The accurate quantification of grid carbon emission factors (CEFs) is foundational for robust carbon accounting, effective climate policy, and credible corporate sustainability reporting. Traditional national-average CEFs are fundamentally inadequate, perpetuating the “copper plate” fallacy by ignoring profound spatial and temporal heterogeneity within interconnected power systems. This review critically evaluates the emerging paradigm of “grid hierarchical and zonal division” as a necessary response to this challenge. We systematically analyze the limitations of existing methodologies, highlighting the significant gap between top-down administrative calculations and bottom-up physical flow tracing. As our central contribution, we propose an integrated three-layer framework that synthesizes established but previously siloed concepts, physical flow modeling, policy boundary definition, and data architecture, into a unified structure. The novelty lies in their explicit integration and the modeling of interdependencies across layers: a Physical Flow Layer (‘engine’), a Policy Boundary Layer (‘rulebook’), and a Data and Calculation Layer (‘nervous system’). Our comparative analysis demonstrates that no single methodology is universally superior; a strategic, hybrid application across the hierarchy is essential. A structured case-based analysis applying the framework to China’s West-East Electricity Transfer corridor demonstrates its practical utility, with illustrative estimates from published comparative analyses suggesting that different accounting choices for cross-border electricity can result in differences of 15%–30% in an importing region’s reported Scope 2 emissions. The review identifies critical challenges data transparency, treatment of electricity imports, and lack of standardization, and proposes actionable pathways. Future research frontiers include dynamic real-time CEFs, artificial intelligence for forecasting and zoning, and blockchain for data integrity. This framework provides an essential blueprint for next-generation grid CEFs indispensable for guiding a precise and efficient energy transition.
A multi-agent transaction optimization decision-making method based on blockchain Berge-NS equilibrium is proposed, aiming to protect user interests and achieve carbon reduction objectives. Firstly, by improving the utility function of electricity users and quantifying the impact of blockchain technology on the electricity utility of market entities, a blockchain based P2P electricity trading architecture for microgrids is constructed, and a blockchain network and utility function for electricity users are designed; Secondly, the Evolutionary Game Theory based on bounded rationality decision-making is introduced to construct a Berge-NS game model on both sides of electricity supply and demand. The distributed iterative algorithm and step size control method are used to solve the Nash equilibrium, and the strategy evolution of demand side subjects in the game process is studied through dynamic processes; Finally, numerical simulations were conducted to analyze the trading strategies of bilateral contract markets, centralized trading markets, and dual layer decision-making models for electricity sellers, verifying the feasibility and effectiveness of the models and algorithms. The experimental results show that the proposed multi-agent trading Berge-NS decision-making method for microgrid electricity market exhibits certain performance advantages in reducing carbon emissions, lowering user electricity costs, and improving user satisfaction.
Stephen Oko Gyan Torto, Rupendra Kumar Pachauri, Jai Govind Singh, Shubham Tiwari · 7 authors
Global projects are mobilizing technologies to fight power generation curtailment and smooth demand by exploiting excess energy via transactive energy management and control. Sharing and transferring energy between microgrids helps manufacturers and businesses create energy autonomously. The transition to Multi-Vector Multi-Agent Energy Systems (MMV-ES) demands a paradigm shift from traditional centralized control to decentralized, market-based coordination. Transactive Energy Management (TEM) has emerged as a key enabler in this context, supporting local flexibility, peer-to-peer (P2P) trading, and integrated energy vectors across distributed assets. This review systematically decomposes and classifies the existing state of TEM from several perspectives: the market topology, the interaction of the agent, game-theoretic models and the real deployment challenges. Moreover, two game-theory formulations (cooperative and non-cooperative) were given special attention and a detailed comparison between Shapley value and Nucleolus was provided as approaches for fair cost allocation. To enhance the adaptability of the market and the overall efficiency of the system, we introduce the Transactive Energy Reformulation Model (TE-RM), a hybrid model combining AI-powered congestion pricing with coalition formation and fairness-based incentives. The comparative tables in this paper summarize TEM and TE-RM's strengths and weaknesses and compare it to the centralized and conventional DSM methodologies. Lastly, key research gaps including scalability, regulatory fit, and AI model interpretability are reviewed, and future directions are proposed for the integration of future advanced technologies (e.g., reinforcement learning, blockchain, IoT) to enable stable, fair and interoperable energy markets.
The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends.