Peer-to-Peer Energy Trading in Smart Energy Communities: A Lyapunov-Based Energy Control and Trading System

Zhu, Hailing; Ouahada, Khmaies; Abu‐Mahfouz, Adnan M.

doi:10.1109/access.2022.3167828

Cited by 28 publications

(15 citation statements)

References 40 publications

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“…Similar results are obtained by [46] through its study on the problem of a smart community, composed of a group of grid-connected prosumers with controllable loads, renewable generations, and energy storage systems. It demonstrates that the energy exchange in the proposed scalable energy trading system results in considerable increases in energy cost reductions and renewable energy usage efficiency.…”

Section: B Existing Solutions For Building Energy Managementsupporting

confidence: 78%

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

et al. 2023

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The emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the development of a methodology for the energy management, combining photovoltaics and storage systems, considering as the main case study a multi-story building characterized by a high density of households, used to generate data which allow feasibility foresights. The physical model of the algorithm is composed by two main elements: the photovoltaics modules and the battery energy storage system. In addition, to gain information about the real-time consumption a machine learning module is included in our approach to generate predictions about the near future demand. The benefits provided by the method are evaluated with an economic analysis, which computes the return of the investment using the real consumptions of a Boarding School, located in Turin (Italy). The case study analyzed in this article showed an increase in purchased energy at the minimum price from 25% to 91% and a 55% reduction in the electricity bill compared to most solutions on the market, with no additional costs and a stabilizing effect on the grid. Finally, the economic analysis shows that the proposed method is a profitable investment, with a breakeven point of thirteen years, due to the very simple implementation and the zero additional cost requested.INDEX TERMS Deep learning, energy management systems, energy storage, environmental economics, renewable energy sources. FRANCESCO GRIMACCIA (Senior Member, IEEE) received the M.S. and Ph.D. degrees (cum laude) in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2003 and 2007, respectively. He is currently an Associate Professor with the Energy Department, Politecnico di Milano. His main research interest includes softcomputing technique development and applications in different electric engineering fields, such as unmanned systems, photovoltaics, and other energy-harvesting devices. He is a member of the IEEE Computational Intelligence Society and the Vice-President of the Associazione Italiana di Elettrotecnica, Elettronica and the Automazione, Informatica e Telecomunicazioni, Milan Section.

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Section: B Existing Solutions For Building Energy Managementsupporting

confidence: 78%

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

et al. 2023

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“…Blockchain-based Smart grid management Pop et al, 2018;Alladi et al, 2019;Cioara et al, 2020;Badra and Borghol, 2021;Chikezie et al, 2021;Samy et al, 2021 Peer-to-peer energy trading Lee et al, 2019;Pee et al, 2019;Seven et al, 2020;Thakur and Breslin, 2020;Chikezie et al, 2021;Niloy et al, 2021 IoT-based Trading in Industrial IoT Lin et al, 2017;Hou et al, 2019;Li et al, 2020b;Zhu et al, 2022 Trading in general IoT Ku et al, 2017;Verma and Prakash, 2019;Nguyen et al, 2020Nguyen et al, , 2021Salvi et al, 2020;Kaushik and Bagga, 2021;Nadeem et al, 2021 Blockchain and IoT based Utilizing both technologies Sanju et al, 2018;Hou et al, 2019;Baig et al, 2020;Li et al, 2020b Implementation platforms Ethereum Sanju et al, 2018;Lee et al, 2019;Baig et al, 2020;Li et al, 2020b;Seven et al, 2020;Chikezie et al, 2021 Hyperledger fabric Sanju et al, 2018;Ali et al, 2020b;Syed et al, 2020Smart, Micro, Mini Grid Ku et al, 2017Pop et al, 2018;Cioara et al, 2020;Salvi et al, 2020;…”

Section: Recent Researchmentioning

confidence: 99%

A survey on peer-to-peer energy trading for local communities: Challenges, applications, and enabling technologies

Nadeem

2022

Front. Comput. Sci.

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Electricity is still a highly demanding resource in developing countries, especifically in rural areas. Local communities in developing countries are now focusing on generating their own electricity using solar and trading the energy which is in access to the supplier. Energy trading at the peer-to-peer level is a novel paradigm. It allows customers to contribute energy to the grid and power generation and supply companies. In this paper, we investigate the energy trading enabling technologies, their applications, challenges, and benefits to the local communities from various aspects. We analyse how blockchain and IoT technologies can help energy trading at the customer level from recent research proposals. We also present our analysis of recent research on the implementation of energy trading technologies and future directions.

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“…However, the economic benefits of individual nanogrids are not considered. A joint energy control and trading system is developed for smart communities in [33], where a Lyapunov-based online energy control and trading algorithm is combined with a double auction mechanism, assuming transmission losses due to energy exchange are negligible within energy communities in close proximity. The proposed double auction mechanism clears the energy trading market at the equilibrium price where the quantity demanded equals the quantity offered.…”

Section: Related Workmentioning

confidence: 99%

Transmission Loss-Aware Peer-to-Peer Energy Trading in Networked Microgrids

2022

Self Cite

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Networked microgrids (MGs) have a great potential to improve the efficiency, reliability, resilience, security, and sustainability of power supply services. Peer-to-peer (P2P) energy trading built on a smart information system in networked MGs is an emerging economic approach to facilitate energy sharing among networked MGs to achieve mutual cost-effective operation and improve the reliability and stability of energy supply service. Such a distributed and competitive energy trading market urges the need for an efficient energy trading strategy that incentivizes the self-interested MGs with various energy production and consumption profiles to participate in energy trading. In this paper, we propose a distributed real-time P2P energy trading strategy that integrates energy trading into energy management and enables the MGs with renewable energy sources (RESs) and energy storage systems (ESSs) to manage their storage scheduling, energy supply, and energy trading in a dynamic manner, jointly considering the randomness of renewable energy generation and load demand, operational constraints of ESSs and transmission losses associated with energy exchange. The proposed energy control and bidding algorithm allows each MG to dynamically and independently determine its energy control actions and price-quantity bids/offers, while the proposed trading pair matching algorithm matches the MGs on a many-to-many basis with respect to their individual payoffs, which couple price-quantity bids/offers of the MGs with distance-dependent energy transmission losses associated with energy exchange. Numerical simulation results demonstrate that the proposed distributed energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while reducing energy transmission losses within the system.

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Peer-to-Peer Energy Trading in Smart Energy Communities: A Lyapunov-Based Energy Control and Trading System

Cited by 28 publications

References 40 publications

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

An Efficient Artificial Intelligence Energy Management System for Urban Building Integrating Photovoltaic and Storage

A survey on peer-to-peer energy trading for local communities: Challenges, applications, and enabling technologies

Transmission Loss-Aware Peer-to-Peer Energy Trading in Networked Microgrids

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