Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant

Nemer A. Amleh; Ali T. Al Awami

doi:10.1109/PSC.2016.7462855

Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant

Nemer A. Amleh, Ali T. Al Awami

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

Abstract

In this paper, a virtual power plant (VPP) that consists of generation and controllable demand is enabled to participate in the wholesale market. VPP makes renewable energy sources (RES) and distributed generations (DGs) controllable and observable to the system operator. The main objective is to introduce a scheme that optimizes the bidding strategies and maximizes the VPP's profit on day-ahead basis. To achieve this goal, the VPP trades energy externally with a wholesale market, and trades energy and demand response (DR) internally with the consumers in its territory. That is, when generation exceeds demand, the VPP sells the excess energy to the market, and it buys energy from the market when the generation and reduction in demand due to DR scheme are less than the required demand in its territory. Fuzzy optimization is proposed in this work to consider the uncertainty in the RES.

Original language	English
Title of host publication	Clemson University Power Systems Conference, PSC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509006878
DOIs	https://doi.org/10.1109/PSC.2016.7462855
State	Published - 28 Apr 2016

Publication series

Name	Clemson University Power Systems Conference, PSC 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Demand response (DR)
Elasticity factor
Fuzzy Optimization
Mixed Integer Nonlinear programming (MINLP)
Virtual Power Plant (VPP)

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/PSC.2016.7462855

Cite this

Amleh, N. A., & Al Awami, A. T. (2016). Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant. In Clemson University Power Systems Conference, PSC 2016 Article 7462855 (Clemson University Power Systems Conference, PSC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PSC.2016.7462855

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title = "Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant",

abstract = "In this paper, a virtual power plant (VPP) that consists of generation and controllable demand is enabled to participate in the wholesale market. VPP makes renewable energy sources (RES) and distributed generations (DGs) controllable and observable to the system operator. The main objective is to introduce a scheme that optimizes the bidding strategies and maximizes the VPP's profit on day-ahead basis. To achieve this goal, the VPP trades energy externally with a wholesale market, and trades energy and demand response (DR) internally with the consumers in its territory. That is, when generation exceeds demand, the VPP sells the excess energy to the market, and it buys energy from the market when the generation and reduction in demand due to DR scheme are less than the required demand in its territory. Fuzzy optimization is proposed in this work to consider the uncertainty in the RES.",

keywords = "Demand response (DR), Elasticity factor, Fuzzy Optimization, Mixed Integer Nonlinear programming (MINLP), Virtual Power Plant (VPP)",

author = "Amleh, \{Nemer A.\} and \{Al Awami\}, \{Ali T.\}",

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year = "2016",

month = apr,

day = "28",

doi = "10.1109/PSC.2016.7462855",

language = "English",

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Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant. / Amleh, Nemer A.; Al Awami, Ali T.
Clemson University Power Systems Conference, PSC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7462855 (Clemson University Power Systems Conference, PSC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Optimal demand response bidding and pricing mechanism with fuzzy optimization

T2 - Application for a virtual power plant

AU - Amleh, Nemer A.

AU - Al Awami, Ali T.

PY - 2016/4/28

Y1 - 2016/4/28

N2 - In this paper, a virtual power plant (VPP) that consists of generation and controllable demand is enabled to participate in the wholesale market. VPP makes renewable energy sources (RES) and distributed generations (DGs) controllable and observable to the system operator. The main objective is to introduce a scheme that optimizes the bidding strategies and maximizes the VPP's profit on day-ahead basis. To achieve this goal, the VPP trades energy externally with a wholesale market, and trades energy and demand response (DR) internally with the consumers in its territory. That is, when generation exceeds demand, the VPP sells the excess energy to the market, and it buys energy from the market when the generation and reduction in demand due to DR scheme are less than the required demand in its territory. Fuzzy optimization is proposed in this work to consider the uncertainty in the RES.

AB - In this paper, a virtual power plant (VPP) that consists of generation and controllable demand is enabled to participate in the wholesale market. VPP makes renewable energy sources (RES) and distributed generations (DGs) controllable and observable to the system operator. The main objective is to introduce a scheme that optimizes the bidding strategies and maximizes the VPP's profit on day-ahead basis. To achieve this goal, the VPP trades energy externally with a wholesale market, and trades energy and demand response (DR) internally with the consumers in its territory. That is, when generation exceeds demand, the VPP sells the excess energy to the market, and it buys energy from the market when the generation and reduction in demand due to DR scheme are less than the required demand in its territory. Fuzzy optimization is proposed in this work to consider the uncertainty in the RES.

KW - Demand response (DR)

KW - Elasticity factor

KW - Fuzzy Optimization

KW - Mixed Integer Nonlinear programming (MINLP)

KW - Virtual Power Plant (VPP)

UR - https://www.scopus.com/pages/publications/84973868605

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DO - 10.1109/PSC.2016.7462855

M3 - Conference contribution

AN - SCOPUS:84973868605

T3 - Clemson University Power Systems Conference, PSC 2016

BT - Clemson University Power Systems Conference, PSC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Optimal demand response bidding and pricing mechanism with fuzzy optimization: Application for a virtual power plant

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Cite this