### Abstract

Original language | English |
---|---|

Pages (from-to) | 105863-105871 |

Number of pages | 8 |

Journal | IEEE Access |

Volume | 7 |

DOIs | |

Publication status | Published - 1 Aug 2019 |

### Keywords

- Pricing strategy
- volatility cost
- correlation coefficient
- decentralized demand response
- wavelet transforms

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*IEEE Access*,

*7*, 105863-105871. https://doi.org/10.1109/ACCESS.2019.2932499

**A Pricing Strategy Reflecting the Cost of Power Volatility to Facilitate Decentralized Demand Response.** / Zhang, Zhong; Li, Furong; Shi, Heng.

Research output: Contribution to journal › Article

*IEEE Access*, vol. 7, pp. 105863-105871. https://doi.org/10.1109/ACCESS.2019.2932499

}

TY - JOUR

T1 - A Pricing Strategy Reflecting the Cost of Power Volatility to Facilitate Decentralized Demand Response

AU - Zhang, Zhong

AU - Li, Furong

AU - Shi, Heng

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Previous pricing strategies including time-of-use price and dynamic price reflect system marginal cost and calculate consumers’ bills according to the quantity of their electricity usage. Little effort is made to understand the impact of power volatility on total production costs. This paper thus proposes a novel pricing strategy reflecting the cost arising from power volatility. Firstly, the impact of volatility on the production cost is investigated to quantify volatility cost. Secondly, a novel pricing model is proposed to allocate the volatility cost to consumers and renewable energy generations (REGs). It can reveal the coupling relationship between an individual load/REG curve and the system load curve. Thirdly, under the proposed pricing strategy, customers/REGs help to flatten the system load curve and reduce the production cost in a decentralized manner, which is certificated theoretically based on the Haar wavelet transforms. Validation on residential level loads shows that the volatility and peak-to-valley difference of aggregated load curve is reduced by 34.07% and 19.81%, respectively. The problem of synchronous response among customers faced by hourly price strategies is addressed by the proposed strategy. A test on megawatt-level loads shows a 61.95% reduction in system load volatility and a 2.21% reduction in production cost. It also reduces the peak-to-valley difference by 6.52%.

AB - Previous pricing strategies including time-of-use price and dynamic price reflect system marginal cost and calculate consumers’ bills according to the quantity of their electricity usage. Little effort is made to understand the impact of power volatility on total production costs. This paper thus proposes a novel pricing strategy reflecting the cost arising from power volatility. Firstly, the impact of volatility on the production cost is investigated to quantify volatility cost. Secondly, a novel pricing model is proposed to allocate the volatility cost to consumers and renewable energy generations (REGs). It can reveal the coupling relationship between an individual load/REG curve and the system load curve. Thirdly, under the proposed pricing strategy, customers/REGs help to flatten the system load curve and reduce the production cost in a decentralized manner, which is certificated theoretically based on the Haar wavelet transforms. Validation on residential level loads shows that the volatility and peak-to-valley difference of aggregated load curve is reduced by 34.07% and 19.81%, respectively. The problem of synchronous response among customers faced by hourly price strategies is addressed by the proposed strategy. A test on megawatt-level loads shows a 61.95% reduction in system load volatility and a 2.21% reduction in production cost. It also reduces the peak-to-valley difference by 6.52%.

KW - Pricing strategy

KW - volatility cost

KW - correlation coefficient

KW - decentralized demand response

KW - wavelet transforms

U2 - 10.1109/ACCESS.2019.2932499

DO - 10.1109/ACCESS.2019.2932499

M3 - Article

VL - 7

SP - 105863

EP - 105871

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -