A bidding strategy determination method, a virtual power plant, an electronic device, and a storage medium

Abstract

The application provides a bidding strategy determination method, a virtual power plant, an electronic device and a storage medium. The method comprises the following steps: under first power balance constraints and thermal balance constraints, a day-ahead transaction bidding strategy meeting multiple renewable energy power generation power scenarios is determined; in an intraday transaction stage, a bidding period of a previous round of intraday transaction is taken as a time node, and an initial bidding strategy of a current round of intraday transaction period in the day-ahead transaction bidding strategy is optimized in combination with second power balance constraints of intraday imbalance transaction volume, so that a target bidding strategy is obtained. The application determines the bidding strategy in combination with multiple renewable energy power generation power scenarios and intraday rolling correction, can overcome multiple uncertain factors that a virtual power plant needs to face when making a bidding strategy, and improves the scientificity and effectiveness of the power plant bidding strategy.

Description

Technical Field

[0001] This invention relates to the field of integrated power grid control technology, and more specifically, to a method for determining bidding strategies, a virtual power plant, electronic equipment, and a storage medium. Background Technology

[0002] The bidding strategy of virtual power plants refers to the process by which virtual power plants optimize the start-up, shutdown, output, or charging and discharging plans of various distributed resources through their internal energy management system (EMS) in day-ahead or intraday market transactions, based on load forecasts, renewable energy (RES) output forecasts, and market price signals, and submit segmented bid-volume curves to the electricity market authorities accordingly.

[0003] With the advancement of the "dual carbon" target, the penetration rate of distributed renewable energy, represented by wind power and photovoltaics, in the power system continues to rise. Virtual power plants (VPPs), as key carriers for the aggregation of distributed energy resources (DERs) in the new power system, provide a crucial technological path to enhance the flexibility and market participation of the power system by integrating flexible resources such as renewable energy, electric / thermal energy storage systems, and electric vehicles.

[0004] However, the day-ahead output forecasts for renewable energy sources such as wind and solar power are subject to significant uncertainties. This means that virtual power plants must confront multiple uncertainties when formulating bidding strategies, including: the actual output of renewable energy, fluctuations in user-side load demand, electricity market price trends, and the bidding strategies of other market participants. These factors are intertwined and directly affect the scientific validity and effectiveness of virtual power plant bidding strategies. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a bidding strategy determination method, a virtual power plant, an electronic device and a storage medium, which can combine multiple renewable energy power generation scenarios and intraday rolling corrections to determine the bidding strategy, overcome the multiple uncertainties that virtual power plants need to face when formulating bidding strategies, and improve the scientificity and effectiveness of power plant bidding strategies.

[0006] In a first aspect, embodiments of this application provide a method for determining a bidding strategy, which is applied to a virtual power plant, and the method includes:

[0007] Under the first power balance constraint and thermal balance constraint, determine the day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios;

[0008] The day-ahead trading bidding strategy is submitted to the electricity market department for bidding; after the electricity market department completes the settlement based on the day-ahead trading bidding strategy, the intraday trading phase begins.

[0009] During the intraday trading phase, the bidding period of the previous intraday trading session is used as the time node. Simultaneously, with the goal of minimizing the cost of the bidding strategy, and under the second power balance constraint that incorporates the intraday imbalance trading volume, the initial bidding strategy of the current intraday trading session in the previous day trading bidding strategy is optimized to obtain the target bidding strategy.

[0010] The cost of the minimum bidding strategy is used to avoid losses caused by the inability of the internal resources integrated by the virtual power plant to fully offset the power imbalance caused by the deviation in electricity volume; the intraday imbalance trading volume is the difference between the first active power trading volume predicted the day before and the second active power trading volume predicted the latest.

[0011] In one possible implementation, the cost of the bidding strategy includes the penalty cost for adjusting anomalies, the fuel cost of the micro generator set, and the operating cost of the power storage device;

[0012] The penalty cost for abnormal regulation refers to the economic penalty cost incurred due to failure to meet the regulation requirements of the integrated energy system composed of the power system and the heat system in the virtual power plant during actual operation.

[0013] In one possible implementation, the expression for minimizing the cost of the bidding strategy is:

[0014] ;

[0015] in, The scheduling duration for intraday trading. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. For the number of energy storage devices, Let be the operating cost of the j-th energy storage device at time t. To adjust the cost of abnormal fines.

[0016] In one possible implementation, the expression for the second power balance constraint of the intraday imbalanced trading volume is:

[0017] ;

[0018] in, The number of micro generator sets, Let be the power of the i-th micro generator set at time t. The number of power storage devices. Let be the output power of the j-th energy storage device at time t. Let t be the renewable energy power generation capacity. Let t represent the active power trading volume in the market before time t. This represents the intraday trading volume imbalance.

[0019] In one possible implementation, determining a day-ahead trading bidding strategy that satisfies multiple renewable energy generation power scenarios under the first power balance constraint and thermal balance constraint includes:

[0020] Under the first power balance constraint and thermal balance constraint, a day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios is determined based on the objective function;

[0021] The objective function includes the fuel cost of the micro generator set, the operating cost of the energy storage device, and the predicted cost of the day-ahead market.

[0022] In one possible implementation, the expression for the objective function is:

[0023] ;

[0024] in, The scheduling duration for transactions completed in the previous day. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. The number of power storage devices. Let be the operating cost of the j-th energy storage device at time t. Let t represent the active power trading volume in the market before time t. Let t be the market forecast cost before time t.

[0025] In one possible implementation, the method further includes:

[0026] The target bidding strategy is submitted to the power market department to participate in the bidding, so that the power market department sends the target bidding strategy to the power grid dispatch department for line congestion verification.

[0027] After receiving a line congestion test result indicating no line congestion from the power market department or the power grid dispatching department, the generating units are dispatched according to the dispatching instructions sent by the power grid dispatching department based on the target bidding strategy.

[0028] Secondly, embodiments of this application also provide a virtual power plant, the virtual power plant comprising:

[0029] The determination module is used to determine the day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios under the first power balance constraint and thermal balance constraint;

[0030] The submission module is used to submit the day-ahead trading bidding strategy to the power market department for bidding; after the power market department completes the settlement based on the day-ahead trading bidding strategy, the intraday trading phase begins.

[0031] The strategy optimization module is used to optimize the initial bidding strategy of the current intraday trading session in the previous intraday trading phase, taking the bidding period of the previous intraday trading as the time node, simultaneously aiming to minimize the bidding strategy cost, and under the second power balance constraint that combines the intraday imbalance trading volume, to obtain the target bidding strategy.

[0032] The cost of the minimum bidding strategy is used to avoid losses caused by the inability of the internal resources integrated by the virtual power plant to fully offset the power imbalance caused by the deviation in electricity volume; the intraday imbalance trading volume is the difference between the first active power trading volume predicted the day before and the second active power trading volume predicted the latest.

[0033] In one possible implementation, the cost of the bidding strategy includes the penalty cost for adjusting anomalies, the fuel cost of the micro generator set, and the operating cost of the power storage device;

[0034] The penalty cost for abnormal regulation refers to the economic penalty cost incurred due to failure to meet the regulation requirements of the integrated energy system composed of the power system and the heat system in the virtual power plant during actual operation.

[0035] In one possible implementation, the expression for minimizing the cost of the bidding strategy is:

[0036] ;

[0037] in, The scheduling duration for intraday trading. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. For the number of energy storage devices, Let be the operating cost of the j-th energy storage device at time t. To adjust the cost of abnormal fines.

[0038] In one possible implementation, the expression for the second power balance constraint of the intraday imbalanced trading volume is:

[0039] ;

[0040] in, The number of micro generator sets, Let be the power of the i-th micro generator set at time t. The number of power storage devices. Let be the output power of the j-th energy storage device at time t. Let t be the renewable energy power generation capacity. Let t represent the active power trading volume in the market before time t. This represents the intraday trading volume imbalance.

[0041] In one possible implementation, the determining module is specifically used to determine a day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios based on an objective function under a first power balance constraint and a thermal balance constraint; wherein the objective function includes the fuel cost of micro generator sets, the operating cost of energy storage devices, and the forecast cost of the day-ahead market.

[0042] In one possible implementation, the expression for the objective function is:

[0043] ;

[0044] in, The scheduling duration for transactions completed in the previous day. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. The number of power storage devices. Let be the operating cost of the j-th energy storage device at time t. Let t represent the active power trading volume in the market before time t. Let t be the market forecast cost before time t.

[0045] In one possible implementation, the virtual power plant further includes: a unit scheduling module;

[0046] The submission module is also used to submit the target bidding strategy to the power market department to participate in the bidding, so that the power market department can send the target bidding strategy to the power grid dispatch department for line congestion verification.

[0047] The unit dispatching module is used to dispatch units according to the dispatching instructions sent by the power grid dispatching department based on the target bidding strategy after receiving a line congestion test result from the power market department or the power grid dispatching department indicating that there is no line congestion.

[0048] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the bidding strategy determination method as described in any of the first aspects.

[0049] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the bidding strategy determination method as described in any of the first aspects.

[0050] This application provides a method for determining bidding strategies, a virtual power plant, an electronic device, and a storage medium. The method includes: determining a day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios under first power balance constraints and thermal balance constraints; during the intraday trading phase, using the bidding period of the previous round of intraday trading as a time node, simultaneously optimizing the initial bidding strategy for the current intraday trading period under a second power balance constraint that incorporates intraday imbalance trading volume, with the goal of minimizing the cost of the bidding strategy, to obtain the target bidding strategy. This application combines multiple renewable energy power generation scenarios and intraday rolling corrections to determine the bidding strategy, which can overcome the multiple uncertainties faced by virtual power plants when formulating bidding strategies, and improve the scientificity and effectiveness of power plant bidding strategies. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This application provides a schematic diagram of the three-party collaborative day-ahead and intraday planning provided in an embodiment.

[0053] Figure 2 This application illustrates a method for determining a bidding strategy according to an embodiment of the present application;

[0054] Figure 3 This paper illustrates a schematic diagram of the power topology of a virtual power plant provided in an embodiment of this application.

[0055] Figure 4 This illustration shows a schematic diagram of the intraday auction rolling process provided in an embodiment of this application;

[0056] Figure 5 A flowchart of a bidding strategy adjustment method provided in an embodiment of this application is shown;

[0057] Figure 6 This application provides a virtual device structure diagram of a virtual power plant according to an embodiment of the present application;

[0058] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0060] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] To enable those skilled in the art to utilize the content of this application, and in conjunction with the specific application scenario of "integrated power grid control technology," the following embodiments are provided. For those skilled in the art, the general principles defined herein can be applied to other embodiments and application scenarios without departing from the spirit and scope of this application. Although this application primarily describes the field of "integrated power grid control technology," it should be understood that this is merely an exemplary embodiment.

[0062] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0063] The following will be combined with the appendix Figure 1The implementation methods of the embodiments of this application will be described in detail below; Figure 1 This illustration shows a schematic diagram of the day-ahead and intraday planning for tripartite collaboration provided in an embodiment of this application. (Attached) Figure 1 As can be seen, this application uses virtual power plant (VPP), electricity market department and grid dispatch department to collaboratively realize the bidding process for day-ahead market transactions and intraday market transactions.

[0064] exist Figure 1 The provided tripartite collaboration framework comprises three main process loops: the outer loop (day-ahead trading phase), the middle loop (intraday trading phase), and the core loop (congestion handling process).

[0065] Reference Figure 2 The diagram illustrates a bidding strategy determination method provided in this application embodiment. This method is applied to virtual power plants. Steps S201 and S202 represent the implementation process for day-ahead trading of the virtual power plant corresponding to the outer ring (day-ahead trading phase), while steps S202-S203 represent the implementation process for the virtual power plant corresponding to the middle ring (intraday trading phase). The exemplary steps of this application embodiment are described below:

[0066] S201. Under the first power balance constraint and thermal balance constraint, determine the day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios.

[0067] In this embodiment, the first power balance constraint requires that the internal power consumption demand of the virtual power plant (VPP) equal the sum of the output of the power storage device, the renewable energy generation, and the power output of the micro gas turbine power generation system (MTGS), minus the trading output in the day-ahead market. This first power balance constraint ensures the power supply and demand balance of the VPP within each time step. The thermal balance constraint requires that the total thermal power output of the micro gas turbine and thermal storage device equal the thermal power consumption within the VPP. This constraint reflects the energy conservation principle of the VPP in thermal dispatch. The renewable energy generation power scenario is used to characterize the set of states of the time-series distribution of renewable energy generation power during the day-ahead trading period under specific meteorological conditions; that is, each renewable energy generation power scenario represents a possible renewable energy RES output situation. The day-ahead trading bidding strategy refers to the bidding strategy determined during the day-ahead trading phase, which includes the bidding strategy for the next day.

[0068] Here, there is significant uncertainty in the day-ahead forecasting of wind and solar power generation. To address this challenge, this application models these uncertainties using multiple renewable energy power generation scenarios during day-ahead operation, i.e., generating multiple renewable energy power generation scenarios within the planning time domain. To ensure the dispatch feasibility of the VPP under uncertain conditions, it is essential to guarantee that the first power balance constraint and thermal balance constraint are met under each renewable energy power generation scenario.

[0069] In addition, refer to Figure 3 The diagram shows the power topology of a virtual power plant provided in this embodiment. Its resources include power resources (micro gas turbines, wind power, and photovoltaics), energy storage devices (small pumped storage, electrochemical storage, and thermal storage devices), and load resources (electrical load and thermal load). Furthermore, the combined heat and power (CHP) characteristics of the micro gas turbines are incorporated into the modeling, with both their thermal and electrical outputs participating in the system's energy balance calculations. The energy storage devices further enhance the VPP's regulatory capabilities in the electricity market through flexible charging and discharging strategies.

[0070] Specifically, the calculation expression for the first power balance constraint is as follows:

[0071] ;

[0072] in, The number of micro generator sets, Let be the power of the i-th micro generator set at time t. The number of power storage devices. Let be the output power of the j-th energy storage device at time t. Let t be the renewable energy power generation capacity. The active power trading volume in the market before time t.

[0073] Specifically, the calculation expression for the thermodynamic equilibrium constraint is as follows:

[0074] ;

[0075] in, The number of micro generator sets, Let be the thermal output power of the micro-turbine in the i-th micro-generator set at time t. For the number of thermal storage devices, Let be the heat output power of the k-th thermal storage device at time t. Let t be the power consumption of heat in the virtual power plant at time t.

[0076] Furthermore, under the first power balance constraint and thermal balance constraint, a day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios is determined, including: under the first power balance constraint and thermal balance constraint, a day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios is determined based on an objective function; wherein, the objective function includes the fuel cost of micro generator sets, the operating cost of energy storage devices, and the predicted price of the day-ahead market.

[0077] The objective function is expressed as follows:

[0078] ;

[0079] in, The scheduling duration for transactions completed in the previous day. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. The number of power storage devices. Let be the operating cost of the j-th energy storage device at time t. Let t represent the active power trading volume in the market before time t. Let t be the market's predicted price before time t.

[0080] Here, the scheduling objective of day-ahead trading in this application is to minimize the operating cost of the VPP (i.e., the objective function). Under the premise of satisfying power balance constraints, and combining the predicted prices of the day-ahead market and the predicted output scenarios of renewable energy, the scheduling of various resources is optimized within the planning time domain according to the time step. Since the operating costs of renewable energy (such as wind power and solar power) are very low, they can be neglected in the objective function.

[0081] S202. Submit the day-ahead trading bidding strategy to the power market department to participate in the bidding; after the power market department completes the settlement based on the day-ahead trading bidding strategy, the intraday trading stage will begin.

[0082] In this embodiment, the virtual power plant submits its day-ahead trading bidding strategy to the power market department to participate in the bidding, so that the power market department sends the day-ahead trading bidding strategy to the grid dispatch department for line congestion verification; after receiving the line congestion verification result sent by the power market department or the grid dispatch department that there is no line congestion, the virtual power plant performs unit dispatch according to the dispatch instructions sent by the grid dispatch department based on the target bidding strategy.

[0083] Here, the power grid dispatching department can not only test line congestion for the day-ahead trading bidding strategy, but also perform other technical verifications, such as power flow calculation and security checks.

[0084] Furthermore, the power grid dispatching department conducts line congestion checks through the following steps: Power flow calculations are performed on all branch lines mn to assess the active power distribution of each line and obtain the active power of all lines; if the active power of all lines is lower than the line capacity, the VPP dispatching plan is considered feasible, the line congestion check result is no line congestion, and the bidding plan is approved (i.e., the day-ahead trading bidding strategy is approved); if there are lines with active power exceeding the line capacity, it indicates that the line is overloaded and congested, and the line congestion check result is that the day-ahead trading bidding strategy has line congestion.

[0085] In addition, the grid dispatching department sends line congestion results to the electricity market department, which then sends the line congestion results to the virtual power plants. Alternatively, the grid dispatching department sends line congestion results to both the electricity market department and the virtual power plants.

[0086] Furthermore, if line congestion exists in the day-ahead trading bidding strategy, the power grid dispatching department will first consider using network topology to solve the line congestion problem in the day-ahead trading bidding strategy. If the network topology change leads to new line overload (i.e., line congestion) or fails to solve the congestion problem of all congested lines, the power grid dispatching department will calculate sensitivity data based on the active power flow of the congested lines and the voltage angle vector of all nodes. The sensitivity data includes the sensitivity of the active power flow of the congested lines to the power flow correction of virtual power plant resources at each node. This sensitivity data is sent to the virtual power plant so that the virtual power plant can adjust the bidding strategy based on the sensitivity data (i.e., the inner loop implementation process).

[0087] S203. During the intraday trading phase, the bidding period of the previous round of intraday trading is used as the time node. Simultaneously, with the goal of minimizing the cost of the bidding strategy, and under the second power balance constraint that combines the intraday imbalance trading volume, the initial bidding strategy of the current intraday trading period in the previous day trading bidding strategy is optimized to obtain the target bidding strategy.

[0088] Specifically, refer to Figure 4 The diagram shown is a schematic of the intraday auction rolling process provided in this application embodiment; each round of intraday trading is divided into a market preparation period and an intraday trading interval, which is further divided into an auction period and a waiting auction period; this application uses the auction period of the previous round of intraday trading as a time node to start the current round of intraday trading, i.e. Figure 4 The first cell in the (k+1)th round is both the bidding period of the kth round and the market preparation period for this round. It aims to use more accurate short-term forecast information to dynamically optimize the VPP's operating strategy, enabling timely rolling optimization of the day-ahead trading bidding strategy and overcoming the problem of unreasonable bidding strategies caused by the uncertainty of day-ahead forecasts for wind and solar power generation.

[0089] Here, this application initiates the current round of bidding strategy optimization during the previous bidding period, which has the following advantages: (1) Utilizing the latest information: At the start of the bidding period, market participants have the latest forecast information and market data, including renewable energy output forecasts, load demand forecasts, and market price trends. This information helps to formulate intraday trading strategies more accurately. (2) Improving response speed: Starting the intraday trading phase immediately after the bidding period allows for a faster response to market changes, such as demand fluctuations or sudden changes in renewable energy output, thereby adjusting the output plan in a timely manner. (3) Optimizing resource allocation: Based on the latest market signals and system status, resources, including distributed energy, energy storage devices, and controllable loads, can be allocated and scheduled more effectively to minimize costs and maximize benefits. (4) Reducing deviation risk: By adjusting the strategy immediately after the bidding period, the deviation between actual output and forecasts can be reduced, thereby reducing penalty costs caused by deviations. (5) Enhancing market competitiveness: Timely adjustment of strategies helps to improve the competitiveness of market participants in competition, better adapt to changes in market supply and demand, and seize trading opportunities. (6) Support grid stability: By optimizing the scheduling of resources within the VPP, the stable operation of the grid can be better supported, the dependence on grid scheduling can be reduced, and the overall reliability of the grid can be improved.

[0090] Minimizing the bidding strategy cost is used to avoid losses (such as market penalties, additional costs of purchasing high-priced electricity) caused by the inability of the integrated internal resources of the virtual power plant (such as energy storage devices, micro gas turbines, adjustable loads, etc.) to fully offset the deviation in electricity output (i.e., the difference between actual power and planned power) resulting from power imbalance. Bidding strategy costs include penalty costs for abnormal regulation, fuel costs of micro generator sets, and operating costs of energy storage devices. The penalty cost for abnormal regulation refers to the economic penalty cost incurred due to failure to meet the regulation requirements of the integrated energy system composed of the power and thermal systems in the virtual power plant during actual operation. The triggering condition is: during the VPP's intraday operation, internal resources cannot fully smooth out fluctuations in renewable energy output and load forecast deviations, resulting in a power imbalance between actual output and the previously declared planned output, generating deviation electricity output, and thus triggering assessment penalties in the electricity market. Intraday imbalance trading volume is the difference between the first active power trading volume predicted the previous day and the second active power trading volume predicted the latest day. The intraday trading session refers to the trading session scheduled by the exchange during this round of trading. For example, the intraday trading session for the first round is from 3:00 to 4:00, and the intraday trading session for the second round is from 4:00 to 5:00.

[0091] For example, if the intraday trading session for this round is from 3:00 to 4:00, then the initial bidding strategy refers to the bidding strategy from 3:00 to 4:00 in the previous day's trading bidding strategy.

[0092] Furthermore, the expression for minimizing the cost of the bidding strategy is:

[0093] ;

[0094] in, The scheduling duration for intraday trading. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. For the number of energy storage devices, Let be the operating cost of the j-th energy storage device at time t. To adjust the cost of abnormal fines.

[0095] Here, the primary objective of the VPP in intraday planning is to address potential power imbalances based on day-ahead market electricity trading information and the latest renewable energy (RES) forecasts. If its internal resources cannot completely eliminate the imbalance, it will generate skewed electricity in the intraday market. To mitigate the impact of such skewed electricity, the VPP resource scheduling objective is set to minimize skewed electricity. Therefore, this application considers the penalty cost of regulating anomalies during optimization.

[0096] Furthermore, the expression for the second power balance constraint of intraday imbalanced trading volume is:

[0097] ;

[0098] in, The number of micro generator sets, Let be the power of the i-th micro generator set at time t. The number of power storage devices. Let be the output power of the j-th energy storage device at time t. Let t be the renewable energy power generation capacity. Let t represent the active power trading volume in the market before time t. This represents the intraday trading volume imbalance.

[0099] In addition, the initial bidding strategy for the current intraday trading session in the previous trading bidding strategy has been optimized. In addition to the second electrical balance constraint, the thermal balance constraint also needs to be considered.

[0100] Furthermore, the method also includes: submitting the target bidding strategy to the power market department to participate in the bidding, so that the power market department sends the target bidding strategy to the grid dispatch department for line congestion verification; after receiving the line congestion verification result sent by the power market department or the grid dispatch department that there is no line congestion, the unit is dispatched according to the dispatch instructions sent by the grid dispatch department based on the target bidding strategy.

[0101] Here, the power grid dispatching department can not only perform line congestion checks on the target bidding strategy, but also conduct other technical verifications, such as power flow calculations and security checks. The process of the power grid dispatching department performing line congestion checks on the target bidding strategy is similar to the process of performing a second line congestion check on the day-ahead trading bidding strategy.

[0102] In addition, the grid dispatching department sends line congestion results to the electricity market department, which then sends the line congestion results to the virtual power plants. Alternatively, the grid dispatching department sends line congestion results to both the electricity market department and the virtual power plants.

[0103] Furthermore, if the target bidding strategy suffers from line congestion, the power grid dispatching department will first consider adopting a network topology to address the line congestion problem. If the network topology change leads to new line overload (i.e., line congestion) or fails to resolve the congestion problem of all congested lines, the power grid dispatching department will calculate sensitivity data based on the active power flow of the congested lines and the voltage angle vector of all nodes. The sensitivity data includes the sensitivity of the active power flow of the congested lines to the power flow correction of virtual power plant resources at each node. This sensitivity data is then sent to the virtual power plant so that the virtual power plant can adjust its bidding strategy based on the sensitivity data (i.e., the inner loop implementation process).

[0104] Here, the traditional VPP optimized operation has the following disadvantages: (1) It lacks the design of a "power market-grid dispatch-VPP" collaborative mechanism, making it difficult for VPP to obtain network congestion information in real time to dynamically adjust its operation strategy; (2) It does not embed the physical constraints (such as power flow limits) and sensitivity analysis of distribution network lines into the VPP dispatch model, making it impossible to guide it to actively participate in congestion management in the form of ancillary services. The above-mentioned defects make it difficult for VPP to effectively ensure the safe operation of the distribution network while improving the consumption of renewable energy, thus restricting the release of its potential as a system flexibility resource.

[0105] To address the challenges to the safe operation of distribution networks (especially line congestion) caused by the high proportion of distributed energy grid connection, this application proposes a virtual power plant collaborative operation framework based on ancillary services (i.e., Figure 1 The core innovation of this framework lies in the design of a two-way collaborative mechanism between VPP and grid dispatch. Through information exchange and policy linkage, it guides VPP resources to actively participate in congestion management in the form of ancillary services. Key considerations include multi-timescale market adaptability, collaborative handling of uncertainties, dual-objective collaborative optimization, and universality of market environments to ensure applicability in complex scenarios.

[0106] Based on the above framework, this application further constructs an optimized decision-making model for VPP participation in multi-level markets. This application innovatively introduces a bilateral contract mechanism to standardize the flexible allocation of demand response (DR) resources in the day-ahead primary energy market and the intraday balancing market, applicable to a typical tiered trading system with "day-ahead as primary and intraday as secondary." This application achieves Pareto improvement in the overall operational efficiency and system security level of VPP by coordinating and optimizing conventional DER scheduling (the process of managing and optimizing the scheduling of distributed energy resources (DERs)) and DR flexible resource allocation (the flexibility and responsiveness of adjusting user-side loads through the demand response (DR) mechanism). This overcomes the aforementioned shortcomings of traditional VPP optimized operation.

[0107] Furthermore, referring to Figure 5 The diagram shows a flowchart of a bidding strategy adjustment method provided in an embodiment of this application. This method is applied to a virtual power plant. The exemplary steps of this embodiment are described below:

[0108] S501. Obtain the active power flow of the congested lines caused by the current bidding strategy and the voltage angle vectors of all nodes. Here, line congestion refers to the line congestion problem existing under the current bidding strategy that the power grid dispatching department cannot resolve by adjusting the network topology.

[0109] S502. Sensitivity data calculated based on the active power flow and the voltage angle vector of all nodes.

[0110] In this application's embodiments, active power flow refers to the actual active power flowing on a specific line in a power system due to the imbalance between generation and load. Sensitivity refers to the degree of influence of the active power flow of congested lines on the power flow correction performed by the virtual power plant at various nodes. "Power flow correction" refers to adjusting the power flow in the power system. The sensitivity data includes the sensitivity of the active power flow of congested lines to the power flow correction of virtual power plant resources at various nodes.

[0111] The current bidding strategy is either the day-ahead bidding strategy during the day-ahead trading phase or the target bidding strategy during the intraday trading phase.

[0112] S503. Adjust the current bidding strategy based on sensitivity data and under the constraint of correction amount.

[0113] Among them, the correction constraint refers to using the sensitivity of the active power flow of the congested line to the power flow correction of the virtual power plant resources at each node, and constraining the numerical relationship between the input power correction of all nodes and the active power correction of the congested line.

[0114] In this embodiment, the sensitivity of the active power flow of the congested line to the power flow correction of each node is used to constrain the numerical relationship between the input power correction of all nodes and the active power correction of the congested line. This includes: using the sensitivity of the active power flow of the congested line to the active power flow correction of the virtual power plant resources at each node, and the sensitivity of the active power flow of the congested line to the reactive power flow correction of the virtual power plant resources at each node, to constrain the numerical relationship between the reactive power input correction of all nodes, the active power input correction, and the active power correction of the congested line.

[0115] The correction amount refers to the amount of adjustment to the original plan or baseline value.

[0116] Here, the traditional sensitivity data is the sensitivity of the active power of the line to the power of each node, directly focusing on the active power of the line, that is, the power part that actually does work. The sensitivity data of this application is the sensitivity of the active power flow of the congested line to the power flow correction of the virtual power plant resources at each node, directly focusing on the active power of the line, that is, the power part that actually does work. In terms of solving line congestion, compared with adjusting the bidding strategy through the traditional sensitivity data, adjusting the bidding strategy through the sensitivity data of this application has the following advantages: (1) More comprehensive power flow analysis: the active power flow includes the sum of active power and reactive power flowing on the line, which provides a more comprehensive analysis of the line load. (2) Reactive power has an important impact on voltage control and line stability, so it is necessary to consider reactive power when solving congestion problems. (3) More accurate congestion identification: by analyzing the power flow, it is possible to more accurately identify which lines are close to or exceed their thermal or voltage limits, thereby more effectively locating congestion problems. (4) Optimize grid stability: Reactive power has a significant impact on grid voltage stability. By considering power flow, grid voltage stability can be optimized simultaneously when adjusting bidding strategies. (5) Improve dispatch efficiency: By analyzing the sensitivity of power flow, grid resources can be dispatched more effectively, such as adjusting power generation, load distribution, or the charging and discharging of energy storage devices, to reduce or avoid line congestion. (6) Reduce economic penalties: In the electricity market, failure to meet supply and demand balance may result in economic penalties. By precisely controlling power flow, penalties caused by power imbalance can be reduced. (7) Enhance system flexibility: Considering power flow allows virtual power plants to respond to grid demand more flexibly, for example, by dynamically adjusting the output of distributed energy resources to alleviate congestion. (8) Comprehensively consider multiple factors: Power flow is a comprehensive reflection of the grid operating status. Considering power flow allows multiple factors to be comprehensively considered when adjusting bidding strategies, such as line capacity, voltage level, and system stability.

[0117] Furthermore, the expression for the correction constraint is as follows:

[0118] ;

[0119] in, The number of nodes participating in the virtual power plant resource. The sensitivity of the active power flow of the congested line to the active power flow correction of the virtual power plant resources at the i-th node is given. Let be the active power input correction amount for the i-th node. The sensitivity of the active power flow of the congested line to the reactive power flow correction of the virtual power plant resources at the i-th node. Let be the reactive power input correction amount for the i-th node. This is the active power correction amount for the congested line.

[0120] Here, the essence of line congestion is that "the actual active power of a certain line exceeds its maximum transmission capacity," which may lead to line overload, voltage collapse, or even tripping, seriously threatening grid security. Internal resource adjustments within the VPP (such as changes in distributed generation output, energy storage charging and discharging, and load shifting) directly affect the active power flowing through the lines. The mechanism of this correction constraint is precisely to control the impact of resource adjustments on line power, thereby fundamentally avoiding or mitigating congestion. By establishing a direct correlation between resource adjustments and line power changes, this constraint provides a quantifiable control basis for the VPP to avoid or resolve line congestion, ultimately achieving "ensuring that the active power of each line does not exceed its safe limit while meeting power demand."

[0121] Furthermore, the VPP scheduling strategy is detailed in the subsequent equations. To simplify the analysis, it is assumed that the power curve remains constant within each time step. The characteristic equations related to the micro gas turbine are (1)-(3).

[0122] (1);

[0123] (2);

[0124] (3);

[0125] In the formula: For natural gas prices, For the efficiency of micro generator sets, This refers to the operating time of the micro generator set.

[0126] Equation (1) represents the fuel cost of the gas micro generator set. Equation (2) represents the thermal output power of the micro gas turbine, and Equation (3) gives the upper limit of the power of each micro gas turbine. and the lower limit of the power of each micro gas turbine .

[0127] The energy storage model is given by formulas (4)-(8):

[0128] ;

[0129] In the formula: The average operating cost of power storage equipment. Let be the energy storage rate of the j-th energy storage device at time t. Let be the energy storage rate of the j-th thermal energy storage device at time t. Let be the discharge efficiency of the j-th energy storage device at time t. Let be the charging efficiency of the j-th energy storage device at time t. Let be the discharge efficiency of the j-th thermal energy storage device at time t. Let be the charging efficiency of the j-th thermal energy storage device at time t. Let j be the minimum capacity of the j-th energy storage device. Let j be the maximum capacity of the j-th energy storage device. Let j be the minimum capacity of the thermal energy storage device. Let be the maximum capacity of the j-th thermal energy storage device.

[0130] Equation (4) represents the operating cost of each energy storage unit, including implementation costs and the cost of using non-thermal storage units. Equations (5) and (7) represent the state of charge of the energy storage device, respectively. The maximum and minimum capacity of the energy storage device can be obtained from formulas (6) and (8).

[0131] (9);

[0132] t represents the scheduling time segment (time / period), which is the smallest time unit for optimized scheduling; e identifies the electric energy storage device, used to distinguish the thermal energy storage device in the document; i represents the number index of the energy storage device, representing the i-th electric energy storage unit in the virtual power plant; The actual charging power of the i-th energy storage device at time t, in kW / MW; the superscript ch represents the charging status; (t) The charging status of the i-th energy storage device at time t; a variable of 0 or 1; when the value is 1, it means that the energy storage device is in the charging state; when the value is 0, it means that the device is not in the charging state. The maximum allowable charging power of the i-th energy storage device at time t is determined by the performance of the energy storage hardware and the health status of the battery, and the unit is kW / MW; {0,1} represents that the variable is 0 or 1, and can only take two discrete values, 0 or 1.

[0133] (10);

[0134] The actual discharge power of the i-th energy storage device at time t, in kW / MW; the superscript dch represents the discharge state (abbreviated as dis in Formula 12, but with the same meaning); The discharge state of the i-th energy storage device at time t is a variable of 0 or 1; when the value is 1, it means that the energy storage device is in the discharge state; when the value is 0, it means that the device is not in the discharge state. The maximum allowable discharge power of the i-th power storage device at time t is determined by the performance of the energy storage hardware and the health status of the battery, and the unit is kW / MW; the meanings of the other parameters are completely consistent with formula (9).

[0135] (11);

[0136] All symbols are completely consistent with the definitions of formulas (9) and (10); the constraint logic states that the sum of two variables equals 1, meaning that at any given time only one variable can be 1 and the other 0:

[0137] 1. =1, =0, the device is only allowed to charge and discharging is prohibited;

[0138] 2. =0, =1, the device is only allowed to discharge and charging is prohibited;

[0139] 3. =0, =0 indicates that the device is in standby (neither charging nor discharging) state, which also satisfies the constraint logic.

[0140] (12);

[0141] The remaining power (SOC) of the i-th energy storage device at time t, in kWh / MWh; At the end of the previous scheduling period ( The remaining power of the i-th energy storage device at time (i.e., the initial power of the current time period); The time step of a single scheduling period (e.g., 15 minutes, 1 hour), in h, is used to convert power (MW) into electricity (MWh). The charging efficiency of the i-th energy storage device is dimensionless and ranges from 0 to 1; it represents the proportion of grid-input electrical energy converted into battery-stored electrical energy during the charging process. The document defaults to a charging efficiency of 98%. The discharge efficiency of the i-th energy storage device is dimensionless and ranges from 0 to 1; it represents the proportion of stored energy in the battery that is converted into output energy during the discharge process. The document defaults to a discharge efficiency of 98%. With formula (10) The same parameter is used, namely the actual discharge power of the i-th energy storage device at time t; other parameters... , , , The meaning is completely consistent with the previous text.

[0142] During charging: >0、 =0, the remaining power increases with charging, and multiplying by the charging efficiency reflects the charging loss.

[0143] During discharge: >0、 =0, the remaining charge decreases as the discharge progresses, and the discharge loss is reflected when divided by the discharge efficiency.

[0144] In standby mode: both charging and discharging power are 0, and the remaining power remains unchanged.

[0145] (13);

[0146] The minimum allowable state of charge (SOC) of a power storage device is determined by the battery's chemical characteristics and is typically 10% to 20% of its rated capacity to avoid over-discharge. The unit is kWh / MWh.

[0147] The upper limit of the remaining power capacity of an energy storage device (maximum allowable SOC), which is the rated capacity of the energy storage, is usually 80% to 100% of the rated capacity to avoid overcharging. The unit is kWh / MWh.

[0148] The actual remaining power of the i-th energy storage device at time t is completely consistent with the definition of formula (12); the remaining parameters , , The meaning is completely consistent with the previous text.

[0149] Equation (9) constrains the upper and lower limits of the charging power of the energy storage device at time t, ensuring that the charging power is within the hardware safety range. At the same time, the start and stop of the charging state are controlled by 0 / 1 variables. Equation (10) constrains the upper and lower limits of the discharging power of the energy storage device at time t, ensuring that the discharging power is within the hardware safety range. At the same time, the start and stop of the discharging state are controlled by 0 / 1 variables. Equation (11) forces the energy storage device to be in one of the states of charging, discharging, or standby at the same time. Simultaneous charging and discharging are absolutely prohibited to avoid physical infeasible conditions such as circulating current and overcurrent in energy storage, ensuring equipment safety and making the optimization model conform to the actual physical characteristics of energy storage. Equation (12) describes the dynamic change relationship of the remaining energy of the energy storage with the charging and discharging behavior. It is the core state equation of energy storage scheduling and accurately calculates the remaining energy of the energy storage (state of charge) at each time. Equation (13) constrains the remaining energy of the energy storage device at any time to be within the safe upper and lower limits, avoiding overcharging and over-discharging to damage the battery and extend the service life of the energy storage. It is the core safety constraint of energy storage operation.

[0150] Furthermore, to reduce the penalty costs caused by imbalance, the active power exchange imbalance of VPP in the trading day market should be minimized as much as possible. Specifically, based on sensitivity data, the current bidding strategy is adjusted under the constraint of correction amount, including: taking minimizing the penalty costs caused by active power exchange imbalance as the adjustment objective, and adjusting the current bidding strategy under the constraint of correction amount based on sensitivity data; where active power exchange imbalance refers to the difference between the planned active power exchange amount and the actual active power exchange amount.

[0151] The calculation expression for the active power exchange imbalance is as follows:

[0152] ;

[0153] in, The penalty cost is due to the power imbalance. For the deviation in power, denoted as the operating time of the micro generator set, and pf as the penalty factor; the penalty factor is a coefficient that quantifies the severity of the impact of unbalanced power on the system and is used to calculate the economic penalty caused by power deviation.

[0154] Furthermore, to reduce congestion on overloaded line mn, sensitivity analysis of resources within the VPP is performed by calculating the active power flow of the nth line. This application provides a formula for the power grid dispatching department to calculate sensitive data, along with the corresponding formula derivation process. The specific steps are as follows:

[0155] Step 1: The following formula (14) gives the active power flow of line mn. The calculation formula is as follows:

[0156] (14);

[0157] in, Let be the voltage at node m. Let be the voltage at node n. Let mn be the conductance of the line; Let mn be the susceptance of the line. Let m be the voltage angle at node m. Let be the voltage angle at node n.

[0158] Step 2, Formula (15) gives the correction for the active power flow of line mn. :

[0159] (15);

[0160] in, For the number of nodes, Voltage at node k The correction amount, The voltage angle at node k The correction amount.

[0161] Step 3: Represent the nodes with counters from 1 to N. Equation (15) can be modified to Equation (16):

[0162] ;

[0163] Among them, on the right side of equation (16), the voltage amplitude correction amount and voltage angle vector It was introduced to reflect changes in the system state.

[0164] Step 4: In equation (17), the active and reactive power flow corrections at the nodes are expressed through the Jacobian matrix. It is related to the node voltage phase angle correction and voltage amplitude correction, and the specific relationship is as follows:

[0165] (17);

[0166] After removing the rows and columns corresponding to the relaxation bus (balanced nodes), the Jacobian matrix... Full-rank invertible, let the inverse matrix be... = Equation (17) can be rewritten as Equation (18):

[0167] (18);

[0168] Where P is active power; Q is reactive power; ΔP is the correction amount for active power injection at the node, that is, the deviation between the planned active power injection and the actual active power injection at the node, which is the core variable for power flow calculation and subsequent line sensitivity analysis; ΔQ is the correction amount for reactive power injection at the node, that is, the deviation between the planned reactive power injection and the actual reactive power injection at the node.

[0169] Step 5: Based on equations (17) and (18), row vectors It possesses all the sensitivities of power flow on line mn to active and reactive power flow corrections, as shown in equation (19):

[0170] (19);

[0171] (20)

[0172] Formula (16) can be modified to formula (20):

[0173] (20);

[0174] In the following relationship, Described as The subvectors only consider the traffic sensitivity caused by the power injection of the i-th node involving VPP resources:

[0175] (twenty one);

[0176] (twenty two);

[0177] Where I is the number of nodes participating in the VPP resources, and Equations (21) and (22) are the calculation methods for the sensitivity data sent by the power grid dispatching department to the virtual power plant.

[0178] Based on the same inventive concept, this application also provides a virtual power plant corresponding to the bidding strategy adjustment method. Since the principle of solving the problem by the virtual power plant in this application is similar to the bidding strategy adjustment method described above in this application, the implementation of the virtual power plant can refer to the implementation of the method, and the repeated parts will not be described again.

[0179] Reference Figure 6 The diagram shown is a structural diagram of a virtual power plant according to an embodiment of this application. The virtual power plant device includes:

[0180] The receiving module 601 is used to acquire the active power flow of the congested line caused by the current bidding strategy and the voltage angle vector of all nodes.

[0181] The calculation module 602 is used to calculate sensitivity data based on the active power flow and the voltage angle vector of all nodes; the sensitivity data includes the sensitivity of the active power flow of the congested line to the power flow correction of the virtual power plant resources at each node, and the line congestion is line congestion that cannot be resolved by adjusting the network topology.

[0182] The adjustment module 603 is used to adjust the current bidding strategy based on the sensitivity data under the correction amount constraint; wherein, the correction amount constraint refers to constraining the numerical relationship between the input power correction amount of all nodes and the active power correction amount of the congested line by using the sensitivity of the active power flow of the congested line to the power flow correction of the virtual power plant resources at each node.

[0183] like Figure 7 As shown in the embodiment of this application, an electronic device 700 includes a processor 701, a memory 702, and a bus. The memory 702 stores machine-readable instructions executable by the processor 701. When the electronic device is running, the processor 701 communicates with the memory 702 via the bus, and the processor 701 executes the machine-readable instructions to perform the steps of the bidding strategy adjustment method described above.

[0184] Specifically, the memory 702 and processor 701 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 701 runs the computer program stored in the memory 702, it can execute the bidding strategy adjustment method mentioned above.

[0185] Corresponding to the above-described bidding strategy adjustment method, this application embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described bidding strategy adjustment method.

[0186] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0187] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0189] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the information processing methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0190] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining a bidding strategy, characterized in that, This method is applied to a virtual power plant, and the method includes: Under the first power balance constraint and thermal balance constraint, determine the day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios; The day-ahead trading bidding strategy is submitted to the electricity market department for bidding; after the electricity market department completes the settlement based on the day-ahead trading bidding strategy, the intraday trading phase begins. During the intraday trading phase, the bidding period of the previous intraday trading session is used as the time node. Simultaneously, with the goal of minimizing the cost of the bidding strategy, and under the second power balance constraint that incorporates the intraday imbalance trading volume, the initial bidding strategy of the current intraday trading session in the previous day trading bidding strategy is optimized to obtain the target bidding strategy. The cost of the minimum bidding strategy is used to avoid losses caused by the inability of the internal resources integrated by the virtual power plant to fully offset the power imbalance caused by the deviation in electricity volume; the intraday imbalance trading volume is the difference between the first active power trading volume predicted the day before and the second active power trading volume predicted the latest.

2. The bidding strategy determination method according to claim 1, characterized in that, The costs of the bidding strategy include the penalty costs for adjusting anomalies, the fuel costs of micro generator sets, and the operating costs of power storage equipment; The penalty cost for abnormal regulation refers to the economic penalty cost incurred due to failure to meet the regulation requirements of the integrated energy system composed of the power system and the heat system in the virtual power plant during actual operation.

3. The bidding strategy determination method according to claim 1 or 2, characterized in that, The expression for minimizing the cost of the bidding strategy is: ； in, The scheduling duration for intraday trading. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. For the number of energy storage devices, Let be the operating cost of the j-th energy storage device at time t. To adjust the cost of abnormal fines.

4. The bidding strategy determination method according to claim 1, characterized in that, The expression for the second power balance constraint of the intraday imbalanced trading volume is: ； in, The number of micro generator sets, Let be the power of the i-th micro generator set at time t. The number of power storage devices. Let be the output power of the j-th energy storage device at time t. Let t be the renewable energy power generation capacity. Let t represent the active power trading volume in the market before time t. This represents the intraday trading volume imbalance.

5. The bidding strategy determination method according to claim 1, characterized in that, The method for determining day-ahead trading bidding strategies that satisfy multiple renewable energy power generation scenarios under the first power balance constraint and thermal balance constraint includes: Under the first power balance constraint and thermal balance constraint, a day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios is determined based on the objective function; The objective function includes the fuel cost of the micro generator set, the operating cost of the energy storage device, and the predicted cost of the day-ahead market.

6. The bidding strategy determination method according to claim 5, characterized in that, The expression for the objective function is: ； in, The scheduling duration for transactions completed in the previous day. The number of micro generator sets, Let be the fuel cost of the micro generator set at time t. The number of power storage devices. Let be the operating cost of the j-th energy storage device at time t. Let t represent the active power trading volume in the market before time t. Let t be the market forecast cost before time t.

7. The bidding strategy determination method according to claim 1, characterized in that, The method further includes: The target bidding strategy is submitted to the power market department to participate in the bidding, so that the power market department sends the target bidding strategy to the power grid dispatch department for line congestion verification. After receiving a line congestion test result from the power market department or the power grid dispatching department indicating no line congestion, the unit is dispatched according to the dispatching instructions sent by the power grid dispatching department based on the target bidding strategy.

8. A virtual power plant, characterized in that, The virtual power plant includes: The determination module is used to determine the day-ahead trading bidding strategy that satisfies multiple renewable energy power generation scenarios under the first power balance constraint and thermal balance constraint; The submission module is used to submit the day-ahead trading bidding strategy to the power market department for bidding; after the power market department completes the settlement based on the day-ahead trading bidding strategy, the intraday trading phase begins. The strategy optimization module is used to optimize the initial bidding strategy of the current intraday trading session in the previous intraday trading phase, taking the bidding period of the previous intraday trading as the time node, simultaneously aiming to minimize the bidding strategy cost, and under the second power balance constraint that combines the intraday imbalance trading volume, to obtain the target bidding strategy. The cost of the minimum bidding strategy is used to avoid losses caused by the inability of the internal resources integrated by the virtual power plant to fully offset the power imbalance caused by the deviation in electricity volume; the intraday imbalance trading volume is the difference between the first active power trading volume predicted the day before and the second active power trading volume predicted the latest.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the bidding strategy determination method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the bidding strategy determination method as described in any one of claims 1 to 7.

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