# A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

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## Abstract

**:**

## 1. Introduction

#### 1.1. Context and Motivation

#### 1.2. Previous Work

#### 1.3. Contribution

- An integrated, flexible real-time optimization and control framework based on a weight-based routing algorithm, significantly improving efficiency by removing communication network constraints usually associated with centralized control schemes.
- A detailed computational study considering technical and environmental parameters.
- Applying the proposed optimization and control algorithm using prototype hardware in an experiment designed to evaluate interaction with real-world data.

#### 1.4. Structure

## 2. Optimization and Control Framework Technical Development

#### 2.1. Generic Framework

#### 2.2. General Description

^{®}environment. Level-2 MATLAB System functions have been used extensively during the design and implementation, providing access to create custom blocks that support multiple input and output ports. Furthermore, this section describes how desktop simulations are reconfigured to validate the optimization and control algorithm using hardware-in-the-loop (HIL) simulation techniques.

#### 2.3. Technical Development

#### 2.4. Optimize and Control Subsystem

^{®}optimize and control block includes three input signals: (1) room temperature (temp_room), (2) current date and time (S0_date), and (3) a demand event signal that indicates the status of a tertiary DR service (des_mode). The block output signals provide: (1) a control signal (ctrl_action) that will alter the space heating temperature setpoint, (2) the current cost of energy usage (tou_tariff), and (3) an indication of the tertiary DR event duration (des_duration). The internal architecture of the optimize and control subsystem is shown in Figure 3.

#### 2.4.1. Thermal Comfort

#### 2.4.2. Electricity Demand Forecasting

#### 2.4.3. Cost (Tariff) Model

#### 2.4.4. Optimization

#### 2.5. Demand Event Signal Subsystem

^{®}model itself is trivial (Figure 5); however, the subsequent sequence of events requires further explanation. Firstly, the objective shifts to making the system ready for a DR event; this includes setting the control action to increase the room temperature in a measured approach by a pre-set value ${T}_{step}(\mathbb{C})$ within the 4-h horizon window. Secondly, there is the objective to ensure the battery energy storage system (BESS) is available with enough charge at the start of the DR event.

#### 2.6. Energy Subsystem

#### 2.7. Building Subsystem

#### 2.8. Scheduler Subsystem

^{®}model of the scheduler subsystem is shown in Figure 8 and includes three input signals and six output signals. The output signals are provided for visual indication of various signal status. A simplified BESS element (ess_subsystem) simulates a battery SOC using a first-order transfer function. Locally defined parameters SOC_hi and SOC_lo set maximum and minimum state of charge values (expressed as a percentage), which determine when the BESS is declared available for use. In this context, initial values are defined in Section 3. The model also includes a self-discharge rate (SDR) which reduces the stored charge of the battery naturally over time.

#### 2.9. Date-Time Subsystem

^{®}model of the date-time subsystem (dt_subsystem) is shown (Figure 9). Its primary function is to provide a date-time element at a sample time of 10 min. The model has been configured to run in real-time during experimental evaluation. By default, dt is set to the current date and time, using format dd-mmm-yyyy hh:mm:ss, with the option to set to any data time during model analysis. The date-time model parameters are reported in Table 4.

## 3. Computational Study

- Thermal comfort model
- Electrical demand forecasting model
- Cost (tariff) model
- Optimizer
- Tertiary DR activity
- Pro-active frequency control

## 4. Experimental Evaluation

^{®}model designed to send/receive serial data, (2) electronic fan speed controller (EFSC) to regulate the heat transfer through flow, (3) a 240 VAC 3 kW box fan portable heater, (4) an Industruino IND.I/O 32u4 Arduino-compatible industrial controller, which includes 2 CH 0 to 10 VDC/4-20 mA 12bit output, and (5) Arduino-compatible remote sensors and communication equipment, including Android smartphone pre-loaded with an app, developed using MIT App Inventor 2 version nb183c. In addition to streaming data into the software environment, the industrial controller on-board liquid crystal display (LCD) panel was codified to visualize the data from remote sensors and user thermal preferences (registered using the smartphone app). These feedback indicators were supplemented by a series of light emitting diodes (LEDs) reporting the status of several decision-making variables.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

AlgorithmA1. Optimize and control algorithm |

inputs: |

temp_room = $\left\{n\right|n\text{}\mathrm{is}\text{}\mathrm{pos},\text{}\mathrm{and}\text{}15.5\text{}\le \text{}n\text{}\le \text{}20.5\}$ ▷ ${T}_{room}\text{}(\mathbb{C})$ |

S0_date ← now() |

$\mathrm{des}\_\mathrm{mode}\text{}=\left\{n\right|n\text{}\mathrm{is}\text{}\mathrm{int},\text{}\mathrm{and}\text{}n\text{}\in \text{}\left\{0,1\right\}\}$ ▷ 0=normal, 1=event |

outputs: |

ctrl_action; tou_tariff; des_duration |

initialise: |

visual_mode; gridmap |

horizon = 4 ▷ duration (h) |

des_mode = 0 |

${T}_{step}$ ($\mathbb{C}$) = $\left\{n|n=2,\text{}\mathrm{and}\text{}n\text{}\in \text{}\left\{2,\text{}3\right\}\right\}$ |

des_duration = $\left\{n|n=40,\text{}\mathrm{and}\text{}n\text{}\in \text{}\left\{30,\text{}40,\text{}50\right\}\right\}$ ▷ duration (min) |

${T}_{min}$ = $\left\{n\right|n=16.0,\text{}\mathrm{and}\text{}15.5\text{}\le \text{}n\text{}\le \text{}17.5\}$ |

$dt$ = {tc, dv, ec, optim} |

for every 10 min interval do |

S0_date ← S0_date + 10 min |

for each $dt$ do |

if $dt$ = tc then |

${T}_{min}^{th}\leftarrow {T}_{min}$ ▷ min temp threshold $(\mathbb{C})$ |

prepare comfort values $\forall Sn=\left\{n|n\text{}\mathrm{is}\text{}\mathrm{an}\text{}\mathrm{integer},\text{}\mathrm{and}\text{}0\text{}\le \text{}n\text{}\le \text{}24\right\}$ |

else if $dt$ = dv then |

require: des_mode; des_duration; ${T}_{step}$ |

prepare demand values $\forall Sn=\left\{\mathrm{n}\right|\mathrm{n}\text{}\mathrm{is}\text{}\mathrm{an}\text{}\mathrm{int},\text{}\mathrm{and}\text{}0\text{}\le \text{}\mathrm{n}\text{}\le \text{}24\}$ |

prepare node path |

else if $dt$ = ec then |

prepare tou values $\forall Sn=\left\{n\right|n\text{}\mathrm{is}\text{}\mathrm{an}\text{}\mathrm{int},\text{}\mathrm{and}\text{}0\text{}\le \text{}n\text{}\le \text{}24\}$ |

end if |

get: ${\delta}_{dt}\left({x}_{n}\right)$ |

prepare gridmap |

adjacency matrix ← digraph ← edgelist ← gridmap |

optimize using dijkstra algorithm |

identify edgepath from start to end node $\forall Sn=\left\{n|n\text{}\mathrm{is}\text{}\mathrm{an}\text{}\mathrm{int},\text{}\mathrm{and}\text{}0\text{}\le \text{}n\text{}\le \text{}24\right\}$ |

if $dt$ = optim then |

prepare control action ▷ ${T}_{{S}_{1}}\text{}(\mathbb{C})$ |

end if |

get: visual_mode |

display: visualization ∈ {horizon, gridmap, bigpath, biggridmap} |

end for |

end for |

## Appendix B

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**Figure 7.**Simulink

^{®}model: (

**a**) Building subsystem; (

**b**) heater; (

**c**) building; (

**d**) daily temperature variation.

**Figure 10.**Gridmap visualization of data type function response at 10-Oct-2019 16:00 over a 4-h horizon window: (

**a**) Occupant thermal comfort feedback response; (

**b**) electricity demand forecast: weekday 24 h; (

**c**) cost (tariff); (

**d**) electricity demand forecast.

**Figure 11.**Optimization response showing individual data types and forecast response: (

**a**) $31\times 72\times 4$ gridmap visualization; (

**b**) gridmap projected onto $11\times 25$ nodemap.

**Figure 12.**Optimization response showing individual data types and forecast response on receipt of demand events signal, ${T}_{step}=3\text{}\mathbb{C}$: (

**a**) $31\times 72\times 4$ gridmap visualization; (

**b**) gridmap projected onto $11\times 25$ nodemap.

**Figure 13.**Simulation study at 10-Oct-2019 16:00 for 24 h with DR event: (

**a**) shows temperature setpoint $(\mathbb{C})$ (TS1), room temperature $(\mathbb{C})$ (Troom), primary power switch signal (PWR), outdoor temperature $(\mathbb{C})$ (Tout), cost (p/kWh) (Cost), and battery energy storage (BESS) state of charge (SOC) (%) (rescaled) (SOC) profiles; (

**b**) shows tariff mode (t_mode), TOU tariff (tariff), demand event signal mode (des_mode), and demand (rescaled) (demand) profiles.

**Figure 14.**Frequency response 10-Oct-2019: (

**a**) impact on mains grid frequency due to simulated load disturbance; (

**b**) proposed model response.

**Figure 15.**Hardware-in-the-loop test environment: (

**a**) Arduino equipment; (

**b**) Android smartphone demonstrator app example screen images; (

**c**) Arduino equipment block identification map; (

**d**) Arduino equipment legend.

**Figure 17.**Visual representations of gridmap data showing 4-h horizon window of predicted values of each data type and optimized temperature profile: (

**a**) on receipt of a simulated DR event signal at 16:50; (

**b**) during the 40 min DR event (20:40 to 21:20).

**Figure 18.**Experimental evaluation recorded results at 6-April-2020 16:00 for 5.5 h with DR event: (

**a**) room temperature (Tr), temperature setpoint (TS1), thermal comfort gridmap data (tc), demand event signal mode (des_mode), and DR event; (

**b**) control action signal (Tu), primary power switch signal (PWR), demand (rescaled) (dv), and DR event.

${\mathit{\tau}}_{\left(\mathit{n}\right)}$ | ${\mathit{u}}_{\mathit{m}\mathit{i}\mathit{n}}$ | ${\mathit{u}}_{\mathit{m}\mathit{a}\mathit{x}}$ |
---|---|---|

1 | 0 | 0 |

2 | 10 | 40 |

3 | 5 | 20 |

4 | 15 | 70 |

5 | 3 | 12 |

6 | 7 | 30 |

7 | 0 | 0 |

Parameter | Description | Value |
---|---|---|

$Ki$ | Secondary ALFC integral gain | 1.667 × 10^{−3} |

$R$ | Governor speed regulator | $0.05$ Hz/pu MW |

$Tg$ | Governor time constant | 0.25 s |

$Tt$ | Turbine time constant | 0.60 s |

$H$ | Inertia time constant | 5 s |

$D$ | Load damping constant | 0.8 s |

$C1$ | Constant | 10 × 10^{6} |

$\Delta Pd$ | Continency load | 75 MW |

Parameter | Value |
---|---|

c | 1005.4 |

Rth | 0.0015 |

C2 | 3.6 × 10^{3} |

C3 | 0.0199 |

C7 | 15 |

C8 | 1800 |

Parameter | Value |
---|---|

C5 | 600 |

C6 | 1.157412771135569 × 10^{−5} |

dt | dd-mmm-yyyy hh:mm:ss |

Parameter | Value | Description |
---|---|---|

SO_date | 10-Oct-2019 16:00 | Stage 0 date time |

des_begin | 10-Oct-2019 16:40 | Notification of DR event |

${T}_{min}^{th}\text{}(\mathbb{C})$ | $16.5$ | Minimum temperature threshold |

${T}_{step}\text{}(\mathbb{C})$ | $3$ | Temperature step increase |

${T}_{room}$ ($\mathbb{C}$) | 18 | Room temperature |

Horizon (h) | 4 | Forecast horizon |

$D{R}_{t}$ (min) | 40 | Tertiary DR event duration |

SOC_hi | 0.8 | State of charge maximum threshold |

SOC_lo | 0.2 | State of charge minimum threshold |

© 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/).

## Share and Cite

**MDPI and ACS Style**

Williams, S.; Short, M.; Crosbie, T.; Shadman-Pajouh, M. A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services. *Energies* **2020**, *13*, 4191.
https://doi.org/10.3390/en13164191

**AMA Style**

Williams S, Short M, Crosbie T, Shadman-Pajouh M. A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services. *Energies*. 2020; 13(16):4191.
https://doi.org/10.3390/en13164191

**Chicago/Turabian Style**

Williams, Sean, Michael Short, Tracey Crosbie, and Maryam Shadman-Pajouh. 2020. "A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services" *Energies* 13, no. 16: 4191.
https://doi.org/10.3390/en13164191