# Impact of Heat Pump Flexibility in a French Residential Eco-District

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## Abstract

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## 1. Introduction

#### 1.1. Background

#### 1.2. Literature Review

#### 1.3. Context and Aims of the Study

#### 1.4. Paper Structure

## 2. Methods for Impact Quantification of Load Shedding

#### 2.1. Indicators for Impact Quantification of Load Shedding

#### 2.1.1. Peak Shaving

#### 2.1.2. Thermal Comfort

- Comfortable: A range of +/− 1 °C about the temperature set-point (T${}_{set}$)
- Slightly uncomfortable: A range of +/− 1 °C and +/− 2 °C about T${}_{set}$
- Uncomfortable: A difference of more than 2 °C with T${}_{set}$

#### 2.1.3. CO${}_{2}$ Emissions Reduction

#### 2.2. Heat Load Profiles Models

#### 2.2.1. Experimental Load Shifting Profile

#### 2.2.2. Thermal Models

#### 2.2.3. Summary of Modeling Approaches

- The ‘Standard’ model: the statistical model from experimental data
- The ‘Simple’ model: thermal model generated by TEASER (Figure 3) with database
- The ‘Enriched’ model: thermal model generated by TEASER (Figure 3) with building envelope data
- The ‘Complex’ model: multi-zone thermal model created with a Pleiades tool.

#### 2.3. Modeling of Load Shedding Scenarios

- One-hour thermal load shedding after one-hour over-heating: In order to over-heat the building outside of the peak period (5 a.m. to 10 a.m.), the load shedding order will be applied from 5 a.m. to 6 a.m.
- Simple one-hour thermal load shedding: In this scenario, the load shedding will be applied in the middle of the peak period, from 7 a.m. to 8 a.m. without any over-heating.

## 3. Results and Discussion

#### 3.1. Results for Peak Shaving

#### 3.1.1. Thermal Load Shedding from 7 a.m. to 8 a.m.

- Most of the rebound effect appears within the two hours following the load shedding (experimental load shifting profile)
- The shed consumption is shifted during the entire day (load shifting profiles from thermal models)

#### 3.1.2. Heat Load Shedding from 5 a.m. to 6 a.m. after an Over-Heating in Preceding Hour

#### 3.2. Results for Thermal Comfort

- On average, load shedding hours are slightly uncomfortable
- On average, other hours of the days are comfortable

#### 3.3. Results for CO${}_{2}$ Emissions Reduction

## 4. Conclusions

- An overheating from 4 a.m. to 5 a.m. before load shedding from 5 a.m. to 6 a.m.
- One-hour load shedding building by building beginning from 6 a.m. to 9 a.m.

- Peak shavingTurning off the heating supply for one hour successively for building by building in an entire district seems to be effective for peak-shaving. Indeed, the transferred load is very diffused (LS${}_{rate}^{h}$ < 25% the first hour and LS${}_{rate}^{h}$ < 10% the following hours) so that the rebound effects of the previous buildings do not cancel the peak reduction obtained by the current load shedding. These results are crucial in the case of a long peak (more than an hour), offering the possibility to shift the load outside consumption peak period.
- Thermal comfortThermal comfort is reduced during the load-shedding hours. Measurements would have to be realized in order to determine if operative temperature evaluation is more reliable when based on the ‘Simple’ model, the ‘Enriched’ model or the ‘Complex’ model. Indeed, the ‘Complex’ model assessed only 0.8% of the time as not comfortable, while this discomfort could cover up to 4% of the time with the ‘Enriched’ model. Moreover, the ‘Enriched’ model gives a minimal operative temperature of 18 °C, while the operative temperatures estimated by the ‘Simple’ and the ‘Complex’ models never reach values below 18.8 °C. The different modeling approaches used do not allow to estimate precisely how much thermal comfort can be reduced and how it will be perceived by occupants but they help the stakeholder understand what could be the issue. In all cases, one solution to investigate the reduction of thermal discomfort could be to reduce heat loads instead of shedding them, or to turn off the thermal load during shorter duration.
- CO${}_{2}$ emission reductionIn the case of CO${}_{2}$ emission reduction, estimation cannot be based only on consumption reduction as CO${}_{2}$ emission for electrical systems have dynamic variations that have to be taken into account. Only by considering dynamic CO${}_{2}$ variations and by calculating the difference between emissions with or without load shedding strategy could lead to a reliable estimation of CO${}_{2}$ emissions variations. Indeed, even with effective consumption diminution, a load shedding strategy could shift consumption from low-CO${}_{2}$ periods to higher-CO${}_{2}$ time slots, increasing the overall CO${}_{2}$ emissions. For instance, in the case of the load shedding after over-heating, the ’Complex’ model assessed 0.14% of energy saving during the month, while the CO${}_{2}$ emissions increased from 0.06%. Therefore, the link between energy saving and CO${}_{2}$ emission reduction has to be realized carefully.

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

COP21 | 21th Conference of the Parties |

CSTB | French Scientific and Technical Center for Building |

DSM | Demand Side Management |

DSO | Distribution System Operator |

GEG | Grenoble Gas and Electricity |

GSHP | Ground Source Heat Pumps |

RTE | French transmission system operator |

TEASER | Tool for Energy Analysis and Simulation for Efficient Retrofit |

TSO | Transmission System Operator |

UNFCCC | United Nations Framework Convention on Climate Change |

## Nomenclature

CO${}_{{2}_{t}}$ | [kg] | CO${}_{2}$ emissions at time t (with load shedding) |

CO${}_{{2}_{t}}^{ref}$ | [kg] | Reference CO${}_{2}$ emissions at time t (without load shedding) |

CO${}_{2}$S${}^{m}$ | [%] | CO${}_{2}$ Saving in a month (Reduction of CO${}_{2}$ emission on the month) |

E${}_{anticipated}$ | [kWh] | Anticipated energy consumption during the hour before the load shedding |

E${}_{cut\_off}$ | [kWh] | Cut-off energy consumption during the load shedding |

E${}_{delayed}$ | [kWh] | Delayed energy consumption during the 23 h after the load shedding |

EG${}_{red}$ | [%] | Expected Gains Reduction (CO${}_{2}$ emissions diminution expected by looking at the energy |

consumption reduction) | ||

ES${}_{rate}^{d}$ | [%] | Energy Saving rate defined 23 h after the load shedding |

ES${}^{m}$ | [%] | Energy Saving in a month (Reduction of energy consumption on the month) |

LS${}_{rate}^{d}$ | [%] | Load Shifting rate defined during a day |

LS${}_{rate}^{h}$ | [%] | Load Shifting rate defined during an hour |

P${}_{t}$ | [kW] | Power consumed at time t (with load shedding) |

P${}_{t}^{ref}$ | [kW] | Reference power consumed at time t (without load shedding) |

T${}_{air}$ | [°C] | Ambient temperature |

T${}_{set}$ | [°C] | Set-point temperature |

T${}_{op}$ | [°C] | Operative temperature |

T${}_{walls}$ | [°C] | Walls temperature |

${\tau}_{ls}^{b}$ | [h] | Beginning of the load shedding |

${\tau}_{ls}^{e}$ | [h] | End of the load shedding |

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**Figure 1.**Representation of a daily heating load curve modification with a load shedding order and associated load shifting rates.

**Figure 2.**Load shifting rate profiles. (

**a**) One-hour residential heat load shedding without pre-heating (

**b**) One-hour residential heat load shedding after one-hour pre-heating.

**Figure 3.**Scheme of the RC (Resistance Capacity) equivalent model generated by Tool for Energy Analysis and Simulation for Efficient Retrofit (TEASER).

**Figure 4.**Load shifting rate profiles during a day for the multiple one-hour thermal load shedding from 5 a.m. to 10 a.m. Example of the ’Standard’ model for load shifting rate profiles for 5 buildings.

Load Shedding after over-Heating | |||
---|---|---|---|

Comfort level/Models | Reduced | Enriched | Complex |

Comfortable | 98.5% | 96% | 100% |

Slightly uncomfortable | 1.5% | 3.6% | 0% |

Uncomfortable | 0% | 0.4% | 0% |

**Table 2.**Consumption and CO${}_{2}$ emission reduction in January for the simple load shedding strategy (a) and for the load shedding after over-heating strategy (b).

(a) Load Shedding | |||

Models | Simple | Enriched | Complex |

ES${}_{rate}^{d}$ | −10.0% | 13.1% | 5.5% |

ES${}^{m}$ | 0.40% | 0.50% | 0.22% |

CO${}_{2}$S${}^{m}$ | 0.38% | 0.50% | 0.16% |

EG${}_{red}$ | 3.2% | 0.70% | 28% |

(b) Load Shedding after over-Heating | |||

Models | Simple | Enriched | Complex |

ES${}_{rate}^{d}$ | 3.1% | 2.1% | 2.4% |

ES${}^{m}$ | 0.21% | 0.22% | 0.14% |

CO${}_{2}$S${}^{m}$ | 0.01% | 0.05% | −0.06% |

EG${}_{red}$ | 94% | 76% | 144% |

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## Share and Cite

**MDPI and ACS Style**

Pajot, C.; Delinchant, B.; Maréchal, Y.; Frésier, D. Impact of Heat Pump Flexibility in a French Residential Eco-District. *Buildings* **2018**, *8*, 145.
https://doi.org/10.3390/buildings8100145

**AMA Style**

Pajot C, Delinchant B, Maréchal Y, Frésier D. Impact of Heat Pump Flexibility in a French Residential Eco-District. *Buildings*. 2018; 8(10):145.
https://doi.org/10.3390/buildings8100145

**Chicago/Turabian Style**

Pajot, Camille, Benoit Delinchant, Yves Maréchal, and Damien Frésier. 2018. "Impact of Heat Pump Flexibility in a French Residential Eco-District" *Buildings* 8, no. 10: 145.
https://doi.org/10.3390/buildings8100145