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Behaviour Demand Response in District Heating—A Simulation-Based Assessment of Potential Energy Savings^{ †}

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## Abstract

**:**

## 1. Introduction

## 2. Model-Based BDR Optimisation

#### 2.1. Occupant Behaviour Model

#### 2.2. Building/Network Model

- Alter the zone/circuit air temperature set-points in the model such that $\mathrm{t}\le \mathrm{t}\prime \le \mathrm{t}+\mathsf{\tau}\left[\mathrm{a}\right]\wedge \mathrm{a}\in {\mathrm{A}}_{\mathrm{q}}\wedge {\mathsf{\alpha}}_{\mathrm{qz}}\ne 0\Rightarrow {\mathrm{s}}_{\mathrm{z}\mathrm{t}\prime}=\mathsf{\sigma}\left[\mathrm{a}\right]$ and compute the resulting mean air temperatures ${\mathcal{S}}^{\prime}$ to assess the thermal behaviour of relevant overall circuits/zones
- Estimate or measure the actual temperatures of the affected context areas $\overline{{s}_{\mathrm{z}}{}^{\prime}}$ and compare them to the computed expected mean air temperatures ${s}_{\mathrm{z}}^{\prime}$ resulting from the building/network model. Taking into consideration the relative size of the context area, re-run the simulation with mean air temperature set-points set to ${s}_{\mathrm{z}}:={s}_{\mathrm{z}}+{\mathsf{\alpha}}_{\mathrm{qz}}\left(\overline{{s}_{\mathrm{z}}{}^{\prime}}-{s}_{\mathrm{z}}^{\prime}\right)$ to obtain the energy $e$required by the building/network assuming the demand-response action has been taken.
- We can apply any function $f$ to account for varying energy prices on dynamic markets where required so the resulting impact is now given by $\mathsf{\theta}\left[\mathrm{a},\mathrm{t}\right]=\mathrm{f}\left[e\right]$.

#### 2.3. Linking the Occupant Behaviour Model with the Building/Network Model

- Exclusivity: ${m}_{it}\ne 0\Rightarrow \left({a}_{i}\in {\mathcal{E}}_{\mathcal{r}}\Rightarrow \forall {a}_{j}\in {\mathcal{E}}_{\mathcal{r}}\backslash \left\{{a}_{i}\right\},t\le s<t+\mathsf{\tau}\left[{a}_{i}\right]:{m}_{js}=0\right)$
- Refractory period: ${m}_{it}\ne 0\Rightarrow \forall t<s<t+\mathsf{\rho}\left[{a}_{i}\right]:{m}_{is}=0$
- Feasibility: ${m}_{it}\ne 0\Rightarrow \mathsf{\varphi}\left[{a}_{i},t\right]=1$
- Presence: ${m}_{it}\ne 0\Rightarrow \exists {c}_{q}\in \mathcal{C}:{a}_{i}\in {\mathcal{A}}_{\mathcal{q}}\wedge \mathsf{\chi}\left[{m}_{it},{c}_{q},t\right]=1$

## 3. Results

^{3}(2.29%) and distributed across 9 of the 15 heating circuits of the CIT Bishopstown campus during the heating season 2018/2019. The behavioural model has been calibrated with 100 participants to run a simulation-based assessment of potential energy savings achievable through triggering BDR messages (Figure 2). Energy savings of up to 4.5% have been achievable.

## Acknowledgments

## References

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**MDPI and ACS Style**

Beder, C.; Blanke, J.; Klepal, M.
Behaviour Demand Response in District Heating—A Simulation-Based Assessment of Potential Energy Savings. *Proceedings* **2019**, *20*, 2.
https://doi.org/10.3390/proceedings2019020002

**AMA Style**

Beder C, Blanke J, Klepal M.
Behaviour Demand Response in District Heating—A Simulation-Based Assessment of Potential Energy Savings. *Proceedings*. 2019; 20(1):2.
https://doi.org/10.3390/proceedings2019020002

**Chicago/Turabian Style**

Beder, Christian, Julia Blanke, and Martin Klepal.
2019. "Behaviour Demand Response in District Heating—A Simulation-Based Assessment of Potential Energy Savings" *Proceedings* 20, no. 1: 2.
https://doi.org/10.3390/proceedings2019020002