67 results about "Power market" patented technology

The application discloses a power market electricity price mechanism coordinated evolution implementation method, and particularly relates to the technical field of power market operation and regulation; based on a multi-dimensional power market scene set, evolution track characteristic parameter sets of the electricity price mechanism are extracted; a similarity clustering algorithm is used to divide the electricity price mechanism into coupled mechanism clusters, and a coupling degree coefficient matrix is constructed; a coordinated evolution function group is constructed based on the coupling degree to represent the linkage response relationship between the mechanisms; a disturbance factor is introduced to optimize the robustness of the model, forming a robust evolution model; the optimized model is applied to the current operating state of the target area, and the electricity price mechanism state prediction result of the next period is output; the electricity price tearing point and the mechanism conflict point in the prediction result are further identified, and the high-risk regulation interval is marked; the application can realize dynamic linkage modeling and stability early warning between multiple mechanisms, and improve the adaptability and regulation precision of the electricity price mechanism under high uncertainty scenarios.

The application discloses a kind of based on risk scene's electric power market parallel clearing method and system, it is characterized by: S1, multi-dimensional risk scene synchronization generation;S2, scene space-time feature extraction;S3, scene clustering group;S4, distributed parallel clearing calculation;S5. Risk index aggregation analysis.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.The application improves risk coverage by scene coupling generation, solves the problem that scene and clearing calculation are separated and cannot capture multiple risk coupling effects;Through distributed architecture for clearing calculation, improve the efficiency of calculation;Through risk index, the cross-market chain reaction effect can be quantified.

The invention discloses an XGBoost-based power generation quotation rationality evaluation method, system and equipment and a medium, and relates to the technical field of power market operation and supervision, and the method comprises the steps: collecting historical operation and market environment data, obtaining five evaluation indexes through a prediction model, elastic analysis, matching degree calculation and clustering analysis, fusing the indexes to construct a feature vector, and obtaining a power generation quotation rationality evaluation result. And training a classification model to evaluate the rationality of quotation, and generating a monitoring early warning signal when the matching degree is too low, the price response is too strong, the quotation deviation is too large and the behavior mode is abnormal by continuously tracking the change trend of key indexes. According to the invention, by fusing the multi-dimensional dynamic features and adopting the elastic time sequence matching and interpretable machine learning model, a power generation quotation behavior intelligent supervision closed loop integrating comprehensive evaluation, accurate quantification and active early warning is constructed, and the comprehensiveness, accuracy, objectivity and timeliness of market supervision are significantly improved.

The application belongs to the technical field of power system operation and planning, and relates to a construction method of a distributed photovoltaic aggregator participating in a power market transaction architecture, comprising: 1, establishing an upper leader model with the goal of maximizing the distributed photovoltaic aggregator's income; 2, establishing a lower follower model with market transaction center social welfare maximization clearing as follower 1 and distribution system operator safety checking as follower 2; 3, solving a one master and multiple slave Stackelberg game model; the application provides a scientific and reasonable decision basis for the distributed photovoltaic aggregator participating in the power market transaction, helps to improve the income of the distributed photovoltaic aggregator, at the same time, can guarantee the safe and stable operation of the distribution system, and promotes the healthy and sustainable development of the power market.

This invention discloses a resource collaborative scheduling and demand response optimization system and method for virtual power plants in industrial parks. It achieves global collaborative scheduling through a two-layer Actor-Critic network architecture, and uses a fusion of particle swarm optimization and fuzzy control to achieve grid-connected stability control. A three-layer fusion optimization architecture is constructed to complete the continuous amplitude calculation of demand response and the generation of equipment-level instructions. A unified algorithm support is provided by an intelligent optimization module. This invention effectively solves the problems of weak adaptability of existing global optimization models for virtual power plants in industrial parks, low level of intelligent decision-making in specialized sub-scenarios, and difficulty in coordinating multi-timescale scheduling and refined execution. It can achieve robust optimization and refined control in uncertain scenarios, balancing system economy, stability, and operational reliability. It is suitable for virtual power plants in industrial parks to efficiently participate in the power market and resource collaborative scheduling.

The application provides a kind of energy storage participation multi-dimensional power market optimization control method based on quantification attribution.The application carries out initial configuration, constructs energy storage participation multi-dimensional power market clearing model, obtains benchmark scheme and optimal scheme, quantification attribution based on path integral, optimizes control energy storage decision according to attribution result.The application specifically designs the path construction method for mapping marginal benefit using optimal Lagrange multiplier information, proposes the benefit decomposition attribution scheme of riemann approximation based on the path construction method, and generates the power distribution instruction and declaration strategy of energy storage system in the next scheduling period by taking the attribution result as feedback signal.The application can solve the decoupling problem of energy storage value under the coupling of complex physical and market constraints, clearly quantifies the contribution share of energy storage in multi-dimensional power market, optimizes the energy storage scheduling scheme, and provides support for energy storage investment decision and safe and economic operation of new power system.

This invention discloses a method, electronic device, and medium for intelligent decision-making by participants in the electricity market, comprising: acquiring and standardizing power and price data for each segment declared by each generating unit in the power generation system; clustering the standardized power and price data for each segment declared by each generating unit, and generating bidding scenarios by random sampling based on the distribution of the clustering results; constructing an equivalent model based on LSTM, wherein the equivalent model includes a historical technical data estimation model and a future technical data prediction model; wherein the historical technical data estimation model responds to the bidding scenario and market clearing results, and outputs the estimated value of the cumulative bids of historical equivalent competitors; the future technical data prediction model responds to the load data, season, and weather conditions of the future trading day to be predicted, and predicts the cumulative bid data of equivalent competitors.

The application discloses a resource scheduling method, system and device based on dynamic coupling weight and a medium, and belongs to the technical field of power systems. The method is as follows: acquiring characteristic parameters corresponding to each power market, demand parameters, operation parameters of a power grid and power flow parameters of a power transmission section; calculating a first weight factor based on the characteristic parameters, the operation parameters and the power flow parameters, and calculating a second weight factor based on the operation parameters and the power flow parameters; determining a constraint condition according to the first weight factor and the second weight factor corresponding to each market participant; and optimizing and solving with the target of minimizing the cost of each market participant in each power market under the constraint condition to obtain a resource scheduling scheme, and controlling each market participant to execute the resource scheduling scheme. Therefore, the application can improve the resource scheduling effect and reduce the cost.

A control method and a control device of an energy storage air conditioning system are disclosed. The control method comprises: in response to receiving a scheduling instruction from a load scheduling platform, determining a target time-sharing load of the energy storage air conditioning system in a preset time period; based on the target time-sharing load, determining a load distribution strategy of the energy storage air conditioning system; and according to the load distribution strategy, performing time-sharing load distribution on multiple air conditioning units of the energy storage air conditioning system, so that a sum of energy efficiency ratios of the multiple air conditioning units meets a first preset requirement. The control method can make the energy storage air conditioning system meet scheduling requirements, user requirements and economic requirements in an electricity market environment.

This invention provides a method and apparatus for assessing the utilization rate of new energy sources in a regional power system, relating to the field of new energy utilization technology. This method constructs a multi-objective decision-making model integrating economic efficiency, low carbon emissions, and system security constraints. It comprehensively considers multiple practical operating factors such as cross-sectional transmission limits, system frequency stability, ramp-up capability, and energy storage operation characteristics. This enables a more realistic simulation of the dispatching behavior of a regional power system under complex operating conditions, overcoming the problem of traditional assessment methods that tend to be overly optimistic due to neglecting the trade-off between system security and economic efficiency. Thus, it achieves a more scientific and reasonable quantitative assessment of new energy utilization rates. This method can not only accurately calculate the average utilization rates of wind power and photovoltaic power within a specified assessment period, but also reveal the game relationship between the three objectives of "security-economy-green" through Pareto front analysis, providing strong technical support for new energy planning and layout, flexible resource allocation, and power market mechanism design.

The application provides a power spot transaction electricity auxiliary decision-making method and system based on improved attention, and relates to the field of power transaction auxiliary decision-making. The correlation of multi-source features and spot prices is calculated, the dynamic interaction relationship between the multi-source features changing over time is calculated, and the key features are screened based on the correlation results and the dynamic interaction relationship. The key features are input into a deep learning model of an improved attention mechanism, and based on the processing of the feature attention layer, the local time attention layer and the long short-term memory network, the power spot price prediction result is obtained. Based on the spot price prediction result, combined with the load demand prediction, the power generation cost and the market transaction rules, a transaction electricity decision-making model with the maximum profit as the target is constructed, and the optimal transaction electricity is determined. The application determines the optimal transaction electricity based on multi-source features, improves the scientificity and rationality of transaction decision-making in a complex market environment, and guarantees the stable operation of the power market.

This invention discloses an intelligent collaborative operation system for joint peak shaving of thermal power and new energy sources, comprising: a cloud-based collaborative optimization layer deployed in a remote dispatch center, used to execute day-ahead multi-scenario optimized dispatching, cross-regional resource aggregation, and power market trading strategy generation; an edge-based intelligent decision-making layer deployed on the power plant-side edge server, performing rolling optimization on a minute-level cycle, integrating ultra-short-term forecast data to correct day-ahead plans, and completing collaborative power allocation for multi-modal energy storage; and a three-layer edge control terminal including a thermal power edge control terminal, a new energy edge control terminal, and a multi-modal energy storage terminal, used to directly interact with equipment and execute power commands. This invention, through a three-layer collaborative architecture of cloud, edge, and edge control terminals, achieves closed-loop feedback of day-ahead planning, intraday correction, and real-time control, and utilizes the complementary characteristics of multi-modal energy storage to reduce the frequency of load changes in thermal power units, improve the absorption capacity of new energy sources and the economic efficiency of grid operation, and enhance the resilience of the grid to disturbances.

This invention provides a method and system for optimizing frequency regulation transactions in virtual power plants based on fractional heavy-tailed noise modeling, belonging to the field of power market technology. By systematically collecting and preprocessing grid frequency, market settlement, and generation data from virtual power plants, it achieves deep fusion and effective governance of multi-source data, providing high-quality, structured input for subsequent analysis and significantly improving data-driven decision-making capabilities. Using a voltage characteristic model, the energy and response time distribution required for dynamic and static frequency responses can be calculated separately, enabling quantitative and structured analysis of grid frequency regulation needs. This allows virtual power plants to more accurately assess the matching relationship between their own response potential and system requirements. This provides important technical support for the market-oriented and large-scale application of distributed resources under new power systems.

The application discloses an adaptive unit combination optimization method and system based on extreme scenario driving, constructs an uncertainty set in robust optimization; determines an overall model of adaptive robust optimization unit combination according to the constructed uncertainty set; converts the overall model of adaptive robust optimization unit combination into a random programming model with vertices and extreme scenarios, adds a full-scenario feasibility constraint and an unexpected constraint into the random programming model, and realizes adaptive unit combination optimization based on extreme scenario driving. The method solves the defects that the conventional uncertainty set robust optimization and the random programming do not satisfy the unexpectedness and full-scenario feasibility, and the scheduling instruction obtained by the model can be used for high-proportion new energy power market unit combination optimization decision.

The application discloses a kind of devices and methods for assisting individual power users to participate in power market quotation, device includes data input module: the type of power market that individual power user participates in is input, the transaction period under the type of participation, the power that each transaction period participates in;Data storage module is arranged by device operator, in networked state, daily update operator issued market information and user information by each device;Data analysis module: for the certain power market of certain time scale of individual power user, carry out the analysis of suggested quotation based on the historical transaction information of power market and the analysis of suggested quotation based on the historical transaction result of individual power user;Data output module: the result obtained by data analysis module, the visualized suggested quotation output for user reference, weaken the hindrance of individual power user participating in power market, promote its enthusiasm of participating in power market transaction, promote demand side resource to participate in power market, guarantee the safe and stable operation of power system.

The application discloses a security-constrained unit commitment fast solving method, system, equipment and medium based on a space-time graph attention network, which comprises the following steps: inputting graph structure input data into a pre-trained space-time graph attention model for multi-task joint reasoning to obtain start-stop state pre-judgment probability and activity prediction score; fixing the binary decision variable of unit start-stop based on the start-stop state pre-judgment probability, screening the line transmission constraint based on the activity prediction score, constructing a reduced mixed integer linear programming model and solving the model, verifying the feasibility of the whole network power flow after obtaining a candidate scheduling scheme, outputting a final scheduling scheme if the verification is passed, and modifying and re-solving the reduced mixed integer linear programming model based on the violation information if the verification exists constraint violation. The application overcomes the problem of slow solving speed of the traditional method under a large-scale system, and can meet the time requirement of future virtual power plants and distributed power sources participating in power market clearing.

The application discloses a kind of based on energy consumption side's province and land two-level non-fossil energy power consumption accounting method, belong to power system data management and green energy accounting technical field.The method follows user subject and consumption base principle, first determines accounting period and boundary, collects total social power consumption, power market transaction, cross-provincial power flow direction and other basic data;Secondly, a provincial accounting model is constructed, and five types of power consumption, including medium and long-term transaction, cross-provincial net inflow and self-generation and self-use, are integrated to calculate the total consumption of the province;At the same time, a city-level accounting model is constructed, and the city consumption is accounted according to the user types of direct entry into the city and proxy power purchase, combined with the provincial allocation coefficient.The application realizes the coordination and unification of physical power and green certificate rights and interests, solves the problems of repeated calculation and province-city disconnection in existing accounting, and provides reliable technical support for government energy-saving assessment, renewable energy responsibility weight calculation and non-fossil energy consumption accounting.

The application provides a self-adaptive accelerated solving method and system for power spot market clearing, and belongs to the field of power market optimal dispatch. The method comprises the following steps: constructing a historical database, extracting node features and edge features through feature engineering, constructing a graph neural network prediction model, adjusting the prediction model after training in combination with boundary data, and generating a predicted solution through prediction; based on the predicted solution, an adaptive time period reduction method containing a statistical envelope index and a consistency discrimination criterion is used to establish a variable scale time period model and obtain a coarse-grained solution; the coarse-grained solution is expanded into an initial fine-grained solution, the model scale is reduced, and the initial fine-grained solution is sequentially adjusted through start-stop structure adjustment, scale-intra feasibility repair and scale-inter feasibility repair to obtain a fine feasible solution meeting the full physical constraints of the original time period, and the solving of the large-scale power spot market clearing is completed. Under the premise of ensuring the physical feasibility and economic rationality of the clearing result, the solving efficiency and stability are significantly improved.

A transmission / distribution system operation coordination-based HVDC operation system is provided, based on the operation of HVDC (High Voltage Direct Current) to expand supply stability, improve power quality, and enhance the economic efficiency of system operation throughout the domestic power system. The HVDC operation system is characterized by comprising: a power system management terminal that generates power system operation information; a plurality of HVDCs that generate HVDC availability information; a power market management server that generates power market operation information; distribution network operation information; a distribution system management computer that generates distribution network generation planning information and real-time distribution network generator operation information using distribution network generator bidding information and transmission system distribution network generation capacity acceptance information among the power market operation information; and an HVDC operation computer that generates HVDC operation planning information and distribution network generation capacity acceptance information based on the distribution network generation planning information, and generates real-time control information for controlling the operation of the HVDC based on the HVDC operation planning information, power system operation information, and distribution network operation information.

The application discloses a power grid frequency safety optimization method, device, equipment and medium. By quantifying physical inertia and virtual inertia uniformly and realizing dynamic partitioning based on electrical distance, accurate identification and quantitative evaluation of inertia void in the power grid are realized. Further, the minimum inertia demand of the partition is introduced into the power market clearing model as a rigid safety constraint, allowing new energy to participate in market bidding as a virtual inertia service provider, and through the optimization of joint decision of power and inertia resources, the resource allocation scheme with the lowest total social cost is automatically generated on the premise of ensuring frequency safety, realizing the coordinated optimization of safety and economy. Finally, the marginal price of inertia resources is extracted from the optimization model, and a node inertia scarcity index is further constructed, which is converted into an intuitive economic signal and frequency safety warning, thereby systematically improving the stability and power supply reliability of power grid operation.

The application discloses an optimization decision method for distributed lithium battery energy storage participating in a power market, and relates to the technical field of the power market. The method comprises the following steps: constructing a profit model and a cost model of the distributed lithium battery energy storage participating in the power market, constructing a profit function of the distributed lithium battery energy storage participating in the power market based on the profit model and the cost model, and determining the power purchase capacity and the power sale capacity of the distributed lithium battery energy storage participating in the power market and the bidding capacity of the distributed lithium battery energy storage in the power market when the profit function is maximum under the constraint conditions of the state of charge of the distributed lithium battery energy storage and the self characteristics of the distributed lithium battery energy storage. The method solves the technical problem that the existing distribution strategy is not good, so that the distribution strategy cannot well guide the distributed battery to enter the power market.

The present application relates to the field of new energy power system and optimal scheduling, in particular to a kind of multi-time scale energy management method of light storage grid-connected system, comprising S1, based on day-ahead operation forecast data, under the condition that energy storage operation constraint, grid-connected constraint and system power balance constraint are satisfied, to establish day-ahead scheduling model with light storage grid-connected system operation cost, S2, introduce uncertainty modeling mechanism in day-ahead scheduling model, generate day-ahead scheduling scheme considering uncertainty, S3, construct intraday operation optimization model under shorter time scale, and combine system real-time operation state, to the rolling correction of day-ahead scheduling scheme, S4, construct penalty function to the deviation of intraday operation decision relative to day-ahead scheduling scheme, combine day-ahead scheduling scheme and output the final operation control instruction of light storage grid-connected system, the present application system effectively suppresses minute-level power fluctuation, while ensuring the safe operation of system, reduces the transaction cost with power market and prolongs the service life of energy storage.

The application discloses a knowledge graph updating method and system based on a semantic large model, and belongs to the technical field of data processing. The application trains a power marketing semantic large model by using a plurality of knowledge graph triples of a power marketing knowledge graph. Then, a plurality of business entity texts of to-be-processed business data are obtained by performing coarse-grained entity segmentation processing on the to-be-processed business data. Then, a plurality of prompt word texts of the power marketing knowledge graph are generated by using the knowledge graph triples, and the power marketing semantic large model is guided to perform fine-grained entity extraction by using a plurality of semantic input texts constructed by the business entity texts and the prompt word texts. The power marketing knowledge graph is verified incrementally by using business triples, an incremental verification result is obtained, and the power marketing knowledge graph is updated based on the incremental verification result and the business triples, so that the technical problem that the power marketing knowledge graph is not accurately updated in the prior art can be solved.

The application belongs to the field of power system automation and control technology, and relates to a method for aggregating distributed resources and optimizing market bidding based on chino polyhedron, comprising the following steps: 1, establishing a distributed resource model, wherein the distributed resource model comprises an air conditioning model, a distributed energy storage model, a diesel generator model and a distributed photovoltaic model; 2, performing inner approximation on the feasible region of the distributed resources based on the chino polyhedron; and 3, aggregating the distributed resources to participate in the optimization decision of the power market; by performing inner approximation on the feasible region of the distributed resources based on the chino polyhedron, the application effectively simplifies the aggregation calculation complexity and improves the calculation efficiency, and provides an effective decision method for the distributed resources participating in the power market.

The application discloses a cloud-edge collaborative-based power system balance optimization regulation method and device, wherein the method comprises the following steps: acquiring a target point set of a market residual demand curve; solving a strategy bidding model of a generator set which is established in advance according to the target point set of the market residual demand curve to obtain an optimal strategy bidding coefficient of the generator set; judging whether the current market is balanced according to the optimal strategy bidding coefficient of the generator set and a preset balance condition, wherein when the current market is balanced, generating an energy balance optimization regulation scheme of the market according to the optimal strategy bidding coefficient of the generator set. Thus, the problems that key information of the market cannot be timely and fairly disclosed in the power market optimization scheduling, and the efficiency of the power market is reduced are solved.

The application relates to the field of power market analysis, and discloses a power demand side management optimization method based on electricity-carbon coupling and dynamic carbon emission, which comprises the following steps: obtaining multiple demand response schemes and multiple energy generator unit data in a power market; constructing a multiple energy generator unit market model according to the multiple energy generator unit data in the power market; constructing a two-stage power market clearing model based on the multiple energy generator unit market model, wherein the two-stage power market clearing model comprises a first-stage model and a second-stage model, the first-stage model is a market planning and dispatching model, and the second-stage model is a demand response model; inputting each demand response scheme into the two-stage power market clearing model in sequence to obtain market operation parameter analysis results under each demand response scheme; and obtaining market performance analysis and demand response decision through comparison of all market operation parameter analysis results. The application can realize expansion of implementation comparison of different market-oriented demand response schemes.

The wind, light and water cross-region joint bidding and DC power transmission optimization method belongs to the field of new energy power market operation and cross-region DC power transmission optimization, and aims to solve the problems of sending end wind and light base cross-region participating in receiving end market bidding and DC power transmission plan optimization. The invention constructs a sending end wind and light and receiving end cascade hydropower joint bidding body, and participates in medium and long term and day-ahead market through cross-region DC channel; the medium and long term establishes an upper layer bidding model with the maximum difference contract income as the target, and a lower layer social welfare maximization dispatching model containing DC operation characteristic constraint; the day-ahead establishes an upper layer CFD power decomposition and day-ahead bidding model, and a lower layer day-ahead dispatching model containing cascade hydropower output; the master-slave game iteration is adopted to obtain the optimal bidding strategy, market dispatching result and DC power transmission scheme. The invention couples CFD and DOCC constraints, relies on water power to suppress wind and light fluctuation, stabilizes new energy income, improves market economy and cross-region cooperation ability, and improves new energy consumption level.

The application discloses a coal blending optimization system for a thermal power unit raw coal bunker, and belongs to the technical field of power system optimization. The system comprises a data acquisition module, a capacity price assessment parameter analysis module, a bunker blending optimization module and an execution feedback module. The capacity price assessment requirement is deeply integrated into the blending strategy, and the dual goals of assessment standard and operation economy are achieved, thereby solving the problem of traditional blending ignoring assessment constraints, improving the utilization rate of raw coal resources, adapting to the new regulation of power market capacity price assessment, adopting a multi-objective optimization model combined with an improved genetic algorithm, matching a time sequence prediction mechanism and refined resource scheduling rules, balancing the income, cost and emission of the multi-objective model, ensuring the high efficiency and precision of the improved algorithm, predicting the load and raw coal fluctuation in advance, scientifically distributing the raw coal according to the priority in the resource scheduling, improving the adaptability and real-time performance of the blending scheme, and avoiding the risk of assessment failure caused by unexpected situations.

The application discloses a kind of electric power bilateral transaction settlement deviation strategy optimization method, system and storage medium, belong to electric power market transaction technical field.The application obtains user historical load data, electricity demand prediction and transaction package parameters in real time through data interface;Based on probability distribution, construct load random fluctuation model, generate multi-scenario simulation load curve using Monte Carlo algorithm;Parallel trial calculation is carried out to candidate package using simulation settlement engine;The optimal transaction package is dynamically screened by multi-objective optimization function, and transaction instruction is generated;In transaction execution, deviation is monitored in real time by closed-loop feedback module, and contract adaptive adjustment is triggered.System includes five modules of data acquisition, random modeling, simulation settlement, strategy optimization and closed-loop feedback, integrates LSTM-GARCH hybrid prediction, GPU accelerated calculation, block chain storage technology.Improves the accuracy and adaptability of deviation strategy, reduces settlement risk, optimizes transaction efficiency, supports stable operation of electric power market.

This invention provides a robust optimization scheduling method and system for virtual power plants considering multiple uncertainties, relating to the field of power market dispatching technology. The method includes: establishing a virtual power plant dispatching model based on ARIMA-SVR, where uncertainties include distributed energy generation, electricity prices, and load demand; extracting features of the uncertainties, constructing an uncertainty set, and transforming the virtual power plant dispatching model into a robust optimization scheduling model; and using a particle swarm optimization algorithm to solve the robust optimization scheduling model and optimize the dispatching strategies for each energy resource within the virtual power plant. This invention, by constructing a robust optimization scheduling model considering multiple uncertainties and combining it with advanced solving algorithms, achieves optimized dispatching of virtual power plants in complex market environments, improving the dispatching efficiency, economic benefits, and ability to cope with uncertainties in complex market environments.