Risk Based Day-ahead Energy Resource Management with Renewables via Computational Intelligence

Pratik Mochi; Kartik S. Pandya; Dharmesh Dabhi; Vipul N. Rajput

doi:10.20508/ijrer.v12i2.12965.g8475

Risk Based Day-ahead Energy Resource Management with Renewables via Computational Intelligence

Pratik Mochi, Kartik S. Pandya, Dharmesh Dabhi, Vipul N. Rajput

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

An indeterminate and variable nature of renewable energy sources like solar photovoltaic, wind power, load consumption, electric vehicles trips and market spot prices, make the operation and control of energy management system quite complex. Also, it is expected that the system should be consistent and resilient in case of extreme events like faults, hurricanes etc. This paper has used the risk based optimization strategies considering uncertainty of aforementioned parameters to minimize the operational cost of the aggregator. A 13-bus practical distribution system with 15-scenarios (03-scenarios as extreme events with high impact) are considered as a test system. WCCI-2018 award winning, Enhanced Velocity Differential Evolutionary Particle Swarm Optimization (EVDEPSO) computational intelligence method has been used to solve this problem.

Original language	English
Pages (from-to)	921-929
Number of pages	9
Journal	International Journal of Renewable Energy Research
Volume	12
Issue number	2
DOIs	https://doi.org/10.20508/ijrer.v12i2.12965.g8475
Publication status	Published - Jun 2022
Externally published	Yes

Keywords

Electricity market
Energy management
Optimization
Smart grid

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10.20508/ijrer.v12i2.12965.g8475

Cite this

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title = "Risk Based Day-ahead Energy Resource Management with Renewables via Computational Intelligence",

abstract = "An indeterminate and variable nature of renewable energy sources like solar photovoltaic, wind power, load consumption, electric vehicles trips and market spot prices, make the operation and control of energy management system quite complex. Also, it is expected that the system should be consistent and resilient in case of extreme events like faults, hurricanes etc. This paper has used the risk based optimization strategies considering uncertainty of aforementioned parameters to minimize the operational cost of the aggregator. A 13-bus practical distribution system with 15-scenarios (03-scenarios as extreme events with high impact) are considered as a test system. WCCI-2018 award winning, Enhanced Velocity Differential Evolutionary Particle Swarm Optimization (EVDEPSO) computational intelligence method has been used to solve this problem.",

keywords = "Electricity market, Energy management, Optimization, Smart grid",

author = "Pratik Mochi and Pandya, {Kartik S.} and Dharmesh Dabhi and Rajput, {Vipul N.}",

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T1 - Risk Based Day-ahead Energy Resource Management with Renewables via Computational Intelligence

AU - Mochi, Pratik

AU - Pandya, Kartik S.

AU - Dabhi, Dharmesh

AU - Rajput, Vipul N.

PY - 2022/6

Y1 - 2022/6

N2 - An indeterminate and variable nature of renewable energy sources like solar photovoltaic, wind power, load consumption, electric vehicles trips and market spot prices, make the operation and control of energy management system quite complex. Also, it is expected that the system should be consistent and resilient in case of extreme events like faults, hurricanes etc. This paper has used the risk based optimization strategies considering uncertainty of aforementioned parameters to minimize the operational cost of the aggregator. A 13-bus practical distribution system with 15-scenarios (03-scenarios as extreme events with high impact) are considered as a test system. WCCI-2018 award winning, Enhanced Velocity Differential Evolutionary Particle Swarm Optimization (EVDEPSO) computational intelligence method has been used to solve this problem.

AB - An indeterminate and variable nature of renewable energy sources like solar photovoltaic, wind power, load consumption, electric vehicles trips and market spot prices, make the operation and control of energy management system quite complex. Also, it is expected that the system should be consistent and resilient in case of extreme events like faults, hurricanes etc. This paper has used the risk based optimization strategies considering uncertainty of aforementioned parameters to minimize the operational cost of the aggregator. A 13-bus practical distribution system with 15-scenarios (03-scenarios as extreme events with high impact) are considered as a test system. WCCI-2018 award winning, Enhanced Velocity Differential Evolutionary Particle Swarm Optimization (EVDEPSO) computational intelligence method has been used to solve this problem.

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KW - Energy management

KW - Optimization

KW - Smart grid

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Risk Based Day-ahead Energy Resource Management with Renewables via Computational Intelligence

Abstract

Keywords

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