Probabilistic Microgrid Investment Planning with Integrated Game-Theoretic Demand Response Management

Mohseni, S; Brent, AC

Probabilistic Microgrid Investment Planning with Integrated Game-Theoretic Demand Response Management

Mohseni, S

Brent, AC

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Power Systems, 2024, Part F3613, pp. 23-56
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Mohseni, S https://orcid.org/0000-0001-7367-3757
dc.contributor.author	Brent, AC
dc.date.accessioned	2025-02-02T00:38:03Z
dc.date.available	2025-02-02T00:38:03Z
dc.date.issued	2024-01-01
dc.identifier.citation	Power Systems, 2024, Part F3613, pp. 23-56
dc.identifier.isbn	9789819766222
dc.identifier.uri	http://hdl.handle.net/10453/184784
dc.description.abstract	This Chapter presents an innovative modelling framework for advancing integrated metaheuristic-based energy planning optimization, with a focus on resilient, renewable community microgrids. The study brings attention to a critical aspect of long-term sustainable energy system planning by illuminating biases intrinsic to consumer preference-based demand response projections. Central to the framework is the emphasis on market-driven sectoral flexibility procurement, achieved through sealed-bid auctions, thereby optimizing social welfare, improving demand response capacity liquidity, and promoting sectoral stability. To this end, the integration of game theory is highlighted as a means to leverage demand-side flexibility for community-level clean energy projects. Further, navigating the uncertainties stemming from non-dispatchable sources and smart grid interventions, the Chapter advocates for a comprehensive evaluation involving holistic uncertainty-aware models for renewable system planning. In this context, a novel probabilistic investment planning framework is introduced, which incorporates climatological, demand, and price uncertainties within a stochastic microgrid sizing model integrated with demand response solutions. The significance of this framework lies in its capacity to address gaps in uncertainty-aware methodologies and the integration of operational and investment planning. By synergizing intricate modelling with practical implementation considerations, the Chapter establishes a guiding framework that not only enriches scholarly deduction, but also aids practical, sustainable energy decision-making. The efficacy of the approach is substantiated through numerical simulations of a case study in Aotearoa New Zealand, to validate the applicability of the results.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Power Systems
dc.relation.ispartofseries	Power Systems
dc.relation.isbasedon	10.1007/978-981-97-6623-9_2
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Probabilistic Microgrid Investment Planning with Integrated Game-Theoretic Demand Response Management
dc.type	Chapter
utslib.citation.volume	Part F3613
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/DVC (Research)
pubs.organisational-group	University of Technology Sydney/DVC (Research)/Research Office
utslib.copyright.status	closed_access	*
dc.date.updated	2025-02-02T00:38:02Z
pubs.publication-status	Published
pubs.volume	Part F3613

Abstract:

This Chapter presents an innovative modelling framework for advancing integrated metaheuristic-based energy planning optimization, with a focus on resilient, renewable community microgrids. The study brings attention to a critical aspect of long-term sustainable energy system planning by illuminating biases intrinsic to consumer preference-based demand response projections. Central to the framework is the emphasis on market-driven sectoral flexibility procurement, achieved through sealed-bid auctions, thereby optimizing social welfare, improving demand response capacity liquidity, and promoting sectoral stability. To this end, the integration of game theory is highlighted as a means to leverage demand-side flexibility for community-level clean energy projects. Further, navigating the uncertainties stemming from non-dispatchable sources and smart grid interventions, the Chapter advocates for a comprehensive evaluation involving holistic uncertainty-aware models for renewable system planning. In this context, a novel probabilistic investment planning framework is introduced, which incorporates climatological, demand, and price uncertainties within a stochastic microgrid sizing model integrated with demand response solutions. The significance of this framework lies in its capacity to address gaps in uncertainty-aware methodologies and the integration of operational and investment planning. By synergizing intricate modelling with practical implementation considerations, the Chapter establishes a guiding framework that not only enriches scholarly deduction, but also aids practical, sustainable energy decision-making. The efficacy of the approach is substantiated through numerical simulations of a case study in Aotearoa New Zealand, to validate the applicability of the results.

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http://hdl.handle.net/10453/184784