Demand response simulation platform and method supporting autonomous agent game
1. A demand response simulation platform supporting autonomous agent gaming, the simulation platform comprising:
the human-computer interaction interface layer is used for receiving the selection of a user on the autonomous intelligent agent, setting the power data of the autonomous intelligent agent and the power data of a power consumer, and transmitting the set power data of the autonomous intelligent agent and the power data of the power consumer to the database server layer;
the demand response method layer is used for selecting an initial demand response scheme of the autonomous intelligent agent from historical demand response schemes of a database server layer according to power data of the autonomous intelligent agent selected by a user and power data of power users, adjusting the electric quantity and/or the electricity price of the autonomous intelligent agent by taking the benefit maximization of the autonomous intelligent agent as a target based on the initial demand response scheme of the autonomous intelligent agent, carrying out game among all autonomous intelligent agents participating in a demand simulation process, and further changing or adding the demand response scheme of the autonomous intelligent agent based on a game result;
and the database server layer is used for storing the power data of each main intelligent agent, the historical demand response scheme of each main intelligent agent and the changed or newly added demand response scheme.
2. The simulation platform of claim 1, wherein the autonomous agent comprises: power generation enterprises, power transmission enterprises, power selling enterprises and power consumers.
3. The simulation platform of claim 2, wherein the human-machine interaction interface layer comprises:
the power generation enterprise unit is used for accepting the selection of a user when the autonomous intelligent agent selected by the user is a power generation enterprise, and is also used for setting power data of the power generation enterprise and transmitting the set power data to the database server layer, wherein the power data of the power generation enterprise comprises: electricity price, electricity quantity and cost;
a power transmission enterprise unit for accepting a selection of a user when the autonomous agent selected by the user is a power transmission enterprise, and also for setting power data of the power transmission enterprise and transmitting the set power data to a database server layer, the power data of the power transmission enterprise including: power transmission and distribution amount, power transmission cost and line loss rate;
the power selling enterprise unit is used for receiving the selection of the user when the autonomous intelligent agent selected by the user is a power selling enterprise, and is also used for setting the power data of the power selling enterprise and transmitting the set power data to the database server layer, wherein the power data of the power selling enterprise comprises: electricity selling price, user electricity consumption, user category, peak and valley electricity price fluctuation, electricity transmission and distribution price, market share and equipment use cost;
a power consumer unit for accepting a user's selection when the autonomous agent selected by the user is a power consumer, and for setting power data of the power consumer and transmitting the set power data to a database server layer, the power data of the power consumer including: electricity usage, electricity load, electricity cost, power quality, and service level satisfaction.
4. The simulation platform of claim 3, wherein the demand response method layer comprises:
the reading module is used for reading the power data of the autonomous intelligent agent selected by the user and the power data of the power consumer from the database server layer;
the prediction module is used for predicting the power data of the power consumer in the next period according to the power data of the power consumer;
the determining module is used for selecting an initial demand response scheme of the autonomous intelligent agent selected by the user from historical demand response schemes of a database server layer according to the predicted power data of the power consumer in the next period and the power data of the autonomous intelligent agent selected by the user;
the game module is used for adjusting the electric quantity and/or the electricity price of the autonomous intelligent agent by taking the maximization of the self interest acquired by the autonomous intelligent agent as a target based on the initial demand response scheme of the autonomous intelligent agent selected by the user, playing games among all the autonomous intelligent agents participating in the demand simulation process, and further changing or adding the demand response scheme of the autonomous intelligent agent based on the game result;
and the implementation management module is used for managing and implementing the demand response scheme of each main intelligent agent corresponding to each power data of the power consumer.
5. The emulation platform of claim 4, wherein the gaming module is specifically configured to:
the power generation enterprises adjust the generated energy and/or the electricity price for selling electricity to the electricity selling enterprises according to the market electricity demand, and the self benefit maximization is obtained;
the power transmission enterprise adjusts the power transmission amount according to the maximum bearing capacity of the power grid, and obtains the maximum benefit of the power transmission enterprise;
the electricity selling enterprise adjusts the electricity purchasing amount of the electricity selling enterprise and the electricity selling price of the electricity selling to the electricity users according to the electricity price of the electricity generating enterprise and the electricity purchasing amount of the electricity users, and carries out peak clipping and valley filling on the power grid to obtain the maximum benefit of the electricity selling enterprise;
on the basis of meeting the self power consumption requirement, the power consumer adjusts the self power consumption to obtain the self benefit maximization according to the change of the electricity selling price.
6. The simulation platform of claim 4, wherein the implementation management module comprises:
the resource management unit is used for regularly maintaining and managing the basic information of the autonomous intelligent agent;
the scheme management unit is used for maintaining and managing the demand response scheme of the autonomous agent;
a scenario implementation unit for executing a demand response scenario;
the system comprises a scheme execution monitoring unit, a demand response scheme optimization unit and a demand response unit, wherein the scheme execution monitoring unit is used for monitoring the energy utilization load of an autonomous intelligent agent needing to optimize the demand response scheme;
and the scheme effect analysis unit is used for carrying out statistical analysis on the historical energy consumption data of the autonomous intelligent agent needing to optimize the demand response scheme.
7. The simulation platform of claim 1, wherein the database server layer comprises:
the original database module is used for storing the classification information of the autonomous agent;
the application database module is used for storing the power data of the autonomous agent;
the policy information database module is used for storing various policy information;
the demand response resource library module is used for storing historical demand response schemes;
and the historical response information base module is used for storing historical demand response information.
8. A demand response simulation method supporting autonomous agent gaming, the method comprising:
step 1: the method comprises the steps that a user selection of an autonomous intelligent agent is received on the basis of a human-computer interaction interface layer, power data of the autonomous intelligent agent and power data of a power consumer are set, and the power data of the autonomous intelligent agent and the power data of the power consumer are transmitted to a database server layer;
step 2: selecting an initial demand response scheme of the autonomous intelligent agent from historical demand response schemes of a database server layer based on power data of the autonomous intelligent agent selected by a user and power data of power users by using a demand response method layer, adjusting the electric quantity and/or the electricity price of the autonomous intelligent agent by taking the benefit maximization of the autonomous intelligent agent as a target based on the initial demand response scheme of the autonomous intelligent agent, carrying out game among all autonomous intelligent agents participating in a demand simulation process, and further changing or adding the demand response scheme of the autonomous intelligent agent based on a game result;
and 3, storing the requirement response scheme of the autonomous intelligent agent selected by the changed or newly added user in a database server layer.
9. The method of claim 8, wherein the selecting, with the demand response methodology layer, an initial demand response scenario for the autonomous agent from historical demand response scenarios at a database server layer based on the user-selected power data for the autonomous agent and the power data for the power consumer comprises:
reading the power data of the autonomous agent and the power data of the power consumer selected by the user from the database server layer by using the demand response method layer;
predicting the power data of the power consumer in the next period according to the power data of the power consumer read from the database server layer;
and selecting an initial demand response scheme of the autonomous intelligent agent selected by the user from historical demand response schemes of a database server layer according to the predicted power data of the power consumer in the next period and the power data of the autonomous intelligent agent selected by the user.
10. The method of claim 8, wherein accepting a user selection of an autonomous agent based on the human-computer interaction interface layer, setting power data for the autonomous agent and power data for a power consumer, and transmitting the setting power data for the autonomous agent and power data for the power consumer to the database server layer comprises:
if the autonomous intelligent agent selected by the user is a power generation enterprise unit, the power generation enterprise unit using the human-computer interaction interface layer accepts the selection of the user, then sets power data of a power generation enterprise and transmits the set power data to a database server layer, wherein the power data of the power generation enterprise comprises: electricity price, electricity quantity and cost;
if the autonomous intelligent agent selected by the user is a power transmission enterprise unit, the power transmission enterprise unit using the human-computer interaction interface layer receives the selection of the user, then sets power data of a power transmission enterprise and transmits the set power data to a database server layer, wherein the power data of the power transmission enterprise comprises: power transmission and distribution amount, power transmission cost and line loss rate;
if the autonomous intelligent agent selected by the user is the electricity selling enterprise unit, the electricity selling enterprise unit utilizing the human-computer interaction interface layer receives the selection of the user, then the power data of the electricity selling enterprise is set, and the set power data is transmitted to the database server layer, wherein the power data of the electricity selling enterprise comprises: electricity selling price, user electricity consumption, user category, peak and valley electricity price fluctuation, electricity transmission and distribution price, market share and equipment use cost;
if the autonomous agent selected by the user is the power subscriber unit, the power subscriber unit using the human-computer interaction interface layer accepts the selection of the user, then sets power data of the power subscriber and transmits the set power data to the database server layer, wherein the power data of the power subscriber comprises: electricity usage, electricity load, electricity cost, power quality, and service level satisfaction.
11. The method of claim 8, wherein said adjusting the power and/or electricity prices of said autonomous agents to target their own benefits maximization, playing a game among all autonomous agents participating in a demand simulation process, and further modifying or adding a demand response scheme for said autonomous agents based on the game result comprises:
adjusting the generated energy and/or the electricity price for selling electricity to an electricity selling enterprise according to the market electricity demand to obtain the maximum benefit of the electricity generating enterprise;
adjusting the power transmission amount according to the maximum bearing capacity of the power grid to obtain the maximum benefit of a power transmission enterprise;
adjusting the electricity purchasing quantity of the electricity selling enterprises to the electricity generating enterprises and the electricity selling price of the electricity selling to the electricity consumers according to the electricity price of the electricity generating enterprises and the electricity purchasing quantity of the electricity consumers, and carrying out peak clipping and valley filling on the power grid to obtain the maximum benefit of the electricity selling enterprises;
on the basis of meeting the self power demand, the power consumption of the power consumer is adjusted according to the power price change of the power sold by the power selling enterprise, so that the benefit maximization of the power consumer is obtained.
Background
Under the background of ubiquitous power Internet of things, demand side resource optimization and regulation are carried out, so that the power grid is safer to operate and more lean to manage. The main body of a demand response user under the new situation of the Internet of things generally presents a million-level growth trend, the development form of demand response business is gradually participated in a demand response system in an automatic contract form from the prior art taking invitation response as a main part, the number of the main body and the terminal participating in the demand response presents the million-level growth trend, the prior business coordination and risk control mechanism which is researched, evaluated, invited and negotiated by people and is mainly used for meeting the demand under the new situation cannot meet the demand under the new situation, and the accuracy is low, so that a high-reliability demand response simulation platform needs to be designed, and the reliability of the demand response full-chain business is ensured.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a demand response simulation platform supporting autonomous intelligent agent game, which comprises the following steps:
the human-computer interaction interface layer is used for receiving the selection of a user on the autonomous intelligent agent, setting the power data of the autonomous intelligent agent and the power data of a power consumer, and transmitting the set power data of the autonomous intelligent agent and the power data of the power consumer to the database server layer;
the demand response method layer is used for selecting an initial demand response scheme of the autonomous intelligent agent from historical demand response schemes of a database server layer according to power data of the autonomous intelligent agent selected by a user and power data of power users, adjusting the electric quantity and/or the electricity price of the autonomous intelligent agent by taking the benefit maximization of the autonomous intelligent agent as a target based on the initial demand response scheme of the autonomous intelligent agent, carrying out game among all autonomous intelligent agents participating in a demand simulation process, and further changing or adding the demand response scheme of the autonomous intelligent agent based on a game result;
and the database server layer is used for storing the power data of each main intelligent agent, the historical demand response scheme of each main intelligent agent and the changed or newly added demand response scheme.
Preferably, the autonomous agent includes: power generation enterprises, power transmission enterprises, power selling enterprises and power consumers.
Further, the human-computer interaction interface layer includes:
the power generation enterprise unit is used for accepting the selection of a user when the autonomous intelligent agent selected by the user is a power generation enterprise, and is also used for setting power data of the power generation enterprise and transmitting the set power data to the database server layer, wherein the power data of the power generation enterprise comprises: electricity price, electricity quantity and cost;
a power transmission enterprise unit for accepting a selection of a user when the autonomous agent selected by the user is a power transmission enterprise, and also for setting power data of the power transmission enterprise and transmitting the set power data to a database server layer, the power data of the power transmission enterprise including: power transmission and distribution amount, power transmission cost and line loss rate;
the power selling enterprise unit is used for receiving the selection of the user when the autonomous intelligent agent selected by the user is a power selling enterprise, and is also used for setting the power data of the power selling enterprise and transmitting the set power data to the database server layer, wherein the power data of the power selling enterprise comprises: electricity selling price, user electricity consumption, user category, peak and valley electricity price fluctuation, electricity transmission and distribution price, market share and equipment use cost;
a power consumer unit for accepting a user's selection when the autonomous agent selected by the user is a power consumer, and for setting power data of the power consumer and transmitting the set power data to a database server layer, the power data of the power consumer including: electricity usage, electricity load, electricity cost, power quality, and service level satisfaction.
Further, the demand response method layer includes:
the reading module is used for reading the power data of the autonomous intelligent agent selected by the user and the power data of the power consumer from the database server layer;
the prediction module is used for predicting the power data of the power consumer in the next period according to the power data of the power consumer;
the determining module is used for selecting an initial demand response scheme of the autonomous intelligent agent selected by the user from historical demand response schemes of a database server layer according to the predicted power data of the power consumer in the next period and the power data of the autonomous intelligent agent selected by the user;
the game module is used for adjusting the electric quantity and/or the electricity price of the autonomous intelligent agent by taking the maximization of the self interest acquired by the autonomous intelligent agent as a target based on the initial demand response scheme of the autonomous intelligent agent selected by the user, playing games among all the autonomous intelligent agents participating in the demand simulation process, and further changing or adding the demand response scheme of the autonomous intelligent agent based on the game result;
and the implementation management module is used for managing and implementing the demand response scheme of each main intelligent agent corresponding to each power data of the power consumer.
Further, the gaming module is specifically configured to:
the power generation enterprises adjust the generated energy and/or the electricity price for selling electricity to the electricity selling enterprises according to the market electricity demand, and the self benefit maximization is obtained;
the power transmission enterprise adjusts the power transmission amount according to the maximum bearing capacity of the power grid, and obtains the maximum benefit of the power transmission enterprise;
the electricity selling enterprise adjusts the electricity purchasing amount of the electricity selling enterprise and the electricity selling price of the electricity selling to the electricity users according to the electricity price of the electricity generating enterprise and the electricity purchasing amount of the electricity users, and carries out peak clipping and valley filling on the power grid to obtain the maximum benefit of the electricity selling enterprise;
on the basis of meeting the self power consumption requirement, the power consumer adjusts the self power consumption to obtain the self benefit maximization according to the change of the electricity selling price.
Further, the implementation management module includes:
the resource management unit is used for regularly maintaining and managing the basic information of the autonomous intelligent agent;
the scheme management unit is used for maintaining and managing the demand response scheme of the autonomous agent;
a scenario implementation unit for executing a demand response scenario;
the system comprises a scheme execution monitoring unit, a demand response scheme optimization unit and a demand response unit, wherein the scheme execution monitoring unit is used for monitoring the energy utilization load of an autonomous intelligent agent needing to optimize the demand response scheme;
and the scheme effect analysis unit is used for carrying out statistical analysis on the historical energy consumption data of the autonomous intelligent agent needing to optimize the demand response scheme.
Preferably, the database server layer includes:
the original database module is used for storing the classification information of the autonomous agent;
the application database module is used for storing the power data of the autonomous agent;
the policy information database module is used for storing various policy information;
the demand response resource library module is used for storing historical demand response schemes;
and the historical response information base module is used for storing historical demand response information.
The invention provides a demand response simulation method supporting autonomous intelligent agent game based on the same inventive concept, which comprises the following steps:
step 1: the method comprises the steps that a user selection of an autonomous intelligent agent is received on the basis of a human-computer interaction interface layer, power data of the autonomous intelligent agent and power data of a power consumer are set, and the power data of the autonomous intelligent agent and the power data of the power consumer are transmitted to a database server layer;
step 2: selecting an initial demand response scheme of the autonomous intelligent agent from historical demand response schemes of a database server layer based on power data of the autonomous intelligent agent selected by a user and power data of power users by using a demand response method layer, adjusting the electric quantity and/or the electricity price of the autonomous intelligent agent by taking the benefit maximization of the autonomous intelligent agent as a target based on the initial demand response scheme of the autonomous intelligent agent, carrying out game among all autonomous intelligent agents participating in a demand simulation process, and further changing or adding the demand response scheme of the autonomous intelligent agent based on a game result;
and 3, storing the requirement response scheme of the autonomous intelligent agent selected by the changed or newly added user in a database server layer.
Preferably, the selecting an initial demand response scenario of the autonomous agent from historical demand response scenarios of a database server tier by a demand response method tier based on the power data of the user-selected autonomous agent and the power data of the power consumer comprises:
reading the power data of the autonomous agent and the power data of the power consumer selected by the user from the database server layer by using the demand response method layer;
predicting the power data of the power consumer in the next period according to the power data of the power consumer read from the database server layer;
and selecting an initial demand response scheme of the autonomous intelligent agent selected by the user from historical demand response schemes of a database server layer according to the predicted power data of the power consumer in the next period and the power data of the autonomous intelligent agent selected by the user.
Preferably, the accepting, by the human-computer interaction interface layer, a selection of an autonomous agent by a user, setting power data of the autonomous agent and power data of a power consumer, and transmitting the set power data of the autonomous agent and the set power data of the power consumer to the database server layer includes:
if the autonomous intelligent agent selected by the user is a power generation enterprise unit, the power generation enterprise unit using the human-computer interaction interface layer accepts the selection of the user, then sets power data of a power generation enterprise and transmits the set power data to a database server layer, wherein the power data of the power generation enterprise comprises: electricity price, electricity quantity and cost;
if the autonomous intelligent agent selected by the user is a power transmission enterprise unit, the power transmission enterprise unit using the human-computer interaction interface layer receives the selection of the user, then sets power data of a power transmission enterprise and transmits the set power data to a database server layer, wherein the power data of the power transmission enterprise comprises: power transmission and distribution amount, power transmission cost and line loss rate;
if the autonomous intelligent agent selected by the user is the electricity selling enterprise unit, the electricity selling enterprise unit utilizing the human-computer interaction interface layer receives the selection of the user, then the power data of the electricity selling enterprise is set, and the set power data is transmitted to the database server layer, wherein the power data of the electricity selling enterprise comprises: electricity selling price, user electricity consumption, user category, peak and valley electricity price fluctuation, electricity transmission and distribution price, market share and equipment use cost;
if the autonomous agent selected by the user is the power subscriber unit, the power subscriber unit using the human-computer interaction interface layer accepts the selection of the user, then sets power data of the power subscriber and transmits the set power data to the database server layer, wherein the power data of the power subscriber comprises: electricity usage, electricity load, electricity cost, power quality, and service level satisfaction.
Preferably, the step of adjusting the electric quantity and/or the electricity price of the autonomous agent by taking the self interest maximization of the autonomous agent as a target, playing the game among all autonomous agents participating in the demand simulation process, and further changing or adding a demand response scheme of the autonomous agent based on a game result includes:
adjusting the generated energy and/or the electricity price for selling electricity to an electricity selling enterprise according to the market electricity demand to obtain the maximum benefit of the electricity generating enterprise;
adjusting the power transmission amount according to the maximum bearing capacity of the power grid to obtain the maximum benefit of a power transmission enterprise;
adjusting the electricity purchasing quantity of the electricity generating enterprises of the electricity selling enterprises and the electricity selling price of the electricity selling users according to the electricity price of the electricity generating enterprises and the electricity purchasing quantity of the electricity purchasing users, and carrying out peak clipping and valley filling on the power grid to obtain the maximum benefit of the electricity selling enterprises;
on the basis of meeting the self power demand, the power consumption of the power consumer is adjusted according to the power price change of the power sold by the power selling enterprise, so that the benefit maximization of the power consumer is obtained.
Compared with the closest prior art, the invention has the following beneficial effects:
1. the invention provides a demand response simulation platform and method supporting autonomous intelligent agent game, which comprises the following steps: the system comprises a human-computer interaction interface layer, a demand response method layer and a database server layer; the human-computer interaction interface layer is used for receiving the selection of a user on the autonomous intelligent agent, setting the power data of the autonomous intelligent agent and the power data of a power consumer, and transmitting the set power data of the autonomous intelligent agent and the power data of the power consumer to the database server layer; the demand response method layer is used for selecting an initial demand response scheme of the autonomous intelligent agent from historical demand response schemes of a database server layer according to power data of the autonomous intelligent agent selected by a user and power data of power users, adjusting the electric quantity and/or the electricity price of the autonomous intelligent agent by taking the benefit maximization of the autonomous intelligent agent as a target based on the initial demand response scheme of the autonomous intelligent agent, carrying out game among all autonomous intelligent agents participating in a demand simulation process, and further changing or adding the demand response scheme of the autonomous intelligent agent based on a game result; the database server layer is used for storing the power data of each main intelligent agent, the historical demand response scheme and the changed or newly added demand response scheme of each main intelligent agent; the technical scheme provided by the invention supports dynamic demand response scheme updating, solves the reliability of autonomous negotiation and interaction between users and a demand response system under the condition that massive demand response users participate, and ensures safe and efficient operation of demand response services and a power grid.
2. The simulation platform in the technology provided by the invention can optimize the demand response information of each main agent, thereby improving the resource utilization rate of the social power industry.
Drawings
FIG. 1 is a block diagram of a demand response simulation platform supporting autonomous agent gaming provided in embodiments of the present invention;
FIG. 2 is a flowchart of a human-computer interaction interface layer provided in an embodiment of the present invention;
FIG. 3 is a flow chart of the operation of a demand response method layer provided in an embodiment of the present invention;
FIG. 4 is a schematic illustration of a power market demand response provided in an embodiment of the present invention;
FIG. 5 is a business architecture diagram of a demand response simulation platform supporting autonomous agent gaming provided in embodiments of the present invention;
FIG. 6 is a flow chart of a demand response simulation method supporting autonomous agent gaming provided by the present invention;
fig. 7 is a specific flowchart of a demand response simulation method for supporting autonomous agent gaming according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The demand response simulation platform supporting the autonomous intelligent agent game, provided by the invention, is analyzed from two aspects of a system architecture and a service architecture of the demand response simulation platform.
System architecture of demand response simulation platform
The invention provides a demand response simulation platform supporting autonomous intelligent agent gaming, which is described by combining a system architecture of the demand response simulation platform with a figure 1, wherein the demand response simulation platform is abstracted into a layered structure and comprises a human-computer interaction interface layer, a demand response method layer and a database server layer;
the database server layer can store electricity utilization data of related users, various policy information, a preset demand response scheme and historical demand response information, wherein the electricity utilization data and the various policy information are needed in the operation process of the demand response simulation platform;
the demand response method layer can realize the interaction process between the user and the power generation and power selling enterprises;
the human-computer interaction interface layer provides a graphical interface which is convenient for a user to use, and the user can participate in the process of demand response simulation by selecting different roles, so that more efficient energy consumption and profit maximization are realized;
the technology applied by the demand response simulation platform supporting the autonomous agent game comprises the following steps: database technology, WEB technology, algorithm development, optimization technology and multiparty game theory.
Wherein, the human-computer interaction interface layer is specifically used for:
the human-computer interaction interface layer provides a graphical interface convenient for a user to use, the user logs in and manages the power consumption of the power user in real time and the overall operation condition of the simulation platform through the preset authority of the simulation platform, wherein the administrator authority monitors the power consumption condition of the power user in real time and monitors the overall operation condition of the simulation platform; the user authority can participate in the process of demand response simulation by selecting different roles, wherein the roles comprise roles of four autonomous agents, namely power generation enterprises, power selling enterprises, power transmission enterprises and power users, and the power generation price, the power generation quantity and the power generation cost can be set for the power generation enterprises; the transmission and distribution amount, the transmission cost and the line loss rate can be set for transmission enterprises; for an electricity selling enterprise, electricity selling price, user electricity consumption, user category, peak valley price fluctuation, power transmission and distribution price, market share and equipment use cost can be set; the power consumption, the power load, the power cost, the power quality, and the service level satisfaction may be set for the power usage. By modifying the market strategy, the information of different power generation and power selling roles and the like, the simulation output of different types of demand response corresponding to the optimal demand response scheme can be realized so as to meet the new market model in the future, and the specific working flow is shown in fig. 2.
The human-computer interface layer provided by the embodiment supports the addition of roles, so that the simulation platform can be dynamically updated to meet the requirements of a new market on the simulation platform in the future. The demand response method layer is specifically configured to:
the demand response method layer can realize the interaction process of power consumers, power generation enterprises, power selling enterprises and power transmission enterprises, and optimize the benefit of the autonomous intelligent agent while realizing more efficient energy consumption; the reading module of the demand response method layer reads the acquired power data of the power consumers from the server database layer, the prediction module predicts the power data of the power consumers in the next time period by using the information, then the determination module selects a proper demand response scheme from the server database layer as an initial demand response scheme according to the predicted power data and the historical demand response condition, generates real-time electricity price and excitation of the next time period and issues the real-time electricity price and the excitation to the power consumers, the game module changes the power consumption modes of the power consumers according to the electricity price and the excitation, the demand response method can change or newly add the demand response scheme of the autonomous intelligent agent selected by the user according to the power consumption modes of the power consumers, and the specific working flow is shown in fig. 3; and after the demand response scheme of the autonomous agent selected by the changed or newly added user is obtained, the implementation management module implements and manages the simulation platform based on the demand response scheme of the autonomous agent selected by the changed or newly added user.
Wherein, implement the management module, include: the resource management unit is used for regularly maintaining and managing the basic information of the autonomous intelligent agent; the scheme management unit is used for maintaining and managing the demand response scheme of the autonomous agent; a scenario implementation unit for executing a demand response scenario; the system comprises a scheme execution monitoring unit, a demand response scheme optimization unit and a demand response unit, wherein the scheme execution monitoring unit is used for monitoring the energy utilization load of an autonomous intelligent agent needing to optimize the demand response scheme; and the scheme effect analysis unit is used for carrying out statistical analysis on the historical energy consumption data of the autonomous intelligent agent needing to optimize the demand response scheme.
The demand response layer provided by the embodiment can ensure the reliability of autonomous negotiation and interaction between the user and the demand response system under the condition that a large number of demand response users participate, and ensure the safe and efficient operation of the demand response service and the power grid. The database server layer is specifically configured to:
the database server layer can store power data, various policy information, preset demand response schemes and historical demand response information of related users required in the operation process of the demand response simulation platform, provide data support for the demand response method, read the power utilization information and the demand response schemes of the users from the database by the demand response method, and simultaneously store the result of demand response and the generated new demand response schemes into the database.
In the game process, the electricity selling company attracts power users by pushing out different power packages, and the maximization of customer satisfaction and electricity selling profits is expected to be realized, namely the balance of explicit income and implicit income is considered. The electricity selling company adjusts the electricity utilization peak value through the electricity price, implements demand response, and enables the electricity utilization peak value of the power consumer to be reduced, so that the maximization of the self profit is obtained, and the income improvement is realized. The user receives the influence of dynamic electricity price on the basis that can satisfy self power consumption demand, in order to obtain the minimizing of self cost, can reduce the power consumption in peak period. Through dynamic electricity price adjustment based on demand response, the resource utilization rate of the social power industry is improved.
In this embodiment, performing the demand response of the power market based on the demand response simulation platform specifically includes, as shown in fig. 4:
the power generation enterprises, the power transmission enterprises, the power selling enterprises and the power users play games with one another, and the power grid maximum bearing and power demand response play games between the power generation enterprises and the power transmission enterprises; carrying out non-cooperative game of price and cooperative game of market supply and demand relation and power consumer price formulation between a power generation enterprise and a power selling enterprise; the power transmission enterprises and the power selling enterprises play the game of the maximum power grid bearing and power demand response; the power selling enterprise and the power users perform incomplete information dynamic price game, game of peak power demand of the users and maximum bearing capacity of the power selling enterprise, and game of explicit income and implicit income;
the power generation enterprise conducts game with other autonomous intelligent bodies according to the power generation price, the power generation amount, the fuel cost and the management/operation cost of the power generation enterprise, the power transmission enterprise conducts game with other autonomous intelligent bodies according to the power transmission and distribution, the line loss rate, the power transmission and distribution cost, the network service cost and the blocking management cost, the power selling enterprise conducts game with other autonomous intelligent bodies according to the power selling price of power users, the actual power consumption of the users, the user category, the peak valley price fluctuation, the power transmission and distribution price, the market share and the equipment investment maintenance cost, and the power users conduct game with other autonomous intelligent bodies according to the power cost, the power quality and the service book evaluation satisfaction degree;
the profit of the power transmission enterprise is released on the power selling side and is under the big background of power transmission and distribution price improvement, the power grid company is only responsible for the transmission process, and the government checks and decides the profit;
the income of the electricity selling enterprises rises, the standard electricity price is reduced, the user satisfaction rises, more customers are attracted, the public praise degree and the enterprise image of the electricity selling enterprises are improved, and the core competitiveness of the electricity selling enterprises is enhanced;
the profit of the electricity selling enterprise is calculated based on an actual profit calculation factor, the actual profit calculation factor is determined by contract electricity purchasing cost of the electricity selling enterprise and the electricity generating enterprise, contract electricity price with the electricity generating enterprise, electricity utilization prediction of power users, real-time market electricity price, real-time market reference electricity price, deviation value between prediction and actual consumption electricity and influence coefficient of electricity on electricity price, when the prediction electricity is more than the actual electricity, the electricity selling enterprise needs to consume the electricity with low price compensation; when the actual electric quantity is larger than the predicted electric quantity, the electric power selling enterprises need to purchase electric energy at high price.
Business framework of demand response simulation platform
The implementation of the service of the demand response simulation in this embodiment is implemented based on a service framework of a demand response simulation platform, and is described with reference to fig. 5, where the service framework of the simulation platform is composed of an application platform, a support platform, and a specific implementation part, the service framework is specifically implemented based on reinforcement learning, demand response, and markov decision, the support platform in the service framework provides tool information support for an upper application platform, and the application platform includes: power supply and demand analysis, power supply and demand prediction, power intelligent simulation, customer satisfaction analysis, dynamic electricity price pricing and report generation; the tool comprises: and other information management tools such as a database management tool and a model management tool are used for integrating data and training the model, and the model management tool is used for managing the model and the algorithm module. The platform simulates each role in the power market, responds to an incentive mechanism, changes the conventional power consumption mode, and improves the reliability of autonomous negotiation and interaction between users and a demand response system, so that a high-credibility demand response platform is constructed.
Example 2
Based on the simulation platform provided above, the present invention provides a demand response simulation method supporting autonomous agent gaming, as shown in fig. 6, including:
step 1: the method comprises the steps that a user selection of an autonomous intelligent agent is received on the basis of a human-computer interaction interface layer, power data of the autonomous intelligent agent and power data of a power consumer are set, and the power data of the autonomous intelligent agent and the power data of the power consumer are transmitted to a database server layer;
step 2: the method comprises the steps that a power data of an autonomous agent selected by a user and a power data of a power user utilize a demand response method layer to select an initial demand response scheme of the autonomous agent from historical demand response schemes of a database server layer, the power and/or electricity price of the autonomous agent is adjusted by taking the benefit maximization of the autonomous agent as a target based on the initial demand response scheme of the autonomous agent, a game is played among all autonomous agents participating in a demand simulation process, and the demand response scheme of the autonomous agent is further changed or added based on a game result;
and 3, storing the requirement response scheme of the autonomous intelligent agent selected by the changed or newly added user in a database server layer.
The overall flow chart is shown in fig. 7, and specifically includes:
the user first selects the autonomous agents, sets the number of autonomous agents and the corresponding power data. The demand response method layer firstly reads collected power data of the power consumers from a database, predicts the power data of the power consumers in the next time period by using the data, then selects a proper demand response scheme from the database according to the power data of the power consumers in the next time period and historical demand response conditions, generates real-time electricity price and incentive in the next time period and sends the real-time electricity price and the incentive to the power consumers, the power consumers change own power consumption modes according to the electricity price and the incentive, the demand response method calculates the cost and income of each party according to the power consumption modes of the users, changes or newly adds the demand response scheme, balances the benefits of each party as far as possible, and finally stores the changed or newly added demand response scheme of the autonomous intelligent agent selected by the users in the database server layer.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
