AU2020327343A1

AU2020327343A1 - Virtual aggregation system and method for regional energy complex

Info

Publication number: AU2020327343A1
Application number: AU2020327343A
Authority: AU
Inventors: Chen Fang; Donghan FENG; Shanshan SHI; Yiwei SHI; Haojing WANG; Qin Xu; Sumin YU; Qiqi ZHANG; Yu Zhang; Yun ZHOU
Original assignee: Shanghai Jiaotong University; State Grid Shanghai Electric Power Co Ltd; East China Power Test and Research Institute Co Ltd
Current assignee: Shanghai Jiaotong University; State Grid Shanghai Electric Power Co Ltd; East China Power Test and Research Institute Co Ltd
Priority date: 2020-06-03
Filing date: 2020-12-09
Publication date: 2021-12-23
Also published as: CN111641207A; WO2021244000A1; CN111641207B

Abstract

The present disclosure provides a virtual aggregation system and method for a regional energy complex with a charging station as a key node. The system is based on a power Internet of Things (IoT) architecture, including three architectural layers: a perception layer, a network layer, and an application layer, and the following modules: a data acquisition module, an operation status management module, a distributed power generation device output forecast module, a charging station load forecast module, an operation plan development module, a real-time scheduling optimization module, an internal transaction platform module, and an external transaction platform module. The present disclosure reduces negative impact of charging load on a power grid, reduces costs of upgrading and expansion of a power distribution network, and fully consumes electric energy generated by electric power generation with distributed renewable energy. Renewable energy is efficiently used and a rate of wind and light abandonment is reduced by providing grid-connected distributed energy storage rental services and by using a forecast module and a scheduling module in the system. Energy is managed at an information flow level, thereby helping control costs of the energy complex. Therefore, the present disclosure is more widely applied.

Description

VIRTUAL AGGREGATION SYSTEM AND METHOD FOR REGIONAL ENERGY COMPLEX TECHNICAL FIELD

[0001] The present disclosure relates to the field of electric energy dispatching and management technologies, and specifically, to a virtual aggregation system and method for a regional energy complex with a charging station as a key node.

BACKGROUND

[0002] In recent years, an installed capacity of distributed power generation has grown rapidly. The advantage of distributed power generation is that it is close to a load side, suitable for nearby consumption, and can reduce line laying investment and electric energy transmission losses in transmission and distribution. To implement nearby consumption of distributed power generation, the National Development and Reform Commission and the National Energy Administration issued the "Notice on Launching Pilot Market-based Trading of Distributed Power Generation" (hereinafter referred to as the "Notice") in 2017, allowing a distributed power generation project to have a power transaction with nearby users within a voltage level range of a distribution network.

[0003] Distributed renewable energy participates in a power transaction at a power distribution network level, and can provide users with electric energy at a lower price than conventional retail electricity. However, in the power transaction, distributed power generation usually lacks initiative in a power transaction process due to a relatively small capacity and unstable output, and user-side flexibility needs to be used or mobilized through proper multi-energy complementation of integrated energy service providers, to reduce uncertain impact of power generation with the distributed renewable energy, and improve flexibility and stability of power supply. The concept of virtual power plant came into being. The virtual power plant implements aggregation and coordinative optimization of distributed source through an advanced information communication technology and software system. However, the virtual power plant participates in a power supply coordinative management system of a power market and a power grid in a form of a power plant. This does not resolve a nearby consumption problem of distributed power generation.

[0004] Power generation methods with renewable energy such as photovoltaic and wind are greatly affected by the climate, fluctuate significantly, and are relatively unstable. With energy storage, a utilization rate of renewable energy can be significantly improved. However, investment costs of energy storage at a current stage are high, owners of distributed power generation are less willing to equip their own energy storage, and a utilization rate of a single type of energy storage in distributed power generation is limited. Some scholars have proposed that energy storage resources can be shared with users through cloud energy storage (that is, a type of network-connected distributed energy storage) to reduce power consumption costs for the users, and cloud energy storage providers make profits by providing energy storage services. Willingness of electric power users to use cloud energy storage is usually low, and it is uncertain whether such a business mode is feasible.

[0005] Digitalization is an important support for the supply of energy services in the background of prosperous construction of a power Internet-of-Things (IoT). Interconnection between distributed energy sources, interaction between distributed energy sources and the grid, and sharing of resources among market players all require digital technology support. The construction of the power IoT provides technical support for diversified aggregation forms of distributed energy. In contrast, conventional forms of distributed energy aggregation, such as microgrids, require a large investment in the construction of power grids at an early stage.

[0006] It is found through retrieval that:

[0007] In the Chinese utility model patent No. CN206041652U disclosed on March 22, 2017 and entitled "ELECTRIC ENERGY DISTRIBUTION SYSTEM FOR DISTRIBUTED VIRTUAL POWER PLANT", the system is an internal transaction system and electric energy distribution system of a virtual power plant, and does not relate to management of an external transaction with a power grid. The system does not include special load of an electric vehicle charging station, and complementarity of charging station load and distributed power generation is not considered. The system does not use a two-step scheduling method involving day-ahead and intraday scheduling.

[0008] In the Chinese invention patent application No. CN105761109A, filed on July 13, 2016 and entitled "INTELLIGENT MANAGEMENT SYSTEM FOR ENERGY MANAGEMENT AND ELECTRIC POWER TRANSACTION OF VIRTUAL POWER PLANT, AND OPTIMIZED OPERATION METHOD FOR SAME", the system and method are directed to virtual power plants serving as power generators participating in electric power market-based transactions, with a restricted scope of application. The system and method do not include special load of an electric vehicle charging station, and complementarity of charging station load and distributed power generation is not considered. The system and method do not provide any information network architecture with application significance under the background of the power IoT.

[0009] At present, no description or report of a technology similar to the present disclosure has been found, and no similar information has been collected at home and abroad.

SUMMARY

[0010] In view of the foregoing disadvantages in the prior art, the present disclosure provides a virtual aggregation system and method for a regional energy complex with a charging station as a key node.

[0011] The present disclosure is achieved by the following technical solutions.

[0012] In a first aspect of the present disclosure, a virtual aggregation system for a regional energy complex is provided, including:

[0013] a data acquisition module, where the data acquisition module obtains time series data of a distributed power generation device, an energy storage system, and a charging station according to an information acquisition instruction of an operation status management module;

[0014] the operation status management module, where the operation status management module, serving as a data management center and a data scheduling interface, processes the time series data of the distributed power generation device, the energy storage system, and the charging station to form operation data, records status data and external environment data of the distributed power generation device, the energy storage system, and/or the charging station, and calls and outputs the status data and the external environment data;

[0015] a distributed power generation device output forecast module, where the distributed power generation device output forecast module forecasts medium and long term, day-ahead, and intraday output of the distributed power generation device according to the data output by the operation status management module;

[0016] a charging station load forecast module, where the charging station load forecast module forecasts day-ahead and intraday load of the charging station according to the data output by the operation status management module;

[0017] an internal transaction platform module, where the internal transaction platform module implements multi-type transaction settlement within the system according to the data output by the operation status management module and external transaction information provided by an external transaction platform;

[0018] an external transaction platform module, where the external transaction platform module conducts an external transaction with a power grid according to a real-time electricity price and a real-time operation status obtained by a real-time scheduling optimization module;

[0019] the operation plan development module develops an operation plan of power generation and consumption of a next day for the distributed power generation device, the energy storage system, and the charging station according to day-ahead output forecast information obtained by the distributed power generation device output forecast module, day-ahead load forecast information of the charging station obtained by the charging station load forecast module, and historical electricity price information obtained by the external transaction platform module; and

[0020] the real-time scheduling optimization module performs real-time scrolling scheduling optimization on the system according to the operation plan developed by the operation plan development module, intraday output forecast information obtained by the distributed power generation device output forecast module, intraday load forecast information of the charging station obtained by the charging station load forecast module, and real-time electricity price information provided by the external transaction platform module, to obtain a real-time scheduling policy for the distributed power generation device, the energy storage system, and the charging station.

[0021] Preferably, the data acquisition module acquires the time series data based on a specified sampling period after receiving the information acquisition instruction.

[0022] Preferably, the time series data includes real-time power generation of the distributed power generation device, remaining power and a status of the energy storage system, and real-time charging power of each charging pile, a quantity of current charging vehicles, and a charged time in the charging station; and

[0023] Preferably, the data acquisition module performs multi-source heterogeneous data acquisition in each area by using distributed sensor networks WSNs in an Internet of Things (IoT), summarizing acquisition results to an area base station based on dynamic features of an IoT terminal by separately selecting a 5GNR standard and a TCP/IP transmission protocol, and transmitting the acquisition results to the operation status management module through an information exchange port based on a 5GNR air interface architecture.

[0024] Preferably, the operation status management module sends a data acquisition instruction of the distributed power generation device, the energy storage system, and/or the charging station to the data acquisition module through an information exchange port based on a 5G wireless communications standard and a TCP/IP transmission protocol, and receives time series data from the data acquisition module; and stores, analyzes, and corrects the received time series data, removes abnormal data, and converts the time series data into operation status data of a required time scale; in addition, the operation status management module records the status data and the environment data, calls and outputs the operation data, the status data, and/or the environment data to the distributed power generation device output forecast module, the charging station load forecast module, and/or the internal transaction platform module.

[0025] Preferably, the status data includes charging and discharging times and charging and discharging depth of the energy storage system, a cumulative power generation capacity of the distributed power generation device, cumulative load and a historical maintenance record of the charging station, and/or a device parameter of the distributed power generation device.

[0026] Preferably, the environment data includes geographic information, real-time road traffic information, and historical traffic information of the distributed power generation device, and/or a wind power forecast value and a lighting forecast value of a location of the distributed power generation device.

[0027] Preferably, the distributed power generation device output forecast module calls real-time and historical power generation capacities and device parameters of the distributed power generation device and a wind power forecast value and a lighting forecast value of a location of the distributed power generation device through a data calling interface provided by the operation status management module, then performs medium and long term, day-ahead, and intraday forecast on a power generation capacity of the distributed power generation device, collates and stores forecast results, and provides the forecast results for the operation plan development module and the real-time scheduling optimization module.

[0028] Preferably, the distributed power generation device output forecast module performs the medium and long term, day-ahead, and intraday forecast on the power generation capacity of the distributed power generation device by using commercial wind turbine power forecast software and/or photovoltaic power forecast software.

[0029] Preferably, the charging station load forecast module calls real-time and historical charging station load, a quantity of charging piles, a device parameter, real-time relevant road traffic information, and historical traffic information through a data calling interface provided by the operation status management module, constructs a day-ahead charge load curve based on historical data and performs intraday forecast correction for total load of the charging station through a commercial Intemet-of-Vehicles (IoV) platform API, collates and stores forecast results, and provides forecast result information for the operation plan development module and the real-time scheduling optimization module.

[0030] Preferably, the operation plan development module calls next-day time distribution of a day-ahead forecast power generation capacity of the distributed power generation device, next-day time distribution of day-ahead forecast load of the charging station, and electricity price information for each period of a next day through data calling interfaces provided by the operation status management module, the distributed power generation device output forecast module, the charging station load forecast module, and the real-time scheduling optimization module, and establishes an optimization model of an objective function of minimizing operation costs of the next day, where the optimization model of the objective function is shown as follows: T T T T

minZ(7PBAt - TPj t + [PtTAt) + Mc Scs + Mpv +Mwind -Stind t=1 t=1 t=1 t=1

BS

[0031] in the formula: WTn, 7T are respectively a price at which the system purchases electricity from the power grid and sells electricity to the power grid in a time period t; 7 Tis a grid-crossing fee charged by the power grid; P, Pis are respectively powers for purchasing electricity from the power grid and selling electricity to the power grid in the time period t; P[ is a power for the system to flow in the power grid in the time period t; M,,, Mpy, Mwind are respectively penalty coefficients for a forecast bias of a charging requirement, a forecast bias of a power generation capacity of a photovoltaic power generation device, and a forecast bias of a power generation capacity of a wind power generation device; o5s is a forecast bias of a charging requirement of the charging station in the time period t; Sev is a forecast bias of a power generation capacity of a distributed photovoltaic power generation device in the time periodt;Sot'nd is a forecast bias of a power generation capacity of a distributed wind power generation device in the time period t; and T is a total quantity of time periods; and

[0032] develops a next-day operation plan through solution by using a mixed integer nonlinear stochastic programming algorithm, and outputs information about the developed operation plan to the real-time scheduling optimization module.

[0033] Preferably, the real-time scheduling optimization module performs scrolling optimization solution on an intraday operation plan in an operation day according to the information about the operation plan provided by the operation plan development module, by using intraday power generation capacity forecast information provided by the distributed power generation device output forecast module, intraday load forecast information provided by the charging station load forecast module, and real-time electricity price information of the current day as decision support information, according to real-time information that is of a device status and a power generation status of the distributed power generation device, an energy storage system status, and a load station status and that is provided by the operation status management module, and by using a mathematical optimization algorithm; and adjusts and refines information about the intraday operation plan based on an optimization solution result, to generate a real-time scheduling optimization policy and external power transaction plan information for the distributed power generation device, the energy storage system, and the charging station, and separately outputs the real-time scheduling optimization policy and the external power transaction plan information to the external transaction platform module and control terminals of the distributed power generation device, the energy storage system, and the charging station.

[0034] Preferably, the internal transaction platform module calls real-time and historical power generation capacities of the distributed power generation device, real-time and historical charge/discharge volumes of the energy storage system, and real-time and historical load of each charging pile of the charging station through a data calling interface provided by the operation status management module, and obtains external transaction information through a data calling interface provided by the external transaction platform module;

[0035] Preferably, the internal transaction platform module uses a blockchain decentralized data storage technology to implement an electronic contract, notarized measurement, and fee settlement, and implement multi-type transaction settlement within the system internal; and

[0036] Preferably, the external transaction platform module calls a total power generation capacity of the distributed power generation device and total power consumption of all the charging piles of the charging station through a data calling interface of the operation status management module, interacts with a power grid enterprise, obtaining and storing real-time electricity price information, conducting an external transaction with the power grid enterprise, and provides electricity price information for the operation plan development module and the real-time scheduling optimization module; and executes external power transaction plan information provided by the real-time scheduling optimization module.

[0037] Preferably, the system is based on a power IoT architecture, including a perception layer, a network layer, and an application layer, where:

[0038] the data acquisition module is located at the perception layer;

[0039] the operation status management module is located at the network layer; and

[0040] the distributed power generation device output forecast module, the charging station load forecast module, the operation plan development module, the real-time scheduling optimization module, the internal transaction platform module, and the external transaction platform module are separately located at the application layer.

[0041] In another aspect of the present disclosure, a virtual aggregation method for a regional energy complex is provided, including:

[0042] obtaining time series data of a distributed power generation device, an energy storage system, and a charging station according to an information acquisition instruction of an operation status management module;

[0043] processing the time series data of the distributed power generation device, the energy storage system, and the charging station to form operation data, recording status data and external environment data of the distributed power generation device, the energy storage system, and/or the charging station, and calling and outputting the status data and the external environment data;

[0044] forecasting medium and long term, day-ahead, and intraday output of the distributed power generation device according to the outputted operation data, status data, and environment data;

[0045] forecasting day-ahead and intraday load of the charging station according to the outputted operation data, status data, and environment data;

[0046] developing an operation plan of power generation and consumption of a next day for the distributed power generation device, the energy storage system, and the charging station according to obtained day-ahead output forecast information, day-ahead load forecast information of the charging station, and historical electricity price information;

[0047] performing real-time scheduling optimization according to the developed operation plan, intraday output forecast information, intraday load forecast information of the charging station, and real-time electricity price information, to obtain a real-time operation policy for the distributed power generation device, the energy storage system, and the charging station;

[0048] conducting an external transaction with a power grid according to a real-time electricity price and operation statuses of the distributed power generation device, the energy storage system, and the charging station; and

[0049] implementing multi-type transaction settlement within the system according to the outputted operation data, status data, and environment data and external transaction information.

[0050] The present disclosure has at least one of the following beneficial effects:

[0051] As the electric vehicle population gradually increases, new charging load puts pressure on a power grid. According to the virtual aggregation system and method for the regional energy complex provided in the present disclosure, an energy scheduling and management system with a charging station as a key node is used, so that negative impact of electric vehicle charging load on the power grid is reduced, and costs of upgrading and expansion of a power distribution network are reduced.

[0052] The virtual aggregation system and method for the regional energy complex provided in the present disclosure can fully consume electric energy generated by electric power generation with distributed renewable energy. The energy complex enables efficient use of renewable energy and reduces a rate of wind and light abandonment by providing grid-connected distributed energy storage rental services, and by using a forecast module and a scheduling module in the system.

[0053] According to the virtual aggregation system and method for the regional energy complex provided in the present disclosure, energy is managed at an information flow level, without a need to transform an existing power distribution network. This is an asset-light operation mode and helps control costs of the energy complex.

[0054] The virtual aggregation system and method for the regional energy complex provided in the present disclosure can be more widely applied, provided that the energy complex can serve as either an electric power generator side and a load side of power consumption in different situations as an electricity price changes over time.

BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Other features, objectives, and advantages of the present disclosure will become more apparent from a reading of the detailed description of non-limiting embodiments with reference to the following accompanying drawings.

[0056] FIG. 1 is a schematic structural diagram of a virtual aggregation system for a regional energy complex with a charging station as a key node according to a preferred embodiment of the present disclosure.

[0057] FIG. 2 is a diagram of time series in a virtual aggregation system for a regional energy complex with a charging station as a key node according to a preferred embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] Embodiments of the present disclosure are to be described in detail below. The embodiments are implemented on the premise of the technical solutions of the present disclosure, and detailed implementations and specific operation processes are provided. It should be noted that a person of ordinarily skilled in the art can further make several variations and improvements without departing from the concept of the present disclosure. These variations and improvements all fall within the protection scope of the present disclosure.

[0059] An embodiment of the present disclosure provides a virtual aggregation system for a regional energy complex. The system is based on a power IoT technology, uses a charging station as a key node, and integrates distributed energy on a power consumption side and electric vehicle charging load.

[0060] As shown in FIG. 1, the virtual aggregation system for the regional energy complex in this embodiment is based on the power IoT architecture, including three architectural layers: a perception layer, a network layer, and an application layer, and the following modules: four data acquisition modules, an operation status management module, a distributed power generation device output forecast module, a charging station load forecast module, an operation plan development module, a real-time scheduling optimization module, an internal transaction platform module, and an external transaction platform module. Specifically:

[0061] The data acquisition module obtains time series data of a distributed power generation device, an energy storage system, and a charging station according to an information acquisition instruction of the operation status management module.

[0062] The operation status management module, serving as a data management center and a data scheduling interface, processes the time series data of the distributed power generation device, the energy storage system, and the charging station to form operation data, records status data and external environment data of the distributed power generation device, the energy storage system, and/or the charging station, and calls and outputs the status data and the external environment data.

[0063] The distributed power generation device output forecast module forecasts medium and long term, day-ahead, and intraday output of the distributed power generation device according to the data output by the operation status management module.

[0064] The charging station load forecast module forecasts day-ahead and intraday load of the charging station according to the data output by the operation status management module.

[0065] The internal transaction platform module implements multi-type transaction settlement within the system according to the data output by the operation status management module and external transaction information provided by an external transaction platform.

[0066] The external transaction platform module conducts an external transaction with a power grid according to a real-time electricity price and a real-time operation status obtained by the real-time scheduling optimization module.

[0067] The operation plan development module develops an operation plan of power generation and consumption of a next day for the distributed power generation device, the energy storage system, and the charging station according to day-ahead output forecast information obtained by the distributed power generation device output forecast module, day-ahead load forecast information of the charging station obtained by the charging station load forecast module, and historical electricity price information obtained by the external transaction platform module.

[0068] The real-time scheduling optimization module performs real-time scrolling scheduling optimization on the system according to the operation plan developed by the operation plan development module, intraday output forecast information obtained by the distributed power generation device output forecast module, intraday load forecast information of the charging station obtained by the charging station load forecast module, and real-time electricity price information provided by the external transaction platform module, to obtain a real-time scheduling policy for the distributed power generation device, the energy storage system, and the charging station.

[0069] In a preferred embodiment, the data acquisition module belongs to the perception layer of the IoT architecture, and is physically located in the distributed power generation device, the energy storage system, and a charging pile terminal. The data acquisition module includes a sensor, a memory, a transceiver, and a processor, and is mainly configured to obtain the time series data of the distributed power generation device, the energy storage, and the charging pile. An operation process of the module is as follows: After receiving the information acquisition instruction from the operation status management module, the module collects, based on a specified sampling period, real-time power generation of the distributed power generation device (which may be a distributed wind turbine and/or a distributed photovoltaic device), remaining power and a status of the energy storage system, and real-time charging power of a charging pile, a quantity of current charging vehicles, and a charged time in the charging station. In each area, multi-source heterogeneous data acquisition is performed by using a technology of IoT distributed sensor network wireless sensor networks (WSNs), acquisition results are summarized to an area base station based on dynamic features of an IoT terminal by separately selecting a GNR standard and a TCP/IP transmission protocol, and the acquisition results are transmitted to a big data center through an information exchange port based on a 5GNR air interface design and are provided for the operation status management module.

[0070] In a preferred embodiment, the operation status management module belongs to the network layer of the IoT architecture, and is physically located in the big data center. The big data center is a collection and distribution center and a management center for data related to system operation, and is responsible for data processing and providing a data calling interface for another module related to data analysis. An operation process of the module is as follows: The operation status management module sends, through the information exchange port based on a G wireless communication technology and a TCP/IP protocol, the information acquisition instruction to the distributed power generation device, an energy storage system, and the charging pile in a geographical area integrated and managed by the system, and receives the time series data from the data acquisition module, including the real-time power generation of the distributed power generation device (which may be a distributed wind turbine and/or a distributed photovoltaic device), the remaining power and the status of the energy storage system, and the real-time charging power of each charging pile in the charging station, the quantity of charging vehicles, and the charged time. The received time series data is stored, analyzed, and corrected, abnormal data is removed, and time series information is converted into operation data of a required time scale, for easy calling by each module. In addition, the operation status management module records related status data such as charging and discharging times, a cumulative power generation capacity of distributed power generation, cumulative load and a historical maintenance record of the charging station, and a device parameter of the distributed power generation device. A public commercial Internet-of-Vehicles (IoV) platform API (for example, an application with a real-time traffic acquisition capability and permissions such as Baidu Maps) is called at regular time intervals to obtain real-time road traffic information and historical traffic information. A weather forecast platform API (for example, a platform with a real-time weather acquisition capability and permissions such as Moji Weather) is called at regular time intervals to obtain environment data such as a wind power forecast value and a lighting forecast value of a location of the distributed power generation, and geographic information of the distributed power generation device. Then, an information retrieval outlet and a data calling communications interface are provided for a real-time data processing result, historical operation data, road traffic information, historical traffic information, and the wind power forecast value and the lighting forecast value of the location of the distributed power generation, and the information is provided for the distributed power generation device output forecast module, the charging station load forecast module, the internal transaction platform module, and the real-time scheduling optimization module.

[0071] In a preferred embodiment, the distributed power generation device output forecast module belongs to the application layer of the IoT architecture, is physically located in the big data center, and is configured to perform medium and long term, day-ahead, and intraday forecast on output of a distributed power generation device. The module calls real-time and historical power generation capacities and related device parameters of the distributed power generation device and meteorological information such as a wind power forecast value and a lighting forecast value of the location of the distributed power generation device through a database interface provided by the operation status management module, then performs medium and long term, day-ahead, and intraday forecast on a power generation capacity of the distributed power generation device by using commercial wind turbine power generation forecast software (such as Previento) and/or photovoltaic power generation forecast software (such as Suncast), and collates and stores forecast results. The module provides an information retrieval outlet and a data calling communication interface for the forecast results, and provides information about the forecast results for the operation plan development module and the real-time scheduling optimization module through the data calling communication interface.

[0072] In a preferred embodiment, the power generation capacity of the distributed power generation device is forecast by calling commercial power generation forecast software.

[0073] In a preferred embodiment, the charging station load forecast module belongs to the application layer of the IoT architecture, is physically located in the big data center, and is configured to perform day-ahead and intraday load forecast on charging station load. The module calls real-time and historical charging station load statuses, a quantity of charging piles, a device parameter, real-time relevant road traffic information, and historical traffic information through a database interface provided by the operation status management module, to perform day-ahead and intraday forecast on total load of the charging station. A forecasting method is as follows: A daily load base curve is constructed by using historical load data and a charging station load forecast model, and then real-time road traffic information is used as an adjustment amount to correct a forecast value. Forecast results are collated and stored, and an information retrieval outlet and a data calling communication interface are provided for the output of processing results of these data. Information about the forecast results are provided for the operation plan development module and the real-time scheduling optimization module through the data calling communication interface.

[0074] In a preferred embodiment, a public commercial IoV platform API is called to forecast charging load.

[0075] In a preferred embodiment, the operation plan development module belongs to the application layer of the IoT architecture, is physically located in the big data center, and is configured to develop a plan of power generation and consumption of a next day for the distributed power generation device and the energy storage system according to power generation and consumption forecast information and electricity price information. The module is called once a day to make an operation plan for the next day. Next-day time distribution of a day-ahead forecast power generation capacity of the distributed power generation device, next-day time distribution of day-ahead forecast load of the charging station, and electricity price information for each period of a next day are called through data calling communication interfaces provided by the operation status management module, the distributed power generation device output forecast module, the charging station load forecast module, and the external transaction platform module. Investment costs and operation costs are comprehensively considered to establish an optimization model of an objective function of minimizing operation costs of the next day. The optimization model includes constraints such as an energy storage capacity limit, a market transaction limit, uncertainty factors of power generation and consumption data. Then, a next-day operation plan is developed through solution by using a random mixed integer optimization algorithm embedded in optimization software. An information retrieval outlet and a data calling communication interface are provided for the output of day-ahead operation plan information, and the day-ahead operation plan information is provided for the real-time scheduling optimization module through the data calling communication interface. In the foregoing embodiment of the present disclosure, the operation status management module may be a controller or a chip; and the distributed power generation device output forecast module, the charging station load forecast module, the operation plan development module, the real-time scheduling optimization module, the internal transaction platform module, and the external transaction platform module may be independent controllers or chips, or may be integrated on one chip.

[0076] In the preferred embodiment,

[0077] the optimization model of the objective function is shown as follows: T T T T

minmn (7T~ptBAt (Y, P t - 7~ptAt + 7[ptTAt) + M" . -uP t +xP t) +,Mc Y' (es+M (5S ~++Mwind. tv +Mwind -, Sti (wind t=1 t=1 t=1 t=1

[0078] In the formula: 7T, 7T are respectively a price at which the system purchases electricity from the power grid and sells electricity to the power grid in a time period t; 7 Tis a grid-crossing fee charged by the power grid; PB, Pt are respectively powers for purchasing electricity from the power grid and selling electricity to the power grid in the time period t; P[ is a power for the system to flow in the power grid in the time period t; Mc,, Mev, Mwind are respectively penalty coefficients for a forecast bias of a charging requirement, a forecast bias of a power generation capacity of a photovoltaic power generation device, and a forecast bias of a power generation capacity of a wind power generation device; Scs is a forecast bias of a charging requirement of the charging station in the time period t; o5v is a forecast bias of a power generation capacity of a distributed photovoltaic power generation device in the time period t; thi"d is a forecast bias of a power generation capacity of a distributed wind power generation device in the time period t; and T is a total quantity of time periods.

[0079] A next-day operation plan is developed through solution by using a mixed integer nonlinear stochastic programming algorithm, and information about the developed operation plan is output to the real-time scheduling optimization module.

[0080] In a preferred embodiment, the real-time scheduling optimization module belongs to the application layer of the IoT architecture, is physically located in the big data center, and is configured to perform real-time scheduling operation on the system based on the operation plan. The module performs scrolling optimization solution on an intraday operation plan in an operation day according to the information about the day-ahead operation plan provided by the operation plan development module, by using intraday power generation forecast information provided by the distributed power generation device output forecast module, intraday load forecast information provided by the charging station load forecast module, and real-time electricity price information of the current day as decision support information, according to real-time system operation status information that is of a device status of a distributed energy unit, a power generation status, an energy storage status, and a load status and that is provided by the operation status management module, and by using mathematical optimization software. In addition, the information about the intraday operation plan is adjusted and refined based on an optimization solution result, to generate a real-time scheduling optimization policy and an external power transaction plan for the distributed power generation device, the energy storage system, and the charging station. An information retrieval outlet and a data calling communication interface are provided for the output of information about the real-time scheduling optimization policy and the external power transaction plan, and the information about the real-time scheduling optimization policy is provided for control terminals of the distributed power generation device, the energy storage system, and the charging station, and the external transaction platform module through the data calling communication interface.

[0081] In a preferred embodiment, the internal transaction platform module belongs to the application layer of the IoT architecture, is physically located in the big data center, provides an internal transaction settlement platform for an aggregation entity, and provides settlement of multiple types transactions such as an electricity transaction and energy storage rental. The module calls wind turbine real-time and historical power generation capacities, photovoltaic real-time and historical power generation capacities, real-time and historical charge/discharge volumes of energy storage, and real-time and historical load of each charging pile through the data calling interface of the operation status management module; and calls external transaction information through the data calling interface provided by the external transaction platform. The module uses a blockchain decentralized data storage technology to implement an electronic contract, notarized measurement, and fee settlement. In terms of the electronic contract, an owner of the distributed power generation device signs an electricity transaction contract and an energy storage rental contract in the form of blockchain smart contracts with the charging station. The electricity transaction contract stipulates an electricity settlement price and a settlement period for distributed power generation and the charging station. The energy storage rental contract stipulates that the owner of the distributed power generation device pays a particular amount of energy storage rental fee to the charging station, and the rental fee is determined based on an output fluctuation degree and a power generation capacity of the distributed power generation device. Automatic settlement of internal transactions using blockchain smart contract features is both transparent and highly efficient. The module provides an information calling outlet for the output of internal transaction information.

[0082] In a preferred embodiment, the external transaction platform module belongs to the application layer of the IoT architecture, is physically located in the big data center, and is configured to conduct a transaction with the power grid according to a real-time electricity price and an operation status. Content of the transaction includes an electricity fee and a grid-crossing fee. It is assumed that the power grid adopts a real-time electricity price, that is, the electricity price dynamically changes according to electricity market transactions. Part of the electric energy can be automatically consumed within the system, overall power consumption or an overall power generation capacity is relatively small, and the system has a relatively weak capability to participate in the electricity market as a main body. Therefore, it is assumed herein that the system serves as a price acceptor of a real-time electricity price and does not participate in the electricity market. The module obtains total power consumption/a total power generation capacity of the system through the data calling interface of the operation status management module. The module interacts with a power grid enterprise to obtain real-time electricity price information and store the electricity price information. The module executes planned transaction information provided by the real-time scheduling optimization module, and completes a 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 information about the real-time and historical electricity prices for the operation plan development module and the real-time scheduling optimization module.

[0083] The virtual aggregation system for the regional energy complex in this embodiment uses the charging station as a key node, is based on a power IoT technology and a modem metering and control communication technology, and includes an operation framework of the regional energy complex of different types of distributed power generation devices. An aggregation manner of the complex is aggregation of an information flow level, that is, an electric vehicle charging station, distributed power generation, and an energy storage device are controlled collaboratively through communications devices.

[0084] As the key node of the regional energy complex, the charging station has triple identities. First, the charging station serves as a "brain" of the energy complex, is equipped with a data center having a relatively strong calculation capability, and undertakes functions of energy complex operation plan formulation, real-time coordination control, internal main settlement, and an external power grid transaction. Second, the charging station serves as main load of the regional energy complex, and consumes electric energy generated by distributed power generation. Third, the charging station serves as a provider of grid-connected distributed energy storage resources, is equipped with an energy storage device with a particular capacity, and provides energy storage resources for the regional energy complex. It should be noted that, because the charging station obtains a large amount of low-price electric energy through distributed power generation and is the biggest beneficiary in the complex, the charging station is used as the key node and is equipped with a data center. However, the data center is not strongly bound to the charging station and can be decoupled from the charging station when necessary.

[0085] Another embodiment of the present disclosure provides a virtual aggregation method for a regional energy complex with a charging station as a key node. The method includes:

[0086] obtaining time series data of a distributed power generation device, an energy storage system, and a charging station according to an information acquisition instruction of an operation status management module;

[0087] processing the time series data of the distributed power generation device, the energy storage system, and the charging station to form operation data, recording status data and external environment data of the distributed power generation device, the energy storage system, and/or the charging station, and calling and outputting the status data and the external environment data;

[0088] forecasting medium and long term, day-ahead, and intraday output of the distributed power generation device according to the outputted operation data, status data, and environment data;

[0089] forecasting day-ahead and intraday load of the charging station according to the outputted operation data, status data, and environment data;

[0090] developing an operation plan of power generation and consumption of a next day for the distributed power generation device, the energy storage system, and the charging station according to obtained output forecast information, charging station load forecast information, and electricity price information;

[0091] performing real-time scheduling optimization according to the developed operation plan, to obtain a real-time electricity price and an operation status of the distributed power generation device, the energy storage system, and/or the charging station;

[0092] conducting an external transaction with a power grid according to the real-time electricity price and the operation status of the distributed power generation device, the energy storage system, and/or the charging station; and

[0093] implementing multi-type transaction settlement within the system according to the outputted operation data, status data, and environment data and external transaction information.

[0094] It should be noted that, the steps in the method provided in the present disclosure may be implemented by using corresponding modules, apparatuses, units, or the like in the system. A person skilled in the art may implement the step procedure of the method with reference to the technical solutions of the system, that is, the embodiments in the system may be understood as preferred examples to implement the method. Details are not described herein.

[0095] The following further describes in detail the technical solutions in the foregoing embodiments of the present disclosure with reference to the accompanying drawings and specific application examples.

[0096] In this application example, the regional energy complex is at an early stage of trial operation, and a volume of distributed power generation inside the complex is relatively small. Therefore, an electric vehicle charging station can consume most of electric energy generated by distributed power generation. The remaining electric energy that cannot be automatically consumed is to be traded with the power grid at a real-time electricity price.

[0097] A block diagram of the system in this application example is shown in FIG. 1. A perception layer of the system consists of data acquisition modules located in a charging pile, a photovoltaic device, and a wind turbine. A network layer implements transmission of data at the perception layer data through wireless, microwave, and other signal transmission media, and implements interconnection between the IoT and a conventional telecommunications network with the help of a IoT gateway. An operation status management module is located at the network layer. An application layer is all located in a data center, and includes six modules: a distributed power generation device output forecast module, a charging station load forecast module, an operation plan development module, a real-time scheduling optimization module, an internal transaction platform module, and an external transaction platform module.

[0098] A schematic diagram of time series in the system is shown in FIG. 2. A specific process is as follows:

[0099] 1. Data acquisition and processing: When the system starts to operate, the operation status management module first sends an information acquisition instruction to a distributed power generation device, an energy storage device, and a charging pile in a geographical area integrated and managed by the system. The data acquisition module located at the perception layer receives the information acquisition instruction from the operation status management module, then controls a control metering device located in a terminal, collects real-time power generation of a distributed wind turbine, real-time power generation of a distributed photovoltaic device, remaining power and a status of the energy storage system, real-time charging power of a charging pile of the charging station, a quantity of current charging vehicles, and a charged time based on a specified sampling period by using a technology of IoT distributed sensor networks WSNs, then summarizes acquisition results to an area base station through the WSNs, and provides the information for the operation status management module through an information exchange port equipped with an IoT gateway. The operation status management module at the network layer analyzes and corrects the foregoing time series data from the data acquisition module upon reception, removes abnormal data, and finally converts time series information into operation status information with a time scale of 1 min, that is,1 min average power generation of the distributed wind turbine, 1 min average power generation of the distributed photovoltaic device, remaining power of the energy storage system, and 1 min average power consumption power of the charging station, for easy calling by each module. In addition, the operation status management module records related information such as charging and discharging times, a cumulative power generation capacity of distributed power generation, cumulative load and a historical maintenance record of the charging station, and a device parameter and geographical information of the distributed power generation device. The operation status management module calls an IoV platform API every 15 min to obtain real-time road traffic information and historical traffic information, and calls a weather forecast platform API every 15 min to obtain a wind power forecast value and a lighting forecast value of a location of the distributed power generation. The operation status management module stores a processing result of the obtained data, and provides the information for the distributed power generation device output forecast module, the charging station load forecast module, the internal transaction platform module, and the real-time scheduling optimization module through a database calling interface based on an open data base connectivity (ODBC).

[0100] 2. Development of a day-ahead plan: On the day before operation, the distributed power generation device output forecast module performs day-ahead forecast on a power generation capacity of the distributed power generation device by calling real-time and historical power generation capacities and related device parameters of the distributed power generation device from the operation status management module, and weather information such as a next-day wind power forecast value and a next-day lighting forecast value of a location of the distributed power generation device, and by using a self-built power generation capacity forecast model of the distributed power generation device, or existing wind turbine power generation forecast software such as Previento, and photovoltaic power generation forecast software such as Suncast, and collates and stores forecast results. A day-ahead forecast result is next-day time distribution of the power generation capacity of the distributed power generation device. A minimum time scale thereof is 30 min. Day-ahead power generation forecast information is provided for the operation plan development module through a data calling communication interface.

[0101] The charging station load forecast module calls real-time and historical load statuses of the charging station, a quantity of charging piles, and a device parameter from the operation status management module. A daily load base curve is constructed by using historical load data and a charging station load forecast model, and total forecast load and time distribution of the charging station for the next day are output, with 30 min as a minimum time scale. Day-ahead power generation forecast information is provided for the operation plan development module through a data calling communication interface.

[0102] The operation plan development module calls time distribution of a day-ahead forecast power generation capacity of the distributed power generation device, next-day time distribution of day-ahead forecast load of the charging station, and electricity price information for each period of a next day. A mathematical optimization software module based on C language is used, and investment costs and operation costs are comprehensively considered to establish an objective function, constraints including an energy storage capacity limit, a market transaction limit, uncertainty factors of power generation and consumption data are established, a hybrid optimization model for operation plans is established, and then a next-day operation plan is developed through resolution by using a random mixed integer optimization algorithm embedded in optimization software. Operation plan information, that is, an optimal power generation capacity per time scale, power consumption, charge/discharge volumes of the energy storage device, an internal settlement electricity price, and a planned power purchase/sale from/to the power grid, is provided for the real-time scheduling optimization module with a minimum time scale of 30 min.

[0103] Intraday scheduling optimization: On the day of operation, the distributed power generation device output forecast module corrects a day-ahead forecast value by calling weather information such as a next-hour wind power forecast value and a next-hour lighting forecast value of a location of the distributed power generation device, and by using a self-built power generation capacity forecast model of the distributed power generation device, or existing wind turbine power generation forecast software such as Previento, and photovoltaic power generation forecast software such as Suncast, and collates and stores an intraday forecast result. The intraday forecast result is a time scale of the power generation capacity of the distributed power generation device, and a minimum time scale thereof is 1 min. The intraday forecast result is provided for the real-time scheduling optimization module through a data calling interface.

[0104] The charging station load forecast module calls real-time relevant road traffic information and historical traffic information, and corrects a day-ahead load forecast value of the charging station by using the real-time road traffic information as an adjustment amount. An intraday load forecast output result is charging station load and time distribution within one hour, with 1 min as a minimum time scale. The forecast result is provided for the real-time scheduling optimization module.

[0105] The real-time scheduling optimization module performs optimization solution on a next-hour operation plan according to the information about the operation plan provided by the operation plan development module, by using intraday power generation forecast information provided by the distributed power generation device output forecast module, intraday load forecast information provided by the charging station load forecast module, and electricity price information of the current day as decision support information, according to real-time system operation status information that is of a device status of the distributed energy unit, a power generation status, an energy storage status, and a load status and that is provided by the operation status management module, and by using mathematical optimization software. In addition, operation plan information with time scale of 30 min is adjusted and refined based on an optimization solution result, to generate a real-time scheduling optimization policy, that is, an optimal power generation capacity per time scale of the distributed energy unit, optimal charge/discharge volumes of the energy storage device, and a planned power purchase/sale from/to the power grid of the next hour, with a time scale of1 min. The foregoing information is transmitted to a control device located at the perception layer through the network layer to complete the operation plan.

[0106] Internal transaction: An owner of the distributed power generation device signs an electricity transaction contract and an energy storage rental contract in the form of smart contracts with the energy complex. The electricity transaction contract stipulates an electricity settlement price for distributed power generation and the charging station, and the price is adjusted according to a real-time electricity price of the power grid. A settlement period is divided into weekly and monthly settlement according to a type of a contract with the owner of the distributed power generation device. The energy storage rental contract stipulates that the owner of the distributed power generation device pays a particular amount of energy storage rental fee to the charging station on a monthly basis, and the rental fee is determined based on an output fluctuation degree and a power generation capacity of the distributed power generation device. The internal transaction platform module obtains real-time and historical power generation capacities of the distributed power generation device, real-time and historical charge/discharge volumes of the energy storage, and real-time and historical load of each charging pile through the data calling interface of the operation status management module. A blockchain decentralized data storage technology is used to implement a notarized measurement and fee settlement. After the transaction is completed, an information calling outlet is provided for the output of internal transaction information.

[0107] External transaction: The external transaction platform module obtains total power consumption/a total power generation capacity of the system through the data calling interface of the operation status management module, obtains real-time electricity price information through the power grid information communication software API, and stores the electricity price information. The electricity price information is provided for the operation plan development module and the real-time scheduling optimization module. The external transaction platform module conducts a transaction with the power grid according to planned transaction information provided by the real-time scheduling optimization module. Content of the transaction includes an electricity fee and a grid-crossing fee. settlement is carried out in monthly or other units in accordance with stipulations of the power grid.

[0108] The virtual aggregation system and method for the regional energy complex provided in the foregoing embodiments of the present disclosure resolve at least one of the following technical problems:

[0109] As a new type of power grid load that has proliferated in recent years, electric vehicle fast-charging stations have the following features: The load has high uncertainty and strong volatility. Charging load is significantly affected by traffic flows, and a day and night load gap is large. High-power charging load has impact on the power grid. It is easy to find that the load features of the electric vehicle fast-charging stations and output features of distributed power generation can complement each other to some extent in time and energy dimensions. According to the system and method in the foregoing embodiments of the present disclosure, combining the two through virtual aggregation to implement energy complementarity can resolve both problems of on-site consumption of distributed renewable energy and an increase in load caused by the charging stations to the power grid.

[0110] According to the system and method in the foregoing embodiments of the present disclosure, a user that rents energy storage is limited to a distributed power supply. The distributed power supply has a high demand for energy storage and a stronger willingness to pay. According to the system and method in the foregoing embodiments of the present disclosure, a transaction mode, a transaction platform, and a transaction rule for an energy storage rental transaction between an energy integrated service provider and distributed energy are designed. Investment pressure of the owner of distributed power generation is relieved, and a role of the energy storage in power regulation is fully exploited.

[0111] According to the system and method in the foregoing embodiments of the present disclosure, distributed energy is aggregated at an information flow level, thereby reducing device investment at the physical layer. Interconnection and intercommunication of distributed energy, power consumption load, and an energy storage apparatus are implemented at an information level with the help of an advanced power IoT technology frame. Coordinated control of the distributed energy, the power consumption load, and the energy storage apparatus is implemented with the help of an advanced control metering device.

[0112] According to the system and method in the foregoing embodiments of the present disclosure, an energy scheduling and management system with a charging station as a key node is used, so that negative impact of electric vehicle charging load on the power grid is reduced, costs of upgrading and expansion of a power distribution network are reduced, and electric energy generated by electric power generation with distributed renewable energy can be fully consumed. The energy complex provides grid-connected distributed energy storage rental services and uses a forecast module and a scheduling module in the system, so that renewable energy is efficiently used and a rate of wind and light abandonment is reduced. In addition, energy is managed at an information flow level, without changing an existing power distribution network. This belongs to an asset-light operation mode and helps control costs of the energy complex. Therefore, the system and method can be more widely applied, provided that the energy complex can serve as either an electric power generator side and a load side of power consumption in different situations as an electricity price changes over time.

[0113] The specific embodiments of the present disclosure are described above. It should be understood that the present disclosure is not limited to the above specific implementations, and a person skilled in the art can make various variations or modifications within the scope of the claims without affecting the essence of the present disclosure.

Claims

CLAIMS: 1. A virtual aggregation system for a regional energy complex, comprising: a data acquisition module, wherein the data acquisition module obtains time series data of a distributed power generation device, an energy storage system, and a charging station according to an information acquisition instruction of an operation status management module; the operation status management module, wherein the operation status management module, serving as a data management center and a data scheduling interface, processes the time series data of the distributed power generation device, the energy storage system, and the charging station to form operation data, records status data and external environment data of the distributed power generation device, the energy storage system, and/or the charging station, and calls and outputs the status data and the external environment data; a distributed power generation device output forecast module, wherein the distributed power generation device output forecast module forecasts medium and long term, day-ahead, and intraday output of the distributed power generation device according to the data output by the operation status management module; a charging station load forecast module, wherein the charging station load forecast module forecasts day-ahead and intraday load of the charging station according to the data output by the operation status management module; an internal transaction platform module, wherein the internal transaction platform module implements multi-type transaction settlement within the system according to the data output by the operation status management module and external transaction information provided by an external transaction platform; an external transaction platform module, wherein the external transaction platform module conducts an external transaction with a power grid according to a real-time electricity price and a real-time operation status obtained by a real-time scheduling optimization module; the operation plan development module develops an operation plan of power generation and consumption of a next day for the distributed power generation device, the energy storage system, and the charging station according to day-ahead output forecast information obtained by the distributed power generation device output forecast module, day-ahead load forecast information of the charging station obtained by the charging station load forecast module, and historical electricity price information obtained by the external transaction platform module; and the real-time scheduling optimization module performs real-time scrolling scheduling optimization on the system according to the operation plan developed by the operation plan development module, intraday output forecast information obtained by the distributed power generation device output forecast module, intraday load forecast information of the charging station obtained by the charging station load forecast module, and real-time electricity price information provided by the external transaction platform module, to obtain a real-time scheduling policy for the distributed power generation device, the energy storage system, and the charging station.
2. The virtual aggregation system for the regional energy complex according to claim 1, wherein the data acquisition module further comprises any one or more of the following: -the data acquisition module acquiring the time series data based on a specified sampling period after receiving the information acquisition instruction; -the time series data comprising: real-time power generation of the distributed power generation device, remaining power and a status of the energy storage system, and real-time charging power of each charging pile, a quantity of current charging vehicles, and a charged time in the charging station; and -the data acquisition module performing multi-source heterogeneous data acquisition in each area by using distributed sensor networks WSNs in an Internet of Things (IoT), summarizing acquisition results to an area base station based on dynamic features of an IoT terminal by separately selecting a 5GNR standard and a TCP/IP transmission protocol, and transmitting the acquisition results to the operation status management module through an information exchange port based on a 5GNR air interface architecture.
3. The virtual aggregation system for the regional energy complex according to claim 1, wherein the operation status management module further comprises any one or more of the following: -the operation status management module sending a data acquisition instruction of the distributed power generation device, the energy storage system, and/or the charging station to the data acquisition module through an information exchange port based on a 5G wireless communications standard and a TCP/IP transmission protocol, and receiving time series data from the data acquisition module; and storing, analyzing, and correcting the received time series data, removing abnormal data, and converting the time series data into operation status data of a required time scale; in addition, the operation status management module recording the status data and the environment data, calling and outputting the operation data, the status data, and/or the environment data to the distributed power generation device output forecast module, the charging station load forecast module, and/or the internal transaction platform module; -the status data comprising: charging and discharging times and charging and discharging depth of the energy storage system, a cumulative power generation capacity of the distributed power generation device, cumulative load and a historical maintenance record of the charging station, and/or a device parameter of the distributed power generation device; and

-the environment data comprising: geographic information, real-time road traffic information, and historical traffic information of the distributed power generation device, and/or a wind power forecast value and a lighting forecast value of a location of the distributed power generation device.
4. The virtual aggregation system for the regional energy complex according to claim 1, wherein the distributed power generation device output forecast module further comprises any one or more of the following: -the distributed power generation device output forecast module calling real-time and historical power generation capacities and device parameters of the distributed power generation device and a wind power forecast value and a lighting forecast value of a location of the distributed power generation device through a data calling interface provided by the operation status management module, then performing medium and long term, day-ahead, and intraday forecast on a power generation capacity of the distributed power generation device, collating and storing forecast results, and providing the forecast results for the operation plan development module and the real-time scheduling optimization module; and -the distributed power generation device output forecast module performing the medium and long term, day-ahead, and intraday forecast on the power generation capacity of the distributed power generation device by using commercial wind turbine power forecast software and/or photovoltaic power forecast software.
5. The virtual aggregation system for the regional energy complex according to claim 1, wherein the charging station load forecast module calls real-time and historical charging station load, a quantity of charging piles, a device parameter, real-time relevant road traffic information, and historical traffic information through a data calling interface provided by the operation status management module, constructs a day-ahead charge load curve based on historical data and performs intraday forecast correction for total load of the charging station through a commercial Internet-of-Vehicles (IoV) platform API, collates and stores forecast results, and provides forecast result information for the operation plan development module and the real-time scheduling optimization module.
6. The virtual aggregation system for the regional energy complex according to claim 1, wherein the operation plan development module calls next-day time distribution of a day-ahead forecast power generation capacity of the distributed power generation device, next-day time distribution of day-ahead forecast load of the charging station, and electricity price information for each period of a next day through data calling interfaces provided by the operation status management module, the distributed power generation device output forecast module, the charging station load forecast module, and the real-time scheduling optimization module, and establishes an optimization model of an objective function of minimizing operation costs of the next day, wherein the optimization model of the objective function is shown as follows: T T T T min mn (7T~ptBAt (Y, P t - 7~ptAt + 7[ptTAt) + M"< -uP t +xP t) +,Mc Y' (es+M (5S ~+M+Mwind.wind tv + Mwind - Y Sti t=1 t=1 t=1 t=1 in the formula: 7TB, wT are respectively a price at which the system purchases electricity from the power grid and sells electricity to the power grid in a time period t; 7 Tis a grid-crossing fee charged by the power grid; PB, Pt are respectively powers for purchasing electricity from the power grid and selling electricity to the power grid in the time period t; P[ is a power for the system to flow in the power grid in the time period t; Mc,, Mev, Mwind are respectively penalty coefficients for a forecast bias of a charging requirement, a forecast bias of a power generation capacity of a photovoltaic power generation device, and a forecast bias of a power generation capacity of a wind power generation device; o5cs is a forecast bias of a charging requirement of the charging station in the time period t; Sev is a forecast bias of a power generation capacity of a distributed photovoltaic power generation device in the time periodt;Sotind is a forecast bias of a power generation capacity of a distributed wind power generation device in the time period t; and T is a total quantity of time periods; and develops a next-day operation plan through solution by using a mixed integer nonlinear stochastic programming algorithm, and outputs information about the developed operation plan to the real-time scheduling optimization module.
7. The virtual aggregation system for the regional energy complex according to claim 1, wherein the real-time scheduling optimization module performs scrolling optimization solution on an intraday operation plan in an operation day according to the information about the operation plan provided by the operation plan development module, by using intraday power generation capacity forecast information provided by the distributed power generation device output forecast module, intraday load forecast information provided by the charging station load forecast module, and real-time electricity price information of the current day as decision support information, according to real-time information that is of a device status and a power generation status of the distributed power generation device, an energy storage system status, and a load station status and that is provided by the operation status management module, and by using a mathematical optimization algorithm; and adjusts and refines information about the intraday operation plan based on an optimization solution result, to generate a real-time scheduling optimization policy and external power transaction plan information for the distributed power generation device, the energy storage system, and the charging station, and separately outputs the real-time scheduling optimization policy and the external power transaction plan information to the external transaction platform module and control terminals of the distributed power generation device, the energy storage system, and the charging station.
8. The virtual aggregation system for the regional energy complex according to claim 1, further comprising any one or more of the following: -the internal transaction platform module calling real-time and historical power generation capacities of the distributed power generation device, real-time and historical charge/discharge volumes of the energy storage system, and real-time and historical load of each charging pile of the charging station through a data calling interface provided by the operation status management module, and obtaining external transaction information through a data calling interface provided by the external transaction platform module; -the internal transaction platform module using a blockchain decentralized data storage technology to implement an electronic contract, notarized measurement, and fee settlement, and implement multi-type transaction settlement within the system internal; and -the external transaction platform module calling a total power generation capacity of the distributed power generation device and total power consumption of all the charging piles of the charging station through a data calling interface of the operation status management module, interacting with a power grid enterprise, obtaining and storing real-time electricity price information, conducting an external transaction with the power grid enterprise, and providing electricity price information for the operation plan development module and the real-time scheduling optimization module; and executing external power transaction plan information provided by the real-time scheduling optimization module.
9. The virtual aggregation system for the regional energy complex according to any one of claims 1 to 8, wherein the system is based on a power IoT architecture, comprising a perception layer, a network layer, and an application layer, wherein: the data acquisition module is located at the perception layer; the operation status management module is located at the network layer; and the distributed power generation device output forecast module, the charging station load forecast module, the operation plan development module, the real-time scheduling optimization module, the internal transaction platform module, and the external transaction platform module are separately located at the application layer.
10. A virtual aggregation method for a regional energy complex, comprising: obtaining time series data of a distributed power generation device, an energy storage system, and a charging station according to an information acquisition instruction of an operation status management module; processing the time series data of the distributed power generation device, the energy storage system, and the charging station to form operation data, recording status data and external environment data of the distributed power generation device, the energy storage system, and/or the charging station, and calling and outputting the status data and the external environment data; forecasting medium and long term, day-ahead, and intraday output of the distributed power generation device according to the outputted operation data, status data, and environment data; forecasting day-ahead and intraday load of the charging station according to the outputted operation data, status data, and environment data; developing an operation plan of power generation and consumption of a next day for the distributed power generation device, the energy storage system, and the charging station according to obtained day-ahead output forecast information, day-ahead load forecast information of the charging station, and historical electricity price information; performing real-time scheduling optimization according to the developed operation plan, intraday output forecast information, intraday load forecast information of the charging station, and real-time electricity price information, to obtain a real-time operation policy for the distributed power generation device, the energy storage system, and the charging station; conducting an external transaction with a power grid according to a real-time electricity price and operation statuses of the distributed power generation device, the energy storage system, and the charging station; and implementing multi-type transaction settlement within the system according to the outputted operation data, status data, and environment data and external transaction information.

FIG. 1

FIG. 2