Extending the Application of the Smart Readiness Indicator—A Methodology for the Quantitative Assessment of the Load Shifting Potential of Smart Districts
Abstract
:1. Introduction
2. Background
2.1. Regulative Background and Current Developments on the SRI
2.2. Smart Districts
2.3. The Potential of Load Shifting in Buildings for the Integration of RES
3. Methodology
“What is the potential of the district to take energy from the grid, store it over a certain period of time and again dispatch it back to the grid? What are the potential CO2 emission savings associated with the load shifting potential of the district?”
3.1. Adaptation of the Previously Published Methodology
- AF: Attenuation Factor to regulate the SRI related to storage efficiency.
- : Definition of the required minimal efficiency of the storage system. Substitute for the definition: with the minimal required efficiency factor and the maximal required losses.
- Λ: Definition point of the minimal efficiency . At this point the Attenuation Factor (AF) is always 0.63.
- k: defines how fast the SRI veers with a low efficiency towards 0. With a low k the SRI is slowly reduced. With a high k the AF (SRI will be cut off) is rapidly reduced with minimal efficiency from 1 to 0.
- EP: Energy Performance, i.e., the efficiency of the system .
3.2. Enlargement to the District Scale
- N
- Number of buildings
- EDi
- Energy demand of the building i per energy source for the selected time period τ.
- SRIi
- SRI for the building i.
- EDDist
- Energy demand for the whole district.
- SRIDist
- SRI for the whole district.
- SP
- Storage potential of the building (Relationship of SC/ED; dimensionless).
- LPDist
- Load shift potential for the whole district.
- Bi
- Building i.
- SPi
- Storage potential for building i.
- EDi
- Energy demand for building i.
- N
- Number of Buildings.
- Step 1: At this first step the level of the individual i-number of building includes the data from the energy performance certificate.
- Step 2: At this second step, the SRI has been calculated for the i-number of buildings with the interaction between the energy grid and the building considered separately at this point.
- Step 3: In the third step an a-priori assessment of the storage potential is created from the SRI, which is based on the storage capacity (SC) and energy demand (ED) of the i-number of buildings. At this step the proportion of the storage potential (SP) of the building that is available for a load shifting for the grid is calculated. A building that cannot feed any energy back into the grid is related to a low SP. Thus, a building that cannot shift loads bi-directionally has to consume the stored energy itself, which is less beneficial to the network.
- Step 4: In this fourth step, the product of the storage potential (SP) and energy demand (ED) over all buildings within the district delivers an a priori assessment of the load shifting potential (LPDist) for a whole district.
3.3. Approximation of CO2 Savings Potential
- CO2Curr
- Actual CO2 emissions per kWh.
- CO2renew
- CO2 emissions per kWh from renewable energy sources.
- CO2a
- Potential total CO2 savings per year.
4. Application of the Methodology in Theoretical District Use Case
4.1. Description of Theorectical District Use Case
- Base Case: For the base case the buildings have been assessed according to the generic data as outlined above. For this case it is assumed that the buildings cannot store, actively load or unload energy to and from the grid. The activity coefficient (AC) is subsequently assumed to be either not available (n/a) in case of e.g., a thermal energy network or (0) where there is no active interaction with the grid, e.g., relating to power or gas.
- Scenario 1: For the first new scenario, a moderate refurbishment of the building envelope is assumed with a 50% improvement compared to the base case. In addition, the gas connection is substituted with a one-directional thermal grid connection and electrical batteries are considered for residential as well as office buildings.
- Scenario 2: In this scenario, the building shell is improved by 90% compared to the base case, thus a high-performance building shell has been implemented. Similar to scenario 1, the gas connection has been severed and the buildings are connected to a low temperature bi-directional district heating system. RES and batteries are included in all buildings.
- Scenario 3: For this scenario the district is doubled in size (18 buildings compared to 9 buildings of the above scenarios) and constitutes a mix of the Base Case and Scenario 1. It is assumed that half of the buildings remain as described in the base case (i.e., un-refurbished) and the other half is considered with a moderate refurbishment as described in Scenario 1.
- Scenario 4: In this scenario, the district is tripled in size (27 buildings) and constitutes a mix of the Base Case, Scenario 1 and Scenario 2. It is assumed that one third of the buildings remains as described in the base case (i.e., un-refurbished), one third is refurbished and follows the characteristics of Scenario 1 and the remaining third follows Scenario 2.
4.2. Results of Theoretical District Use Case
5. Discussion
6. Conclusions
Supplementary Materials
Supplementary File 1Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Scenario | No. of Buildings | Building Envelope | Electrical Storage/Grid | Thermal Storage/Grid | Gas Storage/Grid |
---|---|---|---|---|---|
Base Case | 9 | Un-refurbished | No active storage; one directional connection | No active storage; no thermal grid | No active storage; one directional connection |
Scenario 1 | 9 | Improved by 50% | Active storage bi-directional connection | No active storage; one-directional connection (thermal grid) | No connection |
Scenario 2 | 9 | Improved by 90% | Active storage bi-directional connection | Active storage bi-directional connection (thermal grid) | No connection |
Scenario 3 | 18 | Half of the district (9 buildings) as per Base Case/other half of the district (9 buildings) as per Scenario 1 | |||
Scenario 4 | 27 | One third of the district (9 buildings) as per Base Case/one third of the district (9 buildings) as per Scenario 1/one third of the district (9 buildings) as per Scenario 2 |
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Märzinger, T.; Österreicher, D. Extending the Application of the Smart Readiness Indicator—A Methodology for the Quantitative Assessment of the Load Shifting Potential of Smart Districts. Energies 2020, 13, 3507. https://doi.org/10.3390/en13133507
Märzinger T, Österreicher D. Extending the Application of the Smart Readiness Indicator—A Methodology for the Quantitative Assessment of the Load Shifting Potential of Smart Districts. Energies. 2020; 13(13):3507. https://doi.org/10.3390/en13133507
Chicago/Turabian StyleMärzinger, Thomas, and Doris Österreicher. 2020. "Extending the Application of the Smart Readiness Indicator—A Methodology for the Quantitative Assessment of the Load Shifting Potential of Smart Districts" Energies 13, no. 13: 3507. https://doi.org/10.3390/en13133507