# A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response

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## Abstract

**:**

## 1. Introduction

## 2. Existing Baseline Estimation Methods

#### 2.1. High X of Y

#### 2.2. Last Y Days

#### 2.3. Regression

## 3. Methodology for Free Rider Mitigation in Incentive Settlements of DR Programs Offered to Residential Customers

#### 3.1. Research Framework for the Proposed Model

#### 3.2. Regression

#### 3.3. Incentive Payment Rule

#### Methodology for Obtaining Suitable Rate (${\mathrm{R}}^{\ast}$)

#### 3.4. Evaluation Metrics

#### 3.4.1. Baseline Performance Metrics

#### 3.4.2. Incentive Accuracy Evaluation Metrics

## 4. Case Study: Incentive Scale Analysis in a Korean Residential DR Program

#### 4.1. Input Data

#### 4.2. Proxy Day Selection

#### 4.3. Free Rider Problem

#### 4.4. Results of the Regression

#### 4.4.1. Regression Model Selection

#### 4.4.2. Performance Evaluation

#### 4.5. Payment Rule Analysis

#### Incentive Settlement Evaluation

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Flowchart of the proposed customer baseline load (CBL) estimation and incentive settlement method and corresponding evaluation processes.

**Figure 2.**Average hourly demand for residential peak-time rebate (PTR) program customers in Seoul, Korea, from June through September.

**Figure 9.**Free rider distributions for (

**a**) high 4 of 5, (

**b**) regression, and (

**c**) proposed two-step methodology using both regression and the new payment rule.

**Figure 10.**Variation in payment accuracy evaluation using the threshold rate, in terms of (

**a**) relative excess payment, and (

**b**) total excess payment.

**Table 1.**Details for the high X of Y methods used by several utility companies and independent system operators (ISOs).

Utility/ISO | CBL Method |
---|---|

SDG&E | high 3 of 5 |

Ontario | high 4 of 5 |

PJM | high 3 of 10 |

NewYork ISO | high 5 of 10 |

PowerCents DC | high 3 of 20, high 5 of 20, high 10 of 20 |

Utility/ISO | CBL Method |
---|---|

Wisconsin Energy | last 3 days |

NewYork ISO | last 10 days |

Ontario | last 5 days |

CAISO | last 10 days, last 4 days |

Utility/ISO | CBL Method |
---|---|

ERCOT | Regression of energy use during previous 20 like days |

BG&E | Regression of energy use during event window based on weather and day of week |

SDG&E | Regression of energy use during event window based on CDH, month, and day of week |

CBL Method | Description—Derived from Equation (4) |
---|---|

Regression w/o CDH (4 eligible days) | ${\mathrm{CBL}}_{\mathrm{i},\mathrm{d},\mathrm{t}}=\mathsf{\alpha}+{\displaystyle \sum _{\mathrm{n}=1}^{4}}{\mathsf{\beta}}_{\mathrm{n}}{\mathrm{L}}_{\mathrm{i},\mathrm{d}-\mathrm{n},\mathrm{t}}+\mathsf{\epsilon}$ |

Regression w/o CDH (3 eligible days) | ${\mathrm{CBL}}_{\mathrm{i},\mathrm{d},\mathrm{t}}=\mathsf{\alpha}+{\displaystyle \sum _{\mathrm{n}=1}^{3}}{\mathsf{\beta}}_{\mathrm{n}}{\mathrm{L}}_{\mathrm{i},\mathrm{d}-\mathrm{n},\mathrm{t}}+\mathsf{\epsilon}$ |

Regression w/o CDH (2 eligible days) | ${\mathrm{CBL}}_{\mathrm{i},\mathrm{d},\mathrm{t}}=\mathsf{\alpha}+{\displaystyle \sum _{\mathrm{n}=1}^{2}}{\mathsf{\beta}}_{\mathrm{n}}{\mathrm{L}}_{\mathrm{i},\mathrm{d}-\mathrm{n},\mathrm{t}}+\mathsf{\epsilon}$ |

Regression w/ CDH (2 eligible days) | ${\mathrm{CBL}}_{\mathrm{i},\mathrm{d},\mathrm{t}}=\mathsf{\alpha}+{\mathsf{\beta}}_{0}{\mathrm{sumCDH}}_{\mathrm{i},\mathrm{d},\mathrm{t}}+{\displaystyle \sum _{\mathrm{n}=1}^{2}}{\mathsf{\beta}}_{\mathrm{n}}{\mathrm{L}}_{\mathrm{i},\mathrm{d}-\mathrm{n},\mathrm{t}}+\mathsf{\epsilon}$ |

Regression w/ CDH (1 eligible days) | ${\mathrm{CBL}}_{\mathrm{i},\mathrm{d},\mathrm{t}}=\mathsf{\alpha}++{\mathsf{\beta}}_{0}{\mathrm{sumCDH}}_{\mathrm{i},\mathrm{d},\mathrm{t}}+{\mathsf{\beta}}_{1}{\mathrm{L}}_{\mathrm{i},\mathrm{d}-1,\mathrm{t}}+\mathsf{\epsilon}$ |

CBL Method | Average Relative Error [%] | Sum of Absolute Error [kWh] | Absolute Error Per Customer/Event [kWh] |
---|---|---|---|

Regression w/o CDH (4 eligible days) | 19% | 83,217 | 0.60 |

Regression w/o CDH (3 eligible days) | 5% | 59,780 | 0.43 |

Regression w/o CDH (2 eligible days) | 5% | 57,140 | 0.41 |

Regression w/ CDH (2 eligible days) | 2% | 61,958 | 0.45 |

Regression w/ CDH (1 eligible days) | 2% | 68,466 | 0.50 |

Metric | Ideal Result | high 4 of 5 | Regression | Regression and New Payment Rule |
---|---|---|---|---|

Load reduction (kWh) | 197 | 529 | 391 | 144 |

Total payment ($) | 163.51 | 439.07 | 324.53 | 119.52 |

Metric | high 4 of 5 | Regression | Regression and New Payment Rule |
---|---|---|---|

Free riders (number) | 744 | 547 | 81 |

Free rider (%) | 83.04 | 61.05 | 9.04 |

Total error ($) | 275.75 | 161.08 | −44.05 |

Relative excess payment (%) | 62.87 | 49.72 | −37.07 |

© 2018 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**

Lee, E.; Jang, D.; Kim, J. A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response. *Energies* **2018**, *11*, 3417.
https://doi.org/10.3390/en11123417

**AMA Style**

Lee E, Jang D, Kim J. A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response. *Energies*. 2018; 11(12):3417.
https://doi.org/10.3390/en11123417

**Chicago/Turabian Style**

Lee, Eunjung, Dongsik Jang, and Jinho Kim. 2018. "A Two-Step Methodology for Free Rider Mitigation with an Improved Settlement Algorithm: Regression in CBL Estimation and New Incentive Payment Rule in Residential Demand Response" *Energies* 11, no. 12: 3417.
https://doi.org/10.3390/en11123417