Information interaction and optimal scheduling method for prosumers in an energy community considering the profit driven degree

Sep 29, 2026

Upload by Damian

Driven by energy internet evolution and distributed generation growth, energy users increasingly act as prosumers with both production and consumption capabilities. The paper proposes a prosumer trade model that incorporates a profit driven degree, quantified via Weber-Fechner’s law to capture real-time price sensitivity and demand urgency. Information exchange among neighboring prosumers enables a distributed convergence optimization for consistent transaction power, and an optimal scheduling model minimizes total operating costs. Simulations on an IEEE-33 node system with 19 prosumers show a 17.1% community cost reduction and up to 41.2% fewer grid purchases, with ADMM convergence in about 10 iterations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Literature review

Trading strategies and behavioral models for prosumers

Information interaction mechanisms among prosumers

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Information interaction and optimal scheduling method for prosumers in an energy community considering the profit driven degree Hong Zhang1, Haiyun Dong1, Guanmu Wang1, Daoxing Sun1, Juan Lou1 & Gang Li2􀀍 Driven by the evolution of the energy internet, distributed energy growth, and power system modernization, consumers are transforming into prosumers with both production and consumption capabilities. This paper develops a prosumer trade model incorporating profit driven degree. A profit driven mechanism, based on Weber-Fechner’s law, measures how trading decisions respond to real-time electricity price sensitivity and demand urgency. Through information exchange among neighboring prosumers, a distributed convergence optimization method is applied to rapidly determine consistent transaction power. An optimal scheduling model is built to minimize operating costs for all prosumers, solved via the alternating direction multiplier method. Using the IEEE-33 node distribution network with 19 prosumers, simulations verify the model’s effectiveness. Results show a 17.1% reduction in total community cost and up to 41.2% reduction in main grid purchases when profit driven behavior and information interaction are considered. The synchronous ADMM algorithm converges in about 10 iterations, demonstrating the model’s economic efficiency, enhanced clean energy utilization, and computational suitability for engineering applications. Keywords Energy community, Prosumer, Profit driven degree, Information interaction, Distributed consensus-based optimal scheduling With the deep integration of the Internet of Things and the energy grid1, traditional electricity users are transforming into prosumers with dual attributes of energy production and consumption. Such users can install distributed power generation equipment to generate electricity independently, and excess electricity can be traded with each other or fed back to the grid2,3. As a new type of energy management platform, the energy community allows prosumers to interact with energy and information through local power networks and communication networks4. This model not only improves energy efficiency5 but also provides more economic benefits for prosumers6. How to optimize the energy trading strategy between prosumers to reduce the overall cost of the community and improve the consumption capacity of distributed energy has become an important issue in current research. Literature review Regarding trading strategies of prosumers in energy communities, prior studies have explored prosumer trading patterns and behaviors from multiple perspectives. Sabzevari et al.7 developed a “data purification + multialgorithm forecasting” framework to counter renewable data tampering, quantifying prediction accuracy across methods to enhance market decision resilience. Chong et al.8 proposed a cooperative game model based on the Shapley value, innovatively applied to end-to-end power trading to ensure equitable benefit allocation. Guan et al.9 designed a P2P trading mechanism in regional electricity markets with multiple prosumers, considering heterogeneous energy demands and low-carbon preferences. Considering prosumers’ psychological gap effects and bounded rationality, a distributed electricity market trading mechanism based on a Stackelberg game was constructed10. Bâra et al.11 embedded a decision-support method within an energy assistant to facilitate local 1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Jilin 132012, China. 2School of Mechanical and Control Engineering, Baicheng Normal University, Baicheng 137000, China. 􀀍 email: [ligang@bcnu.edu.cn](ligang@bcnu.edu.cn) [www. nature.com/scientificreports/](www. nature.com/scientificreports/) market trading. Liu et al.12 proposed a dynamic game model for photovoltaic pr

FAQs

What problem does the paper address in energy communities with prosumers?

It addresses how to optimize prosumer energy trading to reduce the community’s overall cost and improve distributed energy utilization.

How is the profit driven degree defined and used?

The profit driven degree is quantified using Weber-Fechner’s law to measure how trading decisions respond to real-time electricity price sensitivity and demand urgency.

What method and network setup are used to evaluate the scheduling approach?

The paper builds an optimal scheduling model solved via alternating direction multiplier method, and verifies it using the IEEE-33 node distribution network with 19 prosumers.

Sep 29, 2026

Upload by Damian