综合智慧能源2026，Vol.48Issue(9)：68-83,16.DOI:10.3969/j.issn.2097-0706.2026.09.006
贝叶斯声誉与扩散策略势博弈驱动的跨园区算力-电力双边匹配
Cross-park computing-electricity bilateral matching driven by Bayesian reputation and diffusion-policy potential game
摘要
Abstract
Cross-region computing-electricity trading among autonomous parks under China's"Eastern Data,Western Computing"initiative is obstructed by many coupled difficulties.A cross-park trading organization mechanism is demanded that endogenizes trust evolution,ensures matching stability,and efficiently solves the induced networked game.A cross-park computing-electricity bilateral matching market was established in which the sellers'fulfillment reliability was characterized as an unobservable latent variable coupled with their green endowment,and a composite transaction utility spanning price,latency,carbon intensity,and reputation was defined.A Bayesian reputation evolution mechanism was designed,where sellers'reputations were updated by Beta-posterior recursion,and a memory retention factor was introduced to achieve the exponential decay of historical evidence.It was proved that the reputation score converged almost surely to the actual fulfillment capability,and the posterior variance contracted at a rate inversely proportional to the effective sample size.Based on this,a reputation-weighted Gale-Shapley deferred-acceptance bilateral matching auction was constructed,proving that the matching outcome had no blocking pairs.The sellers'pricing-aggressiveness game over the interconnection network was modeled as a weighted potential game,which was solved by Fermi dynamics with adaptive selection intensity.The potential function was proved to increase monotonically along the evolutionary trajectory and converge to a network Nash equilibrium.A graph neural network(GNN)-based potential game solver and a diffusion-policy generative decision-making method were proposed.The results showed that,in an 8-buyer/6-seller,24-period cross-region case,the mean steady-state estimation error of the reputation mechanism was 0.102,and the error ranged from 0.071 to 0.081 for the parks with sufficient trading frequency.This indicated that the mechanism reliably recovered sellers'true trustworthiness.Compared with the price-only matching baseline,social welfare rose from 0.410 to 0.756(+84.4%)and the average carbon intensity fell from 0.303 to 0.272 t/(PFlops·h)(-10.5%),whereas the clearing price shifted only from 517 to 522 yuan/(PFlops·h).It indicated that the gain was mainly attributed to accurate identification of high-quality,low-carbon sellers rather than from price increases.Adding the networked potential game lifted welfare further to 0.880,with its performance envelope over the five normalized dimensions—social welfare,total emission reduction,manipulation resistance,matching fairness,and latency satisfaction—fully enclosing those of the price-only matching,latency-aware,and reputation-only configurations,thereby achieving a Pareto improvement over the price-only matching baseline.The potential function rose monotonically from 341.9 to 421.0 under Fermi dynamics.The GNN solver reached 95%convergence within about 10 iterations,realizing about 50%convergence acceleration compared with the pointwise Fermi iteration requiring approximately 20 iterations.As the number of parks increased from 6 to 96,its solution time scaled with a log-log slope of about 1.15(nearly linear)versus about 1.9(nearly quadratic)for Fermi,and the difference in solution time widened to more than one order of magnitude.The diffusion policy produced a decision distribution standard deviation of 0.317,substantially higher than the 0.020 obtained by the deterministic policy,exhibiting a pronounced bimodal structure.In terms of robustness,as the share of maliciously misreporting sellers increased from 0 to 50%,the social welfare achieved by the proposed mechanism decreased from 0.822 to 0.741,representing only a 9.8%reduction.In contrast,the naive mean reputation method experienced a decrease from 0.791 to 0.358,corresponding to a 54.8%reduction.As the renewable energy penetration rate of sellers increased from 30%to 90%,the reduction in carbon intensity per unit of computing power achieved by the proposed mechanism increased monotonically from 9.7%to 17.3%.Endogenizing Bayesian trust within matching,incorporating strategic coupling into a potential game framework,and accelerating equilibrium solving through graph learning and generative decision-making jointly improve the social welfare,low-carbon performance,and manipulation resistance of cross-park computing-electricity trading without raising clearing prices.Moreover,the carbon reduction value of the mechanism is amplified as the power generation mixes become cleaner,and its solution advantages become more prominent as markets grow larger,forming a synergistic reinforcement with the evolution of the power generation mixes under the"dual carbon"goals.The proposed mechanism complements the three-level Stackelberg-evolution-alternating direction method of multipliers coordination mechanism for a single park in terms of research objects,mechanism design,and solution methods,together forming a complete methodological framework for computing-electricity coordination.关键词
跨园区算力-电力交易/双边匹配/贝叶斯声誉/势博弈/扩散策略/图神经网络/算电协同Key words
cross-park computing-electricity trading/bilateral matching/Bayesian reputation/potential game/diffusion policy/graph neural network/computing-electricity coordination分类
能源科技引用本文复制引用
程乐峰,余风,孙润宝,邹涛..贝叶斯声誉与扩散策略势博弈驱动的跨园区算力-电力双边匹配[J].综合智慧能源,2026,48(9):68-83,16.基金项目
国家自然科学基金项目(52171331) （52171331）
广东省普通高校创新团队项目(自然科学)(2024KCXTD031) （自然科学）
广州市教育局高校科研项目(2024312278) National Natural Science Foundation of China(52171331) （2024312278）
Innovation Team Project for General Universities in Guangdong Province(2024KCXTD031) （2024KCXTD031）
University Research Project of Guangzhou Education Bureau(2024312278) （2024312278）
