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Economically Driven Constrained Opti...

Ghosh, Avik,

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Economically Driven Constrained Optimization and Control: Algorithms, Formal Guarantees, and Applications in Optimal Microgrid Sizing and Dispatch /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Economically Driven Constrained Optimization and Control: Algorithms, Formal Guarantees, and Applications in Optimal Microgrid Sizing and Dispatch // Avik Ghosh.
Author:	Ghosh, Avik,
Description:	1 electronic resource (201 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
Contained By:	Dissertations Abstracts International87-03B.
Subject:	Energy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32113047
ISBN:	9798293838097

Economically Driven Constrained Optimization and Control: Algorithms, Formal Guarantees, and Applications in Optimal Microgrid Sizing and Dispatch /
Ghosh, Avik,

Economically Driven Constrained Optimization and Control: Algorithms, Formal Guarantees, and Applications in Optimal Microgrid Sizing and Dispatch /Avik Ghosh. - 1 electronic resource (201 pages)

Source: Dissertations Abstracts International, Volume: 87-03, Section: B.

The modern electricity grid is fast changing with a shift from traditional centralized generation sources like fossil-fuel and nuclear power plants to distributed variable renewable energy sources (VRES) like wind and photovoltaic (PV) generators. However, VRES are intermittent in nature which can potentially lead to power imbalance in the electric grid, thereby risking grid stability. A cost-effective approach to mitigate these intermittencies is by integrating dispatchable energy resources like electric vehicles (EVs) and battery energy storage systems (BESS) in commercial microgrids to minimize the power fluctuations and provide additional grid services. However, for commercial microgrid operators to avail the maximum benefits from the dispatchable energy resources, it is important to appropriately size them during installation, and once installed it is vital to optimally dispatch them in real-time to realize the maximum electricity cost savings - two critical directions of research we contribute to, in this dissertation.In the first part of the dissertation, we focus on developing EV charging infrastructure sizing solutions - to assist building owners in making a quick and informed choice for different 'smart'/'dumb' and unidirectional/bidirectional EV charging strategies (V0G/V1G/V2G). We first analyzed the impact of an idealized EV commuter fleet workplace charging on commercial building electricity costs following the installation of a variable number of EV charging stations by leveraging an economic optimization model. Next, we developed accurate analytical expressions to estimate the optimal EV charging behavior and benefits even without economic optimization using the exogenous model inputs.In the second part of the dissertation, we first focus on developing application-agnostic, computationally inexpensive, real-time model predictive control (MPC) algorithms with provable guarantees. Here, we developed an adaptive relaxation-based stochastic MPC framework for generic discrete linear time invariant systems with provable nonconservative chance constraint satisfaction in closed-loop. Next, we proposed an economic MPC formulation for generic deterministic discrete non-linear time varying systems, with provable no-worse closed-loop asymptotic economic cost guarantees as compared to any arbitrary solution of the system under the same constraints, known only until the current time-step. Second, we demonstrated that both our proposed methods, when implemented for a real-life microgrid with BESS, VRE generation and load demand, shows superior electricity cost savings as compared to the traditional and state-of-the-art methods.

English

ISBN: 9798293838097Subjects--Topical Terms:

876794
Energy.
Subjects--Index Terms:

Battery energy storage systems

Economically Driven Constrained Optimization and Control: Algorithms, Formal Guarantees, and Applications in Optimal Microgrid Sizing and Dispatch /
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502 $b Ph.D. $c University of California, San Diego $d 2025.
520 $a The modern electricity grid is fast changing with a shift from traditional centralized generation sources like fossil-fuel and nuclear power plants to distributed variable renewable energy sources (VRES) like wind and photovoltaic (PV) generators. However, VRES are intermittent in nature which can potentially lead to power imbalance in the electric grid, thereby risking grid stability. A cost-effective approach to mitigate these intermittencies is by integrating dispatchable energy resources like electric vehicles (EVs) and battery energy storage systems (BESS) in commercial microgrids to minimize the power fluctuations and provide additional grid services. However, for commercial microgrid operators to avail the maximum benefits from the dispatchable energy resources, it is important to appropriately size them during installation, and once installed it is vital to optimally dispatch them in real-time to realize the maximum electricity cost savings - two critical directions of research we contribute to, in this dissertation.In the first part of the dissertation, we focus on developing EV charging infrastructure sizing solutions - to assist building owners in making a quick and informed choice for different 'smart'/'dumb' and unidirectional/bidirectional EV charging strategies (V0G/V1G/V2G). We first analyzed the impact of an idealized EV commuter fleet workplace charging on commercial building electricity costs following the installation of a variable number of EV charging stations by leveraging an economic optimization model. Next, we developed accurate analytical expressions to estimate the optimal EV charging behavior and benefits even without economic optimization using the exogenous model inputs.In the second part of the dissertation, we first focus on developing application-agnostic, computationally inexpensive, real-time model predictive control (MPC) algorithms with provable guarantees. Here, we developed an adaptive relaxation-based stochastic MPC framework for generic discrete linear time invariant systems with provable nonconservative chance constraint satisfaction in closed-loop. Next, we proposed an economic MPC formulation for generic deterministic discrete non-linear time varying systems, with provable no-worse closed-loop asymptotic economic cost guarantees as compared to any arbitrary solution of the system under the same constraints, known only until the current time-step. Second, we demonstrated that both our proposed methods, when implemented for a real-life microgrid with BESS, VRE generation and load demand, shows superior electricity cost savings as compared to the traditional and state-of-the-art methods.
546 $a English
590 $a School code: 0033
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650 4 $a Electrical engineering. $3 649834
650 4 $a Mechanical engineering. $3 649730
650 4 $a Alternative energy. $3 3436775
653 $a Battery energy storage systems
653 $a Microgrids
653 $a Model predictive control
653 $a Optimization
653 $a Variable renewable energy sources
690 $a 0791
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690 $a 0548
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710 2 $a University of California, San Diego. $b Mechanical and Aerospace Engineering. $e degree granting institution. $3 3816116
720 1 $a Kleissl, Jan $e degree supervisor.
720 1 $a Martinez, Sonia $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 87-03B.
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792 $a 2025
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