Source network load storage cooperative control system

Abstract

The invention discloses a source network load storage cooperative control system, and the system comprises the following modules: an uploading module which is used for uploading the historical power information of a local power generation end, a power distribution end, a power utilization end, and an energy storage end; a data analysis module; the calculation module analyzes and calculates the obtained data to find the optimal working time period and load capacity of each type of equipment; the virtual power plant system establishes and stores a virtual power plant model; the electrical load management platform comprises a user file management module, a response protocol management module, a load dynamic monitoring and sensing module, a multi-scale load prediction module, an adjustable resource potential evaluation module, a demand response strategy engine module, an optimization scheduling and control instruction generation module, a user incentive settlement module and an energy efficiency and carbon efficiency analysis module. A storage management platform; and the regulation and control device stores a virtual power plant optimization regulation and control model. According to the method, the cross-regional and multi-time-scale dynamic adjustment requirement can be met conveniently, comprehensive, considerable, measurable and controllable power balance maintenance is achieved, and flexible and adjustable resources are reasonably applied.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of load and storage coordination, and particularly relates to a source-grid-load-storage coordinated control system. BACKGROUND

[0002] Source-grid-load-storage is an advanced power operation mode. "Source" refers to various power sources, such as hydropower, thermal power, wind power, photovoltaic power and the like; "grid" is a power network responsible for power transmission and distribution; "load" refers to power load, covering industrial, commercial, residential and other power demand; and "storage" refers to energy storage equipment capable of adjusting the time and space distribution of power. The four are coordinated and interacted to improve the stability and flexibility of the power system. With the increasing proportion of intermittent power sources such as wind power and photovoltaic power, the rigid operation mode of the traditional power system "source following load" cannot meet the flexibility requirements of the new power system.

[0003] There is a prominent contradiction between rigid growth of energy consumption and energy security supply: in many regions, power consumption is still dominated by thermal power, but coal power is far away from the coal production area, and the cost of coal procurement by power plants is relatively high. In addition, the hydropower in various regions has been basically developed, and the potential for increasing production is limited. Hydropower bears multiple control tasks, and the coordination is difficult. Due to the uneven time and space distribution of water resources, and the high proportion of rainfall in the flood season of major rivers, and the different times of rainfall in different rivers, the power generation capacity is greatly affected by the strong rainfall process in the flood season, and it is difficult to predict the power generation in the flood season, and it is more complex and difficult to accurately prepare the planning curve. There are contradictions between the comprehensive water use departments such as navigation, power generation, urban water supply and agricultural irrigation in the dry season, among which the contradiction between navigation and power generation is the most prominent, and the planning of the navigation flow in the dry season becomes complex and difficult.

[0004] Furthermore, despite the robust growth of renewable energy, its intermittent output leads to insufficient power supply support. Furthermore, weak grid structures in some regions hinder the reliable export of renewable energy. Existing control strategies, which mostly rely on centralized optimization, struggle to adapt to dynamic adjustments across regions and timescales. The installation of renewable energy capacity has led to a high degree of randomness, indirectness, and volatility in the power system. Under the new energy system, the power system will be dominated by renewable energy. Its power output is subject to natural conditions, making it uncontrollable and unable to match load fluctuations. With the massive integration of renewable energy and electric vehicles, the power system's information perception capabilities are insufficient, and existing control measures are unable to provide comprehensive visibility, measurement, and control. This increases uncertainty in the supply and demand balance between the two sides of the power system. The fluctuating, intermittent, and low energy density of renewable energy power generation increases uncertainty in the supply and demand balance between the two sides of the power system, and the power system's dispatching and balancing mechanisms urgently need to be optimized. In extreme weather conditions, it is difficult to ensure sufficient power supply margin to support reliable power system operation. Due to the intermittent, random, and volatile nature of renewable energy output, large-scale grid integration has led to significant temporal and spatial imbalances in the power balance, placing significant pressure on peak and frequency regulation, and necessitating the deployment of more reserve regulation capacity. The increasing availability of flexible and adjustable resources, such as energy storage, electric vehicles, and air conditioning, can provide the grid with abundant adjustable response capacity, enhancing the interaction between "source, grid, load, and storage." These resources play a vital role in power system peak shaving, smoothing renewable energy fluctuations, providing frequency regulation and standby, and providing ancillary services. However, the research on flexible and adjustable resources is still in the theoretical stage, and many issues remain. Summary of the Invention

[0005] In order to overcome the above-mentioned shortcomings of the existing technology, the present invention proposes a source-grid-load-storage collaborative control system, which is convenient for adapting to the dynamic adjustment needs across regions and multiple time scales, and is fully observable, measurable, and controllable, maintaining power balance and rationally applying flexible and adjustable resources.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a source-grid-load-storage coordinated control system, including the following modules:

[0007] The upload module is used to upload historical power information of local power generation, distribution, consumption, and energy storage terminals;

[0008] A data analysis module is used to perform data analysis based on historical power information and transmit the data to the calculation module;

[0009] The calculation module analyzes and calculates the obtained data to find the optimal working hours and load of each type of equipment;

[0010] A virtual power plant system establishes and stores a virtual power plant model; the virtual power plant model includes a stable power generation load, an intermittent power generation load, and a small and micro-new energy dispatchable resource pool; the new energy dispatchable resource pool is a resource pool that aggregates dispersed small and micro-new energy devices through a cloud platform; the small and micro-new energy devices include electric vehicle charging piles, solar water heaters, and air conditioning loads that can be used to power the power grid; first, the electricity generated by the intermittent power generation load is dispatched to the power grid for power supply; if there is still surplus electricity, it is stored in the energy storage system; if there is insufficient power, electricity is purchased from the power grid, and the electricity in the energy storage system is sold to the power grid when the electricity price is high; power is transmitted to the energy storage system and sold to the power grid, and the generated heat is utilized in a hierarchical manner;

[0011] The electricity load management platform includes a user profile management module, a response protocol management module, a load dynamic monitoring and perception module, a multi-scale load forecasting module, an adjustable resource potential assessment module, a demand response strategy engine module, an optimization scheduling and control instruction generation module, a user incentive settlement module, and an energy efficiency and carbon efficiency analysis module; the user profile management module is used to register user information and record electricity consumption characteristics; the response protocol management module is used to sign a demand response contract and clarify the interruptible load capacity, response speed, and compensation mechanism; the load dynamic monitoring and perception module is used to obtain data from smart meters and IoT sensors and identify the sub-item load curves of air conditioners and EV charging piles. The IoT sensor data includes power, voltage, and harmonic data; the multi-scale load forecasting module is used for short-term load forecasting and ultra-short-term load forecasting; short-term load forecasting provides day-ahead information for virtual power plants Basis for dispatching plan; ultra-short-term load forecast rolling correction of real-time control instructions; the adjustable resource potential assessment module is used to calculate the maximum adjustment power and response delay of each small and micro new energy device, and is also used to establish thermodynamic modeling to predict the impact curve of air conditioning temperature adjustment every 1°C on room temperature to avoid exceeding the user comfort limit; the demand response strategy engine module generates a time-of-use electricity price strategy and formulates an interruptible load bidding mechanism according to the preset demand response strategy; the optimized dispatching and control instruction generation module receives the dispatching instructions of the virtual power plant, decomposes them into specific equipment, and generates control instructions under the constraints of grid security, user costs, and carbon emissions; the user incentive settlement module displays real-time electricity prices and the benefits of demand response to residents through the mobile APP; the energy efficiency and carbon efficiency analysis module is used to analyze unit output value and carbon footprint tracking, mark green electricity consumption periods, and generate carbon emission reduction reports;

[0012] The storage management platform monitors the SOC status of grid-side energy storage power stations and electric vehicle batteries, calculates battery loss costs, estimates battery replacement time, and evaluates adjustable capacity and response speed;

[0013] A control device stores a virtual power plant optimization control model; the virtual power plant optimization control model is established according to the optimal working period and load of various types of equipment, and is used to output a control strategy, and send the strategy information to the power grid dispatching center or to the equipment management personnel based on the control strategy.

[0014] Furthermore, the stable power generation load includes a hydropower station and a thermal power station; the intermittent power generation load includes a wind power station and a photovoltaic power station.

[0015] Furthermore, the specific method of aggregating into a new energy dispatchable resource pool is as follows:

[0016] Small and micro new energy equipment is equipped with intelligent communication modules to monitor the designated data of small and micro new energy equipment and obtain the original data;

[0017] Clean, analyze, and convert the format of the raw data into standardized data that can be processed within the platform;

[0018] Perform resource modeling on small and micro new energy devices according to their types; the resource modeling is based on the prediction of charging or energy saving of the small and micro new energy devices;

[0019] Establish an aggregation model: Aggregate the resource models of individual small and micro new energy devices in time and space dimensions to predict key indicators of the entire resource pool at different time scales; the key indicators include baseline load, upward adjustment potential, downward adjustment potential, and response duration.

[0020] Furthermore, the time scale is minute level, hour level, or day level.

[0021] Furthermore, an intelligent communication module is installed on the electric vehicle charging pile or the electric vehicle itself through the Internet of Vehicles to collect the charging status, charging power, expected full time and charging plan in real time;

[0022] Install an intelligent communication module on the water tank or controller to monitor water temperature, water level, ambient temperature, collector temperature, electric auxiliary heating status and power;

[0023] Install the module on the air conditioner indoor unit or smart thermostat to monitor real-time power, operating mode, set temperature, indoor and outdoor temperature;

[0024] Furthermore, resource modeling of small and micro new energy equipment is carried out according to their types as follows:

[0025] Establish a charging and discharging model for electric vehicles based on user driving habits and battery characteristics, and predict the future available charging window, maximum / minimum adjustable power, and transferable power;

[0026] Develop a thermodynamic model for solar water heaters, taking into account solar irradiation forecasts based on historical usage habits and weather forecasts for hot water demand, heat storage capacity, and the adjustable range of electric auxiliary heating power;

[0027] A thermal inertia model is established for the air conditioning load to predict the indoor temperature change trajectory, power-temperature sensitivity, and adjustable power range.

[0028] Furthermore, the historical power information of the power generation end, power distribution end, power consumption end, and energy storage end is specifically as follows:

[0029] Power generation end: daily power generation and daily average power generation;

[0030] Distribution end: line voltage, current, power factor, distribution equipment load rate, voltage deviation, frequency deviation, harmonic content; Power consumption end: user's daily power consumption, daily power load curve, user industry classification, power utilization efficiency; Energy storage end: Energy storage equipment's charge and discharge SOC range, charge and discharge power average, charge and discharge power, energy storage equipment's SOH, cumulative charge and discharge times, charge and discharge efficiency.

[0031] Furthermore, the establishment of the virtual power plant optimization control model includes the following steps:

[0032] (1) Set the optimization objective function; calculate the minimum operating cost of the virtual power plant, which is the electricity purchase cost - electricity sales revenue + power generation cost:

[0033]

[0034] Among them, P grid (t) is the amount of electricity purchased from the power grid during period t; P sell (t) is the amount of electricity sold to the grid during period t; P i (t) is the output of the i-th generating equipment in period t; L curt (t) is the load reduction during period t; c grid (t) is the unit price of electricity purchased from the power grid during period t; r grid (t) is the price of electricity sold to the grid during period t; c i (t) is the unit price of electricity generated by the i-th power generation equipment in period t; c DR (t) is the demand response compensation unit price during period t;

[0035] (2) Set key constraints:

[0036] Power balance constraint: ∑P i (t)+P grid (t)+P dis (t) = L(t) - L curt (t)+P ch (t); where P dis(t) is the discharge amount during the t period; L(t) is the load amount during the t period; P ch (t) is the charge capacity during period t;

[0037] Equipment output limit constraint: P i min ≤P i (t)≤P i max Among them, P i min is the minimum technical output of thermal power units, P i max is the maximum power that the wind turbine can generate;

[0038] Energy storage dynamic constraints: Where SOC(t) is the state of charge during period t; η ch is the charging efficiency during period t, η dis is the discharge efficiency during period t;

[0039] (3) Setting multi-timescale optimization targets, including day-ahead planning targets, intraday rolling targets, and real-time control targets; day-ahead planning targets determine unit start-up and shutdown, and energy storage charging and discharging plans; intraday rolling targets correct forecast deviations; and real-time control targets enable instant adjustment of energy storage charging and discharging power;

[0040] (4) Based on the optimal working period and load of each type of equipment, the dispatch model is modeled and the corresponding control optimization strategy is output: for photovoltaic power generation, priority is given to full power generation during the peak sunshine period, and the surplus power is stored or sold to the power grid; for thermal power generation, it is responsible for base load or peak load regulation to avoid frequent start and stop; for hydropower, it is released during peak electricity price periods to generate electricity, taking into account ecological flow constraints; random optimization is used to deal with wind and solar uncertainty: random planning is performed based on the scenario method to obtain several wind and solar output scenarios; decision variables are defined and the upper limit of the power purchase is set; the objective function is set to minimize the expected cost;

[0041] (5) Output of the control optimization strategy of the energy storage system: Set the charging period and the discharging period, where the charging period = {t|c grid (t)<moving average electricity price}; discharge period={t|c grid (t) > threshold};

[0042] (6) Output of optimization strategy for regulation of small and micro new energy equipment: For electric vehicles and charging piles, charging is carried out during low electricity price / high green electricity period, and V2G participates in frequency regulation; for air conditioners, the indoor temperature is restricted to the comfortable zone to reduce peak load;

[0043] (7) Generate grid dispatching instructions and equipment control instructions based on the control optimization strategies of various types of equipment.

[0044] Compared with the prior art, the beneficial effects of the present application are: through multi-time scale optimization and accurate modeling of device characteristics, the present application realizes the following effects: for the power generation side: maximizing renewable energy consumption, reducing the operation cost of thermal power; for the power grid side: providing flexible peak shaving and frequency modulation capability, relieving congestion; for the user side: obtaining economic benefits on the premise of ensuring energy quality; for environmental benefits: reducing carbon emissions, helping to achieve the double carbon goal; adapting to the dynamic adjustment demand of cross-regional and multi-time scale, achieving comprehensive observability, measurability and controllability, maintaining power balance and reasonably applying flexible adjustable resources. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description.

[0046] Figure 1 The system architecture diagram of a source network load storage collaborative control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0048] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0049] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances. In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0050] As Figure 1As shown, an embodiment of the present invention provides a source-grid-load-storage coordinated control system, including the following modules:

[0051] A source-grid-load-storage coordinated control system includes the following modules:

[0052] The upload module is used to upload historical power information of local power generation, distribution, consumption, and energy storage terminals;

[0053] A data analysis module is used to perform data analysis based on historical power information and transmit the data to the calculation module;

[0054] The calculation module analyzes and calculates the obtained data to find the optimal working hours and load of each type of equipment;

[0055] A virtual power plant system establishes and stores a virtual power plant model; the virtual power plant model includes a stable power generation load, an intermittent power generation load, and a small and micro-new energy dispatchable resource pool; the new energy dispatchable resource pool is a resource pool that aggregates dispersed small and micro-new energy devices through a cloud platform; the small and micro-new energy devices include electric vehicle charging piles, solar water heaters, and air conditioning loads that can be used to power the power grid; first, the electricity generated by the intermittent power generation load is dispatched to the power grid for power supply; if there is still surplus electricity, it is stored in the energy storage system; if there is insufficient power, electricity is purchased from the power grid, and the electricity in the energy storage system is sold to the power grid when the electricity price is high; power is transmitted to the energy storage system and sold to the power grid, and the generated heat is utilized in a hierarchical manner;

[0056] The electricity load management platform includes a user profile management module, a response protocol management module, a load dynamic monitoring and perception module, a multi-scale load forecasting module, an adjustable resource potential assessment module, a demand response strategy engine module, an optimization scheduling and control instruction generation module, a user incentive settlement module, and an energy efficiency and carbon efficiency analysis module; the user profile management module is used to register user information and record electricity consumption characteristics; the response protocol management module is used to sign a demand response contract and clarify the interruptible load capacity, response speed, and compensation mechanism; the load dynamic monitoring and perception module is used to obtain data from smart meters and IoT sensors and identify the sub-item load curves of air conditioners and EV charging piles. The IoT sensor data includes power, voltage, and harmonic data; the multi-scale load forecasting module is used for short-term load forecasting and ultra-short-term load forecasting; short-term load forecasting provides day-ahead information for virtual power plants Basis for dispatching plan; ultra-short-term load forecast rolling correction of real-time control instructions; the adjustable resource potential assessment module is used to calculate the maximum adjustment power and response delay of each small and micro new energy device, and is also used to establish thermodynamic modeling to predict the impact curve of air conditioning temperature adjustment every 1°C on room temperature to avoid exceeding the user comfort limit; the demand response strategy engine module generates a time-of-use electricity price strategy and formulates an interruptible load bidding mechanism according to the preset demand response strategy; the optimized dispatching and control instruction generation module receives the dispatching instructions of the virtual power plant, decomposes them into specific equipment, and generates control instructions under the constraints of grid security, user costs, and carbon emissions; the user incentive settlement module displays real-time electricity prices and the benefits of demand response to residents through the mobile APP; the energy efficiency and carbon efficiency analysis module is used to analyze unit output value and carbon footprint tracking, mark green electricity consumption periods, and generate carbon emission reduction reports;

[0057] The storage management platform monitors the SOC status of grid-side energy storage power stations and electric vehicle batteries, calculates battery loss costs, estimates battery replacement time, and evaluates adjustable capacity and response speed;

[0058] A control device stores a virtual power plant optimization control model; the virtual power plant optimization control model is established according to the optimal working period and load of various types of equipment, and is used to output a control strategy, and send the strategy information to the power grid dispatching center or to the equipment management personnel based on the control strategy.

[0059] Furthermore, the stable power generation load includes a hydropower station and a thermal power station; the intermittent power generation load includes a wind power station and a photovoltaic power station.

[0060] Furthermore, the specific method of aggregating into a new energy dispatchable resource pool is as follows:

[0061] Small and micro new energy equipment is equipped with intelligent communication modules to monitor the designated data of small and micro new energy equipment and obtain the original data;

[0062] Clean, analyze, and convert the format of the raw data into standardized data that can be processed within the platform;

[0063] Perform resource modeling on small and micro new energy devices according to their types; the resource modeling is based on the prediction of charging or energy saving of the small and micro new energy devices;

[0064] Establish an aggregation model: Aggregate the resource models of individual small and micro new energy devices in time and space dimensions to predict key indicators of the entire resource pool at different time scales; the key indicators include baseline load, upward adjustment potential, downward adjustment potential, and response duration.

[0065] Furthermore, the time scale is minute level, hour level, or day level.

[0066] Furthermore, an intelligent communication module is installed on the electric vehicle charging pile or the electric vehicle itself through the Internet of Vehicles to collect the charging status, charging power, expected full time and charging plan in real time;

[0067] Install an intelligent communication module on the water tank or controller to monitor water temperature, water level, ambient temperature, collector temperature, electric auxiliary heating status and power;

[0068] Install the module on the air conditioner indoor unit or smart thermostat to monitor real-time power, operating mode, set temperature, indoor and outdoor temperature;

[0069] Furthermore, resource modeling of small and micro new energy equipment is carried out according to their types as follows:

[0070] Establish a charging and discharging model for electric vehicles based on user driving habits and battery characteristics, and predict the future available charging window, maximum / minimum adjustable power, and transferable power;

[0071] Develop a thermodynamic model for solar water heaters, taking into account solar irradiation forecasts based on historical usage habits and weather forecasts for hot water demand, heat storage capacity, and the adjustable range of electric auxiliary heating power;

[0072] A thermal inertia model is established for the air conditioning load to predict the indoor temperature change trajectory, power-temperature sensitivity, and adjustable power range.

[0073] Furthermore, the historical power information of the power generation end, power distribution end, power consumption end, and energy storage end is specifically as follows:

[0074] Power generation end: daily power generation and daily average power generation;

[0075] Distribution end: line voltage, current, power factor, distribution equipment load rate, voltage deviation, frequency deviation, harmonic content; Power consumption end: user's daily power consumption, daily power load curve, user industry classification, power utilization efficiency; Energy storage end: Energy storage equipment's charge and discharge SOC range, charge and discharge power average, charge and discharge power, energy storage equipment's SOH, cumulative charge and discharge times, charge and discharge efficiency.

[0076] Furthermore, the establishment of the virtual power plant optimization control model includes the following steps:

[0077] (1) Set the optimization objective function; calculate the minimum operating cost of the virtual power plant, which is the electricity purchase cost - electricity sales revenue + power generation cost:

[0078]

[0079] Among them, P grid (t) is the amount of electricity purchased from the power grid during period t; P sell (t) is the amount of electricity sold to the grid during period t; P i (t) is the output of the i-th generating equipment in period t; L curt (t) is the load reduction during period t; c grid (t) is the unit price of electricity purchased from the power grid during period t; r grid (t) is the price of electricity sold to the grid during period t; c i (t) is the unit price of electricity generated by the i-th power generation equipment in period t; c DR (t) is the demand response compensation unit price during period t;

[0080] (2) Set key constraints:

[0081] Power balance constraint: ∑P i (t)+P grid (t)+P dis (t) = L(t) - L curt (t)+P ch (t); where P dis (t) is the discharge amount during the t period; L(t) is the load amount during the t period; P ch (t) is the charge capacity during period t;

[0082] Equipment output limit constraint: P i min ≤P i (t)≤P i max Among them, P i min is the minimum technical output of thermal power units, P i max is the maximum power that the wind turbine can generate;

[0083] Energy storage dynamic constraints: Where SOC(t) is the state of charge during period t; η ch is the charging efficiency during period t, η dis is the discharge efficiency during period t;

[0084] (3) Setting multi-timescale optimization targets, including day-ahead planning targets, intraday rolling targets, and real-time control targets; day-ahead planning targets determine unit start-up and shutdown, and energy storage charging and discharging plans; intraday rolling targets correct forecast deviations; and real-time control targets enable instant adjustment of energy storage charging and discharging power;

[0085] (4) Based on the optimal working period and load of each type of equipment, the dispatch model is modeled and the corresponding control optimization strategy is output: for photovoltaic power generation, priority is given to full power generation during the peak sunshine period, and the surplus power is stored or sold to the power grid; for thermal power generation, it is responsible for base load or peak load regulation to avoid frequent start and stop; for hydropower, it is released during peak electricity price periods to generate electricity, taking into account ecological flow constraints; random optimization is used to deal with wind and solar uncertainty: random planning is performed based on the scenario method to obtain several wind and solar output scenarios; decision variables are defined and the upper limit of the power purchase is set; the objective function is set to minimize the expected cost;

[0086] (5) Output of the control optimization strategy of the energy storage system: Set the charging period and the discharging period, where the charging period = {t|c grid (t)<moving average electricity price}; discharge period={t|c grid (t) > threshold};

[0087] (6) Output of optimization strategy for regulation of small and micro new energy equipment: For electric vehicles and charging piles, charging is carried out during low electricity price / high green electricity period, and V2G participates in frequency regulation; for air conditioners, the indoor temperature is restricted to the comfortable zone to reduce peak load;

[0088] (7) Generate grid dispatching instructions and equipment control instructions based on the control optimization strategies of various types of equipment.

[0089] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A source-grid-load-storage coordinated control system, characterized in that: Includes the following modules: A source-grid-load-storage coordinated control system includes the following modules: The upload module is used to upload historical power information of local power generation, distribution, consumption, and energy storage terminals; A data analysis module is used to perform data analysis based on historical power information and transmit the data to the calculation module; The calculation module analyzes and calculates the obtained data to find the optimal working hours and load of each type of equipment; A virtual power plant system establishes and stores a virtual power plant model; the virtual power plant model includes a stable power generation load, an intermittent power generation load, and a small and micro-new energy dispatchable resource pool; the new energy dispatchable resource pool is a resource pool that aggregates dispersed small and micro-new energy devices through a cloud platform; the small and micro-new energy devices include electric vehicle charging piles, solar water heaters, and air conditioning loads that can be used to power the power grid; first, the electricity generated by the intermittent power generation load is dispatched to the power grid for power supply; if there is still surplus electricity, it is stored in the energy storage system; if there is insufficient power, electricity is purchased from the power grid, and the electricity in the energy storage system is sold to the power grid when the electricity price is high; power is transmitted to the energy storage system and sold to the power grid, and the generated heat is utilized in a hierarchical manner; The electricity load management platform includes a user profile management module, a response protocol management module, a load dynamic monitoring and perception module, a multi-scale load forecasting module, an adjustable resource potential assessment module, a demand response strategy engine module, an optimization scheduling and control instruction generation module, a user incentive settlement module, and an energy efficiency and carbon efficiency analysis module; The user profile management module is used to register user information and record electricity consumption characteristics; the response protocol management module is used to sign a demand response contract and clarify the interruptible load capacity, response speed, and compensation mechanism; the load dynamic monitoring and perception module is used to obtain data from smart meters and IoT sensors, and identify the sub-item load curves of air conditioners and EV charging piles. The IoT sensor data includes power, voltage, and harmonic data; the multi-scale load forecasting module is used for short-term load forecasting and ultra-short-term load forecasting; short-term load forecasting provides a basis for day-ahead scheduling plans for virtual power plants; ultra-short-term load forecasting provides rolling corrections to real-time control instructions; the adjustable resource potential assessment module is used to calculate the maximum load of each small and micro new energy device The maximum adjustment power and response delay are also used to establish thermodynamic modeling to predict the impact curve of each 1°C interval of air conditioning temperature adjustment on room temperature to avoid exceeding the user comfort limit; the demand response strategy engine module generates a time-of-use electricity price strategy and formulates an interruptible load bidding mechanism based on the preset demand response strategy; the optimized scheduling and control instruction generation module receives the scheduling instructions of the virtual power plant, decomposes them into specific equipment, and generates control instructions under the constraints of grid security, user costs, and carbon emissions; the user incentive settlement module displays real-time electricity prices and demand response benefits to residents through a mobile APP; the energy efficiency and carbon efficiency analysis module is used to analyze unit output value and carbon footprint tracking, mark green electricity consumption periods, and generate carbon emission reduction reports; The storage management platform monitors the SOC status of grid-side energy storage power stations and electric vehicle batteries, calculates battery loss costs, estimates battery replacement time, and evaluates adjustable capacity and response speed; A control device stores a virtual power plant optimization control model; the virtual power plant optimization control model is established according to the optimal working period and load of various types of equipment, and is used to output a control strategy, and send the strategy information to the power grid dispatching center or to the equipment management personnel based on the control strategy.

2. A source-grid-load-storage coordinated control system according to claim 1, characterized in that: The stable power generation load includes hydropower stations and thermal power stations; the intermittent power generation load includes wind power stations and photovoltaic power stations.

3. The source-grid-load-storage coordinated control system according to claim 1, characterized in that: The specific method of aggregating into a new energy dispatchable resource pool is: Small and micro new energy equipment is equipped with intelligent communication modules to monitor the designated data of small and micro new energy equipment and obtain the original data; Clean, analyze, and convert the format of the raw data into standardized data that can be processed within the platform; Perform resource modeling on small and micro new energy devices according to their types; the resource modeling is based on the prediction of charging or energy saving of the small and micro new energy devices; Establish an aggregation model: Aggregate the resource models of individual small and micro new energy devices in time and space dimensions to predict key indicators of the entire resource pool at different time scales; the key indicators include baseline load, upward adjustment potential, downward adjustment potential, and response duration.

4. The source-grid-load-storage coordinated control system according to claim 1, characterized in that: The time scale is minute level, hour level, and day level.

5. The source-grid-load-storage coordinated control system according to claim 1, characterized in that: Install intelligent communication modules on electric vehicle charging piles or electric vehicles themselves through the Internet of Vehicles to collect real-time charging status, charging power, estimated full time and charging plan; Install an intelligent communication module on the water tank or controller to monitor water temperature, water level, ambient temperature, collector temperature, electric auxiliary heating status and power; Install the module on the air conditioner indoor unit or smart thermostat to monitor real-time power, operating mode, set temperature, and indoor and outdoor temperatures.

6. The source-grid-load-storage coordinated control system according to claim 1, characterized in that: The resource modeling of small and micro new energy equipment is carried out according to their types as follows: Establish a charging and discharging model for electric vehicles based on user driving habits and battery characteristics, and predict the future available charging window, maximum / minimum adjustable power, and transferable power; Develop a thermodynamic model for solar water heaters, taking into account solar irradiation forecasts based on historical usage habits and weather forecasts for hot water demand, heat storage capacity, and the adjustable range of electric auxiliary heating power; A thermal inertia model is established for the air conditioning load to predict the indoor temperature change trajectory, power-temperature sensitivity, and adjustable power range.

7. The source-grid-load-storage coordinated control system according to claim 1, characterized in that: The historical power information of the power generation end, power distribution end, power consumption end, and energy storage end is specifically as follows: Power generation end: daily power generation and daily average power generation; Distribution end: line voltage, current, power factor, distribution equipment load rate, voltage deviation, frequency deviation, harmonic content; Power consumption end: user's daily power consumption, daily power load curve, user industry classification, power utilization efficiency; Energy storage end: Energy storage equipment's charge and discharge SOC range, charge and discharge power average, charge and discharge power, energy storage equipment's SOH, cumulative charge and discharge times, charge and discharge efficiency.

8. The source-grid-load-storage coordinated control system according to claim 1, characterized in that: The establishment of the virtual power plant optimization control model includes the following steps: (1) Set the optimization objective function; calculate the minimum operating cost of the virtual power plant, which is the electricity purchase cost - electricity sales revenue + power generation cost: Among them, P grid (t) is the amount of electricity purchased from the power grid during period t; P sell (t) is the amount of electricity sold to the grid during period t; P i (t) is the output of the i-th generating equipment in period t; L curt (t) is the load reduction during period t; c grid (t) is the unit price of electricity purchased from the power grid during period t; r grid (t) is the price of electricity sold to the grid during period t; c i (t) is the unit price of electricity generated by the i-th power generation equipment in period t; c DR (t) is the demand response compensation unit price during period t; (2) Set key constraints: Power balance constraint: ∑P i (t)+P grid (t)+P dis (t) = L(t) - L curt (t)+P ch (t); where P dis (t) is the discharge amount during the t period; L(t) is the load amount during the t period; P ch (t) is the charge capacity during period t; Equipment output limit constraint: P i min ≤P i (t)≤P i max Among them, P i min is the minimum technical output of thermal power units, P i max is the maximum power that the wind turbine can generate; Energy storage dynamic constraints: Where SOC(t) is the state of charge during period t; η ch is the charging efficiency during period t, η dis is the discharge efficiency during period t; (3) Setting multi-timescale optimization targets, including day-ahead planning targets, intraday rolling targets, and real-time control targets; day-ahead planning targets determine unit start-up and shutdown, and energy storage charging and discharging plans; intraday rolling targets correct forecast deviations; and real-time control targets enable instant adjustment of energy storage charging and discharging power; (4) Based on the optimal working period and load of each type of equipment, the dispatch model is modeled and the corresponding control optimization strategy is output: for photovoltaic power generation, priority is given to full power generation during the peak sunshine period, and the surplus power is stored or sold to the power grid; for thermal power generation, it is responsible for base load or peak load regulation to avoid frequent start and stop; for hydropower, it is released during peak electricity price periods to generate electricity, taking into account ecological flow constraints; random optimization is used to deal with wind and solar uncertainty: random planning is performed based on the scenario method to obtain several wind and solar output scenarios; decision variables are defined and the upper limit of the power purchase is set; the objective function is set to minimize the expected cost; (5) Output of the control optimization strategy of the energy storage system: Set the charging period and the discharging period, where the charging period = {t|c grid (t)<moving average electricity price}; discharge period={t|c grid (t) > threshold}; (6) Output of optimization strategy for regulation of small and micro new energy equipment: For electric vehicles and charging piles, charging is carried out during low electricity price / high green electricity period, and V2G participates in frequency regulation; for air conditioners, the indoor temperature is restricted to the comfortable zone to reduce peak load; (7) Generate grid dispatching instructions and equipment control instructions based on the control optimization strategies of various types of equipment.

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