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Computational Optimization in Operational and Expansion Planning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational Optimization in Operational and Expansion Planning.
作者:	Verma, Pranjal Pragya.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	188 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
附註:	Advisor: Swarup, K. Shanti;Srinivasan, Dipti;Hesamzadeh, Mohammad Reza.
Contained By:	Dissertations Abstracts International84-04A.
標題:	Test systems.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29352382
ISBN:	9798352682432

Computational Optimization in Operational and Expansion Planning.
Verma, Pranjal Pragya.

Computational Optimization in Operational and Expansion Planning. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 188 p.

Source: Dissertations Abstracts International, Volume: 84-04, Section: A.

Thesis (Ph.D.)--National University of Singapore (Singapore), 2022.

This item must not be sold to any third party vendors.

The electricity demand is rising across the globe and the power grids have to be ready to cater to the future loads. The power grid operators aim to operate the grid as economically as possible. The proper readiness of a grid to future load is studied under the blanket of power system expansion planning problems, while the economic operation is studied under the blanket of power system operation problems. Both of the aforementioned problems cover the domain of Power System Planning. In this thesis, both operational and expansion planning problems are studied.The Power System Expansion planning considered in this thesis consists of investment options in generation, transmission and demand management resources. This problem is highly constrained hard problem. For the power system expansion planning problems, a novel Information Exchange based Clustered Differential Evolution (IE-CDE) is proposed and implemented to solve the Hybrid Generation-Transmission Expansion Planning problems. The thesis shows that a hybrid co-optimization leads to optimal solution as compared to a traditional sequential approach where the Generation expansion planning problem is followed by a transmission expansion planning problem. This is primarily because in a sequential planning framework the solver at the first stage is blind to the second stage feasible set and solution values. The thesis extends the Generation-Transmission expansion planning framework to see the effects of Demand Side Management investment options on the overall expansion planning cost.For the operational planning of the grid, this thesis delves into Nash Equilibrium between competing Gencos in markets. The operational study involves the computation of equilibrium among Gencos that compete to maximize their profits in the electricity market auctions. The thesis proposes a novel Affine-plane approximation method to recast the Non-convex Mathematical Program with Equilibrium Constraints (MPEC) for each profit-maximizing Genco into a Linear Program. This allows for converting the equilibrium calculating step traditionally with an Equilibrium Problem with Equilibrium Constraints (EPEC) into an easier to solve Mixed Complementarity Problems (MCP). The thesis uses this novel reformulation to efficiently compute the Bayesian Nash Equilibrium in the power markets and decompose the economic inefficiencies into Imperfect Information and Imperfect Competition Costs.The thesis also discusses the uncertainty handling techniques used in power system planning problems and presents a state-of-the-art survey of these techniques.Overall this thesis has the following two main contributions:• Hybrid Generation-Transmission Expansion planning and benefits of DSM in power systems.• Affine plane approximation to recast the computation of Bayesian Nash Equilibrium as an MCP in wholesale Power markets.

ISBN: 9798352682432Subjects--Topical Terms:

942560
Test systems.

Computational Optimization in Operational and Expansion Planning.
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500 $a Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
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502 $a Thesis (Ph.D.)--National University of Singapore (Singapore), 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The electricity demand is rising across the globe and the power grids have to be ready to cater to the future loads. The power grid operators aim to operate the grid as economically as possible. The proper readiness of a grid to future load is studied under the blanket of power system expansion planning problems, while the economic operation is studied under the blanket of power system operation problems. Both of the aforementioned problems cover the domain of Power System Planning. In this thesis, both operational and expansion planning problems are studied.The Power System Expansion planning considered in this thesis consists of investment options in generation, transmission and demand management resources. This problem is highly constrained hard problem. For the power system expansion planning problems, a novel Information Exchange based Clustered Differential Evolution (IE-CDE) is proposed and implemented to solve the Hybrid Generation-Transmission Expansion Planning problems. The thesis shows that a hybrid co-optimization leads to optimal solution as compared to a traditional sequential approach where the Generation expansion planning problem is followed by a transmission expansion planning problem. This is primarily because in a sequential planning framework the solver at the first stage is blind to the second stage feasible set and solution values. The thesis extends the Generation-Transmission expansion planning framework to see the effects of Demand Side Management investment options on the overall expansion planning cost.For the operational planning of the grid, this thesis delves into Nash Equilibrium between competing Gencos in markets. The operational study involves the computation of equilibrium among Gencos that compete to maximize their profits in the electricity market auctions. The thesis proposes a novel Affine-plane approximation method to recast the Non-convex Mathematical Program with Equilibrium Constraints (MPEC) for each profit-maximizing Genco into a Linear Program. This allows for converting the equilibrium calculating step traditionally with an Equilibrium Problem with Equilibrium Constraints (EPEC) into an easier to solve Mixed Complementarity Problems (MCP). The thesis uses this novel reformulation to efficiently compute the Bayesian Nash Equilibrium in the power markets and decompose the economic inefficiencies into Imperfect Information and Imperfect Competition Costs.The thesis also discusses the uncertainty handling techniques used in power system planning problems and presents a state-of-the-art survey of these techniques.Overall this thesis has the following two main contributions:• Hybrid Generation-Transmission Expansion planning and benefits of DSM in power systems.• Affine plane approximation to recast the computation of Bayesian Nash Equilibrium as an MCP in wholesale Power markets.
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