CN111641207B - Regional energy complex virtual aggregation system and method - Google Patents
Regional energy complex virtual aggregation system and method Download PDFInfo
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Abstract
The invention provides a regional energy complex virtual aggregation system and a method using a charging station as a key node, which are based on an electric power Internet of things architecture and comprise three architecture layers including a perception layer, a network layer and an application layer, wherein the regional energy complex virtual aggregation system comprises eight modules: the system comprises a data acquisition module, an operation state management module, a distributed power generation equipment output prediction module, a charging station load prediction module, an operation plan making module, a real-time scheduling optimization module, an internal transaction platform module and an external transaction platform module. The invention reduces the negative influence of the charging load on the power grid, and saves the cost of upgrading and expanding the power distribution network; and electric energy generated by the distributed renewable energy source power generation is fully consumed. The efficient utilization of renewable energy sources is realized by providing the network-connected distributed energy storage renting service, and the prediction module and the scheduling module in the system, so that the wind discarding and light discarding rate is reduced; the information flow layer manages energy sources, which is beneficial to the cost control of the energy source complex; the application situation is more extensive.
Description
Technical Field
The invention relates to the technical field of electric power energy scheduling and management, in particular to a regional energy complex virtual aggregation system and method taking a charging station as a key node.
Background
In recent years, the installed capacity of the distributed power generation is rapidly increased, and the distributed power generation has the advantages of being close to a load end, being suitable for near-term digestion and reducing the line laying investment and the electric energy transmission loss of a transmission and distribution link. In order to realize near-elimination of distributed power generation, the national origin-changing commission and the national energy agency in 2017 issue a report on the development of market trade test points of distributed power generation (hereinafter referred to as report), and allow the power transaction between the distributed power generation project and nearby users within the voltage level range of the power distribution network.
The distributed renewable energy source participates in the power transaction of the power distribution network, and can provide lower electric energy for users than conventional retail electricity prices, but in the power transaction, distributed power generation often lacks initiative due to the reasons of smaller capacity, unstable output and the like, and the flexibility of the user side is required to be complementarily utilized or mobilized through reasonable multiple energy of an energy comprehensive service provider so as to weaken the uncertainty influence of the distributed renewable energy source power generation, and improve the power supply flexibility and stability. The concept of virtual power plants has been developed, and the virtual power plants realize the aggregation and coordination optimization of distributed energy sources through advanced information communication technology and a software system. However, the virtual power plant participates in a power supply coordination management system of the power market and the power grid operation in the form of a power plant, and the problem of the nearby digestion of distributed power generation is not solved.
The output of the renewable energy power generation modes such as photovoltaic power, wind power and the like is greatly influenced by climate, and the fluctuation is obvious and unstable. By providing energy storage, renewable energy utilization can be significantly improved. However, the investment cost of the energy storage at the current stage is high, the self-owned energy storage will of the distributed power generation owners is low, and the utilization rate of the single type of distributed power generation energy storage is limited. Scholars propose that energy storage resources can be shared with users in a cloud energy storage (i.e. a network-linked distributed energy storage) mode, so that electricity consumption cost is reduced for the users, and cloud energy storage providers benefit by providing energy storage services. The willingness of the power consumer to use the cloud to store energy is generally low, and whether a business model is feasible or not is doubtful.
Under the prosperous construction background of the electric power Internet of things, digitization is an important support for energy service supply. The digital technical support is needed for interconnection among distributed energy sources, interaction between the distributed energy sources and a power grid and sharing of main market resources. The construction of the electric power Internet of things provides technical support for the multi-element aggregation form of the distributed energy sources. And the traditional distributed energy aggregation forms, such as micro-grids, require a great deal of investment in the early stage for constructing the power grid.
The search finds that:
Chinese patent publication No. CN206041652U, publication No. 2017-03-22, a distributed virtual power plant electric energy distribution system, which is a transaction system and an electric energy distribution system inside a virtual power plant, and does not involve external transaction management of an object as a power grid; the system does not contain a special load of an electric vehicle charging station, and the complementarity of the charging station load and the distributed power generation is not considered; the system does not adopt a two-step scheduling method before and during the day.
The system and the method are used for participating in electric power marketing transaction by taking a virtual power plant as a generator aiming at the Chinese patent application with publication number of CN105761109A and publication date of 2016-07-13, namely an intelligent management system for virtual power plant energy management and electric power transaction and an optimal operation method thereof, and have limited application range; the system and the method do not contain the special load of the electric automobile charging station, and the complementarity of the charging station load and the distributed power generation is not considered; the system and the method do not give an information network architecture with application significance in the background of the electric power Internet of things.
No description or report of similar technology is found at present, and similar data at home and abroad are not collected.
Disclosure of Invention
The invention provides a regional energy complex virtual aggregation system and method taking a charging station as a key node aiming at the defects in the prior art.
The invention is realized by the following technical scheme.
According to one aspect of the present invention, there is provided a regional energy complex virtual aggregation system comprising:
the data acquisition module acquires time sequence data of the distributed power generation equipment, the energy storage system and the charging station according to the information acquisition instruction of the running state management module;
the running state management module is used as a data management center and a data scheduling interface, processes time sequence data of the distributed power generation equipment, the energy storage system and the charging station to form running data, records state data of the distributed power generation equipment, the energy storage system and/or the charging station and external environment data, and invokes output;
the distributed power generation equipment output prediction module predicts the output of the distributed power generation equipment in the middle-long period, the day before and the day after according to the data output by the running state management module;
The charging station load prediction module predicts daily loads and daily loads of the charging station according to the data output by the running state management module;
the internal transaction platform module realizes multi-type transaction settlement in the system according to the data output by the running state management module and the external transaction information provided by the external transaction platform;
the external transaction platform module is used for carrying out external transaction with the power grid according to the real-time electricity price and the running condition obtained by the real-time scheduling optimization module;
the operation plan making module makes a next-day power generation operation plan of the distributed power generation equipment, the energy storage system and the charging station according to the daily output prediction information obtained by the distributed power generation equipment output prediction module, the daily charging station load prediction information obtained by the charging station load prediction module and the historical electricity price information obtained by the external transaction platform module;
and the real-time scheduling optimization module performs real-time rolling scheduling optimization on the system according to the operation plan formulated by the operation plan formulation module, the daily output prediction information obtained by the distributed power generation equipment output prediction module, the daily charging station load prediction information obtained by the charging station load prediction module and the real-time electricity price information provided by the external transaction platform module, so as to obtain a real-time scheduling strategy of the distributed power generation equipment, the energy storage system and the charging station.
Preferably, after the information acquisition instruction of the data acquisition module, time sequence data are acquired according to a set sampling period.
Preferably, the time series data includes: real-time power generation power of the distributed power generation equipment, residual electric quantity and state of the energy storage system, real-time charging power of each charging pile in the charging station, current number of charging vehicles and charged time.
Preferably, the data acquisition module adopts the internet of things distributed sensing network WSNs to acquire multi-source heterogeneous data of each zone, and based on the dynamic characteristics of the internet of things terminal, the data acquisition module respectively selects 5GNR standards and TCP/IP transmission protocols to gather acquisition results to a zone base station, and transmits the acquisition results to the running state management module through an information exchange port based on a 5GNR air interface architecture.
Preferably, the running state management module sends data acquisition instructions of the distributed power generation equipment, the energy storage system and/or the charging station to the data acquisition module through an information exchange port based on a 5G wireless communication standard and a TCP/IP transmission protocol, and receives time sequence data from the data acquisition module; storing, analyzing and correcting the received time sequence data, removing abnormal data, and converting the time sequence data into running state data with required time scale; and meanwhile, the running state management module records the state data and the environment data, and calls and outputs the running data, the state data and/or the environment data to the distributed power generation equipment output prediction module, the charging load prediction module and/or the internal transaction platform module.
Preferably, the status data includes: the energy storage system comprises the charge and discharge times, the charge and discharge depth, the accumulated power generation amount of the distributed power generation equipment, the accumulated load of a charging station, a historical overhaul record and/or equipment parameters of the distributed power generation equipment.
Preferably, the environmental data includes: geographic information of the distributed power generation equipment, real-time road traffic information and historical traffic information and/or wind power predicted values and illumination predicted values of the places where the distributed power generation equipment is located.
Preferably, the distributed power generation output prediction module invokes the power generation amount and the equipment parameters of the real-time and historical distributed power generation equipment, the wind power prediction value and the illumination prediction value of the location of the distributed power generation equipment through the data invoking interface provided by the operation data management module, then predicts the power generation amount of the distributed power generation equipment for a medium period, a day and a day, sorts and stores the prediction result, and provides the prediction result to the operation planning module and the real-time scheduling module.
Preferably, the distributed power generation output prediction module predicts the generated energy of the distributed power generation equipment for a medium-term period, a day before and a day in time through commercial fan power generation power prediction software and/or photovoltaic power generation power prediction software.
Preferably, the charging station load prediction module invokes real-time and historical charging station loads, the number of charging piles, equipment parameters, real-time related road traffic flow information and historical traffic flow information through a data calling interface provided by the operation data management module, performs daily charge load curve construction and daily prediction correction based on historical data on the total charging station load through a commercial vehicle networking platform API, sorts and stores prediction results, and provides the prediction result information to the operation plan making module and the real-time scheduling optimization module.
Preferably, the operation plan making module invokes time distribution of day-ahead predicted power generation capacity of the next day distributed power generation equipment, time distribution of day-ahead predicted load of the next day charging station and power price information of each period of the next day through data calling interfaces provided by the operation data management module, the distributed power generation equipment output prediction module, the charging station load prediction module and the real-time scheduling optimization module, and establishes an optimization model taking the minimum running cost of the next day as an objective function;
the objective function of the optimization model is as follows:
wherein:
the prices of electricity purchasing from the power grid and electricity selling to the power grid of the t-period system are respectively set; / >
The method comprises the steps of charging a power grid for passing a network fee; />
The power of electricity purchasing and selling to the power grid in the t period is respectively; />
Power flowing in the grid for the t-period system; m is M cs 、M PV 、M wind The penalty coefficients of the predicted correction value of the charging demand, the penalty coefficient of the predicted correction value of the generating capacity of the photovoltaic power generation equipment and the penalty coefficient of the predicted correction value of the generating capacity of the wind power generation equipment are respectively; />
For period tPredictive correction values of charging demand of a charging station; />
A predicted correction value of the power generation amount of the distributed photovoltaic power generation equipment in a t period; />
A predicted correction value of the power generation amount of the distributed wind power generation equipment in a t period;
and solving and preparing an operation plan of the next day through a mixed integer nonlinear stochastic programming algorithm, and outputting the prepared operation plan information to a real-time scheduling optimization module.
Preferably, the real-time scheduling optimization module operates on the same day according to the operation plan information provided by the operation plan making module, takes the daily power generation amount prediction information provided by the distributed power generation equipment output prediction module, the daily load prediction information provided by the charging station load prediction module and the current day real-time electricity price information as auxiliary decision information, and performs rolling optimization solving of the daily operation plan by using a mathematical optimization algorithm according to the equipment state and the power generation state of the distributed power generation equipment, the real-time information of the energy storage system state and the load station state provided by the operation state management module; and adjusting and refining the daily operation plan information based on the optimization solving result, generating real-time scheduling optimization strategies and external power transaction plan information of various devices, and respectively outputting the real-time scheduling optimization strategies and the external power transaction plan information to the distributed power generation device, the energy storage system, the control terminal of the charging station and the external transaction platform module.
Preferably, the internal transaction platform module invokes real-time and historical power generation capacity of the distributed power generation device, real-time and historical charge and discharge capacity of the energy storage system and real-time and historical load of each charging pile of the charging station through a data invoking interface provided by the running state management module, and obtains external transaction information through a data invoking interface provided by the external transaction platform module.
Preferably, the internal transaction platform module utilizes a blockchain decentralised data storage technology to realize electronic contract, notarization metering and expense settlement, and realizes multi-type transaction settlement inside the system.
Preferably, the external transaction platform module invokes the total power generation amount of the distributed power generation equipment and the total power consumption amount of each charging pile of the charging station through a data invoking interface of the running state management module, interacts with a power grid enterprise, acquires and stores real-time power price information, performs external transaction with the power grid enterprise, and provides power price information to the running plan making module and the real-time scheduling optimization module; and executing external power transaction plan information provided by the real-time scheduling optimization module.
Preferably, the system is based on a power internet of things architecture, comprising a perception layer, a network layer and an application layer, wherein:
The data acquisition module is positioned on the sensing layer;
the running state management module is positioned at a network layer;
the fan photovoltaic output prediction module, the charging station load prediction module, the operation plan making module, the real-time scheduling optimization module, the internal transaction platform module and the external transaction platform module are respectively located at an application layer.
According to another aspect of the present invention, there is provided a regional energy complex virtual aggregation method including:
acquiring time sequence data of the distributed power generation equipment, the energy storage system and the charging station according to the information acquisition instruction of the running state management module;
processing time sequence data of the distributed power generation equipment, the energy storage system and the charging station to form operation data, recording state data of the distributed power generation equipment, the energy storage system and/or the charging station and external environment data, and calling and outputting;
predicting the output of the distributed power generation equipment in the middle-long term, day-ahead and day-ahead according to the output operation data, state data and environment data;
predicting daily loads and daily loads of the charging station according to the output operation data, state data and environment data;
according to the obtained day-ahead output prediction information, the day-ahead load prediction information of the charging station and the historical electricity price information, a next day power generation operation plan of the distributed power generation equipment, the energy storage system and the charging station is formulated;
Real-time scheduling optimization is carried out according to the formulated operation plan, the daily output prediction information, the daily load prediction information of the charging station and the real-time electricity price information, so that a real-time operation strategy of the distributed power generation equipment, the energy storage system and the charging station is obtained;
according to the real-time electricity price and the running conditions of the distributed power generation equipment, the energy storage system and the charging station, carrying out external transaction with the power grid;
and according to the output operation data, state data, environment data and external transaction information, realizing multi-type transaction settlement inside the system.
Due to the adoption of the technical scheme, the invention has at least one of the following beneficial effects:
as the storage amount of the electric automobile is increased, the newly added charging load causes pressure on a power grid; according to the regional energy complex virtual aggregation system and method provided by the invention, the negative influence of the charging load of the electric automobile on the power grid is reduced through the energy scheduling and management system taking the charging station as a key node, and the cost of upgrading and expanding the power distribution network is saved.
The regional energy complex virtual aggregation system and the regional energy complex virtual aggregation method provided by the invention can fully consume electric energy generated by power generation of distributed renewable energy sources. The energy complex realizes the high-efficiency utilization of renewable energy sources and reduces the wind and light abandoning rate by providing the network-connected distributed energy storage renting service and a prediction module and a scheduling module in the system.
The regional energy complex virtual aggregation system and the regional energy complex virtual aggregation method provided by the invention are used for managing energy in an information flow layer, do not need to modify the existing power distribution network, belong to the operation mode of light assets, and are beneficial to the cost control of the energy complex.
The regional energy comprehensive virtual aggregation system and the regional energy comprehensive virtual aggregation method provided by the invention are wider in application situation, only electricity price changes along with time, and the comprehensive energy can be used as a power generation business terminal and an electricity load terminal under different conditions.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of a regional energy complex virtual aggregation system using charging stations as key nodes according to a preferred embodiment of the present invention;
fig. 2 is a schematic timing diagram of an regional energy complex virtual aggregation system using charging stations as key nodes according to a preferred embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in detail: the embodiment is implemented on the premise of the technical scheme of the invention, and detailed implementation modes and specific operation processes are given. It should be noted that variations and modifications can be made by those skilled in the art without departing from the spirit of the invention, which falls within the scope of the invention.
The embodiment of the invention provides a regional energy comprehensive virtual aggregation system which integrates power utilization side distributed energy and electric vehicle charging loads based on the electric power Internet of things technology by taking a charging station as a key node.
As shown in fig. 1, the regional energy complex virtual aggregation system in this embodiment is based on an architecture of an electric power internet of things, and includes three architecture layers including a sensing layer, a network layer, and an application layer, and is divided into eight modules: the system comprises a data acquisition module (i.e. an information acquisition module in fig. 1), an operation state management module, a distributed power generation equipment output prediction module, a charging station load prediction module, an operation plan making module, a real-time scheduling optimization module, an internal trading platform module and an external trading platform module. Wherein:
the data acquisition module is used for acquiring time sequence data of the distributed power generation equipment, the energy storage system and the charging station according to the information acquisition instruction of the running state management module;
the running state management module is used as a data management center and a data scheduling interface, processes time sequence data of the distributed power generation equipment, the energy storage system and the charging station to form running data, records state data of the distributed power generation equipment, the energy storage system and/or the charging station and external environment data, and invokes and outputs the state data;
The distributed power generation equipment output prediction module predicts the output of the distributed power generation equipment in the middle-long period, the day before and the day in time according to the data output by the running state management module;
the charging station load prediction module predicts daily loads and daily loads of the charging station according to the data output by the running state management module;
the internal transaction platform module is used for realizing multi-type transaction settlement in the system according to the data output by the running state management module and the external transaction information provided by the external transaction platform;
the external transaction platform module is used for carrying out external transaction with the power grid according to the real-time electricity price and the running condition obtained by the real-time scheduling optimization module;
the operation plan making module is used for making a next-day power generation operation plan of the distributed power generation equipment, the energy storage system and the charging station according to the day-ahead output prediction information obtained by the distributed power generation equipment output prediction module, the day-ahead charging station load prediction information obtained by the charging station load prediction module and the historical electricity price information obtained by the external transaction platform module;
and the real-time scheduling optimization module performs real-time rolling scheduling optimization on the system according to the operation plan formulated by the operation plan formulation module, the daily output prediction information obtained by the distributed power generation equipment output prediction module, the daily charging station load prediction information obtained by the charging station load prediction module and the real-time electricity price information provided by the external transaction platform module, so as to obtain a real-time scheduling strategy of the distributed power generation equipment, the energy storage system and the charging station.
As a preferred embodiment, the data acquisition module belongs to a sensing layer of the architecture of the Internet of things and is physically located at the distributed power generation equipment, the energy storage system and the charging pile terminal. The method has the main function of acquiring time sequence data of the distributed power generation equipment, the energy storage and the charging pile. The operation process of the module is as follows: after receiving the information acquisition instruction from the running state management module, the module acquires real-time power generation power of the distributed power generation equipment (which can be a distributed fan and/or distributed photovoltaic equipment), residual electric quantity and state of an energy storage system, real-time charging power of a charging station charging pile, the current number of charged vehicles and charged time according to a set sampling period. Each area utilizes the internet of things distributed sensing network WSNs (Wireless Sensor Networks) technology to collect multi-source heterogeneous data, and based on the dynamic characteristics of the internet of things terminal, 5GNR standards and TCP/IP transmission protocols are respectively selected to collect the collection results to the area base station, and the collection results are transmitted to a big data center through an information exchange port designed based on 5GNR air interfaces and provided for an operation state management module.
As a preferred embodiment, the running state management module belongs to an application layer of the architecture of the Internet of things, is physically located in a large data center, is a distributed center and a management center for system running related data, and plays a role in carrying data processing and providing a data calling interface for other modules related to data analysis. The operation process of the module is as follows: the running state management module sends information acquisition instructions to distributed power generation equipment, energy storage equipment and charging piles in a geographic area which is integrally managed by the system through an information exchange port based on a 5G wireless communication technology and a TCP/IP protocol, and receives time sequence data from the data acquisition module, wherein the time sequence data comprise real-time power generation power of the distributed power generation equipment (which can be a distributed fan and/or a distributed photovoltaic device), residual electric quantity and state of the energy storage system, real-time charging power of each charging pile in a charging station, the number of charging vehicles and charged time. And carrying out storage, analysis and correction on the received time sequence data, removing abnormal data, and converting the time sequence information into operation data with required time scale so as to facilitate the call of each module. Meanwhile, the running state management module records state data such as energy storage charging and discharging times, distributed power generation accumulated generating capacity, charging station accumulated load, historical overhaul records, equipment parameters of distributed power generation equipment and the like. And calling an open commercial vehicle networking platform API (such as an application program with real-time traffic acquisition capability and authority such as a hundred-degree map) at fixed time intervals to acquire real-time road traffic flow information and historical traffic flow information, and calling a weather prediction platform API (such as a platform with real-time weather acquisition capability and authority such as ink weather) to acquire environmental data such as wind power predicted values, illumination predicted values and geographic information of distributed power generation equipment of the distributed power generation place. And providing an information retrieval outlet and a data call communication interface for real-time data processing results, historical operation data, road traffic flow information, historical traffic flow information, wind power predicted values and illumination predicted values of the distributed power generation places, and providing information for a distributed power generation equipment output predicted module, a charging station load predicted module, an internal transaction platform module and a real-time scheduling module.
As a preferred embodiment, the output prediction module of the distributed power generation equipment belongs to an application layer of an architecture of the Internet of things, is physically located in a big data center, and is used for performing medium-long-term, day-ahead and day-in prediction of the output of the distributed power generation unit. The module calls weather information such as the generated energy of the distributed power generation unit, relevant equipment parameters, a wind power predicted value, an illumination predicted value and the like in real time and history through a database interface provided by the operation data management module, and then predicts the generated energy of the distributed power generation unit for a medium and long time, daily and daily through commercial fan generated power prediction software (such as preventino) and/or photovoltaic generated power prediction software (such as Suncast) and sorts and stores the prediction results. The module provides an information retrieval outlet and a data calling communication interface for the predicted result, and provides the predicted result information to the operation planning module and the real-time scheduling module through the data calling communication interface.
As a preferred embodiment, commercial power generation prediction software is invoked to predict the power generation capacity of the distributed power generation equipment.
As a preferred embodiment, the charging station load prediction module belongs to an application layer of an Internet of things architecture, is physically located in a big data center, and is used for predicting the daily and intra-daily load of the charging station load. The module calls real-time and historical charging station load conditions, the number of charging piles, equipment parameters, real-time related road traffic flow information and historical traffic flow information through a database interface provided by the operation data management module, and predicts the total load of the charging station in the middle of day and in the middle of day. The prediction method comprises the following steps: and constructing a daily load basic curve through historical load data and a charging station load prediction model, and correcting a predicted value by taking real-time road traffic flow information as an adjustment quantity. And sorting and storing the predicted results, providing an information retrieval outlet and a data call communication interface for the output of the processing results of the data, and providing the predicted result information to an operation planning module and a real-time scheduling module through the data call communication interface.
As a preferred embodiment, the charge load prediction is implemented by calling the disclosed commercial vehicle networking platform API.
As a preferred embodiment, the operation plan making module belongs to an application layer of the architecture of the Internet of things, is physically located in a big data center, and has the function of making a next-day power generation plan of the distributed power generation equipment and the energy storage system according to the power generation prediction information and the power price information. The module is called once a day to make an operation plan for the next day. The data provided by the operation data management module, the distributed power generation equipment output prediction module, the charging station load prediction module and the external transaction platform module are used for calling a communication interface, and calling time distribution of day-ahead predicted power generation capacity of the next-day distributed power generation equipment, time distribution of day-ahead predicted load of the next-day charging station and electric price information of each period of the next day. And (3) comprehensively considering investment cost and operation cost to establish an optimization model with the minimum operation cost of the next day as an objective function, and then solving and working out an operation plan of the next day through a random mixed integer optimization algorithm embedded in optimization software. And providing an information retrieval outlet and a data call communication interface for the output of the day-ahead operation plan information, and providing the day-ahead operation plan information to the real-time scheduling module through the data call communication interface.
In the preferred embodiment of the present invention, in the preferred embodiment,
the objective function of the optimization model is as follows:
wherein:
the prices of electricity purchasing from the power grid and electricity selling to the power grid of the t-period system are respectively set; />
The method comprises the steps of charging a power grid for passing a network fee; />
The power of electricity purchasing and selling to the power grid in the t period is respectively; />
Power flowing in the grid for the t-period system; m is M cs 、M PV 、M wind The penalty coefficients of the predicted correction value of the charging demand, the penalty coefficient of the predicted correction value of the generating capacity of the photovoltaic power generation equipment and the penalty coefficient of the predicted correction value of the generating capacity of the wind power generation equipment are respectively; />
A predicted correction value for the charging demand of the charging station for the period t; />
A predicted correction value of the power generation amount of the distributed photovoltaic power generation equipment in a t period; />
A predicted correction value of the power generation amount of the distributed wind power generation equipment in a t period;
and solving and preparing an operation plan of the next day through a mixed integer nonlinear stochastic programming algorithm, and outputting the prepared operation plan information to a real-time scheduling optimization module.
As a preferred embodiment, the real-time scheduling optimization module belongs to an application layer of the architecture of the Internet of things, is physically located in a big data center, and is used for performing real-time scheduling optimization of the system based on an operation plan. The module uses the daily power generation prediction information provided by the distributed power generation equipment output prediction module, the daily load prediction information provided by the charging station load prediction module and the current day real-time electricity price information as auxiliary decision information according to the daily operation plan information provided by the operation plan making module on the current day, and performs rolling optimization solving of the daily operation plan by using mathematical optimization software according to the equipment state, the power generation state, the energy storage state and the real-time system operation state information of the distributed energy unit provided by the operation state management module. And adjusting and refining the daily operation plan information based on the optimization solving result to generate a real-time dispatching optimization strategy and an external power transaction plan of the distributed power generation equipment, the energy storage system and the charging station. And providing an information retrieval outlet and a data call communication interface for the real-time dispatching optimization strategy information and the output of the external power transaction plan, and providing the real-time dispatching optimization strategy information for the distributed power generation equipment, the energy storage system, the control terminal of the charging station and the external transaction platform module through the data call communication interface.
As a preferred embodiment, the internal transaction platform module belongs to an application layer of the architecture of the Internet of things, is physically located in a large data center, provides a platform for transaction settlement for the inside of an aggregate, and provides settlement for multi-type transactions such as electric quantity transaction, energy storage lease and the like. The module calls the real-time and historical generating capacity of the fan, the photovoltaic real-time and historical generating capacity, the energy storage real-time and historical charging and discharging capacity and the real-time and historical load of each charging pile through a data calling interface of the running state management module; and calling external transaction information through a data calling interface provided by the external transaction platform. The module utilizes blockchain decentralizing data storage technology to realize electronic contract, notarization metering and expense settlement. In terms of electronic contracts, the distributed generation unit owners sign power trade contracts and energy storage renting contracts in the form of blockchain intelligent contracts with charging stations. The power trade contract agrees with the power settlement price and settlement period of the distributed generation and charging station. The energy storage renting contract agrees that an owner of the distributed power generation unit pays a certain amount of energy storage renting cost to the charging station, and the renting cost is determined according to the output fluctuation degree and the generated energy of the distributed power generation unit. The automatic settlement of internal transactions is realized by utilizing the intelligent contract characteristics of the blockchain, and the system has transparency and high efficiency. The module provides an information call outlet for the output of internal transaction information.
As a preferred embodiment, the external transaction platform module belongs to an application layer of the architecture of the Internet of things, is physically located in a big data center, and has the function of performing transaction with a power grid according to real-time electricity prices and running conditions, and transaction contents comprise electricity fees and internet passing fees. The power grid is assumed to adopt real-time electricity prices, namely the electricity prices dynamically change according to the trading situation of the electric power market. Because the system can automatically consume part of electric energy, the electricity consumption or the electricity generation is smaller as a whole, and the capacity of participating in the electric power market as a main body is weaker, the system is assumed to be used as a price receiver of real-time electricity price and does not participate in the electric power market. The module obtains the total power consumption/power generation of the system through a data calling interface of the running state management module. And acquiring real-time electricity price information in an interaction way with a power grid enterprise, and storing the electricity price information. And executing the planned transaction information provided by the real-time scheduling module to complete the transaction with the power grid enterprise. The module provides an information retrieval outlet and an information calling outlet for the historical electricity price, and provides real-time and historical electricity price information to the operation plan making module and the real-time dispatching optimization module;
the regional energy complex virtual aggregation system in the embodiment takes a charging station as a key node, and comprises an operation framework of regional energy complexes of different types of distributed power generation units based on the electric power internet of things technology and the modern metering control communication technology. The aggregation mode of the complex is aggregation of information flow layers, namely the electric vehicle charging station, the distributed power generation and the energy storage equipment are cooperatively controlled through the communication equipment.
The charging station is used as a key node of the regional energy complex and has triple identity. The intelligent energy comprehensive management system is characterized in that a data center with stronger computing capacity is provided as a brain of an energy comprehensive body to bear the functions of energy comprehensive body operation planning, real-time coordination control, internal main body settlement and external power grid transaction; secondly, as the main load of the regional energy complex, the electric energy generated by distributed power generation is consumed; and thirdly, the energy storage device is used as a provider of the network-connected distributed energy storage resources and is provided with energy storage equipment with a certain capacity to provide the energy storage resources for the regional energy complex. Notably, because charging stations obtain a large amount of low-cost electric energy through distributed generation, which is the biggest profitable in the integrated body, the charging stations are provided with data centers as key nodes. However, the data center is not strongly bound to the charging station and can be decoupled from the charging station if necessary.
The invention provides a regional energy complex virtual aggregation method taking a charging station as a key node, which comprises the following steps:
acquiring time sequence data of the distributed power generation equipment, the energy storage system and the charging station according to the information acquisition instruction of the running state management module;
Processing time sequence data of the distributed power generation equipment, the energy storage system and the charging station to form operation data, recording state data of the distributed power generation equipment, the energy storage system and/or the charging station and external environment data, and calling and outputting;
predicting the output of the distributed power generation equipment in the middle-long term, day-ahead and day-ahead according to the output operation data, state data and environment data;
predicting daily loads and daily loads of the charging station according to the output operation data, state data and environment data;
according to the obtained output prediction information, charging station load prediction information and electricity price information, a next-day power generation operation plan of the distributed power generation equipment, the energy storage system and the charging station is formulated;
real-time scheduling optimization is carried out according to the formulated operation plan, and the operation conditions of the real-time electricity price, the distributed power generation equipment, the energy storage system and/or the charging station are obtained;
according to the real-time electricity price and the running condition of the distributed power generation equipment, the energy storage system and/or the charging station, carrying out external transaction with the power grid;
and according to the output operation data, state data, environment data and external transaction information, realizing multi-type transaction settlement inside the system.
It should be noted that, the steps in the method provided by the present invention may be implemented by using corresponding modules, devices, units, etc. in the system, and those skilled in the art may refer to a technical scheme of the system to implement a step flow of the method, that is, an embodiment in the system may be understood as a preferred example of implementing the method, which is not described herein.
The technical solutions in the above embodiments of the present invention are further described in detail below with reference to the accompanying drawings and specific application examples.
In the application example, the regional energy complex is in the initial stage of test operation, the amount of the distributed power generation body in the complex is small, the electric vehicle charging station can digest most of the distributed power generation to generate electric energy, and the rest electric energy which can not be consumed by the user can be used for trading with the power grid in a residual online mode according to the real-time electricity price.
A system block diagram in this application example is shown in fig. 1. The sensing layer of the system consists of a data acquisition module positioned on the charging pile, the photovoltaic equipment and the fan; the network layer realizes the transmission of sensing layer data through signal transmission media such as wireless, microwave and the like, and realizes the interconnection between the Internet of things and the traditional telecommunication network by means of an Internet of things gateway, and a module in the network layer is provided with an operation state management module; the application layer is all located in the data center and comprises six modules, namely a fan photovoltaic output prediction module, a charging station load prediction module, an operation plan making module, a real-time scheduling optimization module, an internal transaction platform module and an external transaction platform module.
Timing diagrams in the system are shown in fig. 2. The specific process is as follows:
1. data acquisition and processing: when the system starts to operate, the running state management module firstly sends information acquisition instructions to distributed power generation equipment, energy storage equipment and charging piles in a geographic area which is integrally managed by the system. The data acquisition module is positioned on the sensing layer, after receiving an information acquisition instruction from the running state management module, the data acquisition module controls the control metering equipment positioned on the terminal, acquires real-time power generation power of the distributed fan, real-time power generation power of the distributed photovoltaic equipment, residual electric quantity and state of the energy storage system, real-time charging power of the charging station charging pile, current number of charged vehicles and charged time according to a set sampling period by using the technology of the Internet of things distributed sensing network WSNs (Wireless Sensor Networks), and then gathers the acquisition result to the zone base station through the WSNs, and provides information to the running state management module by means of an information exchange port equipped with an Internet of things gateway. And after the operation state management module at the network layer receives the time sequence data from the operation information acquisition module, analyzing and correcting the time sequence data, removing abnormal data, and finally converting the time sequence information into operation state information taking 1min as a time scale, namely, 1min average power generation of the distributed fan, 1min average power generation of the distributed photovoltaic equipment, the residual electric quantity of the energy storage system and 1min average power consumption of the charging station, so that the modules can be conveniently called. Meanwhile, the running state management module records the related information such as the times of energy storage and charge and discharge, the accumulated power generation amount of distributed power generation, the accumulated load of a charging station, the historical overhaul record, the equipment parameters of distributed power generation equipment, geographic information and the like. The running state management module calls an API (application program interface) of the Internet of vehicles to obtain real-time road traffic information and historical traffic information every 15 minutes, and calls an API of the weather prediction platform to obtain a wind power predicted value and an illumination predicted value of a distributed power generation place every 15 minutes. The running state management module stores the processing result of the acquired data, and provides information for the distributed power generation output prediction module, the charging station load prediction module, the internal transaction platform module and the real-time scheduling module through a ODBC (Open Data Base Connectivity) database call interface.
2. Planning in the future: and the distributed power generation equipment output prediction module calls the generated energy of the distributed power generation unit, relevant equipment parameters, wind power prediction values, illumination prediction values and other weather information of the next day of the location of the distributed power generation unit in real time and history by the operation data management module, and then carries out daily prediction on the generated energy of the distributed power generation equipment through a self-built distributed power generation equipment generated energy prediction model or existing fan power generation prediction software, such as prevento, and photovoltaic power generation power prediction software, such as Suncast, and collates and stores prediction results. The day-ahead prediction result is the time distribution of the power generation capacity of the next day distributed power generation equipment, the minimum time division is 30min, and the day-ahead power generation prediction information is provided for the operation plan making module through the data calling communication interface.
The charging station load prediction module invokes real-time and historical charging station load conditions, the number of charging piles and equipment parameters of the operation data management module. And constructing a daily load basic curve through the historical load data and the charging station load prediction model, and outputting the predicted total amount and time distribution of the next daily charging station load, wherein the minimum time division is 30 minutes. And providing the daily power generation prediction information to the operation planning module through a data call communication interface.
And the operation plan making module is used for calling the time distribution of the day-ahead predicted power generation capacity of the next-day distributed power generation equipment, the time distribution of the day-ahead predicted load of the next-day charging station and the electricity price information of each time period of the next day. And a mathematical optimization software module based on a C language is adopted, an objective function is established by comprehensively considering investment cost and operation cost, and then a mixed optimization model of an operation plan is solved through a random mixed integer optimization algorithm embedded in the optimization software, so that the operation plan of the next day is made. And providing the operation plan information, namely the optimal power generation amount, the power consumption amount, the charging and discharging amount of the energy storage equipment, the internal settlement power price and the planned power purchase/selling amount from the power grid, for the real-time dispatching optimization module in a manner of taking 30min as the minimum time division.
Day scheduling optimization: on the same day of operation, the distributed power generation equipment output prediction module calls weather information such as a wind power prediction value, an illumination prediction value and the like of the underground one hour where the distributed power generation unit is located, and corrects a day-ahead prediction value through a self-built distributed power generation equipment power generation amount prediction model or existing fan power generation prediction software such as previfent and photovoltaic power generation power prediction software such as Suncast, and sorts and stores a day-ahead prediction result. The daily forecast result is the time division of the power generation capacity of the distributed power generation equipment within one hour, and the minimum time scale is 1min. And the data is provided to the real-time scheduling module through a data calling interface.
The charging station load prediction module invokes real-time related road traffic information and historical traffic information, and corrects a daily charging station load prediction value by taking the real-time road traffic information as an adjustment quantity. The daily load prediction output result is the load and time distribution of the charging station within one hour, and the minimum time scale is 1min. And providing the prediction result to a real-time scheduling module.
The real-time scheduling optimization module uses the daily power generation prediction information provided by the distributed power generation equipment output prediction module, the daily load prediction information provided by the charging station load prediction module and the today electricity price information as auxiliary decision information according to the operation plan information provided by the operation plan making module, and performs optimal solution for the next hour operation plan by utilizing mathematical optimization software according to the equipment state, the power generation state, the energy storage state and the real-time system operation state information of the distributed energy unit provided by the operation data management module. And based on the optimization solving result, the operation plan information with the time division of 30min is adjusted and refined, and a real-time scheduling optimization strategy is generated, namely, the power generation amount of the optimal distributed energy unit, the charging and discharging amount of the optimal energy storage equipment and the planned electricity purchasing/selling amount from the power grid are distributed in each time division of the next hour, wherein the time division is 1min. And transmitting the information to control equipment positioned at a sensing layer through a network layer to complete the operation plan.
Internal transaction: the distributed power generation equipment owners and the energy source complex sign a power transaction contract and an energy storage lease contract in the form of a blockchain intelligent contract expression. The electric power transaction contract agrees with the electric power settlement price of the distributed generation and charging station, and is adjusted according to the real-time power grid price; the settlement period is classified into weekly settlement and monthly settlement according to the type of contract with the distributed generation owners. The energy storage renting contract agrees that an owner of the distributed power generation unit pays a certain amount of energy storage renting cost to the charging station according to month, and the renting cost is determined by the fluctuation degree of the output power and the generated energy of the distributed power generation unit. The internal transaction platform module obtains real-time and historical generating capacity, energy storage real-time and historical charging and discharging capacity and real-time and historical load of each charging pile through a data calling interface of the running state management module. And realizing notarization metering and expense settlement by using a blockchain decentralization data storage technology. After the transaction is completed, an information calling outlet is provided for outputting the internal transaction information.
External transaction: the external transaction platform module obtains the total power consumption/power generation amount of the system through a data calling interface of the running state management module, obtains real-time power price information through the power grid information communication software API, and stores the power price information. And providing the electricity price information to an operation planning module and a real-time operation scheduling module. And according to the planned transaction information provided by the real-time scheduling module, carrying out transaction with the power grid, wherein the transaction content comprises electric charge and internet charge. According to the regulation of the power grid, settlement is carried out by taking month or other units as settlement periods.
The regional energy complex virtual aggregation system and the regional energy complex virtual aggregation method provided by the embodiment of the invention solve at least one of the following technical problems:
the electric automobile fast charging station is used as a novel load of a power grid with the quantity increasing in recent years, and has the following characteristics: the load uncertainty is high and the fluctuation is strong; the charging load is obviously influenced by traffic flow, and the day-night load difference is large; the high-power charging load impacts the power grid. It is not difficult to find that the load characteristics of the electric vehicle quick charging station and the output characteristics of the distributed power generation can be complemented to a certain extent in the time dimension and the energy dimension. According to the system and the method provided by the embodiment of the invention, the two are complementary through the virtual aggregation mode, so that the problems of in-situ consumption of the distributed renewable energy and load increase brought by the charging station to the power grid can be simultaneously solved.
According to the system and the method provided by the embodiment of the invention, the user for renting the energy storage is limited to the distributed power supply, and the distributed power supply has high demand on the energy storage and stronger payment willingness. The system and the method in the embodiment of the invention design a transaction mode, a transaction platform and a transaction rule of the energy storage renting transaction between the energy comprehensive service provider and the distributed energy. The investment pressure of the distributed generation owners is relieved, and the energy storage function in the aspect of power regulation is fully exerted.
The system and the method in the embodiment of the invention realize aggregation of the distributed energy sources on the information flow layer surface, and reduce the equipment investment of the physical layer. The interconnection and intercommunication of the distributed energy, the power load and the energy storage device information layer are realized by means of an advanced electric power internet of things technical framework; the cooperative control of distributed energy, electricity load and energy storage device is realized by means of advanced control metering equipment.
According to the system and the method provided by the embodiment of the invention, through the energy scheduling and management system taking the charging station as a key node, the negative influence of the charging load of the electric automobile on the power grid is reduced, and the cost of upgrading and expanding the power distribution network is saved; the electric energy generated by the distributed renewable energy source power generation can be fully consumed. The energy complex realizes the high-efficiency utilization of renewable energy sources by providing the network-connected distributed energy storage renting service, and a prediction module and a scheduling module in the system, and reduces the wind and light discarding rate; the energy is managed in the information flow layer, the existing power distribution network is not required to be modified, the energy belongs to the operation mode of light assets, and the cost control of an energy complex is facilitated; the application situation is wider, the electricity price only changes along with time, and the comprehensive energy source can be used as a power producer end and a power load end under different conditions.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the claims without affecting the spirit of the invention.
Claims (9)
1. A regional energy complex virtual aggregation system, comprising:
the data acquisition module acquires time sequence data of the distributed power generation equipment, the energy storage system and the charging station according to the information acquisition instruction of the running state management module;
the running state management module is used as a data management center and a data scheduling interface, processes time sequence data of the distributed power generation equipment, the energy storage system and the charging station to form running data, records state data of the distributed power generation equipment, the energy storage system and/or the charging station and external environment data, and invokes output;
the distributed power generation equipment output prediction module predicts the output of the distributed power generation equipment in the middle-long period, the day before and the day after according to the data output by the running state management module;
The charging station load prediction module predicts daily loads and daily loads of the charging station according to the data output by the running state management module;
the internal transaction platform module realizes multi-type transaction settlement in the system according to the data output by the running state management module and the external transaction information provided by the external transaction platform;
the external transaction platform module is used for carrying out external transaction with the power grid according to the real-time electricity price and the running condition obtained by the real-time scheduling optimization module;
the operation plan making module makes a next-day power generation operation plan of the distributed power generation equipment, the energy storage system and the charging station according to the daily output prediction information obtained by the distributed power generation equipment output prediction module, the daily charging station load prediction information obtained by the charging station load prediction module and the historical electricity price information obtained by the external transaction platform module;
the real-time scheduling optimization module performs real-time rolling scheduling optimization on the system according to an operation plan formulated by the operation plan formulation module, daily output prediction information obtained by the distributed power generation equipment output prediction module, daily charging station load prediction information obtained by the charging station load prediction module and real-time electricity price information provided by the external transaction platform module, so as to obtain a real-time scheduling strategy of the distributed power generation equipment, the energy storage system and the charging station;
The running state management module further comprises any one or more of the following:
-the operational status management module sending data acquisition instructions of the distributed power generation equipment, the energy storage system and/or the charging station to the data acquisition module through an information exchange port based on the 5G wireless communication standard and the TCP/IP transmission protocol, and receiving time sequence data from the data acquisition module; storing, analyzing and correcting the received time sequence data, removing abnormal data, and converting the time sequence data into running state data with required time scale; meanwhile, the running state management module records state data and environment data, and calls and outputs the running data, the state data and/or the environment data to the distributed power generation equipment output prediction module, the charging load prediction module and/or the internal transaction platform module;
-the status data comprises: the method comprises the steps of charging and discharging times, charging and discharging depth of an energy storage system, accumulated power generation capacity of distributed power generation equipment, accumulated load of a charging station, historical overhaul records and/or equipment parameters of the distributed power generation equipment;
-the environmental data comprises: geographic information of the distributed power generation equipment, real-time road traffic information and historical traffic information and/or wind power predicted values and illumination predicted values of the places where the distributed power generation equipment is located.
2. The regional energy complex virtual aggregation system of claim 1, wherein the data acquisition module further comprises any one or more of:
-after an information acquisition instruction of the data acquisition module, acquiring time sequence data according to a set sampling period;
-the timing data comprises: real-time power generation power of the distributed power generation equipment, residual electric quantity and state of an energy storage system, real-time charging power of each charging pile in the charging station, current number of charged vehicles and charged time;
the data acquisition module adopts the WSNs to acquire multi-source heterogeneous data of each zone, and based on the dynamic characteristics of the terminal of the Internet of things, the data acquisition module respectively selects 5GNR standards and TCP/IP transmission protocols to gather acquisition results to a zone base station, and the acquisition results are transmitted to the running state management module through an information exchange port based on a 5GNR air interface architecture.
3. The regional energy complex virtual aggregation system of claim 1, wherein the distributed generation output prediction module further comprises any one or more of:
the distributed power generation output prediction module is used for calling the power generation amount and the equipment parameters of the real-time and historical distributed power generation equipment, the wind power prediction value and the illumination prediction value of the place where the distributed power generation equipment is located through the data calling interface provided by the operation data management module, then predicting the power generation amount of the distributed power generation equipment for a medium-term period, a day-ahead period and a day-in period, sorting and storing the prediction result, and providing the prediction result to the operation planning module and the real-time scheduling module;
-the distributed generation output prediction module is used for performing medium-long-term, day-ahead and day-in prediction on the generated energy of the distributed generation equipment through commercial fan generation power prediction software and/or photovoltaic generation power prediction software.
4. The regional energy complex virtual aggregation system of claim 1, wherein the energy complex is configured to generate the energy from the energy complex,
the charging station load prediction module is used for calling real-time and historical charging station loads, the number of charging piles, equipment parameters, real-time related road traffic flow information and historical traffic flow information through a data calling interface provided by the operation data management module, carrying out daily charge load curve construction and daily prediction correction based on historical data on the total charging station load through a commercial vehicle networking platform API, sorting and storing prediction results, and providing the prediction result information to the operation plan making module and the real-time dispatching optimization module.
5. The regional energy complex virtual aggregation system according to claim 1, wherein the operation plan making module invokes time distribution of day-ahead predicted power generation capacity of the next-day distributed power generation equipment, time distribution of day-ahead predicted load of the next-day charging station and electric price information of each period of the next day through data calling interfaces provided by an operation data management module, a distributed power generation equipment output prediction module, a charging station load prediction module and a real-time scheduling optimization module, and establishes an optimization model taking the minimum running cost of the next day as an objective function;
The objective function of the optimization model is as follows:
wherein:
the prices of electricity purchasing from the power grid and electricity selling to the power grid of the t-period system are respectively set; />
The method comprises the steps of charging a power grid for passing a network fee; />
The power of electricity purchasing and selling to the power grid in the t period is respectively; />
Power flowing in the grid for the t-period system; m is M cs 、M PV 、M wind The penalty coefficients of the predicted correction value of the charging demand, the penalty coefficient of the predicted correction value of the generating capacity of the photovoltaic power generation equipment and the penalty coefficient of the predicted correction value of the generating capacity of the wind power generation equipment are respectively; />
A predicted correction value for the charging demand of the charging station for the period t; />
A predicted correction value of the power generation amount of the distributed photovoltaic power generation equipment in a t period; />
A predicted correction value of the power generation amount of the distributed wind power generation equipment in a t period;
and solving and preparing an operation plan of the next day through a mixed integer nonlinear stochastic programming algorithm, and outputting the prepared operation plan information to a real-time scheduling optimization module.
6. The regional energy complex virtual aggregation system according to claim 1, wherein the real-time scheduling optimization module operates on the current day according to the operation plan information provided by the operation plan making module, takes the daily power generation amount prediction information provided by the distributed power generation equipment output prediction module, the daily load prediction information provided by the charging station load prediction module and the current day real-time electricity price information as auxiliary decision information, and performs rolling optimization solving of a daily operation plan by using a mathematical optimization algorithm according to the equipment state and the power generation state of the distributed power generation equipment, the real-time information of the energy storage system state and the load station state provided by the operation state management module; and adjusting and refining the daily operation plan information based on the optimization solving result, generating a real-time dispatching optimization strategy and external power transaction plan information of the distributed power generation equipment, the energy storage system and the charging station, and respectively outputting the real-time dispatching optimization strategy and the external power transaction plan information to a control terminal and an external transaction platform module of the distributed power generation equipment, the energy storage system and the charging station.
7. The regional energy complex virtual aggregation system of claim 1, further comprising any one or more of:
the internal transaction platform module is used for calling the real-time and historical generating capacity of the distributed power generation device, the real-time and historical charging and discharging capacity of the energy storage system and the real-time and historical load of each charging pile of the charging station through a data calling interface provided by the running state management module, and obtaining external transaction information through a data calling interface provided by the external transaction platform module;
-said internal transaction platform module implementing electronic contract, notarization metering and fee settlement using blockchain decentralised data storage techniques, implementing multi-type transaction settlement within the system;
the external transaction platform module is used for calling the total power generation amount of the distributed power generation equipment and the total power consumption amount of each charging pile of the charging station through a data calling interface of the running state management module, interacting with a power grid enterprise, acquiring and storing real-time power price information, carrying out external transaction with the power grid enterprise, and providing the power price information to the running plan making module and the real-time scheduling optimization module; and executing external power transaction plan information provided by the real-time scheduling optimization module.
8. The regional energy complex virtual aggregation system of any one of claims 1 to 7, wherein the system is based on an electrical internet of things architecture comprising a perception layer, a network layer, and an application layer, wherein:
the data acquisition module is positioned on the sensing layer;
the running state management module is positioned at a network layer;
the distributed power generation equipment output prediction module, the charging station load prediction module, the operation plan making module, the real-time scheduling optimization module, the internal transaction platform module and the external transaction platform module are respectively located at an application layer.
9. A method for virtual aggregation of regional energy complexes, comprising:
acquiring time sequence data of the distributed power generation equipment, the energy storage system and the charging station according to the information acquisition instruction of the running state management module;
processing time sequence data of the distributed power generation equipment, the energy storage system and the charging station to form operation data, recording state data of the distributed power generation equipment, the energy storage system and/or the charging station and external environment data, and calling and outputting;
predicting the output of the distributed power generation equipment in the middle-long term, day-ahead and day-ahead according to the output operation data, state data and environment data;
Predicting daily loads and daily loads of the charging station according to the output operation data, state data and environment data;
according to the obtained day-ahead output prediction information, the day-ahead load prediction information of the charging station and the historical electricity price information, a next day power generation operation plan of the distributed power generation equipment, the energy storage system and the charging station is formulated;
real-time scheduling optimization is carried out according to the formulated operation plan, the daily output prediction information, the daily load prediction information of the charging station and the real-time electricity price information, so that a real-time operation strategy of the distributed power generation equipment, the energy storage system and the charging station is obtained;
according to the real-time electricity price and the running conditions of the distributed power generation equipment, the energy storage system and the charging station, carrying out external transaction with the power grid;
according to the output operation data, state data, environment data and external transaction information, realizing multi-type transaction settlement inside the system;
the running state management module further comprises any one or more of the following:
-the operational status management module sending data acquisition instructions of the distributed power generation equipment, the energy storage system and/or the charging station to the data acquisition module through an information exchange port based on the 5G wireless communication standard and the TCP/IP transmission protocol, and receiving time sequence data from the data acquisition module; storing, analyzing and correcting the received time sequence data, removing abnormal data, and converting the time sequence data into running state data with required time scale; meanwhile, the running state management module records state data and environment data, and calls and outputs the running data, the state data and/or the environment data to the distributed power generation equipment output prediction module, the charging load prediction module and/or the internal transaction platform module;
-the status data comprises: the method comprises the steps of charging and discharging times, charging and discharging depth of an energy storage system, accumulated power generation capacity of distributed power generation equipment, accumulated load of a charging station, historical overhaul records and/or equipment parameters of the distributed power generation equipment;
-the environmental data comprises: geographic information of the distributed power generation equipment, real-time road traffic information and historical traffic information and/or wind power predicted values and illumination predicted values of the places where the distributed power generation equipment is located.
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