CN116109335B - New energy dynamic electricity price data management system based on digital twin - Google Patents
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Abstract
The invention relates to the technical field of intelligent energy, in particular to a new energy dynamic electricity price data management system based on digital twinning. The dynamic electricity price comprises a fixed electricity price and a floating electricity price, wherein the fixed electricity price is determined by the actual electricity grid layer and the twin electricity grid layer, and the floating electricity price is determined by the actual electricity grid layer and the twin electricity grid layer together, and the dynamic electricity price comprises the fixed electricity price and the floating electricity price, wherein: the actual power grid layer evaluates the managed actual power grid data information to obtain comprehensive data; and the twin power grid layer constructs a new energy power grid twin body based on new energy data during actual power grid data information management. According to the new energy power grid twin body, future power grid load is predicted through simulation of the new energy power grid, prediction of the comprehensive evaluation end is verified, deviation is obtained in advance, and the verified deviation value is not required to be converted into electricity price at the next moment, so that the new energy power grid twin body can be shown at the moment.
Description
Technical Field
The invention relates to the technical field of intelligent energy, in particular to a new energy dynamic electricity price data management system based on digital twinning.
Background
Compared with the traditional thermal power generating unit, the new energy power generation has great advantages in the aspects of environmental protection, energy conservation and the like, but also has various effects on the power grid, the new energy is mainly influenced by seasons, weather, geography and other factors, the intermittence, volatility and random uncertainty of the power generation bring a plurality of problems to the safe operation of the power grid, and therefore, a dynamic electricity price charging mode is adopted on electricity price.
At present, new energy power generation electricity prices are formed by an economic technology method, and the electricity price standard is formulated and environmental subsidy by taking the new energy field income into consideration in the aspects of measuring and calculating construction cost, energy saving cost, operation and maintenance cost, environmental value and the like. The new energy brings environmental value which is charged into electricity price in the form of subsidy. The method for fixing the electricity price similar to a thermal power plant cannot accurately reflect the value of new energy and the influence on the internet operation of a conventional unit, so that the prediction punishment electricity price is introduced in the prior art, and the verification and deviation measurement are carried out after the prediction punishment electricity price comes in the future, so that the previously verified deviation value is required to be converted into the electricity price at the next moment, and hysteresis occurs in the data management of the dynamic electricity price.
Disclosure of Invention
The invention aims to provide a new energy dynamic electricity price data management system based on digital twinning, which aims to solve the problems in the background technology.
In order to achieve the above object, a new energy dynamic electricity price data management system based on digital twin is provided, which comprises an actual power grid layer and a twin power grid layer, wherein the dynamic electricity price comprises a fixed electricity price and a floating electricity price, the actual power grid layer is used for determining the fixed electricity price, and the actual power grid layer and the twin power grid layer jointly determine the floating electricity price, wherein:
the actual power grid layer evaluates the managed actual power grid data information to obtain comprehensive data;
the twin power grid layer builds a new energy power grid twin body based on new energy data during actual power grid data information management;
the floating electricity price is obtained by comprehensive data under the predictive verification of the twin bodies of the new energy power grid.
As a further improvement of the technical scheme, the actual power grid layer comprises a sensing end, an interaction end, a data end and a comprehensive evaluation end, wherein the sensing end is used for sensing the acquired actual power grid data information, and the interaction end performs data interaction in a transmission mode, wherein:
the data information generated in the sensing and interaction processes is recorded in a data end, and is managed through the data end;
the comprehensive evaluation terminal carries out comprehensive evaluation on the data information managed by the data terminal and obtains comprehensive data.
As a further improvement of the technical scheme, the twin power grid layer comprises a mapping end, a supporting end and an updating end, wherein the mapping end maps new energy data, recorded in the data end, of a new energy power grid to the supporting end, a new energy power grid twin body is constructed through the supporting end, and the updating end iterates the new energy power grid twin body in real time.
As a further improvement of the technical scheme, the data information recorded by the data terminal comprises electric power data, environment data, service data and personnel data.
As a further improvement of the technical scheme, when the comprehensive evaluation terminal performs comprehensive evaluation, the environmental electricity prices of the fixed electricity price and the floating electricity price and the predicted punishment electricity price are calculated, wherein:
the calculation of the fixed electricity price comprises the reaction of the construction cost, the operation cost and the maintenance cost of the new energy power grid;
the environmental electricity price is a value reaction for reducing the emission of the thermal power unit;
the forecast punishment electricity price is a reaction to the forecast deviation of the future new energy power grid.
As a further improvement of the technical scheme, the larger the predicted deviation of the new energy power grid is, the higher the predicted punishment electricity price is, and the calculation formula of the predicted punishment electricity price is as follows:
；
；
in the formula ,punishment electricity price is predicted; />Punishment electricity price proportionality coefficients are predicted; />Is->Moment total load of the power grid; />And predicting the deviation amount for the new energy power grid.
As a further improvement of the technical scheme, the new energy power grid twin predicts future power grid load through simulation of the new energy power grid, so as to verify the prediction of the comprehensive evaluation end and obtain deviation in advance.
As a further improvement of the technical scheme, the new energy power grid twin comprises a resource regulating intelligent agent, a process regulating intelligent agent and a service regulating intelligent agent;
the resource control intelligent agent carries out digital modeling on various distributed new energy sources through resource analysis.
As a further improvement of the technical scheme, the data end adopts a non-relational database to store data when recording data information.
As a further improvement of the technical solution, the interaction of the data in the interaction end adopts an IoT communication protocol.
Compared with the prior art, the invention has the beneficial effects that:
1. in the new energy dynamic electricity price data management system based on digital twinning, the new energy grid twinning body predicts future power grid load through simulating a new energy grid, verifies the prediction of a comprehensive evaluation end, and obtains deviation in advance, namely, floating electricity price is obtained by comprehensive data under the predictive verification of the new energy grid twinning body, so that the predicted punishment electricity price can be verified in advance, and the verified deviation value is not converted into the electricity price at the next moment any more, and can be reflected at the moment.
2. In the new energy dynamic electricity price data management system based on digital twin, the supporting end cooperates with the updating end to continuously iterate and improve the model according to data and knowledge, and timely reflect the running states of equipment and a power grid, so that the problem that the twin body of the new energy power grid can iterate by means of newly generated data after verification accuracy is reduced is solved.
3. In the new energy dynamic electricity price data management system based on digital twinning, modeling of new energy grid twinning bodies is carried out on various distributed energy sources through resource analysis and cooperation with a supporting end, and the dimension and the precision of physical twinning body and digital twinning body modeling and simulation are ensured; the regulating and controlling process agent comprises an operation control function of an energy router, and ensures multi-time scale coordination and multi-source data coupling of digital and physical twin modeling through process analysis and process implementation.
4. In the new energy dynamic electricity price data management system based on digital twin, accurate prediction and optimization of digital and physical twin modeling and simulation are ensured through service analysis and service implementation. And finally, carrying out dynamic interaction fusion of the three through a multi-agent management system to realize the intellectualization of task decomposition and digital twin tasks. Meanwhile, each agent can be further divided into sub-agents and Sun Zhineng according to task requirements.
Drawings
FIG. 1 is a block flow diagram of a dynamic electricity price data management system of the present invention;
FIG. 2 is a block diagram of the internal modules of the actual power grid layer of the present invention;
fig. 3 is a block diagram of the internal modules of the twin grid layer of the present invention.
The meaning of each reference sign in the figure is:
100. an actual power grid layer; 200. twinning the power grid layer;
110. a sensing end; 120. an interaction end; 130. a data end; 140. a comprehensive evaluation terminal;
210. a mapping end; 220. a support end; 230. and updating the end.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Along with the development of new energy, a new energy power grid is introduced into a present power grid, but the new energy power grid is not replaced in the using process, so that the electricity price is calculated by the thermal power unit, the new energy power grid cannot correctly reflect the value of the new energy and the influence on the online operation of a conventional unit, only prediction can be carried out by a new energy power grid manufacturer, in addition, the influence of the new energy on the power grid is limited only by virtue of auxiliary service, the auxiliary service is overall evaluation, and the value exchange of the new energy and the conventional unit cannot be determined in real time.
In order to provide a new energy dynamic electricity price data management system based on digital twin, fig. 1 shows a specific block diagram of the dynamic electricity price data management system, which comprises an actual power grid layer 100 and a twin power grid layer 200, wherein the dynamic electricity price comprises a fixed electricity price and a floating electricity price, the fixed cost of the new energy is reflected through the fixed electricity price, the environmental value and the energy saving and emission reduction value brought by the new energy are reflected through the floating electricity price, the influence of new energy power generation on the power grid operation is reflected, the actual power grid layer 100 is used for determining the fixed electricity price, the actual power grid layer 100 and the twin power grid layer 200 jointly determine the floating electricity price, at present, the new energy power generation prediction method is quite large, but the prediction result is only used for conventional unit power generation arrangement, the new energy field is not constrained, the predicted enthusiasm of the new energy field on the operation is not improved, the new energy field is constrained by adopting punishment electricity price to improve the prediction precision of the new energy field, the punishment electricity price is required to be verified and measured after the coming at a future moment, the time of the validated value is required to be converted into the value at a moment, and the current value is delayed in the dynamic electricity price management.
The actual power grid layer 100 evaluates the managed actual power grid data information, wherein the actual power grid data contains all data covering the thermal power generating unit and the new energy power grid, the comprehensive data is obtained by evaluating the existing data information, and the specific obtained comprehensive data comprises an environmental electricity price, a real-time standby capacity electricity price and a predicted punishment electricity price, and the environmental electricity price of the new energy should be determined by the environmental marginal value of the thermal power, namely, the emission value of the thermal power generating unit is reduced due to the fact that the new energy generates one unit of electric quantity per unit of power in a unit time in a certain power grid running state.
When joint scheduling calculation of new energy participation is performed, inAt the moment, the real-time power of each thermal power generating unit is determined by the following formula:
；
；
；
wherein ,is a system objective function of the thermal power generating unit; />Is a thermal power generating unit->At->Real-time power at time; />Is->The new energy power plant is at->Real-time power at time; /> and />The total number of the thermal power generating units and the new energy power plants is respectively; />、/>Respectively a lower power limit and an upper power limit of the thermal power unit; />Is a new energy power plant in the power gridTotal emitted power at the moment; />Is->The total load of the power grid at the moment;
if in the objective functionFor the power generation cost of the thermal power generating unit, the fitting can be generally performed by using a function of two or more times, so the +.>Continuous and 2 nd order conductive.
Is arranged atThe optimal solution of time is->When->Under the condition that the rated upper and lower limits of the thermal power generating unit are not reached, the following conditions are satisfied:
；
；
wherein ,generating cost for thermal power generating unit>At->Optimal solution of time of day->An output value at;
when the new energy generates power to generate a deviationWhen a new optimal solution is obtained>It should satisfy:
；
due functionContinuous and 2 nd order conductive, when +.>For a period of time sufficient for->At->And (5) linearizing.
In the case of the first embodimentPersonal electric power plant->When the rated power lower limit is reached, the power deviation is not generated after the power of the new energy unit is increased>Then->In the environmental cost function of the thermal power unit, when the new energy unit is added with a power system +.>After that, the environmental value brought by the whole system is changed to +.>And use->Indicate->The gas emission function of the individual power plant is finally->The total environmental electricity price of the new energy unit in the bullet time tends to be 0:
；
wherein ,the environmental electricity price proportionality coefficient; />Representing the deviation of the gas emission function; />The new energy power plant in the power grid is +.>Total emitted power at the moment; />Deviation amount of power generated for new energy；/>Indicate->A gas emission function of the personal thermal power unit; />Is the output value of the system objective function of the thermal power generating unit.
In addition, since the predicted punishment electricity price should be verified and measured after coming in the future, the deviation value verified in the past is converted into the electricity price at the next moment, and the larger the predicted deviation of the new energy power grid is, the higher the predicted punishment electricity price is, and the calculation formula of the predicted punishment electricity price is as follows:
；
；
in the formula ,punishment electricity price is predicted; />Punishment electricity price proportionality coefficients are predicted; />Is->Moment total load of the power grid; />And predicting the deviation amount for the new energy power grid.
The above calculation results in a basic floating power price, but the verification and prediction punishment power price is considered to have hysteresis, so the twin power grid layer 200 of the embodiment constructs a new power grid twin based on new power data when the actual power grid data is managed, the new power grid twin is simulated through a digital twin technology, future power grid loads are predicted by the new power grid simulation, the prediction of the comprehensive evaluation end 140 is verified, the deviation amount is obtained in advance, that is, the floating power price is obtained by the comprehensive data under the new power grid twin predictability verification, the prediction punishment power price can be verified in advance, the verified deviation value is not converted into the power price at the next moment, and the current price can be reflected at the moment.
Further, the actual power grid layer 100 is the basis of a digital twin power grid, and mainly consists of physical elements (including power grid facilities, communication equipment, sensing equipment, data computing equipment, data storage equipment and the like) of the full-physical power grid, and two fundamental tasks of the layer are to realize accurate sensing of data and guarantee real-time communication of the data. The data in the power grid has the characteristics of wide distribution range, large data volume, high data dimension, easiness in external environment interference and the like, and the construction of the new energy power grid twin body has strong timeliness and accuracy requirements on the data, so that the technology of the actual power grid layer 100 needs to be realized from two aspects of data acquisition and perception and data interaction and transmission. In terms of data acquisition and perception, firstly, digital identification technology is needed to carry out omnibearing and standardized digital identification on the assets of a power grid company. Next, to intelligently sense the electrical quantity and the non-electrical quantity of the power grid universe, the actual power grid layer 100 in this embodiment includes a sensing end 110, an interaction end 120, a data end 130 and a comprehensive evaluation end 140, as shown in fig. 2, the sensing end 110 is configured to sense the acquired actual power grid data information, the interaction end 120 performs data interaction in a transmission manner, and the sensing end 110 adopts a common equipment state sensing method based on monitoring multiple physical quantities. Under the background of the ubiquitous electric power Internet of things, the intelligent sensing of the operation state data of the high-dimensional multi-node large-scale complex system of the Internet of things can be utilized, the requirements on protection of sensors, concentrators and communication channels and the like are met, and further sensing optimization can be carried out through edge calculation of the tail end of the Internet of things. The data acquisition efficiency also greatly influences the data acquisition capacity of the power grid, and meanwhile, an asynchronous distributed aggregation layout construction algorithm based on hierarchical clustering is provided, so that the data acquisition efficiency of the intelligent power grid is greatly improved. In the aspect of data interaction and transmission, as the physical power grid has the characteristics of wide coverage area, various communication modes and large communication data volume, the physical power grid layer needs to rely on an infrastructure communication facility to conduct high-speed stable interaction and transmission on data in a new energy power grid twin body among layers, and the main communication modes comprise optical fiber communication, satellite communication, wireless communication and the like. The existing communication technology has the defects of higher communication time delay, higher power consumption, smaller coverage area and the like, and can meet the high standard requirement of data transmission interaction in a digital twin power grid based on the latest generation 5G communication technology. In terms of communication protocols, different IoT (Internet of Things) communication protocols can be selected to realize according to data transmission requirements of different scenes in the twin power grid construction.
Wherein, the data information generated in the above-mentioned sensing and interaction processes are recorded in the data terminal 130, and managed by the data terminal 130;
the comprehensive evaluation terminal 140 performs comprehensive evaluation on the data information managed by the data terminal 130, and obtains comprehensive data, and when the comprehensive evaluation terminal 140 performs comprehensive evaluation, the comprehensive evaluation terminal calculates the environmental electricity prices and the predicted punishment electricity prices of the fixed electricity price and the floating electricity price, wherein:
the calculation of the fixed electricity price comprises the reaction of the construction cost, the operation cost and the maintenance cost of the new energy power grid;
the environmental electricity price is a value reaction for reducing the emission of the thermal power unit;
the prediction penalty electricity price is a reaction to the prediction deviation of the future new energy power grid, wherein the related calculation principle is already mentioned in the above process, and is not described in detail herein.
Further, referring to fig. 3, the twin grid layer 200 includes a mapping end 210, a supporting end 220 and an updating end 230, where the mapping end 210 maps the new energy data about the new energy grid recorded in the data end 130 to the supporting end 220, builds a new energy grid twin through the supporting end 220, and the updating end 230 iterates the new energy grid twin in real time.
Specifically, the twin power grid layer 200 performs data resource collection processing based on the data acquired by the actual power grid layer 100, so as to establish a foundation for the twin power grid layer 200, and mainly starts from aspects of data storage, processing, fusion and the like. The data of the twin grid layer 200 includes power data (electric quantity obtained by sensing of the physical grid layer, physical equipment data of the whole grid), environmental data (meteorological data, geographical data, etc.), business data and personnel data (various behavior data of the staff). Because the data obtained from the actual power grid layer 100 is multi-source heterogeneous data, the data end 130 can adopt a non-relational database such as MongoDB, neo4j and the like to store the data during data storage, and meanwhile, the problem of deep fusion analysis of the multi-source heterogeneous power sensing data needs to be solved. When processing and analyzing the electric power data, as the data volume gradually increases, if the mapping end 210 still adopts cloud computing, the data processing time is too long, so that the functions of the data processing part of the mapping end 210 can be performed in a mode of combining edge computing and cloud computing, and the instantaneity and the accuracy of the twin power grid are not affected. The data center technology can also be used in a digital twin power grid, and because the power data has the characteristics of distributed sources, large data volume, multiple structures and the like, the twin power grid layer 200 needs to serve as a data gathering, managing, excavating and sharing center by constructing a data center which is transversely related and longitudinally communicated, so that a fine data foundation is provided for the construction of a new energy power grid twin body.
The support end 220 builds the equivalent modeling of the new energy power grid twin body by preferably utilizing data driving, and further can perform modeling in a data knowledge driving mixed modeling mode, so that the data knowledge mixed driving characteristic of the digital twin power grid is reflected, and the model is continuously iterated and improved by data and knowledge in cooperation with the update end 230, so that the running states of equipment and the power grid are reflected timely. The method solves the problem that the new energy power grid twin can iterate by means of newly generated data after the verification accuracy is reduced, and is also a reason that the new energy power grid twin cannot be directly used as a prediction result, so that the prediction data can help the new energy power grid twin iterate, the new energy power grid twin verifies the prediction result, and the new energy power grid twin supplement each other, and in addition, when the data is insufficient, lost or the cost of constructing the new energy power grid twin from the beginning is too high, transfer learning based on historical data can be adopted. When the new energy power grid twin body is constructed, the steps of module division and step division can be adopted, for example, twin modeling of the power equipment can be realized firstly, then the power equipment gradually rises to a twin power grid subsystem and a twin local power grid, and finally the power equipment is integrated into the integrated new energy power grid twin body.
Still further, the new energy power grid twin comprises a regulating resource intelligent agent, a regulating process intelligent agent and a regulating service intelligent agent, wherein the regulating resource intelligent agent comprises modeling functions of various distributed energy sources, and modeling of the new energy power grid twin is performed on the various distributed energy sources through the resource analysis matched with the supporting end 220, so that the dimension and the precision of physical twin and digital twin modeling and simulation are ensured; the regulating and controlling process intelligent agent comprises an operation control function of an energy router, and ensures multi-time scale coordination and multi-source data coupling of digital and physical twin modeling through process analysis and process realization; the regulation service agent comprises various visual environment, flow management, data storage and prediction functions, and ensures accurate prediction and optimization of digital and physical twin modeling and simulation through service analysis and service implementation. And finally, carrying out dynamic interaction fusion of the three through a multi-agent management system to realize the intellectualization of task decomposition and digital twin tasks. Meanwhile, each agent can be further divided into sub-agents and Sun Zhineng according to task requirements.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A digital twinning-based new energy dynamic electricity price data management system, which is characterized by comprising an actual power grid layer (100) and a twinning power grid layer (200), wherein the dynamic electricity price comprises a fixed electricity price and a floating electricity price, the actual power grid layer (100) is used for determining the fixed electricity price, and the actual power grid layer (100) and the twinning power grid layer (200) jointly determine the floating electricity price, wherein:
the actual power grid layer (100) obtains comprehensive data by evaluating the managed actual power grid data information;
the actual power grid layer (100) comprises a sensing end (110), an interaction end (120), a data end (130) and a comprehensive evaluation end (140), wherein the sensing end (110) is used for sensing acquired actual power grid data information, and the interaction end (120) performs data interaction in a transmission mode, wherein:
the data information generated in the sensing and interaction processes is recorded in a data end (130), and is managed through the data end (130);
the comprehensive evaluation terminal (140) performs comprehensive evaluation on the data information managed by the data terminal (130) and obtains comprehensive data; when the comprehensive evaluation terminal (140) performs comprehensive evaluation, calculating the environmental electricity prices and the predicted punishment electricity prices of the fixed electricity price and the floating electricity price, wherein:
the calculation of the fixed electricity price comprises the reaction of the construction cost, the operation cost and the maintenance cost of the new energy power grid;
the environmental electricity price is a value reaction for reducing the emission of the thermal power unit, namely, the value of the emission of the thermal power unit is reduced due to the fact that new energy generates one unit of electric quantity per unit time in a certain power grid running state;
when joint scheduling calculation of new energy participation is carried out, the method comprises the following steps ofAt moment, the actual state of each thermal power generating unitThe time power is determined by the following equation:
；
；
；
wherein ,is the system objective function of the thermal power generating unit, +.>The power generation cost of the thermal power generating unit is; />Is a thermal power generating unit->At->Real-time power at time; />Is->New energy power plant->Real-time power at time; /> and />The total number of the thermal power generating units and the new energy power plants is respectively; />、/>Respectively a lower power limit and an upper power limit of the thermal power unit; />The new energy power plant in the power grid is +.>Total emitted power at the moment; />Is->The total load of the power grid at the moment;
the environmental electricity price of the new energy is determined by the environmental marginal value of the thermal power, and the total environmental electricity price of the new energy unit in unit timeThe method comprises the following steps:
；
wherein ,the environmental electricity price proportionality coefficient; />Representing the deviation of the gas emission function; />Is used for generating new energyAn amount of deviation of the electric power; />Indicate->A gas emission function of the personal thermal power unit; />The output value of the system objective function of the thermal power generating unit is obtained;
predicting punishment electricity price is the reaction to the predicted deviation of the future new energy power grid;
the larger the load prediction deviation of the new energy power grid is, the higher the predicted punishment electricity price is, and the calculation formula of the predicted punishment electricity price is as follows:
；
in the formula ,punishment electricity price is predicted; />Punishment electricity price proportionality coefficients are predicted; />Is->Moment total load of the power grid;predicting deviation amount for new energy power grid load; />For the generation cost function, expressed in power +.>The power generation cost of the lower power plant; />Representing deriving a power generation cost function;
the twin power grid layer (200) constructs a new energy power grid twin body based on new energy data during actual power grid data information management;
the floating electricity price is obtained by comprehensive data under the predictive verification of a twin body of a new energy power grid;
the new energy power grid twin predicts future power grid load through simulation of the new energy power grid, so as to verify the prediction of the comprehensive evaluation end (140) and obtain deviation in advance.
2. The digital twinning-based new energy dynamic electricity price data management system according to claim 1, wherein the twinning grid layer (200) comprises a mapping end (210), a supporting end (220) and an updating end (230), the mapping end (210) maps new energy data about a new energy grid recorded in a data end (130) to the supporting end (220), a new energy grid twine is built through the supporting end (220), and the updating end (230) iterates the new energy grid twine in real time.
3. The digital twin based new energy dynamic electricity price data management system according to claim 2, wherein the data information recorded by the data terminal (130) includes power data, environment data, business data and personnel data.
4. The digital twinning-based new energy dynamic electricity price data management system according to claim 3, wherein the new energy grid twins comprise a regulating resource agent, a regulating process agent and a regulating service agent;
the resource control intelligent agent carries out digital modeling on various distributed new energy sources through resource analysis.
5. The digital twin-based dynamic electricity price data management system for new energy according to claim 4, wherein the data terminal (130) uses a non-relational database for data storage when recording data information.
6. The digital twinning-based new energy dynamic electricity price data management system of claim 5, wherein the interaction of data in the interaction end (120) employs IoT communication protocols.
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