# Intelligent Control of Thermal Energy Storage in the Manufacturing Sector for Plant-Level Grid Response

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## Abstract

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## 1. Introduction

- Incorporating TES with the modeling of a stratified TES tank;
- Using TES in two possible scenarios of process simulation;
- Presenting the potential savings from employing the two scenarios utilizing TES and highlighting the novelty of the paper, which is the application of the smart controller integrated with TES, which has a given setpoint to maintain the power usage of industrial facilities.

## 2. Methods

#### 2.1. Process Scenarios

#### 2.2. TES Tank Modeling

_{p}[37]; the incremental height ∆z, which is the height of each node calculated using the tank height Z and the number of nodes N, also plays a role in this term and is calculated in Appendix A. On the right-hand side of the equation, the first term represents the heat loss to the ambient air, where U is the overall heat transfer coefficient to the ambient air [19] and P is the perimeter of the node. The second term represents heat transfer by conduction, with k representing the thermal conductivity coefficient [37]. The last two terms represent the energy of charging and discharging the chilled water going to the process. These two energies are calculated based on upwind energy balance calculations. The (i − 1) node appears in the charging term because the flow is upward, so the upwind node here is (i − 1) with reference to (i), while (i + 1) is used for discharging because of the downward flow, so the upwind node is (i + 1) with reference to (i). Equations (2) and (3) calculate the temperatures at the bottom and top nodes, respectively, which are the boundaries of the tank. The coefficient $\frac{4}{3}$ arises from the assumption that the wall temperature is equal to the water temperature, with the wall being located at a distance of $\frac{\u2206\mathrm{z}}{2}$, leading to the usage of the mentioned coefficient [34].

#### 2.3. Facility Data

#### 2.4. Chiller Model

## 3. Results

#### 3.1. “Fixed Schedule Discharge” vs. “Baseline Process”

#### 3.2. “Smart Discharging” vs. “Baseline Process”

## 4. Discussion

#### 4.1. Comparison between the Three Different Scenarios

#### 4.2. Payback Period for Using the Two Proposed Process Schemes

#### 4.3. Sensitivity Analysis

#### 4.3.1. Setpoint vs. Cost Savings

#### 4.3.2. Size of the Tank vs. Cost Savings

#### 4.3.3. Utility Rates vs. Cost Savings

#### 4.4. Limitations, Assumptions, and Challenges

- Having a flat utility rate for energy and demand charges;
- The process cooling demand is not changing in terms of (kWth).

- A higher or lower optimum estimation of the setpoint for the smart controller;
- The future prediction of the facility’s power demand in order to estimate the setpoints for each upcoming month;
- The total cost of installation of the tank is expensive, so having a larger tank will lead to a very high cost of installation.

#### 4.5. Setpoints of “Smart Discharging” Scheme and Future Work

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

TES | Thermal energy storage |

VFD | Variable-frequency drive |

Symbols | |

A | Tank cross-sectional area (m^{2}) |

ṁ_{ch} | Mass flow rate of charging the tank (kg s^{−1}) |

ṁ_{dsch} | Mass flow rate of discharging the tank (kg s^{−1}) |

∆z | The height of the layer (node) of the tank (m) |

${\mathsf{\rho}}_{\mathrm{i}}$ | Density of the fluid in each node of the tank (kg m^{−3}) |

Z | Tank height (m) |

k | Thermal conductivity of the fluid in the tank (W m^{−1} K) |

C_{p} | Fluid heat capacity (J kg^{−1} K^{−1}) |

U | Tank-fluid-to-ambient-air overall heat transfer coefficient (W m^{−2} K^{−1}) |

T_{amb} | Ambient air temperature (°C) |

P | Tank perimeter (m) |

T_{i} | Temperature of the fluid at each node of the tank (°C) |

T_{ch} | Temperature of charging chilled water for the tank (°C) |

T_{ret} | Temperature of the returning fluid to the tank and the chiller (°C) |

t | Time (s) |

Q_{cooling} | Cooling energy provided by the chillers (kWth) |

N | Number of nodes of the tank |

kWe | Electric power load |

kWth | Thermal power load |

## Appendix A

Parameter | Value | Description |

C_{p} | 4.2 × 10^{3} | Specific heat capacity of water, Equations (1)–(3) [37] (J kg^{−1} K^{−1}) |

A | $\frac{\pi}{4}{\mathrm{D}}^{2}$ | Cross-sectional area of the node (m), D = 5.7 m calculated based on the aspect ratio [38], Equations (1)–(3) |

Z, ∆z | 21.5, $\frac{21.5}{100}$ | Tank height is calculated based on the aspect ratio [38] (m) and nodes’ height, N = 100, Equations (1)–(3) |

k | 0.5 | Thermal conductivity of the fluid in the tank (W m^{−1} K), Equations (1)–(3) [37] |

U | 1 | Overall heat transfer coefficient of the tank fluid to ambient air (W m^{−2} K^{−1}), assumed to be equal in each node, Equations (1)–(3) [19] |

Tamb | Varies according to the weather data [40] | Ambient air temperature (°C), Equations (1)–(3) |

Tch | 8.9 | The chilled water temperature (°C), from the facility data |

Tret | 14.5 | The process return water temperature (°C), from the facility data |

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**Figure 3.**The process scheme with the smart controller based on the real-time demand of the facility.

**Figure 5.**The tank’s different temperature profiles. The blue curve represents a fully charged tank, the red curve represents a fully discharged tank, and the black curve represents a 60% charged tank.

**Figure 6.**Example of the facility demand profile showing on-peak hours following the summer schedule.

**Figure 7.**Contour plot of the chiller profile showing 2D interpolation function for the chiller model.

**Figure 8.**The baseline of the facility data without the chiller along with the facility profile and the chiller profile to explain how the baseline of the facility without the chiller kWe is calculated.

**Figure 18.**Contour plot of the variation in energy rates and their effect on the cost savings in June.

**Figure 19.**Contour plot of the variation in demand rate and on-peak energy rate and their effect on the cost savings in June.

**Figure 20.**Contour plot of the variation in demand rate and off-peak energy rate and their effect on the cost savings in June.

Scenarios | Name |
---|---|

1 | Baseline process |

2 | Fixed schedule discharge |

3 | Smart discharging |

**Table 2.**Facility’s charges’ description with the on-peak and off-peak schedule according to the utility provider, which is Rocky Mountain Power.

Charges Category | Cost (USD) |
---|---|

On-peak demand | USD 14.96 per kW from October to May |

On-peak hours from 6 am to 9 a.m. and from 6 p.m. to 10 p.m. except on weekends | |

USD 16.61 per kW from June to September | |

On-peak hours from 3 p.m. to 10 p.m. except on weekends | |

kWh off-peak | USD 0.02316 per kWh from October to May |

USD 0.0262 per kWh from June to September | |

kWh on-peak | USD 0.04556 per kWh from October to May |

USD 0.0515 per kWh from June to September |

Ambient Air Temperature (°C) | Thermal Power (kWth) | Electrical Power (kWe) |
---|---|---|

35 | 809 | 274 |

27 | 607 | 149.5 |

18 | 404 | 71 |

13 | 202 | 29.8 |

Category\Scenario | Baseline Process | Fixed Schedule Discharge | Percent Saved |
---|---|---|---|

On-peak demand charges | USD 31,722 | USD 29,801 | 6% |

Energy charge kWh off-peak | USD 24,671 | USD 25,537 | −3.5% |

Energy charge kWh on-peak | USD 14,062 | USD 12,949 | 8% |

Total charges | USD 70,455 | USD 68,288 | 3% |

Category\Scenario | Baseline Process | Fixed Schedule Discharge | Percent Saved |
---|---|---|---|

On-peak demand charges | USD 37,575 | USD 35,357 | 6% |

Energy charge kWh off-peak | USD 27,353 | USD 28,442 | −4% |

Energy charge kWh on-peak | USD 18,807 | USD 17,085 | 9% |

Total charges | USD 83,734 | USD 80,885 | 3.5% |

Category\Scenario | Baseline Process | Smart Discharging | Percent Saved |
---|---|---|---|

On-peak demand charges | USD 31,722 | USD 29,801 | 6% |

Energy charge kWh off-peak | USD 24,671 | USD 24,284 | 1.5% |

Energy charge kWh on-peak | USD 14,062 | USD 14,040 | 0.2% |

Total charges | USD 70,455 | USD 68,124 | 3.5% |

Category\Scenario | Baseline Process | Smart Discharging | Percent Saved |
---|---|---|---|

On-peak demand charges | USD 37,575 | USD 34,474 | 8% |

Energy charge kWh off-peak | USD 27,353 | USD 27,293 | 0.2% |

Energy charge kWh on-peak | USD 18,807 | USD 18,126 | 3.5% |

Total charges | USD 83,734 | USD 79,892 | 4.5% |

Month | Setpoint kW |
---|---|

January | 1880 |

February | 1920 |

March | 1900 |

April | 1970 |

May | 1970 |

June | 2055 |

July | 2240 |

August | 2280 |

September | 2270 |

October | 2000 |

November | 1970 |

December | 1880 |

Savings\Scenarios | Fixed Schedule Discharge | Smart Discharging |
---|---|---|

January | USD 2167 | USD 2331 |

February | USD 2167 | USD 2284 |

March | USD 2176 | USD 2209 |

April | USD 2185 | USD 2189 |

May | USD 2711 | USD 3146 |

June | USD 2849 | USD 3842 |

July | USD 1515 | USD 2947 |

August | USD 1045 | USD 2142 |

September | USD 1826 | USD 2395 |

October | USD 2280 | USD 2339 |

November | USD 2131 | USD 2186 |

December | USD 2125 | USD 2413 |

Scenarios | Baseline Process | Fixed Schedule Discharge | Smart Discharging |
---|---|---|---|

Total charges | USD 917,255 | USD 892,078 | USD 886,832 |

Scenario | Cost (USD) | Savings (USD) | Payback Years |
---|---|---|---|

Fixed schedule discharge | USD 200,000 | USD 25,100 | 8 |

Smart discharging | USD 200,000 | USD 30,400 | 6.5 |

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## Share and Cite

**MDPI and ACS Style**

Bahr, M.T.; Immonen, J.; Billings, B.W.; Powell, K.M.
Intelligent Control of Thermal Energy Storage in the Manufacturing Sector for Plant-Level Grid Response. *Processes* **2023**, *11*, 2202.
https://doi.org/10.3390/pr11072202

**AMA Style**

Bahr MT, Immonen J, Billings BW, Powell KM.
Intelligent Control of Thermal Energy Storage in the Manufacturing Sector for Plant-Level Grid Response. *Processes*. 2023; 11(7):2202.
https://doi.org/10.3390/pr11072202

**Chicago/Turabian Style**

Bahr, Mohamed T., Jake Immonen, Blake W. Billings, and Kody M. Powell.
2023. "Intelligent Control of Thermal Energy Storage in the Manufacturing Sector for Plant-Level Grid Response" *Processes* 11, no. 7: 2202.
https://doi.org/10.3390/pr11072202