# Optimal Outage Management Model Considering Emergency Demand Response Programs for a Smart Distribution System

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## Abstract

**:**

## 1. Introduction

#### 1.1. Motivation

#### 1.2. Literature Review

#### 1.3. The Proposed Approach and Contributions

- A novel two-stage multi-objective optimization is provided for outage management.
- The EDRP, as an essential program to decrease the cost of an outage in a smart distribution system, has been considered.
- The combination of the RRC problem, DG dispatch problem and EDRPs considering topological power system constraints are investigated.
- The proposed method is tested on an IEEE 34 bus test system as well as an actual Iranian 66 bus power distribution feeder.

#### 1.4. Paper Organization

## 2. The Role of EDRPs in the ${\mathbf{SOMS}}_{\mathbf{DGs}}^{\mathbf{EDRPs}}$ Program

#### 2.1. Decreasing the Outage Cost

#### 2.2. Increasing the Served Load

## 3. SOMS Framework

- To classify the outages optimally according to the depots and crews’ constraints.
- To reduce the outage cost of the distribution system.
- To reduce the DG cost of the distributed energy resources.
- To reduce the EDRP cost of the smart distribution system.
- To reduce the RRC’s time to obtain the health of the power distribution system.

- The capacity limit of crews’ vehicles and the limits of the resources in each depot.
- The operation limits such as the power flow limit of the distribution system, EDRP limits and DG limits.
- The RRC limit.
- The repair of damaged components.

**The load shedding cost is considered in the objective function of the proposed framework:**Previous work only considered the maximum served load in the outage management problem. However, this paper considers the load shedding cost in the novel structure. Additionally, the load importance is considered, to prioritize high-priority loads.**The EDRP costs are considered in the proposed framework:**The role of the EDRP was not considered in previous work in the smart outage management field.**The dispatch cost for the DGs is considered in the objective function of the proposed problem:**DGs plays an important role in the smart outage management system. The operation cost for the DGs was not considered in previous work.

## 4. SOMS Framework

#### 4.1. The First Stage

- The estimated distance between the outages and each depot.
- The number of resources in each depot.
- The required resources for outage clearing.
- The proficiency of each depot.

#### 4.2. The Second Stage

## 5. Implementation and Results

#### 5.1. IEEE 34 Bus Test System

#### 5.2. Iranian 66 Bus Power Distribution Feeder

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Nomenclature

Acronyms | |

EDRP | Emergency Demand Response Program |

DG | Distributed Generation |

MILP | Mixed Integer Linear Programming |

DSO | Distribution System Operator |

MGs | Micro Grids |

RRCs | Routing Repair Crews |

Set and Indexes | |

$\sigma $ | Index of each depot |

$m/n$ | Index of an outage and damaged component |

$b$ | Index of bus |

$t$ | Index of time |

${N}_{b}$ | Number of buses |

${N}_{t}$ | Number of times |

Parameters | |

$RE{S}^{P}\left(\sigma \right)$ | Existing equipment in each depot |

$Re{s}^{N}\left(m\right)$ | Required component for repairing outage m |

${a}_{1},{a}_{2},{a}_{3},{a}_{4}$ | Cost function coefficients |

$d\left(de{p}_{\sigma},m\right)$ | The distance between outage and depot |

${P}_{d}\left(b,t\right)$ | Load of bus b in time t |

$Pen\left(b\right)$ | Penalty factor (USD/kW) |

${H}_{m}$ | Public hazard index |

$TD\left(\sigma ,m\right)$ | The binary parameter that shows the ability of each crew to repair the outage m |

$Sus\left(b,bp\right)$ | Susceptance of lines |

${P}_{Line}^{max}$ | Maximum power flow in lines |

${P}_{DG}^{max}$ | Maximum power of each DG |

$nc\left(\sigma \right)$ | Number of crews in depot $\sigma $ |

$CAP\left(c\right)$ | The crew vehicle capacity |

$r\left(m,c\right)$ | Repair time for outage m |

$tr\left(m,n,c\right)$ | Traveling time between outage m and n |

Variables | |

${P}_{DG}\left(i,t\right)$ | Power of DG i in time t |

$s\left(\sigma ,m\right)$ | A binary variable that shows the clustering of the outages |

$\delta \left(b,t\right)$ | The angle of the bus |

$u\left(m,t\right)$ | Status of lines and DGs |

$Z\left(m,t\right)$ | Availability of damaged component m |

$x\left(m,n,c,\sigma \right)$ | The binary variable showing when crew c moves from outage m to n |

$y\left(m,c,\sigma \right)$ | The binary variable showing when the crew c visits the outage m |

$RE{C}^{c}\left(c\right)$ | Number of pieces of equipment assigned to crew c |

$AT\left(m,c\right)$ | Arrival time of crew c at outage m |

$q\left(b,t\right)$ | A binary variable that shows the status of each load bus |

$f\left(m,t\right)$ | Indicates the time that outage m is repaired |

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DGs | Min (kW) | Max (kW) | Operation Cost for DGs per kW (USD/kW) |
---|---|---|---|

DG 1 | 0 | 200 | 5 |

DG 2 | 0 | 210 | 7 |

DG 3 | 0 | 120 | 8 |

DG 4 | 0 | 220 | 9 |

Damage | Repair Time (30 min Step) | Needed Resources | |||
---|---|---|---|---|---|

Crew 1 | Crew 2 | Crew 3 | Crew 4 | ||

M 1 | 2.5 | 1.2 | 1.1 | 1.1 | 10 |

M 2 | 3 | 2.3 | 2.1 | 2.2 | 8 |

M 3 | 1.7 | 1.4 | 1.5 | 2 | 9 |

M 4 | 1 | 2.1 | 1.2 | 1 | 6 |

M 5 | 2 | 1.2 | 1 | 1.3 | 8 |

M 6 | 1.3 | 2.1 | 2.1 | 1 | 12 |

M 7 | 1 | 1.1 | 1.5 | 2 | 10 |

Step | Demand Response Price Block (USD/kW) |
---|---|

1 | 0 |

2 | 1 |

3 | 2 |

4 | 3 |

Step Times | Damaged Component |
---|---|

1 | * |

2 | M4 |

3 | M1-M3 |

4 | M2-M5-M6 |

5 | M7 |

DGs | Min (kW) | Max (kW) | Operation Cost of DGs per kW (USD/kW) |
---|---|---|---|

DG 1 | 0 | 100 | 4 |

DG 2 | 0 | 400 | 3 |

DG 3 | 0 | 100 | 5 |

DG 4 | 0 | 200 | 4 |

DG 5 | 0 | 100 | 6 |

DG 6 | 0 | 100 | 9 |

DG 7 | 0 | 200 | 6 |

DG 8 | 0 | 100 | 3 |

Damage | Repair Time (30 min Step) | Needed Resources | |||
---|---|---|---|---|---|

Crew 1 | Crew 2 | Crew 3 | Crew 4 | ||

M 1 | 2.5 | 1.2 | 1.1 | 1.1 | 10 |

M 2 | 3 | 2.3 | 2.1 | 2.2 | 8 |

M 3 | 1.7 | 1.4 | 1.5 | 2 | 9 |

M 4 | 1 | 2.1 | 1.2 | 1 | 6 |

M 5 | 2 | 1.2 | 1 | 1.3 | 8 |

M 6 | 1.3 | 2.1 | 2.1 | 1 | 12 |

M7 | 1 | 1.1 | 1.5 | 2 | 10 |

M 8 | 2 | 2.1 | 2.5 | 1 | 5 |

M 9 | 2 | 1.4 | 1.5 | 2 | 5 |

M10 | 1 | 1.2 | 2.5 | 1 | 7 |

Step Time | Damaged Component |
---|---|

1 | * |

2 | M4-M6-M9 |

3 | M1 |

4 | M8-M10 |

5 | M2-M3-M5-M7 |

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**MDPI and ACS Style**

Dorahaki, S.; Dashti, R.; Shaker, H.R.
Optimal Outage Management Model Considering Emergency Demand Response Programs for a Smart Distribution System. *Appl. Sci.* **2020**, *10*, 7406.
https://doi.org/10.3390/app10217406

**AMA Style**

Dorahaki S, Dashti R, Shaker HR.
Optimal Outage Management Model Considering Emergency Demand Response Programs for a Smart Distribution System. *Applied Sciences*. 2020; 10(21):7406.
https://doi.org/10.3390/app10217406

**Chicago/Turabian Style**

Dorahaki, Sobhan, Rahman Dashti, and Hamid Reza Shaker.
2020. "Optimal Outage Management Model Considering Emergency Demand Response Programs for a Smart Distribution System" *Applied Sciences* 10, no. 21: 7406.
https://doi.org/10.3390/app10217406