How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy
Abstract
:1. Introduction
2. Materials and Methods
- electricity consumptions (storable loads, shiftable loads, non shiftable loads);
- electric-driven heating systems and/or Domestic Hot Water (DHW);
- PV array installation;
- dwelling size;
- the occupancy modelling (occupant number, time scheduling).
Database Description and Sample Users
3. Results and Discussions
3.1. Electric Consumption Time Scheduling of Selected Archetypes
3.2. Users Virtual Aggregation
3.3. Electricity Price Trend on the Italian Spot Market
3.4. Loads Time-Shifting Strategy Identification
3.5. Flexibility Indicators Calculation
4. Conclusions
- 14 dwelling archetypes have been defined by the use of a numerical approach based on a grade scale ranging between 0 and 1; each sample household (i.e., 751) has been compared to the archetypes in order to identify its category; this method leads to a good fitting since, on average, the best grade is equal to 0.81;
- the most representative archetypes, in terms of the highest number of dwellings belonging to them, are the #9, #6, and #5 corresponding to 165, 138, and 102 sample households, respectively;
- from data collected by a survey, the available potential of flexibility related to the dwellings cluster has been calculated and it is equal to 538.95 MWh/year; therefore, the average daily value of flexible loads per dwelling is equal to 1966 Wh/d;
- by simulating a flexible strategy on an RC of Italian residential sector, which is based on the hourly pricing mechanism following the day-ahead market outcomes, and on limitations of power uptakes, monthly and annual indicators have been defined; so doing, the flexible strategy effectiveness can be computed to assess its actual suitability;
- the highest monthly effectiveness values have been registered in the cold season over the non-working days ranging between 0.49 and 0.53. Conversely, in the hot season, the maximum effectiveness values are generally lower compared to the winter ones (i.e., 0.3–0.4) and they occur over the weekdays. In the end, all those results can be outlined by means of a single indicator (annual effectiveness), which, in this case, is 0.34.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Building location
| Kitchen
|
Appendix B
Limit | Percentile | PUN [€/MWh] | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | June | Jul | Aug | Sept | Oct | Nov | Dec | ||
1 | 75th | 66.4 | 65.9 | 63.3 | 60.0 | 59.1 | 60.1 | 64.2 | 63.2 | 73.9 | 73.7 | 65.7 | 63.8 |
2 | 50th | 61.2 | 59.3 | 56.6 | 54.6 | 54.9 | 57.0 | 60.5 | 59.3 | 66.5 | 66.7 | 61.7 | 59.4 |
3 | 50th | 61.2 | 59.3 | 56.6 | 54.6 | 54.9 | 57.0 | 60.5 | 59.3 | 66.5 | 66.7 | 61.7 | 59.4 |
4 | 25th | 52.6 | 52.6 | 49.3 | 48.4 | 49.8 | 52.0 | 56.4 | 56.1 | 59.5 | 56.4 | 52.0 | 50.4 |
Limit | Percentile | PUN [€/MWh] | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | June | Jul | Aug | Sept | Oct | Nov | Dec | ||
1 | 75th | 63.2 | 59.8 | 54.8 | 53.6 | 55.1 | 52.7 | 58.1 | 58.8 | 62.2 | 65.2 | 57.0 | 55.5 |
2 | 50th | 56.0 | 52.9 | 49.8 | 47.8 | 49.4 | 48.7 | 53.5 | 53.8 | 58.4 | 58.3 | 54.1 | 51.9 |
3 | 50th | 56.0 | 52.9 | 49.8 | 47.8 | 49.4 | 48.7 | 53.5 | 53.8 | 58.4 | 58.3 | 54.1 | 51.9 |
4 | 25th | 52.2 | 48.4 | 45.8 | 44.5 | 45.8 | 43.8 | 49.9 | 51.1 | 55.2 | 54.4 | 51.3 | 47.7 |
Limit | Percentile | PUN [€/MWh] | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | June | Jul | Aug | Sept | Oct | Nov | Dec | ||
1 | 75th | 55.9 | 51.7 | 52.0 | 49.1 | 49.5 | 50.0 | 53.1 | 56.2 | 56.7 | 54.8 | 52.7 | 52.5 |
2 | 50th | 51.8 | 47.0 | 47.5 | 43.9 | 42.9 | 43.1 | 45.6 | 51.4 | 54.0 | 51.8 | 49.9 | 46.9 |
3 | 50th | 51.8 | 47.0 | 47.5 | 43.9 | 42.9 | 43.1 | 45.6 | 51.4 | 54.0 | 51.8 | 49.9 | 46.9 |
4 | 25th | 48.7 | 43.4 | 44.4 | 38.4 | 39.5 | 39.4 | 42.3 | 47.7 | 52.3 | 49.4 | 45.3 | 43.3 |
Limit | Percentile | POWER [W] | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | June | Jul | Aug | Sept | Oct | Nov | Dec | ||
1 | 75th | 268 | 309 | 266 | 271 | 243 | 286 | 294 | 320 | 265 | 272 | 305 | 327 |
2 | 50th | 219 | 252 | 212 | 245 | 219 | 220 | 202 | 254 | 216 | 228 | 248 | 259 |
3 | 50th | 219 | 252 | 212 | 245 | 219 | 220 | 202 | 254 | 216 | 228 | 248 | 259 |
4 | 25th | 175 | 223 | 190 | 213 | 191 | 204 | 169 | 206 | 201 | 212 | 228 | 239 |
Limit | Percentile | POWER [W] | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | June | Jul | Aug | Sept | Oct | Nov | Dec | ||
1 | 75th | 333 | 393 | 391 | 338 | 287 | 297 | 260 | 269 | 309 | 272 | 337 | 395 |
2 | 50th | 269 | 323 | 297 | 253 | 265 | 248 | 197 | 229 | 282 | 249 | 276 | 310 |
3 | 50th | 269 | 323 | 297 | 253 | 265 | 248 | 197 | 229 | 282 | 249 | 276 | 310 |
4 | 25th | 133 | 186 | 212 | 167 | 180 | 198 | 171 | 187 | 173 | 201 | 231 | 222 |
Limit | Percentile | POWER [W] | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Jan | Feb | Mar | Apr | May | June | Jul | Aug | Sept | Oct | Nov | Dec | ||
1 | 75th | 348 | 391 | 354 | 349 | 290 | 322 | 245 | 278 | 345 | 278 | 399 | 400 |
2 | 50th | 300 | 298 | 246 | 268 | 258 | 251 | 203 | 230 | 272 | 253 | 365 | 331 |
3 | 50th | 300 | 298 | 246 | 268 | 258 | 251 | 203 | 230 | 272 | 253 | 365 | 331 |
4 | 25th | 185 | 209 | 184 | 200 | 153 | 198 | 167 | 164 | 183 | 172 | 227 | 216 |
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Typifying Aspects (A) | Criterium | Max Grade * (Gmax) | Grade * |
---|---|---|---|
Storable Loads | Relative deviation | 0.15 | (A/Aref) * Gmax or (Aref/A) * Gmax |
Deferrable Loads | Relative deviation | 0.15 | (A/Aref) * Gmax or (Aref/A) * Gmax |
Non-deferrable Loads | Relative deviation | 0.15 | (A/Aref) * Gmax or (Aref/A) * Gmax |
Heating or DHW ** | Energy carrier | 0.05 | Electricity = 0.05; NG ** = 0 |
PV ** array | Installation/lack | 0.05 | Installed = 0.05; Missing = 0.00 |
Dwelling floor surface | Relative deviation | 0.10 | (A/Aref) * Gmax or (Aref/A) * Gmax |
Occupants Number | Relative deviation | 0.10 | (A/Aref) * Gmax or (Aref/A) * Gmax |
Occupancy in time span 8–13 | presence/absence | 0.10 | Present = 0.10; Missing = 0.00 |
Occupancy in time span 13–19 | presence/absence | 0.10 | Present = 0.10; Missing = 0.00 |
Occupancy in time span 19–0 | presence/absence | 0.025 | Present = 0.025; Missing = 0.00 |
Occupancy in time span 0–8 | presence/absence | 0.025 | Present = 0.025; Missing = 0.00 |
TOTAL | 1.00 |
Archetype | Floor Surface [m2] | Heating and DHW * | Cooling * | PV Array | WM ** | DW ** | TD ** |
---|---|---|---|---|---|---|---|
#1 | 49 | NCB | 2 HP | 7; 5; A+ | 6; 7; A | ||
#2 | 101 | NCB | 1 HP | 10; 2.5; A | |||
#3 | 100 | NCB | 1 HP | 7; 5; A+ | |||
#4 | 50 | NCB | 1 HP | 7; 1.5; A+ | 5; 0.5; A | ||
#5 | 100 | CB + HP | 4 HP | 7; 4; A++ | 5; 4; A | 5; 4; A | |
#6 | 65 | CB | 3 HP | 7; 6; A | 12; 3.5; A | 7; 0.5; B | |
#7 | 65 | NCB | 1 HP | 7; 5; A+ | 6; 7; A | ||
#8 | 60 | CB | 7; 2; A++ | 12; 1.5; A+ | |||
#9 | 95 | NCB | 2 HP | 7; 5; A+++ | 12; 8; A+ | ||
#10 | 102 | NCB | 1 HP | 7; 3; A+ | 14; 5; A | ||
#11 | 67 | CB | 10; 5; B | 6; 5; B | |||
#12 | 134 | CB | 7; 6; A | 14; 7; A | 6; 3; B | ||
#13 | 124 | CB | 5; 4; A | 12; 7; A+ | |||
#14 | 123 | NCB + solar collectors | 3.9 kW | 5; 4; A | 12; 7; A+ |
Archetype | Occupants * | Description |
---|---|---|
#1 | 4; (1; 3; 4; 4) | Family with two teenage children and one unemployed parent |
#2 | 2; (0; 0; 2; 2) | Commuter Workers |
#3 | 4; (0; 3; 4; 4) | Family with school-aged children, and one part-time working parent |
#4 | 1; (0; 0; 1; 1) | Commuter Worker |
#5 | 4; (1; 3; 4; 4) | Family with school-aged children, and one home parent |
#6 | 4; (1; 3; 4; 4) | Family with school-aged children and babies, and one unemployed parent |
#7 | 3; (0; 0; 3; 3) | Family with a baby and commuter parents |
#8 | 2; (1; 1; 2; 2) | Commuter worker, awaiting employment |
#9 | 3; (1; 2; 3; 3) | Family with a school-aged child, and one commuter worker |
#10 | 2; (0; 1; 2; 2) | Family of commuter workers |
#11 | 3; (0; 2; 3; 3) | Family with a school-aged child, and two commuter workers |
#12 | 4; (0; 1; 4; 4) | Family with two adult children, two commuter parents |
#13 | 2; (0; 1; 2; 2) | Family with a school-aged child, and two commuter workers |
#14 | 2; (2; 2; 2; 2) | Two Pensioners |
Function | Device | Archetype | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
#1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | #11 | #12 | #13 | #14 | ||
Energy box | Gateway | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Monitoring | Electricity meters | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 |
Multi-sensors (temperature, presence, brightness) | 5 | 6 | 6 | 4 | 6 | 6 | 4 | 4 | 7 | 6 | 3 | 9 | 7 | 7 | |
Windows/doors opening and closing detectors | 7 | 8 | 6 | 5 | 8 | 8 | 5 | 5 | 10 | 10 | 6 | 9 | 12 | 9 | |
Control | Smart Valves | 6 | 5 | 0 | 4 | 3 | 6 | 5 | 3 | 8 | 6 | 0 | 0 | 7 | 0 |
Smart Plugs | 4 | 3 | 4 | 4 | 3 | 4 | 4 | 3 | 3 | 4 | 3 | 5 | 3 | 6 | |
Smart Switches | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 |
Parameters | Archetype | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
#1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | #11 | #12 | #13 | #14 | |
Storable Loads [kWh] | 191 | 106 | 111 | 165 | 950 | 213 | 112 | 49 | 181 | 110 | 46 | 92 | 122 | 81 |
Deferrable Loads [kWh] | 667 | 188 | 549 | 190 | 808 | 714 | 549 | 139 | 915 | 618 | 820 | 1274 | 835 | 556 |
Non-deferrable Loads [kWh] | 2648 | 1024 | 1085 | 879 | 1298 | 1000 | 1099 | 881 | 2384 | 1218 | 1049 | 1754 | 1439 | 959 |
DHW | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
PV array [-] | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Dwelling Floor Surface [m2] | 49 | 101 | 66 | 50 | 100 | 50 | 66 | 60 | 94 | 102 | 67 | 134 | 137 | 110 |
Occupants Number [-] | 4 | 2 | 3 | 1 | 4 | 4 | 2 | 2 | 3 | 2 | 3 | 4 | 3 | 2 |
Occupancy in time span 8–13 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 |
Occupancy in time span 13–19 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Occupancy in time span 19–0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Occupancy in time span 0–8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Archetype | Dwellings Number | Cumulative Surface [m2] | Occupants | Electric Consumptions [kWh/year] | Storable Loads [kWh/year] | Shiftable Loads [kWh/year] |
---|---|---|---|---|---|---|
#1 | 31 (4.1%) | 4782 (5.2%) | 130 (5.1%) | 95,963 (6.7%) | 5310 (1.6%) | 18,081 (8.4%) |
#2 | 16 (2.1%) | 1213 (1.3%) | 36 (1.4%) | 94,444 (6.6%) | 5894 (1.8%) | 17,602 (8.2%) |
#3 | 18 (2.3%) | 1575 (1.7%) | 54 (2.1%) | 95,718 (6.7%) | 10,267 (3.1%) | 17,113 (8%) |
#4 | 14 (1.8%) | 945 (1.0%) | 18 (0.7%) | 91,964 (6.4%) | 7479 (2.2%) | 16,595 (7.7%) |
#5 | 102 (13.5%) | 12,419 (13.7%) | 369 (14.5%) | 262,070 (18.3%) | 178,992 (54.8%) | 16,305 (7.6%) |
#6 | 138 (18.3%) | 18,056 (19.9%) | 531 (20.9%) | 110,935 (7.7%) | 28,897 (8.8%) | 16,048 (7.5%) |
#7 | 14 (1.8%) | 1186 (1.3%) | 31 (1.2%) | 83,364 (5.8%) | 2507 (0.7%) | 15,830 (7.4%) |
#8 | 83 (11%) | 5409 (5.9%) | 194 (7.6%) | 100,642 (7.0%) | 21,062 (6.4%) | 15,467 (7.2%) |
#9 | 165 (21.9%) | 23,820 (26.3%) | 630 (24.8%) | 108,939 (7.6%) | 32,710 (10%) | 14,186 (6.6%) |
#10 | 16 (2.1%) | 1631 (1.8%) | 37 (1.4%) | 77,844 (5.4%) | 3320 (1.0%) | 13,760 (6.4%) |
#11 | 22 (2.9%) | 1822 (2%) | 69 (2.7%) | 71,965 (5%) | 1142 (0.3%) | 13,468 (6.3%) |
#12 | 33 (4.3%) | 4592 (5%) | 135 (5.3%) | 73,460 (5.1%) | 4563 (1.3%) | 12,892 (6%) |
#13 | 32 (4.2%) | 4975 (5.5%) | 115 (4.5%) | 76,550 (5.3%) | 7890 (2.4%) | 12,813 (6%) |
#14 | 67 (8.9%) | 7923 (8.7%) | 189 (7.4%) | 84,345 (5.9%) | 16,070 (4.9%) | 12,686 (5.9%) |
Aggregate | 751 (100%) | 90,355 (100%) | 2538 (100%) | 1,428,203 (100%) | 326,103 (100%) | 212,846 (100%) |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Mancini, F.; Romano, S.; Lo Basso, G.; Cimaglia, J.; de Santoli, L. How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy. Energies 2020, 13, 3359. https://doi.org/10.3390/en13133359
Mancini F, Romano S, Lo Basso G, Cimaglia J, de Santoli L. How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy. Energies. 2020; 13(13):3359. https://doi.org/10.3390/en13133359
Chicago/Turabian StyleMancini, Francesco, Sabrina Romano, Gianluigi Lo Basso, Jacopo Cimaglia, and Livio de Santoli. 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy" Energies 13, no. 13: 3359. https://doi.org/10.3390/en13133359