Hybrid differential evolution with BBO for Genco's multi-hourly strategic bidding

P. Jain; R. Bhakar; S. N. Singh

doi:10.1109/IICPE.2014.7115803

Hybrid differential evolution with BBO for Genco's multi-hourly strategic bidding

P. Jain, R. Bhakar, S. N. Singh

Department of Electronic & Electrical Engineering

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

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Abstract

In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.

Original language	English
Title of host publication	6th India International Conference on Power Electronics (IICPE), 2014
Publisher	IEEE
Pages	1-6
ISBN (Print)	9781479960453
DOIs	https://doi.org/10.1109/IICPE.2014.7115803
Publication status	Published - 29 May 2015
Event	2014 6th IEEE India International Conference on Power Electronics, IICPE 2014 - Kurukshetra, India Duration: 8 Dec 2014 → 10 Dec 2014

Conference

Conference	2014 6th IEEE India International Conference on Power Electronics, IICPE 2014
Country/Territory	India
City	Kurukshetra
Period	8/12/14 → 10/12/14

Keywords

BBO
Bidding Strategy
DE
Electricity Markets
Monte Carlo Simulation

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10.1109/IICPE.2014.7115803

Accepted VersionAccepted author manuscript, 265 KB

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title = "Hybrid differential evolution with BBO for Genco's multi-hourly strategic bidding",

abstract = "In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.",

keywords = "BBO, Bidding Strategy, DE, Electricity Markets, Monte Carlo Simulation",

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AU - Jain, P.

AU - Bhakar, R.

AU - Singh, S. N.

PY - 2015/5/29

Y1 - 2015/5/29

N2 - In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.

AB - In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.

KW - BBO

KW - Bidding Strategy

KW - DE

KW - Electricity Markets

KW - Monte Carlo Simulation

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M3 - Chapter in a published conference proceeding

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BT - 6th India International Conference on Power Electronics (IICPE), 2014

PB - IEEE

T2 - 2014 6th IEEE India International Conference on Power Electronics, IICPE 2014

Y2 - 8 December 2014 through 10 December 2014

ER -

Hybrid differential evolution with BBO for Genco's multi-hourly strategic bidding

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Keywords

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