Virtual power plant optimization scheduling method based on transmission and distribution cooperation

Abstract

The invention provides a transmission and distribution cooperation-based virtual power plant optimal scheduling method, which comprises the following steps of: obtaining an adjustable capability influence parameter of a controllable resource of a virtual power plant, and constructing a controllable resource adjustment characteristic model of the virtual power plant by utilizing the adjustable capability influence parameter; constructing a virtual power plant regulation boundary optimization model, and solving the virtual power plant regulation boundary optimization model to obtain the upper and lower limits of the total power of the virtual power plant; obtaining controllable resource parameters of the power transmission network, constructing a power transmission network optimization scheduling model, solving the power transmission network optimization scheduling model to obtain virtual power plants, power supply and energy storage scheduling plans and a total adjustment instruction, and issuing the plans and instructions to the power distribution network; and constructing a power distribution network instruction decomposition model, solving to obtain distributed controllable resource aggregate adjustment instructions at different access positions, and checking whether a total adjustment instruction requirement is met or not. According to the method, the coordination and economy of the virtual power plant regulation capability at the level of the power transmission network are improved, and the executable performance and safety at the level of the power distribution network are also remarkably enhanced.

Description

Technical Field

[0001] This invention belongs to the field of novel power system dispatch and control technology, and specifically relates to a virtual power plant optimization dispatch method based on transmission and distribution coordination. Background Technology

[0002] With the large-scale integration of high-proportion renewable energy sources, especially wind and solar power, the power volatility and uncertainty of the power system have significantly increased. Traditional regulation mechanisms dominated by centralized adjustable power sources are struggling to meet the new requirements for system balance control and operational safety, posing unprecedented challenges to the power system's flexible regulation capabilities. Against this backdrop, virtual power plants (VPS), as a coordinated control technology system that integrates distributed controllable resources and participates in unified system dispatch, have become an important direction for improving the flexibility and regulation capabilities of new power systems. By aggregating and managing diverse distributed controllable resources such as distributed generation, energy storage, and flexible loads, VPS can not only provide ancillary services and power support on the system side but also achieve demand response and load regulation on the user side, exhibiting good economic efficiency, responsiveness, and scalability. The widespread application of VPS is expected to break through the limitations of traditional regulation resources, construct a multi-level flexible regulation system characterized by source-grid-load coordination, and provide effective support for the friendly integration of high-proportion renewable energy sources.

[0003] The regulation capacity of virtual power plants originates from massive distributed controllable resources, and their scheduling and management naturally exhibit hierarchical characteristics. These massive distributed controllable resources should not be directly incorporated into the provincial-level transmission network scheduling; otherwise, the complexity of the transmission network scheduling decision model would increase by more than three orders of magnitude, leading to solution difficulties. Furthermore, the regulation capacity of centralized controllable resources such as power sources and energy storage is in the hundreds of MW, while the regulation capacity of distributed controllable resources is typically in the tens of MW; optimizing both simultaneously would reduce the model's solution accuracy. Therefore, distributed controllable resources should first be aggregated into virtual power plants of equivalent scale to centralized controllable resources, and then participate in system-level transmission network scheduling. At this point, the power allocation and response coordination among various power sources, energy storage, and multiple virtual power plants can form the target output or power regulation command for system-level control objects. Then, combining the status information of resources within the virtual power plants (such as remaining regulation capacity, cost parameters, and operating constraints) with the status information of the distribution network's carrying capacity, and with the goal of tracking the regulation commands formed by transmission network scheduling, control commands for each distributed controllable resource can be further formed, including changing operating modes, setting power levels, and start-stop control. The hierarchical control system has good hierarchical decoupling and coordination flexibility, and can take into account both system-level regulation objectives and equipment-level operating constraints. It is one of the core technical supports for the efficient participation of virtual power plants in grid interaction.

[0004] In current practice, virtual power plants are considered a transaction organization method, with only the total amount of regulation by virtual power plants being considered. When dispatching virtual power plants, it is assumed that their regulation is insufficient to cause changes in power system flow or operation mode, and will not affect the operational security of the transmission and distribution networks. This allows virtual power plants to decompose regulation instructions according to their own principles (such as allocation based on unit regulation cost or adjustable capacity). However, as the scale of virtual power plants increases, the above assumptions no longer hold, and there is an urgent need for a dispatching method that balances the interactive value of virtual power plants with the security of the power grid itself. Existing research mainly focuses on system-level transmission network dispatching or regional-level distribution network dispatching: when dispatching transmission networks containing virtual power plants, it is assumed that the regulation boundary of virtual power plants is known and the impact of distribution network security constraints on adjustable capacity is not considered. The results of coordinated dispatching of virtual power plants with other centralized controllable resources such as power sources and energy storage may not be fully executed due to distribution network security constraints. When decomposing virtual power plant regulation instructions, it is often assumed that the regulation instructions are known or that the regulation instructions are generated based on incentive signals such as electricity prices. Although the impact of distributed controllable resource regulation on local power flow and voltage can be considered, it fails to integrate with system-level regulation needs. Therefore, there is still a lack of effective methods for balancing the changing demands of the power grid regulation with the safety of the distribution network in virtual power plant dispatching, and for achieving efficient participation of virtual power plants in grid interaction. Furthermore, the existing power grid dispatching system adopts a hierarchical dispatching approach. Provincial dispatching stations are responsible for the coordination of controllable resources at the system level and mainly focus on the power flow status of the transmission network (220kV), thus lacking distribution network parameters and status information. Meanwhile, regional dispatching stations are responsible for maintaining the safe operation of the local distribution network (110kV and below), only possessing local distribution network parameters and status information and lacking provincial-level system-wide data. This management system also requires virtual power plants connected through the distribution network to consider the coordination of the transmission and distribution systems during dispatching. Summary of the Invention

[0005] To address the aforementioned technical challenges, this invention proposes a virtual power plant optimization scheduling method based on transmission and distribution coordination. This method not only improves the coordination and economy of virtual power plant regulation capabilities at the transmission network level but also significantly enhances its feasibility and security at the distribution network level, providing a technical foundation and engineering support for promoting coordinated regulation of power generation, grid, load, and storage in the context of high-proportion renewable energy integration.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A virtual power plant optimization scheduling method based on transmission and distribution coordination includes the following steps:

[0008] Obtain the controllable resource adjustability impact parameters of the virtual power plant, and construct a controllable resource adjustment characteristic model of the virtual power plant based on the controllable resource capacity and the adjustability impact parameters; obtain the basic information of the distribution network to construct a virtual power plant adjustment boundary optimization model, and solve the virtual power plant adjustment boundary optimization model to obtain the upper and lower limits of the total power of the virtual power plant;

[0009] The controllable resource parameters of the transmission network are obtained. The goal is to minimize the sum of the operating cost of conventional power sources and the regulation cost of each controllable resource. The output constraints of each conventional power source unit, the regulation boundary constraints of virtual power plants, the power flow constraints and the security constraints of the transmission network are considered. The optimal scheduling model of the transmission network is constructed, and the optimal scheduling model of the transmission network is solved to obtain the scheduling plans of each virtual power plant, power source and energy storage, the overall regulation command and the distribution network.

[0010] With the optimization objective of minimizing adjustment costs and curtailment penalties, and under the premise of ensuring that the total adjustment of each resource meets the overall adjustment command, the system considers the operational constraints of adjustable equipment, power flow constraints, and distribution network security constraints to construct a distribution network command decomposition model. The system solves for the adjustment commands of distributed controllable resource aggregates at different access locations and verifies whether the adjustment commands of the distributed controllable resource aggregates meet the requirements of the overall adjustment command. If they do, the commands are issued and executed; if they do not, the virtual power plant adjustment boundary is updated and iteratively solved to complete the virtual power plant optimized scheduling.

[0011] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0012] This invention proposes a virtual power plant optimization scheduling method based on transmission and distribution coordination, comprising the following steps: obtaining the influence parameters of the adjustable capacity of controllable resources in the virtual power plant; constructing a controllable resource regulation characteristic model of the virtual power plant based on the controllable resource capacity and the influence parameters; obtaining basic information of the distribution network to construct a virtual power plant regulation boundary optimization model; solving the virtual power plant regulation boundary optimization model to obtain the upper and lower limits of the total power of the virtual power plant; obtaining controllable resource parameters of the transmission network; constructing the transmission network with the objective of minimizing the sum of the operating cost of conventional power sources and the regulation cost of each controllable resource, and considering the output constraints of each conventional power unit, the regulation boundary constraints of the virtual power plant, power flow constraints, and transmission network security constraints. An optimized scheduling model is used to solve the transmission network optimized scheduling model, obtaining the scheduling plans for each virtual power plant, power source, and energy storage, as well as the overall regulation command, which is then issued to the distribution network. With the optimization objective of minimizing regulation costs and curtailment penalties, and ensuring that the total regulation of each resource meets the overall regulation command, a distribution network command decomposition model is constructed, considering the operational constraints of adjustable equipment, power flow constraints, and distribution network security constraints. This model obtains the regulation commands for distributed controllable resource aggregates at different access locations and verifies whether the distributed controllable resource aggregate regulation commands meet the requirements of the overall regulation command. If they do, the commands are issued and executed; otherwise, the virtual power plant regulation boundaries are updated iteratively to complete the optimized scheduling of virtual power plants. This invention constructs a virtual power plant regulation command generation model based on transmission network scheduling and a virtual power plant regulation command decomposition model based on distribution network scheduling, and updates the overall regulation boundary through feedback, forming a two-layer optimization structure with cascading upper and lower levels and closed-loop feedback characteristics. This enables full-process verification and dynamic adjustment of regulation commands. This method not only improves the coordination and economy of virtual power plant regulation capabilities at the transmission network level, but also significantly enhances its feasibility and security at the distribution network level, providing a technical foundation and engineering support for promoting coordinated regulation of power generation, grid, load and storage in the context of high-proportion renewable energy access. Attached Figure Description

[0013] Figure 1 A complete architecture for connecting virtual power plants to the power grid;

[0014] Figure 2 The information transmission architecture for each level of a virtual power plant;

[0015] Figure 3 This is a flowchart of a virtual power plant optimization scheduling method based on transmission and distribution coordination proposed in Embodiment 1 of the present invention;

[0016] Figure 4 This is the process for constructing and evaluating the virtual power plant regulation boundary model as proposed in Embodiment 1 of the present invention;

[0017] Figure 5 This is the process for constructing and solving the power grid optimization scheduling model proposed in Embodiment 1 of the present invention;

[0018] Figure 6 This is the process for constructing and solving the power distribution network optimization scheduling model proposed in Embodiment 1 of the present invention;

[0019] Figure 7 This is a topology diagram of the IEEE 30 transmission network system proposed in Embodiment 1 of the present invention;

[0020] Figure 8 The power output timing sequence of various units proposed in Embodiment 1 of this invention;

[0021] Figure 9 This describes the load regulation of a virtual power plant as proposed in Embodiment 1 of the present invention.

[0022] Figure 10 This is the power distribution network layer architecture proposed in Embodiment 1 of the present invention;

[0023] Figure 11 The result of the distribution network layer instruction decomposition proposed in Embodiment 1 of the present invention. Detailed Implementation

[0024] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0025] Example 1

[0026] Unlike conventional power sources and grid-side energy storage, which are large-capacity controllable resources that are centrally connected to high voltage levels (such as 220kV and above), virtual power plants have regulation capabilities by integrating distributed controllable resources connected to distribution network voltage levels (10kV to 110kV) for coordinated regulation. Figure 1 The complete system architecture for connecting a virtual power plant to the power grid is presented: nodes 1-39 are important nodes of the transmission network, with centralized wind farms, photovoltaic power plants, and large-scale energy storage directly connected to the transmission network, while nodes 40, 47, 53, and below are grid nodes, connected to the main grid via transformers. Figure 1It is evident that the virtual power plant aggregates and manages various resources such as distributed power sources, energy storage systems, and adjustable loads connected through the distribution network. By coordinating the power generation, power consumption, or charging and discharging power of each distributed controllable resource, it appears as an object with adjustable transmission power from the perspective of the transmission network at the grid connection point (such as nodes 40, 47, and 53). It is similar to the characteristics of a power plant and is therefore called a virtual power plant.

[0027] Virtual power plant (VPS) optimal dispatch typically employs a three-tiered hierarchical collaborative system, corresponding to the division of responsibilities between the power grid dispatching level and the resource management entity. The first tier is the provincial dispatching center, responsible for system-level transmission network optimal dispatch. Based on the adjustable resource information and network status of the entire system, it formulates cross-regional and cross-type resource allocation schemes and generates target power adjustment commands for VPS. The second tier consists of municipal-level dispatching agencies and VPS aggregators. Based on their understanding of the VPS's internal resource capabilities and distribution network operating status, they assess the VPS's real-time adjustment boundary and report this boundary to the provincial dispatching center as system dispatching input. They also receive adjustment commands from the provincial dispatching center and prepare for lower-level decomposition. The third tier comprises the distributed controllable resource terminals under the jurisdiction of the VPS aggregators. Specifically, they receive and decompose upper-level adjustment commands, construct resource-level dispatch optimization models, consider resource operating constraints, geographical access characteristics, and distribution network power flow limitations, output device-level control strategies, and implement command execution and feedback.

[0028] Figure 2 The information transmission architecture of virtual power plants at each level is presented. As shown in the diagram: the provincial dispatching level, based on information such as the virtual power plant's regulation boundaries and cost characteristics provided by the regional dispatching level, constructs an optimized dispatching model for the transmission network by integrating the entire system's regulation resources, and issues virtual power plant regulation commands; the regional dispatching level and virtual power plant aggregators assess the regulation capacity boundaries based on the distribution network status and resource simulation results and feed them back to the provincial dispatching level. Simultaneously, after receiving system-level regulation commands, they transform them into decomposed tasks for the virtual power plant's internal operation; at the execution level, after receiving the target regulation amount, the virtual power plant aggregator dispatching center, in conjunction with terminal resources, decomposes the commands, combines distribution network power flow calculations and resource state constraints to optimize and form feasible resource-level regulation commands, and feeds back information such as feasibility and boundary changes to the regional and provincial dispatching levels to form closed-loop control. The entire process achieves decoupling and collaboration of dispatching tasks at different levels, ensuring that system-level optimization goals and resource-level operational feasibility are both considered, effectively improving the response capability of virtual power plants and the safety of grid operation in scenarios with high proportions of renewable energy access.

[0029] Current research on the optimal dispatch of virtual power plants has not yet formed a complete collaborative system covering both transmission and distribution networks, and is mostly limited to independent modeling and optimization at a single level. On the one hand, in system-level transmission network dispatch research, virtual power plants are usually simplified as equivalent centralized adjustable resources, considering only their total regulation capacity and cost parameters, ignoring the operational constraints of their diverse internal resources and their physical access characteristics in the distribution network. As a result, when the optimization results are sent to the virtual power plant for execution, they may not be implemented due to distribution network line constraints or voltage issues, posing a risk of adjustable but unusable resources. On the other hand, instruction decomposition research at the distribution network level often takes given regulation instructions or economic incentives as a premise, lacking a response mechanism to the upper-level transmission network dispatch objectives, making it difficult to ensure that the dispatch results are consistent with the overall system.

[0030] Embodiment 1 of this invention proposes a virtual power plant optimization scheduling method based on transmission and distribution coordination, which consists of three parts: modeling of the regulation characteristics of distributed controllable resources and evaluation of the regulation boundary of virtual power plants, generation of virtual power plant regulation instructions based on transmission network optimization scheduling, and decomposition and verification of virtual power plant instructions based on distribution network optimization scheduling.

[0031] Figure 3 This is a flowchart of a virtual power plant optimization scheduling method based on transmission and distribution coordination proposed in Embodiment 1 of the present invention;

[0032] In step 1, the parameters affecting the controllability and adjustability of the virtual power plant's controllable resources are obtained. Based on the controllable resource capacity, the adjustment characteristic model of the virtual power plant's controllable resources is constructed using the parameters affecting the adjustability. The basic information of the distribution network is obtained to construct the adjustment boundary optimization model of the virtual power plant. The upper and lower limits of the total power of the virtual power plant are obtained by solving the adjustment boundary optimization model of the virtual power plant.

[0033] Figure 4 This is the process for constructing and evaluating the virtual power plant regulation boundary model as proposed in Embodiment 1 of the present invention;

[0034] To obtain the parameters affecting the controllability and adjustability of virtual power plants' controllable resources, specifically: based on the differences in the types of controllable resources of virtual power plants, statistically analyze the parameters of equipment, users, and meteorological conditions that are closely related to their power output.

[0035] If the controllable resource is electric heating, the main equipment parameters include rated power P. EH Heating efficiency η EH The thermal resistance R of the hot water storage tank to air and the wall surface in contact with the ground. a and R g Heat capacity parameter c and capacity parameter m, etc.; user behavior parameters include user water flow rate f and allowable upper and lower limits of water temperature T. min and T max Meteorological parameters include outdoor air temperature and ground temperature (T). a and Tg wait;

[0036] If the controllable resource is air conditioning, the equipment parameters mainly consider the cooling efficiency η of the air conditioning system. AC Room thermal resistance R and heat capacity parameters C, etc.; user behavior parameters include user-set temperature T. set Indoor temperature T in Permissible upper and lower limits of indoor temperature (T) in,max and T in,min Meteorological parameters include outdoor temperature (T). out ;

[0037] If the controllable resource is a distributed power source, the equipment parameters mainly consider the installed capacity and rated power of the power source; in terms of meteorological conditions, solar power generation mainly considers factors such as light intensity and temperature; wind power generation mainly considers factors such as wind speed.

[0038] If the controllable resource is distributed energy storage, the main consideration is the charging and discharging power P of the energy storage system. ESS Battery rated capacity E ESS With charge and discharge efficiency η ESS Physical parameters, but no user behavior parameters or meteorological condition parameters.

[0039] Based on the controllable resource capacity characteristics, adjustment characteristic models for each distributed controllable resource unit or aggregate are constructed. Specifically, for units with large capacity (such as MW level), a unit adjustment characteristic model is constructed with the unit as the controllable object, and for units with small capacity (such as kW level), an aggregate adjustment characteristic model is constructed with the cluster as the controllable object.

[0040] If the controllable resource is electric heating, with the water temperature in the hot water storage tank as the state variable and the heating power as the input variable, a single-unit regulation characteristic model of the industrial heating boiler is formed by constructing a water temperature change process equation based on the principle of spatial heat balance. A first-order temperature change equation is used to describe the water temperature change law, specifically:

[0041]

[0042] Among them, T t+1 The water temperature at time t+1; T t Let η be the water temperature at time t; EH For the thermal efficiency of the electric boiler; P t EH ρ is the heating power of the electric boiler; c is the specific heat capacity of water; f t T represents the circulating water flow rate at time t; s For water supply temperature; T b T represents the return water temperature. a T represents air temperature. g R represents the ground temperature. aR is the thermal resistance of the water tank wall in contact with air. g The wall resistance of the water tank in contact with the ground is denoted as m; the total mass of water in the hot water storage tank is Δt; the heating time from time t to time t+1 is Δt; T min To meet the minimum water temperature required for user comfort; T max To meet the highest water temperature required for user comfort; This is the maximum thermal power of the electric boiler;

[0043] If the controllable resource is air conditioning, with room temperature as the state variable and outdoor temperature and cooling capacity as input variables, the air conditioning cluster adopts the SOC consensus control algorithm. The regulation characteristic model of the air conditioning aggregate model is as follows:

[0044]

[0045] in, The temperature status of the air conditioning cluster at time t+1; The temperature state of the air conditioning cluster at time t; Let t be the power adjustment amount of the air conditioning cluster at time t; This represents the baseline power value of the air conditioning cluster at time t. and These represent the upper and lower limits of the operating power of the air conditioning cluster; A represents the parameter of the first air conditioning cluster, B represents the parameter of the second air conditioning cluster, and C represents the parameter of the third air conditioning cluster.

[0046]

[0047] in, and These represent the maximum and minimum values ​​of the building's thermal resistance, respectively. This represents the average heat capacity of the building. and These represent the maximum and minimum values ​​of the building's cooling efficiency, respectively. and These are the average values ​​of the upper and lower limits of the permissible indoor temperature, respectively. The temperature state of the individual air conditioning unit at time t; n a Total number of air conditioners; The power adjustment amount of the individual air conditioner unit at time t; and These are the upper and lower limits of the operating power of a single air conditioning unit; and These are the average values ​​of the upper and lower limits of the permissible indoor temperature, respectively. Set the initial temperature state value; This represents the average outdoor temperature of the individual air-conditioned unit; T in,max The upper limit of permissible indoor temperature; T in,min The lower limit of permissible indoor temperature; Tset Set the temperature for the user; SOC out,t This refers to the outdoor temperature status of the individual air conditioning unit; T out,t Let be the outdoor temperature at time t;

[0048] If the controllable resource is distributed photovoltaic (PV), and the PV equipment in the same area is treated as a cluster, considering both power generation capacity and inverter control characteristics, the PQ control mode is used to dynamically adjust the output of active and reactive power. The regulation characteristic model of the distributed PV aggregate model is as follows:

[0049]

[0050] in, The actual active power of the distributed photovoltaic aggregate at time t; The maximum active power of the distributed photovoltaic aggregate at time t; The actual reactive power of the distributed photovoltaic aggregate at time t; This refers to the rated capacity of the distributed photovoltaic aggregate; The minimum power factor of a distributed photovoltaic aggregate;

[0051]

[0052] Where, n p This refers to the total number of photovoltaic cells within the same distribution area. The actual active power of a distributed photovoltaic unit at time t; The actual reactive power of a distributed photovoltaic unit at time t; Let be the maximum active power of a distributed photovoltaic unit at time t; The rated capacity of a single distributed photovoltaic unit; This represents the average of the minimum power factors of individual distributed photovoltaic inverters.

[0053] If the controllable resource is distributed energy storage, and the energy storage units in the same area are considered as a cluster, taking into account the active power output limitations during charging and discharging and the energy storage capacity limitations during normal operation, the state of charge of the distributed energy storage units is as follows:

[0054]

[0055] in, The state of charge of the energy storage polymer at time t+1; The state of charge of the energy storage polymer at time t; The discharge efficiency of the energy storage polymer; Let be the discharge power of the energy storage polymer at time t; Let t be the rated energy storage capacity of the energy storage polymer at time t; This represents the maximum state of charge of the energy storage polymer. This represents the minimum state of charge of the energy storage polymer.

[0056]

[0057] Where, n e The total number of energy storage units within the same distribution area; The actual active power of the energy storage unit at time t; This represents the maximum discharge power of a single energy storage cell. This represents the average maximum state of charge of a single energy storage cell. This represents the average minimum state of charge of a single energy storage cell.

[0058] Based on survey parameters, refined modeling of individual equipment, users, and meteorological conditions is performed to predict daily meteorological conditions and user behavior. The parameters of each type of controllable resource are iteratively substituted into the physical model for refined operational simulation, forming daily power baselines for each type of resource. These baselines are then summed to obtain the cluster power baseline.

[0059] The process of identifying overloaded branches using basic information of the distribution network includes combining the operating power baseline of distributed controllable resources with the basic load forecast curve of the distribution network in the basic information of the distribution network, performing power flow calculations at each moment of the distribution network, counting the percentage of time periods in which the load rate of each branch exceeds a preset percentage, and selecting branches with the number of time periods exceeding a preset percentage of the total number of time periods as key branches with high overload risk.

[0060] By combining the operating power baseline of distributed controllable resources with the basic load forecast curve of the distribution network, power flow calculations are performed at various times in the distribution network. The percentage of time periods when the load rate of each branch exceeds 80% is counted, and branches with more than 1 / 3 of the total number of time periods are selected as key branches with high overload risk.

[0061] The process of constructing a virtual power plant regulation boundary optimization model and solving the virtual power plant regulation boundary optimization model to obtain the upper and lower limits of the total power of the virtual power plant includes:

[0062] The virtual power plant regulation boundary model is as follows:

[0063]

[0064] In the formula: Virtual power plant central control command; and Adjustable upper and lower power limits;

[0065] With the goal of maximizing and minimizing the total power of the virtual power plant at each moment, and comprehensively considering equipment operation constraints, power flow constraints, critical branch safety constraints, and regulation command constraints, a virtual power plant regulation boundary optimization model is constructed:

[0066] or

[0067]

[0068] Among them, P t VPP n represents the total power of the virtual power plant. t T represents the total number of scheduling time slots. i,t+1 T represents the water temperature at node i at time t+1; i,t This represents the water temperature at node i at time t; This represents the electric boiler heating power of node i at time t; T represents the maximum heating power of the electric boiler at node i; i,min T represents the minimum water temperature at node i; i,max This represents the maximum water temperature at node i; The temperature state of the air conditioner at node i at time t+1; The temperature state of the air conditioner at time t represents node i; The operating power of the air conditioner at node i at time t; represent Let be the base power value of the air conditioner at node i at time t; The minimum operating power of the air conditioner at node i; This represents the maximum operating power of the air conditioner at node i. The actual active power of the distributed photovoltaic system at time t represents node i; The maximum active power of the distributed photovoltaic system at time t represents node i. The state of charge of node i at time t represents the energy storage state at node i. The state of charge of node i at time t+1 represents the energy storage state at node i. The discharge efficiency of node i at time t represents the energy storage efficiency at node i. The discharge power of node i at time t represents the energy storage capacity at node i. The rated energy storage capacity of node i at time t; The maximum discharge power of the energy stored at node i; The minimum state of charge represents the energy stored at node i; This represents the maximum state of charge of the energy stored at node i; Inject active power into node i at time t; Let be the active power output of the thermal power unit at node i at time t; Let node i be the unadjustable load at time t; The actual active power of branch k at time t; Let be the initial active power of branch k at time t; Let be the change in active power of branch k at time t; The initial active power injected into node i at time t; h represents the change in active power injected into node i at time t. ik ρ is the power transfer distribution factor, reflecting the impact of the change in injected power at node i on the change in power in critical branch k; ρ is the maximum allowable branch load rate. Let n be the maximum active power load of the k-th critical branch; b The total number of nodes;

[0069] Equations (a) to (d) represent equipment operation constraints, which are, in order, constraints for electric heating equipment, air conditioning equipment, distributed photovoltaic, and energy storage equipment; Equation (e) represents node power balance constraints; Equation (f) represents power flow constraints for DC power flow linearization; Equation (g) represents critical branch load rate constraints; and Equation (h) represents regulation command constraints, reflecting the composition of the total power of the virtual power plant from the internal controllable loads.

[0070] The upper and lower limits of the total power of the virtual power plant were obtained by solving the virtual power plant regulation boundary optimization model using the CPLEX solver. and Combined with virtual power plant power baseline Calculate the upper and lower limits of adjustable power:

[0071]

[0072] in, The lower limit of adjustable power; This is the adjustable power limit.

[0073] Report the virtual power plant regulation boundary model according to the standard interface. and boundary parameters and The value of .

[0074] In step 2, the controllable resource parameters of the transmission network are obtained. The goal is to minimize the sum of the operating cost of conventional power sources and the regulation cost of each controllable resource. The output constraints of each conventional power source unit, the regulation boundary constraints of virtual power plants, the power flow constraints, and the security constraints of the transmission network are considered. The optimal scheduling model of the transmission network is constructed, and the optimal scheduling model of the transmission network is solved to obtain the scheduling plans of each virtual power plant, power source and energy storage, the overall regulation command and send them to the distribution network.

[0075] Figure 5 This is the process for constructing and solving the power grid optimization scheduling model proposed in Embodiment 1 of the present invention;

[0076] Obtain all controllable resource parameters, specifically:

[0077] If the controllable resource is a thermal power unit, the equipment parameters mainly include the unit coal consumption cost c. coal Carbon dioxide emission costs Unit cost of standby power units and Unit start-up and shutdown costs and

[0078] If the controllable resource is a new energy unit, the equipment parameters should mainly consider the maximum output. Curtailment penalty coefficient

[0079] If the controllable resource is energy storage, the equipment parameters mainly consider the battery's rated capacity E. ESS Charge and discharge efficiency η ESS Upper and lower limits of the state of charge of energy storage units and

[0080] If the controllable resource is a virtual power plant, the equipment parameters should mainly consider the upper and lower limits of the virtual power plant's regulating power. and Adjusting costs

[0081] The optimal scheduling model for the power transmission network is constructed as follows:

[0082]

[0083] The costs are, in order, coal consumption and carbon emission costs of thermal power units, standby costs, unit start-up and shutdown costs, load shedding costs, penalties for curtailment of new energy units, and virtual power plant regulation costs. coal Cost per unit of coal consumption; Cost of carbon dioxide emissions; k G Numbering of thermal power units; n g This represents the total number of thermal power units. Coal consumption coefficient; The output of the thermal power unit at time t; The unit cost of standby power units in thermal power plants; The unit cost of standby power units in thermal power plants; The backup power provided to thermal power units; The backup power provided to thermal power units; For unit start-up costs; Costs associated with unit shutdown; For the start-up state variables of thermal power units; For the shutdown state variable of the thermal power unit; Ω D A set of nodes connected to a fixed load; This is the load shedding penalty factor; Ω represents the load change at node i. VRE This refers to the set of nodes to which new energy generating units can be connected. This is the penalty coefficient for power curtailment; Ω represents the change in power output of the new energy unit at node i; VPP This refers to the set of nodes connected to the virtual power plant. Adjusting costs for virtual power plants; The change in power of the virtual power plant at node i; This is the operating status variable for thermal power units; the operating status is 1, and the shut-down status is 0. This is the maximum output of the thermal power unit; This is the minimum output of the thermal power unit; This represents the upward power change of a thermal power unit over operating time. This represents the downward power change of a thermal power unit over operating time. For the output of the thermal power unit at time t-1; This is the minimum startup time for thermal power units; This is the minimum shutdown duration for thermal power units. This represents the startup state variable of a thermal power unit at any given moment. Power for new energy equipment; This represents the change in the output of the new energy generating unit at node i; To maximize the output of new energy equipment; L + % is the first reserve requirement factor for load output; L - % is the second reserve requirement factor reserved for load output; W + % represents the first reserve requirement factor for the output of wind and solar turbine units; W - % is the second reserve requirement factor reserved for the output of wind and solar turbine units; Let be the total load of node i at time t; This represents the change in load at node i; Let k be the maximum active power load of the kth branch; VRE Numbering of new energy generating units; Let be the lower limit of the adjustable power of the virtual power plant at time t for node i. Let i be the upper limit of the adjustable power of the virtual power plant at time t;

[0084] Equations (a) to (e) represent the output constraints of conventional power units, which are, in order, the output constraints of thermal power units, the ramping constraints, the start-stop constraints, the output constraints of new energy units, and the standby constraints.

[0085] Equation (f) represents the energy storage constraint, which is constructed based on the distributed energy storage operation characteristic model.

[0086] Equation (g) represents the node power balance constraint. The node injected power is composed of the output of thermal power units, the output of photovoltaic units, the energy storage discharge power, the fixed load, and the virtual power plant power.

[0087] Equation (h) represents the power flow constraint for DC power flow linearization.

[0088] Equation (i) is the branch load rate constraint.

[0089] Equation (j) represents the boundary constraints for the virtual power plant regulation, and the boundary parameters are... and This information comes from information reported by the regional survey.

[0090] The above large-scale mixed quadratic programming problem is solved using CPLEX or Grobi solvers to obtain the dispatch plans for each virtual power plant, power source, and energy storage. Specifically, this includes: the power generation plan for thermal power plants; the charging and discharging plan for energy storage units; the power output plan for wind and solar power; and the overall regulation command for the virtual power plants. The overall regulation command for the virtual power plants is then further distributed to each distribution network.

[0091] In step 3, with the optimization objective of minimizing adjustment costs and curtailment penalties, and under the premise of ensuring that the total adjustment of each resource meets the total adjustment command, the operation constraints of adjustable equipment, power flow constraints, and distribution network security constraints are considered to construct a distribution network command decomposition model. The adjustment commands of distributed controllable resource aggregates at different access locations are obtained, and it is verified whether the adjustment commands of distributed controllable resource aggregates meet the requirements of the total adjustment command. If they do, the command is issued and executed; if they do not, the virtual power plant adjustment boundary is updated and iteratively solved to complete the virtual power plant optimized scheduling.

[0092] Figure 6 This is the process for constructing and solving the power distribution network optimization scheduling model proposed in Embodiment 1 of the present invention;

[0093] The constructed distribution network command decomposition model is as follows:

[0094]

[0095] In the objective function, the costs are, in order, the cost of adjusting the electric heating load, the cost of adjusting the air conditioning load, and the cost of curtailing solar power. ω1 is the first minimized weight coefficient, ω2 is the second minimized weight coefficient, and ω3 is the third minimized weight coefficient, all of which are greater than or equal to 0, and ω1+ω2+ω3=1; Cost coefficient for electric heating load Cost coefficient for air conditioning load This is the penalty coefficient for discarded light; This represents the change in electric heating power. This represents the change in air conditioner power. This represents the change in photovoltaic power output; This is the minimum power factor for photovoltaics; g represents the reactive power of the distributed photovoltaic system at time t of node i; ij Let b be the conductance of branch k; ij The susceptance of branch k; Ui,t Let θ be the voltage magnitude of node i at time t; ij,t Let be the voltage phase angle difference between the two ends of branch ij; A is the branch node correlation matrix; U is the upper limit of the apparent power of branch k; i,min U represents the minimum allowable voltage amplitude at node i during normal operation of the power system. i,max λ represents the maximum allowable voltage amplitude at node i during normal operation of the power system; λ is the penalty coefficient. The virtual power plant general dispatching command issued by the main grid; The reactive power injected into node i at time t; Let be the reactive power of branch k at time t; Let i be the reactive power output of the thermal power unit at time t; Let be the uncontrollable reactive load of node i at time t.

[0096] Equations (a) to (d) represent equipment operation constraints, which are, in order, constraints for electric heating equipment, air conditioning equipment, distributed photovoltaic systems, and energy storage equipment.

[0097] Equation (e) represents the node power balance constraint. The active power injected into the node consists of the output of thermal power units, the output of photovoltaic units, the discharge power of energy storage, fixed loads, electric heating loads, and air conditioning loads. The reactive power injected into the node consists of the output of thermal power units, the output of photovoltaic units, and fixed loads. A is the branch node correlation matrix, used to describe the connection relationship between nodes and branches.

[0098] Equation (f) represents the power flow constraint.

[0099] Equation (g) represents the safety constraints of the distribution network, including branch capacity constraints and node voltage constraints.

[0100] Equation (h) represents the adjustment instruction tracking constraint. The instruction decomposition is based on the total adjustment instruction issued by the main network. The sum of the adjustment amounts of each resource must be constrained by tracking this instruction and is described in the form of a penalty function.

[0101] The process of solving the distribution network command decomposition model includes: linearizing the capacity constraints in the distribution network command decomposition model using a geometric approximation method, so that the inverter capacity constraints can be linearized as follows:

[0102]

[0103] The rated capacity of the photovoltaic system at node i;

[0104] Branch capacity constraints can be linearized as follows:

[0105]

[0106] The power flow constraints are linearized based on the improved DC power flow method, and the specific results are as follows:

[0107]

[0108] in, For the first network loss factor parameter, For the second network loss factor parameters, For the third network loss factor parameters, For the fourth network loss factor parameters, For the fifth network loss factor parameters, This refers to the parameters of the sixth network loss factor;

[0109]

[0110] Among them, U i,0 θ is the initial value of the node voltage amplitude; ij,0 This is the initial value of the phase angle difference;

[0111] Initial values ​​for node voltage amplitude and phase angle difference are set to calculate the initial value of the network loss factor. A linear programming solver is used to solve the linearized optimal scheduling model, and the average absolute error rate of the power of each branch in the system is evaluated. The evaluation equation is:

[0112]

[0113] Where, Δ t Let be the average absolute error rate of the branch power at time t; The theoretical accurate value of the active power of the branch circuit; This is the theoretically accurate value of the reactive power of the branch circuit.

[0114] If the error exceeds the preset accuracy threshold, the network loss factor is updated using the currently obtained voltage amplitude and phase angle difference, and the next round of optimization is performed. The above process continues to iterate until the average absolute error converges to the set accuracy range, and finally the adjustment command of the distributed controllable resource aggregate at different access locations is obtained.

[0115] The process of verifying whether the adjustment instructions of the distributed controllable resource aggregate meet the requirements of the overall adjustment instructions, and issuing and executing the instructions if they meet the requirements, and updating the virtual power plant adjustment boundary iteratively to complete the virtual power plant optimal scheduling process includes:

[0116]

[0117] Where, δ t The error between the adjustment command for the distributed controllable resource aggregate and the overall adjustment command;

[0118] If the error exceeds the preset accuracy threshold, the lines in the distribution network whose load rate reaches the maximum allowable value are counted, and these lines are added as new constraints to the critical branch safety constraints. The virtual power plant regulation boundary is updated iteratively until the regulation instructions of the controllable resource aggregate meet the requirements of the overall regulation instructions. The decomposed regulation instructions obtained from the solution are then issued to each controllable resource for regulation, thus completing the instruction decomposition.

[0119] Methods for adjusting controllable resources include:

[0120] For electric heating loads, the load can be adjusted up or down by changing the operating power of the electric heating equipment or by intermittent start-stop strategies.

[0121] For air conditioning equipment, power regulation is achieved by adjusting the compressor's operating frequency and changing the set temperature;

[0122] For distributed photovoltaic resources, power reduction is achieved by adjusting the output setpoint of the inverter to achieve the regulation purpose;

[0123] For energy storage systems, charging and discharging strategies are executed based on the current state of charge and adjustment commands to meet the target power requirements.

[0124] The virtual power plant optimization scheduling method based on transmission and distribution coordination proposed in Embodiment 1 of this invention not only improves the coordination and economy of virtual power plant regulation capacity at the transmission network level, but also significantly enhances its feasibility and security at the distribution network level, providing a technical foundation and engineering support for promoting coordinated regulation of source, grid, load and storage under the background of high proportion of renewable energy access.

[0125] To fully illustrate the effectiveness of the virtual power plant optimization scheduling method based on transmission and distribution coordination proposed in Embodiment 1 of this invention, the IEEE 30 transmission network system is used as an example for verification. Figure 7 This is a topology diagram of the IEEE 3000 power grid system proposed in Embodiment 1 of the present invention. The system comprises 30 nodes, 41 lines, a 420MW thermal power plant, a 230MW distributed wind power plant, a 600MW distributed photovoltaic power plant, and a 120MW distributed energy storage unit. Virtual power plants with a total load of 113.5MW are also connected to some nodes. The optimized scheduling of the power grid is performed as follows: By investigating the installation model data of the system's lines, a parameter table for the distribution network system can be generated. The parameters of some lines in this system are shown in Table 1.

[0126] Table 1: Installation Locations and Parameters of Some System Lines

[0127]

[0128] By investigating the installation data of the thermal power units, distributed power sources, and energy storage in this system, operating parameter tables for each piece of equipment in the system can be generated. Parameters for some thermal power units, wind power, photovoltaic power, and energy storage in this system are shown in Tables 2-4.

[0129] Table 2: System Thermal Power Unit Assembly Location and Parameters

[0130] Node number Pg(MW) Qg(Mvar) Qmax(Mvar) Qmin(Mvar) Pmax(MW) Pmin(MW) 1 50 0 100 -100 100 25 2 50 0 100 -50 100 25 5 30 0 60 -30 60 15 8 40 0 80 -40 80 20 11 20 0 20 -20 40 10 13 20 0 20 -20 40 10

[0131] Table 3: System Wind and Solar Turbine Assembly Location and Parameters

[0132] Node number type Maximum power (MW) Operating cost (RMB / (MW*h)) Penalty for abandoning electricity (RMB / (MW*h)) 5 WT 40 1 100 7 WT 100 2 95 23 WT 40 1 90 21 WT 50 2 85 5 PV 100 1 50 8 PV 200 1 55 2 PV 100 1 60 7 PV 200 1 65

[0133] Table 4: Installation Location and Parameters of the System Energy Storage Unit

[0134]

[0135] Based on the basic information of virtual power plants reported by the regional survey, a parameter table of virtual power plants connected to the transmission network system can be generated. The parameters of some virtual power plants in this system are shown in Table 5.

[0136] Table 5: Location and Parameters of Some Virtual Power Plants in the System

[0137] Node number Pr(MW) Pmax(pu) Pmin(pu) SOCInit SOCEnd Unit adjustment cost (yuan / MWh) 8 10 1 0 0.2 0.2 100 21 5 1 0 0.2 0.2 120 5 5 1 0 0.2 0.2 140 7 5 1 0 0.2 0.2 160 12 5 1 0 0.2 0.2 180

[0138] Based on the above system information, one day was selected as a typical day. According to the controllable resource characteristics, virtual power plants were divided into two main categories: VPPAC and VPPP2G. Using the transmission network optimization scheduling model constructed in step 2, the output timing of various units in the transmission network scheduling layer and the load regulation of virtual power plants were obtained, as shown below. Figure 8 and Figure 9 As shown, Figure 8 For the power output sequence of various generating units, Figure 9 This describes the load regulation of the virtual power plant. It can be seen that when wind and solar power output is high, the output of thermal power units decreases to supplement the system's energy supply. In this case, thermal power units mainly play a regulating role, while the virtual power plant load is increased to ensure the absorption of wind and solar power. When wind and solar power output is low, the output of thermal power units is high, undertaking the main energy supply to the system, while the virtual power plant load is reduced. Energy storage units store energy when wind and solar power output is high and release the energy when wind and solar power output is low.

[0139] Select virtual power plants VPP7 and VPP10 on nodes 21 and 12, and set their internal topology to be the IEEE 33 distribution network system. See the starting system topology diagram. Figure 10 , Figure 10 The distribution network layer architecture proposed in Embodiment 1 of this invention includes 24 MVA photovoltaic and 5.6 MW electric heating loads as controllable resources. The distribution network command execution breakdown is as follows:

[0140] The load regulation of the virtual power plant at the transmission network level is used as the overall regulation command at the distribution network level. Figure 11 The distribution network layer instruction decomposition result proposed in Embodiment 1 of this invention, wherein, Figure 11 (a) Virtual power plant load regulation command. Using the distribution network command decomposition model constructed in Step 3, the decomposed electric heating load regulation and distributed photovoltaic output regulation are obtained as follows: Figure 11 (b)- Figure 11 As shown in (c) Figure 11 (b) represents the electric heating load adjustment amount. Figure 11 (c) is the photovoltaic output adjustment amount. It can be seen that the distribution network layer can decompose the instructions of the transmission network layer by adjusting the electric heating load and the distributed photovoltaic output: when the total virtual power plant instruction is increased, the electric heating load is increased and the distributed photovoltaic output is decreased; when the total virtual power plant instruction is decreased, the electric heating load is decreased and the distributed photovoltaic output remains unchanged.

[0141] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A virtual power plant optimization scheduling method based on transmission and distribution coordination, characterized in that, Includes the following steps: Obtain the controllable resource adjustability impact parameters of the virtual power plant, and construct a controllable resource adjustment characteristic model of the virtual power plant based on the controllable resource capacity and the adjustability impact parameters; obtain the basic information of the distribution network to construct a virtual power plant adjustment boundary optimization model, and solve the virtual power plant adjustment boundary optimization model to obtain the upper and lower limits of the total power of the virtual power plant; The controllable resource parameters of the transmission network are obtained. The goal is to minimize the sum of the operating cost of conventional power sources and the regulation cost of each controllable resource. The output constraints of each conventional power source unit, the regulation boundary constraints of virtual power plants, the power flow constraints and the security constraints of the transmission network are considered. The optimal scheduling model of the transmission network is constructed, and the optimal scheduling model of the transmission network is solved to obtain the scheduling plans of each virtual power plant, power source and energy storage, the overall regulation command and the distribution network. With the optimization objective of minimizing adjustment costs and curtailment penalties, and under the premise of ensuring that the total adjustment of each resource meets the overall adjustment command, the system considers the operational constraints of adjustable equipment, power flow constraints, and distribution network security constraints to construct a distribution network command decomposition model. The system solves for the adjustment commands of distributed controllable resource aggregates at different access locations and verifies whether the adjustment commands of the distributed controllable resource aggregates meet the requirements of the overall adjustment command. If they do, the commands are issued and executed; if they do not, the virtual power plant adjustment boundary is updated and iteratively solved to complete the virtual power plant optimized scheduling.

2. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 1, characterized in that, Based on controllable resource capacity, a virtual power plant controllable resource regulation characteristic model is constructed using adjustable capacity influence parameters, specifically including: If the capacity of a controllable resource unit is greater than the capacity threshold, construct a unit adjustment characteristic model with the controllable resource unit as the controllable object. If the capacity of a single controllable resource is not greater than the capacity threshold, a cluster of controllable resources is used as the controllable object to construct an aggregate adjustment characteristic model.

3. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 2, characterized in that, If the controllable resource is electric heating, the individual boiler regulation characteristic model is as follows: Among them, T t+1 The water temperature at time t+1; T t Let η be the water temperature at time t; EH For the thermal efficiency of the electric boiler; P t EH ρ is the heating power of the electric boiler; c is the specific heat capacity of water; f t T represents the circulating water flow rate at time t; s For water supply temperature; T b T represents the return water temperature. a T represents air temperature. g R represents the ground temperature. a R is the thermal resistance of the water tank wall in contact with air. g The wall resistance of the water tank in contact with the ground is denoted as m; the total mass of water in the hot water storage tank is Δt; the heating time from time t to time t+1 is Δt; T min To meet the minimum water temperature required for user comfort; T max To meet the highest water temperature required for user comfort; This is the maximum thermal power of the electric boiler; If the controllable resource is air conditioning, the regulation characteristic model of the air conditioning aggregate model is as follows: in, The temperature status of the air conditioning cluster at time t+1; The temperature status of the air conditioning cluster at time t; Let be the power adjustment amount of the air conditioning cluster at time t; This represents the baseline power value of the air conditioning cluster at time t. and These represent the upper and lower limits of the operating power of the air conditioning cluster; A represents the parameter of the first air conditioning cluster, B represents the parameter of the second air conditioning cluster, and C represents the parameter of the third air conditioning cluster. in, and These represent the maximum and minimum values ​​of the building's thermal resistance, respectively. This represents the average heat capacity of the building. and These represent the maximum and minimum values ​​of the building's cooling efficiency, respectively. and These are the average values ​​of the upper and lower limits of the permissible indoor temperature, respectively. The temperature state of the individual air conditioning unit at time t; n a Total number of air conditioners; The power adjustment amount of the individual air conditioner unit at time t; and These are the upper and lower limits of the operating power of a single air conditioning unit; and These are the average values ​​of the upper and lower limits of the permissible indoor temperature, respectively. Set the initial temperature state value; This represents the average outdoor temperature of the individual air-conditioned unit; T in,max The upper limit of permissible indoor temperature; T in,min The lower limit of permissible indoor temperature; T set Set the temperature for the user; SOC out,t This refers to the outdoor temperature status of the individual air conditioning unit; T out,t Let be the outdoor temperature at time t; If the controllable resource is distributed photovoltaic (PV), the regulation characteristic model of the distributed PV aggregate model is as follows: in, The actual active power of the distributed photovoltaic aggregate at time t; The maximum active power of the distributed photovoltaic aggregate at time t; The actual reactive power of the distributed photovoltaic aggregate at time t; This refers to the rated capacity of the distributed photovoltaic aggregate; The minimum power factor of a distributed photovoltaic aggregate; Where, n p This refers to the total number of photovoltaic cells within the same distribution area. The actual active power of a distributed photovoltaic unit at time t; The actual reactive power of a distributed photovoltaic unit at time t; Let be the maximum active power of a distributed photovoltaic unit at time t; The rated capacity of a single distributed photovoltaic unit; This represents the average of the minimum power factors of individual distributed photovoltaic inverters. If the controllable resource is distributed energy storage, the state of charge of the distributed energy storage unit is: in, The state of charge of the energy storage polymer at time t+1; The state of charge of the energy storage polymer at time t; The discharge efficiency of the energy storage polymer; Let be the discharge power of the energy storage polymer at time t; Let t be the rated energy storage capacity of the energy storage polymer at time t; This represents the maximum state of charge of the energy storage polymer. This represents the minimum state of charge of the energy storage polymer. Where, n e The total number of energy storage units within the same distribution area; The actual active power of the energy storage unit at time t; This represents the maximum discharge power of a single energy storage cell. This represents the average maximum state of charge of a single energy storage cell. This represents the average minimum state of charge of a single energy storage cell.

4. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 1, characterized in that, The method further includes: combining the operating power baseline of distributed controllable resources with the basic load prediction curve of the distribution network in the basic information of the distribution network, performing power flow calculation of the distribution network at each moment, counting the proportion of time periods in which the load rate of each branch exceeds a preset proportion, and selecting branches with the number of time periods exceeding a preset proportion of the total number of time periods as key branches with high overload risk.

5. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 3, characterized in that, The process of constructing a virtual power plant regulation boundary optimization model and solving the virtual power plant regulation boundary optimization model to obtain the upper and lower limits of the total power of the virtual power plant includes: The virtual power plant regulation boundary model is as follows: In the formula: Virtual power plant central control command; and Adjustable upper and lower power limits; With the goal of maximizing and minimizing the total power of the virtual power plant at each moment, and comprehensively considering equipment operation constraints, power flow constraints, critical branch safety constraints, and regulation command constraints, a virtual power plant regulation boundary optimization model is constructed: Among them, P t VPP n represents the total power of the virtual power plant. t T represents the total number of scheduling time slots. i,t+1 T represents the water temperature at node i at time t+1; i,t This represents the water temperature at node i at time t; This represents the electric boiler heating power of node i at time t; T represents the maximum heating power of the electric boiler at node i; i,min T represents the minimum water temperature at node i; i,max This represents the maximum water temperature at node i; The temperature state of the air conditioner at node i at time t+1; The temperature state of the air conditioner at time t represents node i; The operating power of the air conditioner at node i at time t; represent Let be the base power value of the air conditioner at node i at time t; The minimum operating power of the air conditioner at node i; This represents the maximum operating power of the air conditioner at node i. The actual active power of the distributed photovoltaic system at time t represents node i; The maximum active power of the distributed photovoltaic system at time t represents node i. The state of charge of node i at time t represents the energy storage state at node i. The state of charge of node i at time t+1 represents the energy storage state at node i. The discharge efficiency of node i at time t represents the energy storage efficiency at node i. The discharge power of node i at time t represents the energy storage capacity at node i. The rated energy storage capacity of node i at time t; The maximum discharge power of the energy stored at node i; The minimum state of charge represents the energy stored at node i; This represents the maximum state of charge of the energy stored at node i; Inject active power into node i at time t; Let be the active power output of the thermal power unit at node i at time t; Let node i be the unadjustable load at time t; The actual active power of branch k at time t; Let be the initial active power of branch k at time t; Let be the change in active power of branch k at time t; The initial active power injected into node i at time t; h represents the change in active power injected into node i at time t. ik ρ is the power transfer distribution factor; ρ is the maximum allowable branch load rate. Let n be the maximum active power load of the k-th critical branch; b The total number of nodes; The upper and lower limits of the total power of the virtual power plant were obtained by solving the virtual power plant regulation boundary optimization model using the CPLEX solver. and Combined with virtual power plant power baseline Calculate the upper and lower limits of adjustable power: in, The lower limit of adjustable power; This is the adjustable power limit.

6. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 5, characterized in that, The proposed optimal scheduling model for the power transmission network is as follows: Among them, c coal Cost per unit of coal consumption; Cost of carbon dioxide emissions; k G Numbering of thermal power units; n g This represents the total number of thermal power units. Coal consumption coefficient; The output of the thermal power unit at time t; The unit cost of standby power units in thermal power plants; The unit cost of standby power units in thermal power plants; The backup power provided to thermal power units; The backup power provided to thermal power units; For unit start-up costs; Costs associated with unit shutdown; For the start-up state variables of thermal power units; For the shutdown state variable of the thermal power unit; Ω D A set of nodes connected to a fixed load; This is the load shedding penalty factor; Ω represents the load change at node i. VRE This refers to the set of nodes to which new energy generating units can be connected. This is the penalty coefficient for power curtailment; Ω represents the change in power output of the new energy unit at node i; VPP This refers to the set of nodes connected to the virtual power plant. Adjusting costs for virtual power plants; The change in power of the virtual power plant at node i; These are the operating state variables of thermal power units; This is the maximum output of the thermal power unit; This is the minimum output of the thermal power unit; This represents the upward power change of a thermal power unit over operating time. This represents the downward power change of a thermal power unit over operating time. The output of the thermal power unit at time t-1; This is the minimum startup time for thermal power units; This is the minimum shutdown duration for thermal power units. This represents the startup state variable of a thermal power unit at any given moment. For the power of new energy equipment; This represents the change in the output of the new energy generating unit at node i; To maximize the output of new energy equipment; L + % is the first reserve requirement factor reserved for load output; L - % is the second reserve requirement factor reserved for load output; W + % represents the first reserve requirement factor for the output of wind and solar turbine units; W - % is the second reserve requirement factor reserved for the output of wind and solar turbine units; Let be the total load of node i at time t; This represents the change in load at node i; Let k be the maximum active power load of the kth branch; VRE Numbering of new energy generating units; Let be the lower limit of the adjustable power of the virtual power plant at time t for node i. Let i be the upper limit of the adjustable power of the virtual power plant at time t; The power grid optimization scheduling model is solved using a linear programming solver to obtain the power generation plan of thermal power plants, the charging and discharging plan of energy storage units, the power generation output plan of wind and solar power, and the overall regulation command of virtual power plants.

7. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 6, characterized in that, The constructed distribution network command decomposition model is as follows: Where ω1 is the first minimized weight coefficient, ω2 is the second minimized weight coefficient, and ω3 is the third minimized weight coefficient, all of which are greater than or equal to 0, and ω1+ω2+ω3=1; Cost coefficient for electric heating load Cost coefficient for air conditioning load This is the penalty coefficient for discarded light; This represents the change in electric heating power. This represents the change in air conditioner power. This represents the change in photovoltaic power output; This is the minimum power factor for photovoltaics; g represents the reactive power of the distributed photovoltaic system at time t of node i; ij Let b be the conductance of branch k; ij The susceptance of branch k; U i,t Let θ be the voltage magnitude of node i at time t; ij,t Let be the voltage phase angle difference between the two ends of branch ij; A is the branch node correlation matrix; U is the upper limit of the apparent power of branch k; i,min U represents the minimum allowable voltage amplitude at node i during normal operation of the power system. i,max λ represents the maximum allowable voltage amplitude at node i during normal operation of the power system; λ is the penalty coefficient. The virtual power plant general dispatching command issued by the main grid; The reactive power injected into node i at time t; Let be the reactive power of branch k at time t; Let i be the reactive power output of the thermal power unit at time t; Let be the uncontrollable reactive load of node i at time t.

8. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 7, characterized in that, The process of solving the distribution network command decomposition model includes: linearizing the capacity constraints in the distribution network command decomposition model using a geometric approximation method, so that the inverter capacity constraints can be linearized as follows: The rated capacity of the photovoltaic system at node i; Branch capacity constraints can be linearized as follows: The power flow constraints are linearized based on the improved DC power flow method, and the specific results are as follows: in, For the first network loss factor parameter, For the second network loss factor parameters, For the third network loss factor parameters, For the fourth network loss factor parameter, For the fifth network loss factor parameters, This refers to the parameters of the sixth network loss factor; Among them, U i,0 θ is the initial value of the node voltage amplitude; ij,0 This is the initial value of the phase angle difference; Initial values ​​for node voltage amplitude and phase angle difference are set to calculate the initial value of the network loss factor. A linear programming solver is used to solve the linearized optimal scheduling model, and the average absolute error rate of the power of each branch in the system is evaluated. The evaluation equation is: Where, Δ t Let be the average absolute error rate of the branch power at time t; The theoretical accurate value of the active power of the branch circuit; This is the theoretically accurate value of the reactive power of the branch circuit.

9. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 8, characterized in that, The process of verifying whether the adjustment instructions of the distributed controllable resource aggregate meet the requirements of the overall adjustment instructions, and issuing and executing the instructions if they meet the requirements, and updating the virtual power plant adjustment boundary iteratively to complete the virtual power plant optimal scheduling process includes: Where, δ t The error between the adjustment command for the distributed controllable resource aggregate and the overall adjustment command; If the error exceeds the preset accuracy threshold, the lines in the distribution network whose load rate reaches the maximum allowable value are counted, and these lines are added as new constraints to the critical branch safety constraints. The virtual power plant regulation boundary is updated iteratively until the regulation instructions of the controllable resource aggregate meet the requirements of the overall regulation instructions. The decomposed regulation instructions obtained from the solution are then issued to each controllable resource for regulation, thus completing the instruction decomposition.

10. The virtual power plant optimization scheduling method based on transmission and distribution coordination according to claim 9, characterized in that, Methods for adjusting controllable resources include: For electric heating loads, the load can be adjusted up or down by changing the operating power of the electric heating equipment or by using an intermittent start-stop strategy. For air conditioning equipment, power regulation is achieved by adjusting the compressor's operating frequency and changing the set temperature; For distributed photovoltaic resources, power reduction is achieved by adjusting the output setpoint of the inverter to achieve the regulation purpose; For energy storage systems, charging and discharging strategies are executed based on the current state of charge and adjustment commands to meet the target power requirements.

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