The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector
Abstract
:1. Introduction
2. Methodology
- 3D model: Required in OBJ format, it includes the buildings under study, the surfaces available for the photovoltaic installation, and the close shading objects (e.g., trees, close buildings). This information is useful to identify a suitable area where photovoltaic modules can be installed.
- Weather file: Required in EnergyPlus Weather (EPW) format, it describes the irradiation context (also considering the horizon line) and the temperature conditions needed to calculate the expected electricity generation of each photovoltaic module.
- Electricity demand: The hourly profile of the annual electric demand of the building (e.g., heat pumps and auxiliary systems, appliances, lighting system, elevators, etc.).
- Technical parameters: These include some optional indices which can affect the generated and consumed electricity profiles. The parameters are the performance ratio (PR), the PV module efficiency, the linear annual efficiency losses, and the temperature coefficient of the modules. For the battery instead, the maximum depth of discharge, round-trip efficiency, and the capacity degradation rate. A percentage for annual linear growth can be specified for the electricity demand. The time horizon to calculate the system performance is also an input.
- Economic inputs: These data include the investment and operational cost for the photovoltaic system and battery, the price of purchased and sold electricity, a possible premium related to a net billing scheme, the annual discount rate of the investment, and the linear annual growth of the price of purchased and sold electricity.
- and are the electricity of the self-consumed and injected into the grid;
- and are the price of electricity bought or sold for the final user, respectively;
- are the operating expense cost of operation and maintenance for PV system based on the installed PV nominal power ();
- are the cost of BESS replacement per BESS capacity ();
- is the sum of the investment cost for the PV and BESS system accordingly to the PV nominal power and the installed BESS capacity;
- is the discount rate.
- Self-sufficiency: Indicates the percentage of energy that can be supplied by PV and BESS.
- Normalized NPV: Is the normalized over the initial investment.
- GHG emission reduction: Indicated the reduction of GHG emission due to the adoption of PV and BESS system, compared to the reference year which is 1990.
3. Case Study
3.1. Residential Appliances
- Number of households is chosen equal to fifty according to the number of single-family houses of the district;
- Type of buildings considered is the single-family house as represented in Figure 2;
- Distribution of people considered is the one present in the default settings of LPG;
- Location chosen is the city of Bolzano, in the North-East of Italy. According to this, the Typical Meteorological Year (TMY) of the selected location has been used for the weather data [27];
- Time resolution is set equal to one hour because this is the time resolution used by the optimization tool described in Section 2.
3.2. Heat Pump Consumption Profiles
- Calculate the yearly cumulative thermal demand of the building.Within the European H2020 project 4RinEU, several dynamic simulations were performed to estimate the annual cumulative thermal demand of different buildings in different climates, before and after different renovation interventions [29] with TRNSYS [30], a well-known dynamic simulation software. We refer to the project report for the details [29]. Since the objective of this work is to consider a future scenario, it has been assumed that all the single-family houses of the district have been renovated with a standard prefabricated façade to improve thermal insulation. Simulation results suggest that for the Continental climate, the specific thermal demand of a renovated single-family house for heating and cooling is in the range of 42.1–50.7 kWh/m/y for heating and equal to 0 kWh/m/y for cooling. Thus, according to the simulation results, for each building, the specific thermal demand has been randomly selected within the given interval. The obtained value has been multiplied by the area of the building, calculated by multiplying the average area of households in the region by a coefficient to introduce variability between buildings of the district. The following formula has been used to calculate the floor area of each building
- Calculate the dimensionless hourly thermal demand of the buildings from the NUTS2 (nomenclature of territorial units for statistics, basic regions for the application of regional policies) code of the region [31], the outdoor air temperature, and the hour of the day. The relationship between the inputs and the dimensionless thermal demand was obtained in a previous study founded by the Hotmaps H2020 project from synthetic load profiles and are available at the project repository on GitLab [32]. For the details of the calculations, we refer to the project report [33].
- Apply a reduction factor during some hours of the day. This is an optional function and simulates the effect of different control logic based on the hour of the day. The reduction factor simulates a smart control and shifts part of the night thermal load during daytime hours. In practice, the thermal load during the defined hours is multiplied by a coefficient between zero and one. Two different scenarios have been considered in the current work: The scenario without demand shifting where the reduction factor has been set to zero. Conversely, for the scenario with demand shifting, the reduction factor has been considered equal to 0.5.
- Scale the modified dimensionless profile for matching the yearly cumulative thermal demand calculated at point 2. In this way, the integral of the thermal demand profile is equal to the annual cumulative thermal demand for both heating and cooling.
- Divide the thermal consumption profiles by a temperature-dependent coefficient of performance COP (heating) or by the energy efficiency ratio EER (cooling) from the performance map of a commercial reversible air to water heat pump. However, even if the detailed modeling of the heat pump is out of the scope of this work, it is important to notice that the losses of the thermal system have not been considered. In reality, shifting the electric consumption will cause an increase in thermal losses.
3.3. Electric Vehicle Charging Profiles
4. Simulation Results
4.1. Scenarios and Key Performance Indicators
- PV nominal power and BESS capacity: The optimization tool provides the cumulative nominal power for the PV energy system and the battery capacity to be installed in the considered district as the output according to the specific analyzed scenarios and the related cost functions.
- Self-sufficiency: Indicates the percentage of load that can be supplied by PV and BESS.
- Normalized NPV: Is the normalized over the initial investment.
- GHG emission reduction: Indicated the reduction of GHG emission due to the adoption of PV and BESS system compared to the reference year which is 1990.
4.2. Flexibility Impact—NPV Cost Function
4.3. Environmental Impact—LCOE Cost Function
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Scenario A | Scenario B | Scenario C | Scenario D | ||
---|---|---|---|---|---|
EV configuration | Night charge | ✔ | |||
RES integration | ✔ | ✔ | ✔ | ||
HP configuration | No shifting | ✔ | ✔ | ||
Red factor 0.5 | ✔ | ✔ | |||
Appliances configuration | Considered | ✔ | ✔ | ✔ | |
Not considered | ✔ |
Parameter | Value | Unit | Reference | |
---|---|---|---|---|
Efficiency | 22.5 | [%] | [36] | |
Module dimension | 0.5 × 0.5 | [m] | [-] | |
Performance ratio | 0.8 | [-] | [37] | |
Photovoltaic system | Temperature coefficient | −0.5 | [%/°C] | [38] |
Cost of the system | 945 | [kWp] | [39] | |
Annual maintenance cost * | 25–40 | [kWp/year] | [40] | |
Linear annual efficiency losses | 0.75 | [%] | [41,42] | |
BESS | Efficiency | 90 | [%] | [-] |
Cost of the system | 350 | [kWh] | [39] | |
General | Cost of the electricity | 0.2341 | [kWh] | [43] |
Price electricity sold | 0 | [kWh] | [-] | |
Annual discount rate * | 0–2 | [%] | [-] | |
Time horizon | 25 | [years] | [-] |
Scenario A | Scenario B | Scenario C | Scenario D | |
---|---|---|---|---|
PV power [kWp] | 83 | 179 | 166 | 78 |
BESS capacity [kWh] | 0 | 113 | 57 | 29 |
Self-sufficiency [%] | 19.8 | 46.8 | 42.9 | 45 |
Normalized NPV [-] | 2.5 | 1.9 | 2.6 | 2.5 |
PV Power [kWp] | BESS Capacity [kWh] | Self-Sufficiency [%] | LCOE [€/kWh] | N. NPV | |
---|---|---|---|---|---|
Scenario D | 174 | 35 | 55 | 0.072 | 0.7 |
Scenario D S1 | Scenario D S2 | |
---|---|---|
PV power [kWp] | 111 | 121 |
BESS capacity [kWh] | 110 | 91 |
Self-sufficiency [%] | 56 | 56 |
LCOE [€/kWh] | 0.093 | 0.086 |
Normalized NPV [-] | 1.1 | 1.1 |
Scenario D | Scenario D S1 | Scenario D S2 | |
---|---|---|---|
[%] | 46 | 56 | 55 |
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Dallapiccola, M.; Barchi, G.; Adami, J.; Moser, D. The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector. Energies 2021, 14, 2326. https://doi.org/10.3390/en14082326
Dallapiccola M, Barchi G, Adami J, Moser D. The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector. Energies. 2021; 14(8):2326. https://doi.org/10.3390/en14082326
Chicago/Turabian StyleDallapiccola, Mattia, Grazia Barchi, Jennifer Adami, and David Moser. 2021. "The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector" Energies 14, no. 8: 2326. https://doi.org/10.3390/en14082326
APA StyleDallapiccola, M., Barchi, G., Adami, J., & Moser, D. (2021). The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector. Energies, 14(8), 2326. https://doi.org/10.3390/en14082326