdf. [Accessed 28 september 260].
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ScienceDirect
Available online at www.sciencedirect.com Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2018) 000–000 ScienceDirect ScienceDirect ScienceDirect Energy Procedia 00 (2018) 000–000
www.elsevier.com/locate/procedia
Energy (2018) 000–000 136–148 EnergyProcedia Procedia155 00 (2017)
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
12th International Renewable Energy Storage Conference, IRES 2018 12th International Renewable Energy Storage Conference, IRES 2018 Prognosis-Based Operating Strategies for Smart Homes with PowerThe 15th International Symposium on District Heating and Cooling to-Heat Applications Prognosis-Based Operating Strategies for Smart Homes with Powera,b,c the feasibility of using a,b,c a,b,c Assessing the heat demand-outdoor Georg Angenendt *, Sebastian Zurmühlen , Hendrik Axelsen , Dirk Uwe Sauera,b,c to-Heat Applications temperaturea,b,c function for a long-term district heata,b,c demand forecast a Chair of Electrochemical Energy Conversion and Storage Systems,a,b,c Institute for Power Electronics and Electrical Drives (ISEA), RWTH a,b,c
Georg Angenendt *, Sebastian Zurmühlen Hendrik Axelsen , Dirk Uwe Sauer Aachen University, ,Germany a,b,c a Aachen Research a Alliance, JARA-Energy, b b Juelich I. Andrić *, A. Pina , P. Ferrão , J. Fournier ., B.Germany Lacarrièrec, O. Le Correc a Chair of Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH a
c Institute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University, Germany Aachen University, GermanyTécnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal IN+ Center for Innovation, Technology and Policy Research - Instituto Superior b Juelich&Aachen Research Alliance, JARA-Energy, b Veolia Recherche Innovation, 291 Avenue Dreyfous Daniel,Germany 78520 Limay, France c c Institute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University, Germany Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
The number of PV battery energy storage systems (PV BESS) as well as the number of heat pumps in domestic households in Abstract Abstractis continuously increasing. Heat pumps enable the use of electricity for both electrical and thermal appliances. Germany Therefore, they can play a major role to enhance the decarbonisation of the heat sector. Heat pumps are operated in combination The number of PVnetworks battery energy storage systems (PV BESS) as well as as one the numbermost of heat pumpssolutions in domestic households in District heating are to commonly the literature effective decreasing the with a thermal storage in order reduce theaddressed switchingincycles of the heat pump.ofAthe combination of a heat pump for system and a PV Germany is gas continuously increasing. Heat pumps enable the use require of electricity for both electrical and thermal appliances. greenhouse emissions from the building sector. These systems high investments which are returned through the heat BESS could enhance the flexibility of such a system. The flexibility of the battery storage is combined with the flexibility of the Therefore, they can play a major role to enhance the of the policies, heat sector. Heat pumpsinarethe operated in combination sales. Due to by thethe changed conditions anddecarbonisation buildingitself renovation heat demand heating system thermal climate storage capacity of the building and the thermal storage unit of the heat future pump. could decrease, with a thermal storage in order to reduce the switching cycles of the heat pump. A combination of a heat pump system and a PV prolongingoperation the investment return Numerous strategies forperiod. PV BESS as well as for heat pump systems already exist. The combination of these two BESS could enhance the flexibility of such a system. The flexibility of the battery storage is combined with the flexibility of the The main scope of paper is to assess the feasibility using heat demand – outdoor temperature function for heat demand systems demands forthis intelligent operation strategies thatofuse thethe flexibility of both components and could enhance the overall heating system by the thermal storage located capacityinofLisbon the building itself and the thermal storage unit ofThe the district heat pump. forecast. The district of Alvalade, (Portugal), was used as a case study. is consisted of 665 energy efficiency within the household. Numerous operation strategies for PV BESS as and well typology. as for heat pump systems already (low, exist. medium, The combination thesedistrict two buildings vary the in both construction Three weather scenarios andofthree This paper that analyses different operationperiod strategies for both electrical and thermal storage systems andhigh) examines the gain in systems demands for intelligent operation strategies that use the flexibility of both components and could enhance the overall renovationbyscenarios developed (shallow, intermediate, deep).battery To estimate theoferror, demandto values were efficiency combinedwere strategies. Operation strategies that enhance lifetime a PVobtained BESS areheat extended efficiently energy efficiency within the household. compared with results a dynamic heat demand model, previously developed and validated by the authors. fulfil the demand of the from heat system additionally. This paper analyses the operation strategies both electrical and thermal storage systems and examines the gain in Theinfluence results showed thatdifferent when strategies only weather change isfor considered, thethe margin of error could be acceptable for some applications The of the operation is investigated by the use of levelized costs of energy (LCOEnergy). Additionally, efficiency by combined strategies. Operation strategies that enhance battery lifetime of a PV BESS are extended to efficiently (thelevelized error in costs annualofdemand was lower for levelized all weather scenarios considered). However, introducing renovation the heat (LCOH), as than well 20% as the costs of electricity (LCOEle), are after calculated to compare the fulfil the demand of the heatincreased system additionally. scenarios, the error value to 59.5%and (depending on the weather and renovation scenarios combination considered). investigated system with conventionalup electricity heat systems. The the operation strategies is on investigated by the the use range of theoflevelized of energy (LCOEnergy). Additionally, Theinfluence value show of ofslope averageapplications within 3.8% upcosts to 8% per decade, corresponds to the The results thatcoefficient PV BESSincreased with power-to-heat enhance the self-consumption and thethat self-sufficiency rate. the levelized costs of heat (LCOH),hours as well as the levelized costs of season electricity (LCOEle), are calculated toofcompare the decrease in the number of heating of 22-139h during the heating (depending on the combination weather and Prognosis based operation strategies are suitable to meet the requirements of both systems. Last but not least the results indicate investigated system with conventional and heat systems. renovation Onelectricity the are other hand, function interceptwith increased for 7.8-12.7% that domesticscenarios power to considered). heat (P2H) systems economically competitive fossil heating systems.per decade (depending on the The results show that PV BESS with power-to-heat applications enhance the self-consumption the self-sufficiency coupled scenarios). The values suggested could be used to modify the function parameters for and the scenarios considered,rate. and Prognosis based operation strategies are suitable to meet the requirements of both systems. Last but not least the results indicate ©improve 2018 The Authors. Published by Elsevier Ltd. the accuracy of heat demand estimations. Thisdomestic is an open access article under the CCare BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) that power to heat (P2H) systems economically competitive with fossil heating systems. Selection and peer-review under responsibility of the scientific committee of the 12th International Renewable Energy Storage © 2017 The Authors. Published by Elsevier Ltd. Conference. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +49 241 80 49373; fax: +49 241 80 92203. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change * Corresponding Tel.: +49 241 80 by 49373; fax: Ltd. +49 241 80 92203. 1876-6102 © 2018 author. The Authors. Published Elsevier address: [email protected] ThisE-mail is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Selection and peer-review under responsibility of the scientific committee of the 12th International Renewable Energy Storage Conference. 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. 1876-6102 © 2017 The Authors. by Elsevierlicense Ltd. This is an open access article underPublished the CC BY-NC-ND 1876-6102 © 2018 The Authors. Published by Elsevier Ltd.(https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of The 15thof International SymposiumRenewable on DistrictEnergy Heating and Cooling. Selection peer-review under under responsibility the scientificlicense committee the 12th International Storage Conference. This is anand open access article the CCofBY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 12th International Renewable Energy Storage Conference. 10.1016/j.egypro.2018.11.061
© 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) of the etscientific committee of (2018) the 12th International Renewable Energy Selection and peer-review under responsibility Georg Angenendt al. / Energy Procedia 155 136–148 137 Storage Conference. Keywords: heat pump; photovoltaics (PV); battery energy storage system (BESS); thermal storage; heat and power coupling; power to heat (P2H)
1.
Introduction
For environmental protection a central goal is to reduce CO2 emissions. Therefore, the German government aims to reduce CO2 emissions in the electricity sector by 61-62 % and in the building sector by 66-67 % until 2030 in comparison to 1990 [1]. Most of the energy demand in residual households occurs for heating, especially for space or water heating. A typical two-person household in Germany has an electricity consumption between 1,500 to 3,000 kWh/a for electricity and a heating demand of 8,000-18,000 kWh/a [2]. In the heating sector, the share of renewable energy was 17.1 % and in the electricity sector 33.7 % in 2016. In Germany, we are facing a growing share of renewable energy. From 2010 to 2016 the share of renewable energy in the electricity sector doubled, but the share in the heating sector only increased by about 50 % [3]. This is why a change in the heating policy is required [4]. An option for decarbonisation of the heating sector is the coupling of the heating sector with the electrical power generation from PV systems in residential households. The coupling of the sectors heat and electricity in prosumer households could lead to a growing share of renewable energy in the heating sector and therefore lead to a further decarbonisation. If electricity is used in the heating sector, a gas connection could become obsolete. One of the major technologies for the sector interconnection in residential households are heat pumps [4]. Heat pumps could reduce the feed-in of PV systems, which could lead to grid-relieving effects [5], [6]. Therefore, the interconnection of the sectors in prosumer households could provide further flexibility to the grid, especially under the consideration of storage technologies. The number of PV battery energy storage systems (PV BESS) is steadily increasing in Germany [7], and so is the number of heat pumps in residential households [8]. Numerous publications deal with the topic of operation strategies for PV BESS, for example [9]. Further publications investigate different operation strategies for the PV-battery-heat-storage systems. In [10] the heat storage system is charged in priority of the battery system. The approaches in [11] and [12] investigate a priority charge of the battery storage system. In [13] the authors examined different topologies and sizing of PV-heat systems. This publication compares the different approaches for the operation of the heat system and combines these approaches with forecast-based operation strategies for PV BESS presented in [14]. The forecast-based operation strategies aim to enhance the battery lifetime in order to increase the economy of such systems. An economical evaluation over system lifetime is used to compare the different operation strategies. To evaluate the economics, a separated observation of the levelized costs of energy (LCOEnergy), the levelized costs of electricity (LCOEle) and the levelized costs of heat (LCOH) is presented. The discussed operation strategies are investigated using simulations of a DC-coupled PV BESS with power-to-heat application, based on real data measurements.
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Nomenclature BESS DC P2H FOS DHW PV SC SoC Tmin Tmax LCOEnergy LCOEle LCOH CO2 EMS BMS HMS 2.
battery energy storage system direct current power to heat forecast-based operation strategies domestic hot water photovoltaics self-consumption state of charge minimum temperature maximum temperature levelized costs of energy levelized costs of electricity levelized costs of heat carbon dioxide energy management system battery management system heat management system
Methodology
The different operation strategies are evaluated based on a power-to-heat model of a smart home. This smart home consists of a PV BESS and a heat system to provide heating power and domestic hot water (DHW). A heat pump couples the electric PV BESS system with the heat system. The following figure gives an overview of the model components. PV
PV converter
Battery
Battery converter
Grid inverter
Grid
Electrical Load
Thermal Load and Storage Fig. 1: Model of the grid-connected DC-coupled PV BESS [15].
2.1
Electrical Model
The electrical model, containing the battery model, is based on the model presented in [16] and [17]. This battery model represents a DC-coupled PV BESS with a lithium-ion battery. The model of the lithium-ion battery is parametrized with the results of aging tests of a 2.15 Ah 18650 cylindrical lithium-nickel-manganese-cobalt-oxide (NMC) battery cell from LG-Chem (LG ICR18650MF1). A separate calendar and cyclic aging model is used based
4
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on [15] and [18]. Input data for the load [19] and PV radiation of the electrical model are based on real data measurement. The modelled BESS has a storage capacity of 10 kWh. The battery converter has a maximum power of 10kW as well as the PV converter and the grid inverter. The PV system has a rated power of 10 kWp. 2.2
Thermal Model
A heat pump couples the thermal model and the electrical model. This heat pump model is based on a Vitocal 200-S from Vissmann [20]. It supplies a heat system with buffer storage and a DHW (domestic hot water) storage. The DHW storage is in parallel to the buffer storage [21]. Both storage units have been modeled according to commercially available systems with 300 l volume each [22], [23]. Figure 2 shows a representation of the heating system.
electric heater
domestic hot water DHW storage
buffer storage
electric heater
heat pump
heating
Fig. 2: Overview of the heating system.
2.3
Operation strategies
The operation of the domestic power to heat system is based on a master-slave-architecture. The energy management system (EMS) receives information from the heat management system (HMS) and the battery management system (BMS). The main task of the EMS is to satisfy the electric energy demand of the household and the heat demand. To satisfy the heat demand, the HMS requests an electric demand for the heat pump. Based on this information the EMS calculates the residual load. The operation of the heat storages and the battery storage depends on the residual load, which is calculated with the following formula:
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PRL = PLH − PPV
5
(1)
PRL = Residual load [W] PLH = Load household with heat pump [W] PPV = PV generation [W] If the residual load is negative, which means that more PV generation is available than needed for the actual demand, the EMS charges the storages. Therefore, the EMS sends a charging order to the different storage technologies in dependency of the applied operation strategy. 2.3.1
Battery system operation
Two different types of battery operation strategies for PV BESS are investigated: the forecast-based operation strategy (FOS) and the maximized self-consumption (max SC) strategy. Both strategies are based on [14] and [16]. 2.3.1.1 Maximized self-consumption The first operation strategy is the maximized self-consumption operation strategy. If the residual load is negative, the PV panel produces more energy than immediately consumed, the battery storage is charged. If the residual load is positive, the battery storage is discharged. If the battery storage is empty, the grid covers the remaining load. If the battery storage is fully charged and there is still surplus energy, this energy is fed into the grid. 2.3.1.2 Forecast-based operation strategy The forecast-based operation strategy (FOS) aims to reduce the battery aging by reducing the average SoC as described in [14]. Therefore, only the amount of energy that will be consumed in the following night is stored in the battery. If the forecast-based operation strategy (FOS) is applied, not all surplus energy from PV will be stored. The BESS is only charged until a maximum SoC limit that stores enough energy to satisfy the energy demand of the following night. In this paper a persistence prognosis is used. The persistence prognosis uses the assumption that the weather changes very slowly. It is assumed that today's solar radiation is similar to the solar radiation yesterday and therefore the PV power will be the same as the day before.
PPV , forecast (t ) = PPV ,measured (t − 1d )
(2)
For the load forecast the assumption is made that the load will be similar to the load last week on the same day [24], which is presented in the following equation.
Pload , forecast (t ) = Pload ,measured (t − 7 d )
(3)
The forecast-based operation strategies are only applied in the summer months, to use the full potential of the storage system during winter. During winter the battery is always fully discharged overnight, because of the lower energy yield from PV in comparison to the consumption. 2.3.2
Heat system operation
The operation strategy of the heat system is not influenced by the operation strategies applied to the battery system. In dependency of the applied operation strategies of the heat system, the EMS charges the battery or the heat storages.
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2.3.2.1 Heat as a passive element The simplest strategy is that the heat system works only as a passive element. In this case, the heat system is used as an additional load. The storage units are only used to reduce the number of cycles of the heat pump and to make sure that the heat pump is only used in an efficient operation mode. The heat storage units are not used to increase the self-consumption of the PV system by storing surplus energy. 2.3.2.2 Priority battery charging The operation strategy “priority battery charging” charges the battery storage system first, because electric energy is a more versatile form of energy than heat. Only if the battery storage is fully charged, in the case of the maximized self-consumption strategy or the BESS is charged until the max SoC limit in case of the FOS and there is still negative residual load available, the thermal storages are charged. The battery is employed if the EMS sends a charging or discharging order to the BMS. Before the EMS sends a charge command to the HMS, the EMS checks if the available negative residual power is enough to run the heat pump. The modeled heat pump is only switched on or off, therefore there has to be enough surplus power to run the heat pump. The algorithm does not check if the surplus will be high enough to run the heat pump for the minimum runtime. 2.3.2.3 Priority heat storage charging The second operation strategy for heat systems charges the heat storage with highest priority. Therefore, the EMS sends a charging order to the HMS if residual power is available. This operation strategy aims to avoid conversion losses of the BESS. If energy is stored in the battery during daytime and used for heating after sunset, conversion losses due to charging and discharging of the battery occur. Therefore, the heat storages are charged in priority, in order to avoid these conversion losses. To avoid that too much energy is stored in the heat system, a persistence prognosis is applied. The assumption is made, that the heat demand will be the same as the heat demand yesterday. Therefore, the residual energy is only charged to the heat storage as long as the thermal energy stored today is less or equal to the total thermal energy of yesterday. 2.4
Levelized costs of energy
To compare the impact of the different operation strategies the levelized costs of energy (LCOEnergy) are used for an economic evaluation. The levelized costs of energy (LCOEnergy) are the weighted sum of the levelized cost of electricity (LCOEle) and the levelized costs of heat (LCOH). Due to the separate observation of LCOEle a comparison with a system without a BESS is possible. Furthermore, the comparison with alternative heating systems, due to the separate observation of the LCOH, is possible. The calculations of the LCOEle are based on previous work [12]. The LCOEle are the net present value of the unit-cost of electricity over the lifetime of a generating asset [25]. Therefore, the LCOEle incorporate all costs over the system’s lifetime including initial investment, cost of operation and maintenance, and cost of capital. This costs cover all investment costs of the electric system, include the cost for the battery, as well as the cost for the three converters and the PV system. Cost for reinvest, as well as savings because of residual values are taken into account accordingly. Falling prices of the BESS due to scale effects in case of reinforcements are minded [26]. The variable costs consist of the cost for the grid exchange, concerning the net present value of the electricity costs and savings due to the PV feed-in tariff. The changes of the electricity price are evaluated by using an electricity price increasing factor. Maintenance costs are considered as fix costs. The following equation (4) depicts the formula to calculate the LCOEle.
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∑ LCOEle =
n t =1 t
I (r , d I ) + Vt (r , dV ) + Ft (r )
∑
n
t =1
7
(4)
Et ( r , d E )
I t : Net present value of the investment in t Vt : Net present value of the variable costs in t Ft : Net present value of the investment in t
Et : Net present value of the investment in t r : Interest Rate d : Discount rate The LCOH are the present value of the unit-cost of heat over the lifetime of a generating asset and therefore calculated with the same method as the LCOEle. The investment costs of the heat system consist of the costs for the heat, the buffer storage, the DHW storage as well as the cost for the pumps. Maintenance costs are considered as fix costs. The variable costs are calculated differently, because they are influenced by the changed behaviour of the electric system. These costs consist mainly of the costs for the additional load required by the heat pump. This load can be either covered by the PV panel or the grid. But these costs could also contain additional battery costs or savings if the lifetime of the BESS is influenced by the heat system. To take this into consideration the calculation of the LCOH is done in two steps. First, the annuity value of the overall balance of costs and revenues (Asumtot) are calculated with the operation of the heat system. In this case the investment costs include the cost for the electric system as well as the cost for the heat system. Furthermore, the variable costs include the electricity consumption by the heat system and the electric system. In a second step the annuity value of the overall balance of costs and revenues of the electricity system (Asumele) without the heat system is calculated. The annuity value of the overall balance of costs and revenues of the heat system (Asumheat) is the difference between this two costs and calculated with the following formula.
Asumheat = Asumtot − Asumele
(5)
The LCOH are the calculated annuity costs divided by the total heat consumption.
LCOH =
∑
(6)
Asumheat
n
t =1
H t (r , d E )
H t : Heat consumption in t The LCOEnergy are the weighted sum of the LCOH and the LCOEle. With this approach an inter sectorial evaluation is possible.
LCOEnergy = LCOG ⋅
Qheat E + LCOE ⋅ elect Etotal Etotal
(7)
With
Etotal = Eelect + Qheat
(8)
8
3.
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Results
In this chapter the PV BESS with power-to-heat application is compared to common concepts of the energy and heat supply. A comparison on the electrical side with a PV generator is examined. Heating power from gas and oil is used as the benchmark of the thermal model. The annual electrical load is roundabout 4700 kWhel and the thermal load is roundabout 13500 kWhth. 3.1
Input parameters
The following tables provide an overview on the economic input data of the model. This data is used to calculate the LCOEnergy. Table 1. Input data for LCOEle calculation Parameter
tcalc icalc cbat ibat icbat ;kfW
cconv Lconv cPVgen LPVgen c feed −in celectricity ielectricity ima int enance
Value
Unit
Description
15
a
calculation period for invest assessment
1.3
%/a
interest rate
400
€/kWh
specific battery cost
7
%/a
annual battery cost degression
13
%
subsidy rate on battery investment cost from KfW funding Q3 and Q4 2017 [27]
173
€/kW
specific converter cost for one converter
20
a
converter lifetime
1170
€/kWp
specific converter cost for one converter
20
a
converter lifetime
0.122
€/kWh
feed-in tariff (Sep. 2017)
0.292
€/kWh
electricity cost (Sep. 2017)
1.85
%/a
annual electricity price increase
1.5
%/a
annual maintenance cost relative to investment cost
Table 2. Input data for LCOH calculation Parameter
cheatpump cbuffer cDHWstorage c pumpDHW c pumpheat ima int enance, heat
3.2
Value
Unit
Description
6795
€
heat pump costs
449
€
buffer storage costs
619
€
DHW storage cost
150
€
Costs for the DHW pump
150
€
Costs for the pump supplying the heat system
50
€/a
annual maintenance costs for the heat system
Impact of system topology on self-consumption and self-sufficiency rate
This chapter compares the different scenarios described above. Therefore, the influence of different operation strategies and different topologies are analysed. The self-sufficiency and the self-consumption rate are shown. Influence on self-consumption rate and self-sufficiency rate are examined on monthly basis. The self-consumption rated is defined as the energy used from the PV generation divided by the energy produced by the PV system.
Author name / Energy Procedia 00 (2018) 000–000 Georg Angenendt et al. / Energy Procedia 155 (2018) 136–148
144
self consumptionrate =
9
(9)
PVused PVgen
100 self-consumption rate [%]
90 80 70 60 50 40 30 20 10 0
Jan
Feb
Mar
PV-BESS without P2H
Apr
May
Jun
Jul
PV-BESS with P2H
Aug
Sep
PV only
Oct
Nov
Dec
PV with P2H
Fig. 3. Self-consumption rate of different systems: 10 kWp PV system; PV BESS with 10 kWp PV system and 10 kWh BESS; 10kW heat pump
If a BESS is installed, the self-consumption rate can be increased by around 30 % on annual basis. This value is highly depended on the battery system size. The use of a heat pump for heat-electricity-coupling can increase the self-consumption rate on annual basis by around 10 % per year. The increase due to power-to-heat application is rather small, because of the seasonal effects especially in summer. In the following, the self-sufficiency rate is analysed. The self-sufficiency rate is defined as the energy from the PV System used to cover the load divided by the total load.
self sufficencyrate =
PVused loadtotal
(10)
The influence of different topologies on the self-sufficiency rate is shown in figure 4. If PV energy is used for the heating sector as well, the total load of the electrical system is increased. Therefore, the self-sufficiency rate of systems with power-to-heat application is rather low during winter. The self-sufficiency rate is dominated by the demand of the heating system. Nevertheless, during summer the thermal demand is relatively low and therefore the influence on the self-sufficiency rate of the system is smaller. The application of a BESS has a high influence on the self-sufficiency rate. This is why seasonal effects influence the self-sufficiency rate.
10
Georgname Angenendt et al. / Energy 155 (2018) 136–148 Author / Energy Procedia 00Procedia (2018) 000–000
145
100
self-sufficency rate [%]
90 80 70 60 50 40 30 20 10 0
Jan
Feb
Mar
PV-BESS without P2H
Apr
May
Jun
Jul
PV-BESS with P2H
Aug
Sep
PV only
Oct
Nov
Dec
PV with P2H
Fig. 4. Self-sufficiency rate of different systems: 10 kWp PV-system, PV BESS with a 10 kWp PV system and a 10 kWh BESS; 10 kW heat pump
3.3
Comparison of the LCOEle
The influence on the electricity costs are examined in the following, therefore the PV BESS is compared to a PV system without a BESS and a grid-only supply. It is important to mind that this analysis is based on a non-optimized PV BESS. Therefore, the cost for the PV BESS system can be reduced significantly if a system is used, which is optimized regarding the size of the components. An optimization of the component sizes is done in [17]. Figure 5 shows the LCOE depending on the system topology. It can be seen that a PV only system without a BESS has the lowest LCOEle. The application of a forecast-based operation strategy can reduce the LCOEle of a PV BESS system, as shown in [16]. 35 30
€ct/kWh
25 20 15 10 5 0
BESS FOS
BESS max SC
PV only
grid
Fig. 5. Levelized costs of electricity (LCOEle) of different systems: 10 kWp PV-system, PV BESS with a 10 kWp PV system and a 10 kWh BESS
Author name / Energy Procedia 00 (2018) 000–000 Georg Angenendt et al. / Energy Procedia 155 (2018) 136–148
146
3.4
11
Comparison of the LCOH
The influence of the different operation strategies presented in chapter 2.3.2 is exanimated in the following and compared to a fossil heating system. Figure 6 illustrates that the battery priority charging of the power-to-heat application leads to the lowest LCOH. Furthermore, the influence of the forecast-based operation strategy is shown. Using a FOS leads to reduced electricity costs, because the battery is operated in a more economical way. When the PV BESS is used in combination with a heat system, the load of the system is higher and this leads to a higher average SoC. This means that the FOS stores more energy in the battery to cover the higher load at night. The resulting higher average SoC reduces the battery lifetime. Therefore, the LCOH are slightly higher when the FOS is applied, because the additional battery aging costs are added to the LCOH as described in chapter 2.4. The resulting LCOH of the PV BESS system with sector coupling is compared to the LCOH of a conventional heating system. The LCOH for the gas and oil system are based on [28] and [29]. 30 25
€ct/kWh
20 15 10 5 0
passiv
battery prior LCOH max SC
heat prior
oil/gas
LCOH FOS
Fig. 6. Levelized costs of heat (LCOH) of a PV BESS system with power-to-heat application (10 kWp PV system; 10 kWh BESS; 10 kW heat pump) and different operation strategies in comparison to fossil heating systems ([28]; [29])
3.5
Comparison of the LCOEnergy
Figure 7 shows the comparison of the LCOEnergy for the aforementioned system topologies and operation strategies. The analysis combines the results of the previous chapters. The LCOEnergy are calculated based on equation (7). In the analysed scenario 74 % of the energy demand is thermal energy and 26 % of the demand is electrical energy. The results implicate that the use of FOS for BESS leads to reduced LCOEnergy. The influence of the presented operation strategies of the power-to-heat application is rather small. Advanced operation strategies of the domestic power to heat system might have a higher influence. Furthermore, the results show that a PV system has a positive influence on the LCOEnergy.
12
Georgname Angenendt et al. / Energy 155 (2018) 136–148 Author / Energy Procedia 00Procedia (2018) 000–000
147
30
€ct/kWh
25 20 15 10 5 0
passiv
battery prior
heat prior
LCOEnergy max SC
grid+oil/gas
PV+oil/gas
LCOEnergy FOS
Fig. 7. Levelized costs of energy (LCOEnergy) of PV BESS system with power-to-heat application (10 kWp PV system; 10 kWh BESS; 10 kW heat pump) and different operation strategies in comparison to fossil heating systems ([28]; [29]) and a PV system (10 kWp).
4.
Conclusion and Discussion
Power-to-heat applications can play a major role in the decarbonisation process. Therefore, this paper investigates a domestic PV BESS system in combination with a heat pump for heat and power coupling. Different operation strategies of the heat and power system are examined. The results show that a battery priority charging is the most economical case. Nevertheless, the influences of the examined strategies for the heat and power system are rather low. Advanced strategies could have a higher impact and help further reduce cost. The resulting LCOEnergy show that power-to-heat applications are economically competitive compared to fossil heating systems, even though a non-optimized heat and power system is investigated. An optimization of component sizes of the heat and power system could lead to further reduction of energy costs. References [1] BMUB Bundesministerium, "Klimaschutzplan 2050 - Klimaschutzpolitische Grundsätze und Ziele der Bundesregierung," Publikation der Bundesregierung. [2] M. Frondel et al., "Erhebung des Energieverbrauchs der privaten Haushalte für die Jahre 2011-2013," März 2015. [3] Umweltbundesamt, "Erneuerbare Energien in Zahlen," 21 Mai 2017. [Online]. Available: http://www.umweltbundesamt.de. [4] Fraunhofer IWES/IBP, "Wärmewende 2030: Schlüsseltechnologien zur Erreichung der mittel- und langfristigen Klimaschutzziele im Gebäudesektor," 2017. [5] dena Deutsche Energie-Agentur GmbH, "dena-NETZFLEXSTUDIE: Optimierter Einsatz von Speichern für Netz- und Marktanwendungen in der Stromversorgung," Berlin, 2017. [6] S. Zurmühlen et al., "Grid-relieving effects of PV battery energy storage systems with optimized operation strategies," in 33rd European Photovoltaic Solar Energy Conference and Exhibition, Amsterdam, 2017. [7] K.-P. Kairies, D. Magnor and D. U. Sauer, "Scientific Measuring and Evaluation Program for Photovoltaic Battery Systems," 2015. [Online]. Available: [8] Bundesverband Wärmepumpe e.V., "waermepumpe.de," 21 January 2017. https://www.waermepumpe.de/presse/pressemitteilungen/details/17-prozent-marktwachstum-machen-2016-zum-waermepumpenrekordjahr/. [Accessed 30 November 2017]. [9] J. Weniger, J. Bergner and V. Quaschning, "Integration of PV power and load forecasts into the operation of residential PV battery systems," Berlin. [10] M. Battaglia et al., "Increased self-consumption and grid flexibility of PV and heat pump systems with thermal and electrical storage," Energy Procedia 135, pp. 358-366, 2017.
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