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Evolutionary smart contracts for virtual power plant trading: integrating prospect theory and multi-stage negotiation in cross-regional energy markets.

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Title: Evolutionary smart contracts for virtual power plant trading: integrating prospect theory and multi-stage negotiation in cross-regional energy markets.
Authors: Cheng, Lefeng1 (AUTHOR) chenglefeng@gzhu.edu.cn, Zhang, Mengya1 (AUTHOR) zhangmengya@e.gzhu.edu.cn, Wang, Kun1,2 (AUTHOR) wangkun@gzhu.edu.cn, Yuan, Minmin1,3 (AUTHOR) mm.yuan@rioh.cn, Liu, Zhiqiang3 (AUTHOR) zq.liu@rioh.cn, Wang, Jie4 (AUTHOR) wangjie@gzhu.edu.cn, Zhang, Kuozhen5 (AUTHOR) kzhzhang@stu.edu.cn, Huang, Pengrong1 (AUTHOR) hpr@e.gzhu.edu.cn
Source: International Journal of Electrical Power & Energy Systems. Dec2025, Vol. 173, pN.PAG-N.PAG. 1p.
Subjects: Prospect theory, Negotiation, Behavioral economics, Distributed power generation, Energy industries, Contract management, Clean energy investment
Abstract: • Integrates prospect theory with evolutionary smart contracts for adaptive VPP trading. • Achieves 15–25% efficiency gains through behaviorally-informed negotiation protocols. • Validates 72-hour contract adaptation windows responding to market evolution. • Demonstrates behavioral heterogeneity with loss aversion varying 2.1–3.4 across VPPs. • Overcomes cross-regional barriers through cultural adaptation mechanisms. Virtual Power Plant (VPP) trading mechanisms confront unprecedented challenges from behavioral complexities and technological uncertainties that conventional rational choice models inadequately address. This research develops an integrated framework combining prospect theory-driven decision modeling with evolutionary smart contracts and multi-stage negotiation protocols to enhance trading effectiveness in cross-regional energy markets. We establish mathematical foundations incorporating loss aversion, probability distortion, and reference-dependent preferences into VPP decision-making, while developing adaptive contracts capable of autonomous evolution responding to market changes. Through composite game-theoretic analysis examining nested interactions between contract evolution and negotiation dynamics, we validate the framework across three comprehensive scenarios: emergency dispatch under extreme weather, renewable energy integration, and cross-regional collaboration. Simulation results demonstrate 15–25% negotiation efficiency improvements compared to traditional mechanisms, with behavioral models capturing significant heterogeneity in loss aversion coefficients (2.1–3.4) across VPP configurations. The evolutionary contracts successfully adapt within 72-hour windows to policy changes and technological developments, while maintaining system stability. Cross-regional analysis reveals how cultural distance and information asymmetries influence trading outcomes, with the framework achieving superior market integration despite these barriers. These findings establish new paradigms for behaviorally-informed energy market design, offering transformative implications for renewable integration and decentralized electricity systems. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• Integrates prospect theory with evolutionary smart contracts for adaptive VPP trading. • Achieves 15–25% efficiency gains through behaviorally-informed negotiation protocols. • Validates 72-hour contract adaptation windows responding to market evolution. • Demonstrates behavioral heterogeneity with loss aversion varying 2.1–3.4 across VPPs. • Overcomes cross-regional barriers through cultural adaptation mechanisms. Virtual Power Plant (VPP) trading mechanisms confront unprecedented challenges from behavioral complexities and technological uncertainties that conventional rational choice models inadequately address. This research develops an integrated framework combining prospect theory-driven decision modeling with evolutionary smart contracts and multi-stage negotiation protocols to enhance trading effectiveness in cross-regional energy markets. We establish mathematical foundations incorporating loss aversion, probability distortion, and reference-dependent preferences into VPP decision-making, while developing adaptive contracts capable of autonomous evolution responding to market changes. Through composite game-theoretic analysis examining nested interactions between contract evolution and negotiation dynamics, we validate the framework across three comprehensive scenarios: emergency dispatch under extreme weather, renewable energy integration, and cross-regional collaboration. Simulation results demonstrate 15–25% negotiation efficiency improvements compared to traditional mechanisms, with behavioral models capturing significant heterogeneity in loss aversion coefficients (2.1–3.4) across VPP configurations. The evolutionary contracts successfully adapt within 72-hour windows to policy changes and technological developments, while maintaining system stability. Cross-regional analysis reveals how cultural distance and information asymmetries influence trading outcomes, with the framework achieving superior market integration despite these barriers. These findings establish new paradigms for behaviorally-informed energy market design, offering transformative implications for renewable integration and decentralized electricity systems. [ABSTRACT FROM AUTHOR]
ISSN:01420615
DOI:10.1016/j.ijepes.2025.111453