"Optimization of Smart Bioenergy Supply Networks Considering Distributed Energy Resources and Peer-to-Peer (P2P)Electricity Trading"

نوع: Type: Thesis

مقطع: Segment: PHD

عنوان: Title: "Optimization of Smart Bioenergy Supply Networks Considering Distributed Energy Resources and Peer-to-Peer (P2P)Electricity Trading"

ارائه دهنده: Provider: Malihe Masoumi

اساتید راهنما: Supervisors: AmirSaman Kheirkhah

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Nafiseh Soleimani-Parviz Fattahi- Farnaz Barzinpour

زمان و تاریخ ارائه: Time and date of presentation: 2025

مکان ارائه: Place of presentation: آمفی تئاتر

چکیده: Abstract: The rapid expansion of urbanization and population growth has led to a significant increase in electricity consumption. Consequently, electricity generation capacity must rise at a comparable pace. In recent years, in response to the challenges and opportunities posed by the high penetration of renewable energy on the demand side and the growing presence of distributed energy resources (DERs)—such as photovoltaic (PV) systems and energy storage units—peer-to-peer (P2P) energy trading has emerged as a promising solution. However, most existing approaches in the literature overlook the role of retailers or energy suppliers in local energy markets, which is inconsistent with real-world conditions, where such entities are still expected to play a critical role. This study proposes a price-based energy management and optimization model for an electricity supply network that incorporates both time-based and incentive-based demand response programs. The model involves the active participation of a retailer and a local energy supplier equipped with waste-to-energy (WTE) technology and is analyzed under both centralized and decentralized decision-making structures. In the centralized framework, where all entities operate as a unified agent, a two-stage optimization is performed. In the first stage, a MILP model determines the optimal service area of each prosumer to minimize energy transmission distances and maximize renewable energy utilization. The second stage employs a MINLP model to optimize energy exchange and storage management across the network. To assess the model's effectiveness, 12 test problems of varying sizes (small, medium, and large) are developed using real-world data from Iran. For large instances, a Lagrangian relaxation algorithm integrated with a problem-specific heuristic is introduced, which demonstrates rapid convergence to near-optimal solutions within practical computation times. In the decentralized structure, the entities interact through a three-level hierarchical decision-making process modeled as a Stackelberg–Nash game. A distributed heuristic algorithm is developed to iteratively update prices and guide the system toward game equilibrium. Economic and environmental impact analyses reveal that the retailer's profit increases by a factor of 16.3, while prosumers’ costs decrease by 60.5%, primarily due to reduced reliance on the wholesale market—resulting in lower CO₂ emissions. A comparison between the centralized and decentralized approaches shows that the centralized scheme enables a more balanced allocation of energy resources and lower dependency on the upstream market (49.74%), whereas the decentralized case results in higher upstream market reliance (66.1%) and decreased utilization of distributed resources.

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