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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.
面頁冊數:	1 online resource (188 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
Contained By:	Dissertations Abstracts International84-04A.
標題:	Algorithms. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29352382click for full text (PQDT)
ISBN:	9798352682432

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

Computational Optimization in Operational and Expansion Planning. - 1 online resource (188 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352682432Subjects--Topical Terms:

536374
Algorithms.
Index Terms--Genre/Form:

542853
Electronic books.

Computational Optimization in Operational and Expansion Planning.
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245 1 0 $a Computational Optimization in Operational and Expansion Planning.
264 0 $c 2022
300 $a 1 online resource (188 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 84-04, Section: A.
500 $a Advisor: Swarup, K. Shanti; Srinivasan, Dipti; Hesamzadeh, Mohammad Reza.
502 $a Thesis (Ph.D.)--National University of Singapore (Singapore), 2022.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Algorithms. $3 536374
650 4 $a Demand side management. $3 3681811
650 4 $a Alternative energy. $3 3436775
650 4 $a Computer science. $3 523869
650 4 $a Energy. $3 876794
650 4 $a Test systems. $3 3686152
650 4 $a Expansion. $3 3690316
650 4 $a Wind power. $3 672558
650 4 $a Electricity. $3 524507
650 4 $a Electricity distribution. $3 3562889
650 4 $a Optimization. $3 891104
650 4 $a Game theory. $3 532607
650 4 $a Linear programming. $3 560448
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0363
690 $a 0984
690 $a 0791
690 $a 0454
690 $a 0338
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a National University of Singapore (Singapore). $3 3352228
773 0 $t Dissertations Abstracts International $g 84-04A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29352382 $z click for full text (PQDT)