138 results about "Carbon price" patented technology

The invention discloses an intelligent building energy-saving optimization platform and method based on carbon footprint tracking, and relates to the technical field of building energy saving and carbon emission management. The method is used for solving the problems of extensive carbon emission evaluation, rigid quota distribution and insufficient energy-carbon collaboration. A three-dimensional carbon density map is constructed by collecting people flow, equipment energy consumption and environment data in real time, and carbon emission hotspots are dynamically identified. And analyzing the association between the power grid and the renewable energy source through a carbon flow tracking model, and correcting a weight output contribution matrix. The characteristics of equipment energy efficiency, building material hidden carbon emission and the like are fused to construct a carbon emission gene entropy, a quota migration strategy is generated in combination with a game algorithm, and oriented transfer from high carbon to low carbon buildings is promoted. A double-ring collaborative framework is constructed, an inner ring chaos search optimization device starts and stops to suppress carbon density fluctuation, an outer ring carbon price mapping adjusts energy storage scheduling, accurate carbon emission tracing, quota dynamic allocation and energy-carbon deep collaboration are achieved, building low-carbon transformation is supported, and the building cluster carbon emission reduction efficiency is improved.

The invention provides a carbon emission digital management system based on a cloud platform. The carbon emission digital management system comprises a data acquisition layer, a hardware resource layer, a data management layer, a platform algorithm layer, an application layer and a display layer, according to the system, through multi-source data acquisition and data analysis based on an AI large model, artificial filling errors are avoided, meanwhile, carbon emission factors are dynamically adjusted through introduction of machine learning and automatic calculation, and the carbon emission accounting precision is comprehensively improved; secondly, the system is combined with a digital twinning technology to help enterprises to realize multi-level carbon emission trend analysis of groups, enterprises, processes, equipment and the like, comprehensive strategy optimization is carried out through multi-level data arrangement, and wider application services are provided; besides, the system is in butt joint with international standards such as the international carbon market and CBAM, can help enterprises to monitor carbon price fluctuation and predict in real time, intelligently adjust carbon tax and provide a compliant carbon transaction scheme, and helps the enterprises to reduce cost, thereby realizing comprehensive upgrading of carbon management from static statistics to intelligent prediction and optimization.

The invention relates to the field of energy dispatching, and particularly provides an intelligent energy station dispatching method and device based on artificial intelligence, a terminal and a medium, and the method comprises the steps: obtaining multi-source data of an energy station, and generating a short-term load prediction result and a medium-and-long-term load prediction result; a multi-objective optimization function is constructed with the minimum energy consumption, the minimum operation cost and the minimum carbon emission, a Pareto optimal solution set is generated through an NSGA-II algorithm, a final operation plan is selected as a scheduling scheme according to the real-time electricity price and carbon price weight, the scheduling scheme is disassembled into executable instructions, and the executable instructions are issued to a field control unit after safety certification; making a maintenance plan according to the medium-term load prediction result, and making expansion and reconstruction according to the long-term load prediction result; and continuously training and optimizing each model. According to the method, multi-source data is utilized, multi-time-scale accurate prediction is realized, multi-target collaborative optimization is carried out, and a scheduling plan is ensured to be safe and reliable.

The invention relates to the technical field of virtual power plant collaborative optimization, and discloses a virtual power plant multi-target carbon economy optimization method based on swarm intelligence, which comprises the following steps: S1, constructing a digital twinborn body of a virtual power plant, and synchronizing distributed energy output, load demand and carbon emission data of a physical VPP (virtual power plant) in real time; s2, establishing an energy-carbon economy dual-objective optimization model; s3, solving the dual-objective optimization model through an improved swarm intelligence algorithm, wherein the algorithm dynamically adjusts the weight of the economical efficiency and the weight of the carbon emission objective; s4, real-time data based on the digital twinborn body; and S5, outputting a Pareto optimal solution set. The total operation cost and the full life cycle carbon emission in the operation cycle of the virtual power plant are taken as double optimization targets, the target weight is adjusted through the dynamic carbon price sensitivity coefficient, carbon market signals are coupled, the decision-making deviation of high implicit carbon and operation pseudo-low carbon of equipment is avoided, and the multi-target decision-making scientificity of the virtual power plant under the carbon constraint is improved.

The invention discloses a low-carbon path real-time planning method and system based on multi-source data fusion and reinforcement learning. According to the invention, through a dynamic weight self-adjustment mechanism, the real-time adaptation capability of low-carbon path planning is significantly improved. Meanwhile, scenes such as traffic flow abrupt change, carbon price abnormity and energy station high load are recognized in real time through high-frequency scanning of multi-source fusion data, weights are dynamically adjusted through a formula, and planning can quickly respond to emergencies. For example, energy weight is reduced during charging pile queuing, emission constraint is enhanced when carbon price rises, planning lag caused by weight fixing in a traditional method is avoided, path selection better fits current environment changes, the problems of congestion and energy supplement shortage can be solved more timely in actual use, emission, time and cost are more accurately balanced under low-carbon constraint, and the method is suitable for popularization and application. In practical application, carbon emission can be effectively reduced, and user time and energy expenditure are saved.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

The invention relates to an integrated energy system optimization method considering a carbon emission reward and punishment mechanism and shared electrochemical energy storage, and the method comprises the following steps: carrying out the modeling of an integrated energy system which comprises energy supply, energy conversion, load and shared electrochemical energy storage; constructing a peak-flat-valley period stepped carbon emission mechanism considering the carbon emission difference of different energies and a dynamic carbon price factor, and determining a corresponding carbon transaction cost / reward according to the total carbon emission of the integrated energy system in a scheduling period; based on a capacity leasing and power service mechanism, modeling is carried out on the cost of the shared electrochemical energy storage mode; an optimization scheduling problem is constructed by taking system operation cost minimization and clean energy utilization rate improvement as targets; and solving the optimization scheduling problem to obtain an optimal output scheme and an energy storage charging and discharging strategy of each energy device in the integrated energy system, thereby realizing optimization of the integrated energy system.

The invention provides a low-carbon integrated energy system modeling method based on a hybrid energy storage system. And constructing a multi-dimensional constraint and cost optimization objective function, and establishing a global optimization framework of the integrated energy system. Wherein the balance constraint and the assembly constraint ensure dynamic matching of electric / hot / cold / gas multi-energy flow and cooperative operation of equipment, and the problem of unbalanced supply and demand of a traditional system is solved. The carbon emission constraint is divided through a dynamic carbon valence interval, the carbon emission and the carbon cost are in nonlinear correlation, a low-carbon technology path is guided, the green certificate constraint converts excess renewable energy power generation into economic benefits, and dual excitation of environmental protection and economy is formed. And the target function comprehensively optimizes equipment investment, maintenance cost and market income, and drives multi-energy complementation and full life cycle cost optimization. The system is adaptive to renewable energy fluctuation, load change and energy price fluctuation through optimal configuration of system output and equipment capacity, and economical and efficient operation under the low-carbon target is achieved.

The invention relates to the technical field of power system resource optimization scheduling, in particular to a deep scheduling method and system for deep peak regulation of a coal-fired unit, and the method comprises the steps: collecting multi-dimensional operation parameters of the unit, a deep peak regulation instruction and a carbon price signal of a carbon emission permit trading market, and achieving timestamp alignment; constructing a five-dimensional security constraint model associated with the economic cost boundary; taking peak regulation income maximization as a target, combining fire coal purchase cost, carbon transaction cost and equipment maintenance cost to construct a target function, adopting a dynamic planning algorithm to solve an economic dispatching scheme, and executing a cross-system cooperation strategy; and on the basis of LSTM-based equipment life prediction and digital twin simulation, triggering a cross-system correction strategy. According to the method, the commercial cooperation problem of economy, environmental protection and equipment reliability in deep peak regulation is solved, and the peak regulation income maximization of the coal-fired unit in an electricity market environment is realized.

The invention belongs to the technical field of power systems, and particularly discloses a micro-grid electricity-carbon joint transaction method and system and a medium, and the method comprises the steps: constructing a regional interconnection micro-grid system model; constructing a local electricity-carbon combined market mechanism, carrying out order matching by adopting a multi-round bilateral auction mode, determining a transaction result in combination with an electricity price and carbon price combined bidding rule, and carrying out trend constraint verification; the method comprises the following steps: constructing a multi-agent Markov decision process model based on a micro-grid model and a market mechanism, and setting a state space, an action space and a multi-target reward function; and a multi-agent near-end strategy optimization algorithm MAPPO-TDSA fusing task decomposition and an attention mechanism is adopted to train each agent strategy network, and settlement with an external power market and a carbon trading market is carried out according to a micro-grid trading execution result and a power carbon quota income and expenditure condition. According to the method, energy-carbon collaborative transaction and intelligent regulation and control between regional micro-grids are realized, and the method has remarkable low-carbon property, autonomy and strategy optimization capability.

The invention relates to the technical field of comprehensive energy system optimization scheduling, in particular to an energy-carbon excitation-based farm comprehensive energy system optimization scheduling method, which comprises the following steps: S1, constructing a comprehensive energy system covering an energy operator and farm users, providing electric heating energy by the operator, and supplying heat by the farm users through biogas power generation, the three types of loads are combined to construct an equipment and load model; s2, carbon potential is calculated based on a carbon emission flow theory, an energy-carbon price is formulated, and a stepped carbon transaction and segmented subsidy mechanism is adopted to stimulate a user to use low-carbon energy; s3, constructing a master-slave game model, performing upper-layer pricing excitation and lower-layer optimization response, and performing joint iterative solution by adopting a genetic algorithm and GUROBI; according to the invention, by constructing a master-slave game scheduling framework and introducing an energy-carbon joint pricing and stepped carbon excitation mechanism, collaborative optimization of system economy and a low-carbon target is realized, and user response enthusiasm and carbon emission reduction potential are effectively improved.

The invention discloses a power distribution network multi-type flexible resource low-carbon scheduling method based on carbon emission flow, and relates to the technical field of power system low-carbon scheduling, and the method comprises the steps: constructing a scheduling time period set of a discrete time domain, and building an equivalent lossless network model; power flow calculation is carried out, and a node-branch carbon flow mapping relation is established; according to the carbon emission intensity of the unit, the carbon flow rate and node carbon emission flow of each branch are calculated; performing differential approximate calculation on the node injection power to obtain a carbon marginal coefficient of each node; generating a time-varying carbon price signal of each node in combination with an external carbon price reference; constructing a weighted combination objective function based on the node time-varying carbon price signal; and solving a weighted combination objective function and constraint conditions by adopting a distributed multi-agent collaborative optimization strategy. According to the invention, collaborative scheduling of multiple types of flexible resources under a low-carbon target is realized, and the carbon emission reduction effect is improved.

The invention provides a power distribution system area energy autonomous optimization method under new energy bearing capacity constraint. The method comprises the following steps: acquiring basic data of a target power distribution system area; a Voronoi-energy flow entropy dynamic space partitioning method is adopted, autonomous units are divided, and energy flow entropy features are calculated; generating initial new energy installed capacity, energy storage capacity and bearing capacity threshold values according to the space boundary and energy flow entropy characteristics of each autonomous unit; a Stackelberg game model is adopted, a dynamic electricity price, a carbon price and a deviation reward and punishment mechanism are introduced, a distributed iteration method is combined, and an operation scheduling strategy of each autonomous unit is determined; and performing rolling optimization and scheduling adjustment according to the feedback data, and dynamically updating the space boundary, the bearing capacity threshold and the scheduling strategy of the autonomous unit. According to the method, the energy management capability of the power distribution system is effectively improved, the new energy resource configuration is optimized, the scheduling cost is reduced, and the flexibility and stability of the system are enhanced.

The invention discloses an optimal scheduling method for a regional integrated energy system, and relates to the technical field of energy optimization. Comprising the following steps: step 1, multi-source data hierarchical acquisition and preprocessing; step 2, multi-time scale uncertainty scene modeling is carried out; step 3, constructing a multi-objective optimization model; 4, intelligent algorithm solving and dynamic scheduling are carried out; 5, carrying out constraint processing and resource integration; and 6, carrying out closed-loop correction and strategy optimization. According to the regional integrated energy system optimization scheduling method, a dynamic relaxation factor mu (t) is introduced to adjust equipment climbing constraint in real time, and a virtual energy storage equivalent model is combined to integrate a transferable load, so that when the load of the system suddenly increases, the equipment power adjustment fluctuation is controlled within 10% of rated power, frequent out-of-limit of energy storage is avoided, and meanwhile, the optimal scheduling of the regional integrated energy system is realized. The grading pricing mechanism excitation system of the stepped carbon transaction model preferentially consumes new energy, and the carbon emission is greatly reduced compared with that of a traditional fixed carbon price model.

The invention provides a power plant carbon emission data full life cycle management method and device, and relates to the technical field of data processing, and the method comprises the steps: collecting production, fuel and coal quality test system multi-source data in real time through a standard interface, and forming a dynamic data flow; predicting the carbon content of the fire coal element based on a BP neural network, and calculating the carbon emission by combining an emission factor method; carrying out back calculation on the discharge amount by using a mass balance method, analyzing and verifying the relevance between data and an accounting result through multiple regression, and triggering correction when an error exceeds a threshold value; a double-stage attention deep learning network is adopted to predict the carbon price in a rolling mode, a carbon asset dynamic analysis result is generated in combination with a quota estimation model, real-time emission data, quota surplus and shortage and the carbon price trend are displayed through a visual interface, and graded early warning is conducted on abnormal data. According to the method, full-life-cycle automatic management is realized, the data acquisition real-time performance and accounting precision are improved, the data authenticity and traceability are guaranteed, and the carbon asset management decision support capability is optimized.

The invention discloses a coal-fired power plant carbon emission intelligent control method and system based on machine learning, and relates to the technical field of coal-fired power plant carbon emission, and the method comprises the four steps: building a three-level sensing network to collect carbon flow data, importing various parameters to construct a carbon flow digital twinborn body, building a real-time mapping link, and obtaining twinborn body boiler data; establishing a solid waste related mapping model, and calculating and outputting a coal blending combustion proportion through an optimal solution; predicting the carbon release amount within 5 minutes in combination with boiler data and a blending combustion ratio, constructing a carbon capture agent model, and judging whether elastic adjustment is triggered or not through an improved Sarsa algorithm game; and analyzing a quota consumption rule based on prediction data, predicting a carbon price, optimizing multiple targets and analyzing a Pareto optimal strategy. According to the method, full-chain collaborative management and control are realized, the carbon emission control precision and intelligent level are improved, environmental protection standard reaching, quota management and cost control are balanced, and low-carbon and efficient operation of a power plant is assisted.

A VCG mechanism-based comprehensive energy system optimization operation method in an electricity-gas-carbon combined market comprises the following steps: constructing a comprehensive energy system coupling model integrating electric power, natural gas, carbon cycle and green certificate transaction, maximizing social welfare through a distributed robust optimization model, processing carbon price and green certificate price uncertainty by adopting a Wasserstein fuzzy uncertainty set, and obtaining a comprehensive energy system optimization model; and calculating an external payment value of each unit by using a VCG mechanism, endogenously determining an electricity price and a gas price, and finally outputting an optimal scheduling scheme meeting multiple constraints. The method can solve the problems that an existing single market mechanism is low in resource allocation efficiency, insufficient in carbon emission reduction excitation and the like, the reliability of multi-market data association analysis is improved by mining energy conversion, carbon circulation and other associations, the robustness under data limitation is enhanced through a Wasserstein uncertainty set, strategy quotation distortion is avoided through a VCG mechanism, and the reliability of multi-market data association analysis is improved. The data analysis reliability and optimization decision accuracy of the complex energy system are remarkably improved, and support is provided for construction of a novel electric power system.

The invention discloses a multi-objective optimization decision-making method and system for sewage treatment based on OLAP reinforcement learning, and solves the problems that an existing sewage treatment control method neglects carbon emission, is extensive in cost control and does not integrate a carbon transaction mechanism. The method comprises the following steps: constructing an OLAP cube, and generating an optimal historical strategy set; establishing a state set according to the sewage environment parameters and the carbon price; an action set is established according to the optimal historical strategy set, and an action priority strategy is set according to the carbon emission weight partition; establishing a multi-target reward function dynamically adjusted according to the carbon price; constructing each controller agent model for training until the strategy converges; and dynamically outputting an optimal strategy by the intelligent agent according to the change of the carbon price. According to the method, a multi-dimensional OLAP cube is constructed to analyze the incidence relation between key variables, a plurality of process control agents are constructed, reinforcement learning collaborative optimization of working condition adjustment is achieved, the carbon emission item optimization weight is adjusted according to the real-time carbon transaction price, and dynamic policy adaptation is achieved.

The invention discloses an integrated energy system multi-time scale optimization method and system considering electric-carbon coupling. The method comprises the following steps: constructing an electric-carbon model of each device in an integrated energy system; constructing an electricity-carbon coupling price model based on the electricity price of the electric power spot market and the floating carbon price; based on the electricity-carbon model of each device and the electricity-carbon coupling price model, constructing a multi-time scale regulation and control optimization model with the goal of minimizing the total cost of the system; and solving the multi-time-scale regulation and control optimization model under the condition of meeting a constraint set comprising a power balance constraint, a reserve capacity constraint, a power-carbon coupling price constraint, an intra-day regulation constraint, a real-time deviation constraint and a power grid security constraint of each device of the system to obtain an optimization scheduling scheme. According to the invention, through deep fusion of an electricity-carbon coupling mechanism, the time-of-use electricity price signal and the multi-time scale carbon price signal of the electric power spot market cooperatively guide the optimal scheduling of the integrated energy system, and the low-carbon toughness and the operation stability of the urban integrated energy system in the electric power spot market are enhanced.

The invention relates to an interval value carbon price prediction method based on multi-source data fusion and deep learning integration, and the method comprises the following steps: obtaining the actual historical transaction data of a carbon market, collecting the maximum price and the minimum price of the daily actual transaction of the carbon market, and determining an interval value carbon price sequence; key external driving factors influencing the carbon transaction price are screened through feature importance analysis, and data alignment is conducted on the key external driving factors of different market transaction days through a cubic spline interpolation method; cEEMDAN-VMD dual decomposition is adopted, and higher-precision multi-scale characterization of an original interval value carbon price sequence is achieved; and by taking the decomposition result and the determined external key driving factor as input and the carbon price prediction interval as output, constructing a carbon price interval prediction model, and finally outputting to obtain the carbon price prediction interval containing upper and lower boundary values. The method can effectively cope with the non-stationary and non-linear characteristics of the carbon valence sequence, and is good in expansibility and adaptability.

The invention discloses an integrated energy system flexible optimization scheduling method based on a large language model. The method comprises the following steps: S1, constructing a multi-source heterogeneous knowledge base and a rule logic coding system of the comprehensive energy system; s2, training a large language model agent with a dynamic reasoning capability in the field of the integrated energy system; s3, analyzing a user input demand, and constructing an optimal scheduling scene comprising adjustable equipment, a scheduling time period and a target weight; s4, dynamically predicting the carbon price and the energy price in the scheduling time period; s5, establishing a flexible optimization scheduling model containing a target function and constraint conditions according to the optimization scheduling scene; and S6, solving the flexible optimization scheduling model, updating model cognition in real time according to user feedback and a scheduling result, and realizing adaptive optimization of a scheduling strategy. According to the invention, the flexibility and adaptability of the optimization scheduling process can be effectively improved, and flexible, efficient and low-carbon operation of the integrated energy system is facilitated.

The invention provides an electricity market transaction optimization system based on deep reinforcement learning, and the system comprises a hybrid prediction subsystem which is used for optimizing an electricity market transaction strategy through electricity price fluctuation prediction, frequency modulation demand prediction and carbon price trend prediction; the multi-agent decision-making subsystem is used for carrying out optimization generation and dynamic adjustment on an electricity market transaction strategy through a plurality of agents with specific tasks; and the risk control subsystem is used for monitoring, evaluating and controlling the transaction risk of the power market through market risk control, ontology risk control and policy risk response, and optimizing the transaction strategy of the energy storage system. According to the technical scheme, multi-target collaborative optimization and cross-time arbitrage of energy storage participating in an electric power spot market, an auxiliary service market and a carbon emission permit market are realized.

The invention, which relates to the technical field of energy management, discloses a multi-source low-temperature waste heat adaptive grid-connected optimization method based on electric-heat cooperative scheduling, comprising the following steps: establishing an economic objective function and a carbon emission objective function according to a dynamic weight coefficient, a node marginal electricity price and a real-time power generation instruction, and performing multi-objective cooperative optimization; generating a power generation equipment power set value; power generation operation is executed based on the power set value of the power generation equipment, hierarchical control instructions of a heat pump and a heat exchanger are generated, meanwhile, equipment operation state parameters are collected to construct a digital twinborn body, and an actual heat supply load value is generated through a compensation algorithm; and updating the global federated learning model parameters by using the deviation between the actual heat supply load value and the heat supply network load prediction curve. According to the method, high-dimensional space-time modeling of the nonlinear coupling relation between the economical efficiency and the carbon emission target is realized, and the technical limitation that real-time fluctuation of the electricity price and the carbon price is difficult to respond is broken through.

An intelligent optimization method for an electrical-carbon coupled virtual power plant based on meta-reinforcement learning and distributed robust optimization relates to the field of virtual power plants, and comprises the following steps: sensing and preprocessing multi-source heterogeneous data, and constructing a multi-dimensional high-quality feature matrix; constructing an electricity-carbon deep coupling model, and quantifying a stepped carbon price risk and user preference synergistic effect; performing element intelligent hierarchical decision optimization, and outputting an optimal cooperation strategy of the power-carbon market; according to the method, the correlation characteristics of dynamic fluctuation and multi-source uncertainty of the electricity and carbon market are fully considered, a stepped carbon price-risk perception model and a user implicit preference collaborative model are constructed, cross-scene dynamic bidding strategy optimization is realized through a Meta-SAC element reinforcement learning algorithm, and the method has the advantages of being high in robustness, high in robustness and high in accuracy. The output randomness of renewable energy sources is processed through Wasserstein distributed robust optimization, multi-agent distributed collaborative scheduling is completed in combination with a federal ADMM algorithm, and finally, strategy dynamic evolution and optimization targets are achieved through a closed-loop feedback mechanism.

The invention relates to the technical field of power system optimization scheduling, in particular to a virtual power plant alliance optimization scheduling method and system based on electricity-carbon certificate transaction, and the method comprises the steps: constructing a virtual power plant alliance cooperation cost minimization model considering the multi-dimensional value flow of electric energy, carbon right and green certificate; a second-class special ordered set is adopted to accurately linearize the stepped carbon price; constructing a dynamic internal collaborative green certificate conversion mechanism, defining an electric energy transaction volume, a carbon transaction volume and a green certificate transaction volume for each virtual power plant, and forming a multi-commodity internal market in which electricity, carbon and green certificates are coupled; carrying out distributed solution on the interior of the alliance by adopting an asynchronous self-adaptive alternating direction multiplier method; and fairly distributing cooperation surplus according to the contribution degree index of each alliance member based on a cooperative game theory. By constructing the multi-dimensional value flow collaborative model and the fair cost allocation mechanism, the total operation cost and carbon emission of the virtual power plant alliance can be remarkably reduced, and meanwhile, the cost allocation fairness is guaranteed to maintain the alliance stability.

The invention discloses an electric power system carbon emission conductivity evaluation method, system and device in a market environment, and a storage medium, and relates to the technical field of electric power market and carbon market cooperative operation, and the method comprises the steps: constructing a decentralized clearing model of an electric power market and a carbon market, and carrying out the first-round clearing; a combined clearing model of the electricity market and the carbon market is constructed to carry out second round clearing, and the variation of the cost of the power generation side under dispersed clearing and combined clearing is output; tracking the power generation carbon emission cost to a load side by using a carbon flow tracking theory, and obtaining the ratio of the carbon emission cost change of a user side according to the carbon price; the carbon emission conductivity of the generator to the load is output based on the ratio of the user-side carbon emission cost change. According to the method, the influence of the carbon price on the power generation cost is quantified through dispersion and joint clearing comparison, accurate distribution of the carbon cost from a power generation side to a load side is realized by using a carbon flow tracking theory, a carbon emission conductivity index is proposed, and visualization and quantification of a carbon cost conduction path are realized.

The invention discloses a group-level carbon management platform and method, and a carbon management system, and the platform comprises an accounting module which collects the carbon emission activity data of each subordinate branch company of a group-level enterprise, carries out the standardized accounting of the carbon emission activity data based on a unified carbon emission factor adaptive to the production category of the branch company, and carries out the calculation of the carbon emission activity data, generating standardized carbon emission performance data; the analysis module compares the standardized carbon emission performance data with the target decomposition value of the branch company corresponding to the standardized carbon emission performance data; if the performance deviation between the standardized carbon emission performance data and the target decomposition value of the target branch company is greater than a risk threshold value through comparison, the decision-making module performs decision-making on the basis of the performance deviation, the emission reduction capability matrix of the group-level enterprise, the carbon asset condition data, the group total quota pool state and the group internal carbon price; determining a resource allocation scheme and a dynamic target correction value under the condition that the total carbon emission of the group does not exceed the total performance target; and the feedback module issues the resource allocation scheme and the dynamic target correction value to related branch companies.

The application provides a big data-based carbon transaction market intelligent analysis system and method, relates to the field of carbon asset analysis, and is characterized in that: a blockchain+digital twin MRV credible closed loop is first created, data is unforgeable, traceable and automatically certified; a 'carbon-energy-economy' coupled multi-scale model is constructed, the carbon price prediction accuracy is improved; an all-industry-chain carbon footprint quota allocation is innovated, and an incentive mechanism is combined to stimulate emission reduction power; a federal learning collaborative supervision is first created, millisecond-level identification of violations, personalized early warning and CBAM compliance deduction are realized; a carbon asset digital twin and cross-chain transfer technology are first created, the linkage of 'carbon data-carbon assets-carbon finance' is promoted, and market liquidity is improved.

The invention relates to the technical field of low-carbon regulation and control of data centers, finally achieves the purpose of industry carbon neutralization, provides a low-carbon regulation and control method, system and equipment for load calculation of a data center and a storage medium, and solves the problems of low accuracy and low effectiveness of low-carbon regulation and control. According to the invention, the real-time computing power of each enterprise and the real-time carbon price data of the carbon trading market are collected; a digital twinborn model is constructed based on the two types of data, and the power consumption, the cold energy consumption and the carbon emission of the data center can be mapped in real time; simulating the carbon emission change under a preset computing power scheduling strategy by means of a digital twinborn model, and generating virtual environment feature data; designing a double-layer game model: receiving the basic data and the virtual environment characteristic data by an upper-layer unit, and determining a final carbon quota distribution proportion; and the lower-layer unit dynamically adjusts the power supply voltage and frequency of the electric equipment, so that low-carbon regulation and control of the calculation load of the data center are realized, and the accuracy and effectiveness of the low-carbon regulation and control of the calculation load of the data center are improved.

The invention belongs to the technical field of electric power, and discloses a virtual power plant energy management collaborative optimization method, system, device and medium, and the method comprises the following steps: S1, constructing a distributed robust optimization multi-market bidding model, and outputting a bidding power plan of a virtual power plant in multiple markets; s2, constructing an internal power market of the virtual power plant and the producer, and calculating a transaction strategy of the virtual power plant and the producer by adopting a Starburg game model; s3, constructing a real-time power consumption deviation management and control mechanism of the production and consumer; calculating a credit value based on the deviation between the real-time electricity consumption and the day-ahead contract amount, and dynamically adjusting a penalty factor and collecting a fine in combination with a historical credit value; s4, constructing a low-carbon scheduling model, defining the equivalent carbon potential of the energy storage equipment, and calculating the carbon emission of the virtual power plant and the producer in combination with each power supply carbon emission factor and the stepped carbon price; and S5, realizing virtual power plant energy management collaborative optimization based on the bidding power plan, the transaction strategy, the deviation management and control result and the carbon cost constraint.