Incentive-based Peer-to-Peer Distributed Energy Trading in Smart Grid Systems

Pankiraj, JemaSharin; Yassine, Abdulsalam; Choudhury, Salimur

doi:10.1109/isncc49221.2020.9297278

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Ptl is the total losses of the proposed converter which can be obtained through pulsing semiconductor loss Psl, transformer loss Ptrl, and heat loss on passive components Ppal shown in (12).…”

Section: Figure 5 Five Power Transmission Directions Of the Designed ...mentioning

confidence: 99%

“…Users can also realize P2P trading through the multi-port bidirectional energy router because of its characteristic that power can be transmitted in two directions. In current research, P2P trading means that users can connect and interact directly to exchange data or information, as opposed to classical "server-client" models with unidirectional data transfer that clients request from a central server [12] shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

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Energy Router for Peer-to-Peer Trading under Emergency with Energy Traceable Functionality

Nie

Zhang²,

et al. 2024

IEEE Access

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Multi-port bidirectional energy router is an important infrastructure for peer-to-peer (P2P) energy trading after power supply interruption. The state-of-the-art multi-port bidirectional energy router lacks the traceability function of an energy source. A triple active bridge (TAB) converter with partial power processing (PPP) progress is employed as multi-port bidirectional energy router. This proposed multi-port bidirectional energy router integrates the energy conversion and signal transmission in one converter, which supports peer-to-peer trading. In detail, the controlling approach of power transmission and the phase perturbation modulation (PPM) method of signal transmission, which realize the energy traceable functionality, are also illustrated in this paper. Finally, Simulation is performed and presented as proof of the feasibility of the designed method.

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Section: Figure 5 Five Power Transmission Directions Of the Designed ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Router for Peer-to-Peer Trading under Emergency with Energy Traceable Functionality

Nie

Zhang²,

et al. 2024

IEEE Access

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Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Yavuz,

Kivanç

2024

IEEE Access

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The concept of Prosumer has enabled consumers to actively participate in Peer-to-Peer (P2P) energy trading, particularly as Renewable Energy Source (RES)s and Electric Vehicle (EV)s have become more accessible and cost-effective. In addition to the P2P energy trading, prosumers benefit from the relatively high energy capacity of EVs through the integration of Vehicle-to-X (V2X) technologies, such as Vehicle-to-Home (V2H), Vehicle-to-Load (V2L), and Vehicle-to-Grid (V2G). Optimization of an Energy Management System (EMS) is required to allocate the required energy efficiently within the cluster, due to the complex pricing and energy exchange mechanism of P2P energy trading and multiple EVs with V2X technologies. In this paper, Deep Reinforcement Learning (DRL) based EMS optimization method is proposed to optimize the pricing and energy exchanging mechanisms of the P2P energy trading without affecting the comfort of prosumers. The proposed EMS is applied to a small-scale cluster-based environment, including multiple (6) prosumers, P2P energy trading with novel hybrid pricing and energy exchanging mechanisms, and V2X technologies (V2H, V2L, and V2G) to reduce the overall energy costs and increase the Self-Sufficiency Ratio (SSR)s. Multi Double Deep Q-Network (DDQN) agents based DRL algorithm is implemented and the environment is formulated as a Markov Decision Process (MDP) to optimize the decision-making process. Numerical results show that the proposed EMS reduces the overall energy costs by 19.18%, increases the SSRs by 9.39%, and achieves an overall 65.87% SSR. Additionally, numerical results indicates that model-free DRL, such as DDQN agent based Deep Q-Network (DQN) Reinforcement Learning (RL) algorithm, promise to eliminate the energy management complexities with multiple uncertainties.INDEX TERMS Energy management system, peer-to-peer energy trading, vehicle-to-home, multi-agent reinforcement learning, deep reinforcement learning, smart grids.

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Enhancing Peer-to-Peer Energy Trading Efficiency and Fairness Through a Hybrid Auction Mechanism in Decentralized Smart Grids

Arora,

M V,

Sharma

et al. 2023

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

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Incentive-based Peer-to-Peer Distributed Energy Trading in Smart Grid Systems

Cited by 3 publications

References 21 publications

Energy Router for Peer-to-Peer Trading under Emergency with Energy Traceable Functionality

Energy Router for Peer-to-Peer Trading under Emergency with Energy Traceable Functionality

Optimization of a Cluster-Based Energy Management System Using Deep Reinforcement Learning Without Affecting Prosumer Comfort: V2X Technologies and Peer-to-Peer Energy Trading

Enhancing Peer-to-Peer Energy Trading Efficiency and Fairness Through a Hybrid Auction Mechanism in Decentralized Smart Grids

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