Double-layer bidding system and method considering influence of virtual power plant on spot market settlement

Abstract

The present application relates to a kind of double-layer bidding system and method considering the influence of virtual power plant on spot market settlement, comprising: identity authentication module, information input module, database module, data processing module, scene generation module, internal optimization module, external optimization module, result check module and result output module.The present application can solve the problem that the influence brought by the participation of virtual power plant is less considered in the current electric power spot market settlement, resulting in insufficient accuracy of settlement price.

Description

Technical Field

[0001] This invention belongs to the field of electricity spot market trading technology, and relates to a two-tier bidding system and method for virtual power plant spot market, particularly a two-tier bidding system and method that considers the impact of virtual power plants on spot market settlement. Background Technology

[0002] With the deepening of power system reform, the continuous improvement of the energy internet construction, and the large-scale grid connection of distributed power sources, the potential of user-side resources is being continuously explored, market players are diversifying, and electricity sales are trending towards direct targeting of end users. Virtual power plants can aggregate the output range and operating costs of internal resources, obtaining their internal flexibility resource output curves and aggregation cost characteristics. Therefore, virtual power plants can serve as a medium for various distributed entities to participate in electricity market transactions. Small entities that cannot participate in electricity transactions independently can participate through the aggregation effect of virtual power plants, realizing value transmission from the electricity market to the virtual power plant, and then from the virtual power plant to the internal aggregated entities. In the above situation, user-side resource consumption behavior and methods also affect market prices. However, most current studies still treat virtual power plants, which aggregate a large number of end users, as price takers in the electricity market, without fully considering the impact of virtual power plants participating in market bidding on electricity settlement prices.

[0003] A search revealed no publicly available literature of the same or similar prior art as this invention. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and propose a two-tier bidding system and method that considers the impact of virtual power plants on spot market settlement. This can solve the problem that the current electricity spot market settlement rarely considers the impact of virtual power plant participation, resulting in insufficient accuracy of settlement prices.

[0005] The present invention solves its practical problem by adopting the following technical solution:

[0006] A two-tier bidding system that considers the impact of virtual power plants on spot market settlement includes: an identity authentication module, an information input module, a database module, a data processing module, a scenario generation module, an internal optimization module, an external optimization module, a result verification module, and a result output module;

[0007] The identity authentication module is used to authenticate the identity of the virtual power plant aggregator, confirm the login to the system, and input the virtual power plant aggregator equipment information.

[0008] The output of the identity authentication module is connected to the information input module, and is used to input the electricity price, electricity volume and corresponding time information according to the market type and declaration mode selected by the virtual power plant.

[0009] The output end of the database module is connected to the information entry module, and historical data is randomly extracted from the database and output to the information entry module.

[0010] The output of the information input module is processed by a data processing module to preprocess the input electricity price, electricity volume and corresponding time information and randomly extracted historical data from the database to obtain data with consistent dimensions.

[0011] The output of the data processing module is connected to the scene generation module. It is used to randomly extract historical data from the preprocessed database, perform Latin hypercube sampling, generate a scene set, and then reduce the scene to generate a classic scene.

[0012] The output of the scenario generation module is connected to the internal optimization module. The internal optimization module is used to coordinate and optimize the internal resources of the virtual power plant based on classic scenarios, so as to minimize the internal cost of the virtual power plant and generate a variety of bidding methods.

[0013] The output of the internal optimization module is connected to the information input module, and the generated bidding method and the internal power dispatching status of the virtual power plant aggregation equipment are re-inputted into the information input module and the data processing module for data noise reduction.

[0014] The outputs of the data processing module and the scenario generation module are respectively connected to the external optimization module. The output of the external scheduling module is connected to the result verification module. The external optimization module is used to simulate the bidding situation of various entities in the day-ahead joint market based on the classic scenario set and fully consider the interaction between the virtual power plant and the market and other market entities. It outputs the winning bids of each entity in the day-ahead energy market and the winning bids of the day-ahead frequency regulation ancillary service market mileage to the result verification module.

[0015] The output of the result verification module is connected to the result output module to obtain the final virtual power plant's winning bid volume, output, and benefits in various markets.

[0016] A two-tier bidding method that considers the impact of virtual power plants on spot market settlement includes the following steps:

[0017] Step 1: Collect virtual power plant data, historical electricity market transaction data, and competitor bidding data for preprocessing;

[0018] Step 2: Based on the virtual power plant data collected in Step 1, generate a scenario set and classic scenarios using competitor marginal cost and market price curves;

[0019] Step 3: Based on classic scenarios, construct an optimized scheduling model for the internal entities of a virtual power plant and generate multiple bidding methods;

[0020] Step 4: Construct the upper-level model in the external optimization model, namely the virtual power plant participating in the electricity spot market bidding model;

[0021] Step 5: Construct the lower-level model in the external optimization model, namely the joint clearing model of the day-ahead energy market and the day-ahead frequency regulation market, and calculate the probability-weighted average of all electricity price forecast scenarios and the day-ahead market clearing price;

[0022] Step 6: Combine the upper and lower layer models from Steps 4 and 5 for optimization, and calculate the total benefits of the virtual power plant under different methods.

[0023] Furthermore, the specific method of step 1 is as follows:

[0024] Data collection includes power generation data from wind turbines and photovoltaic units, power consumption data from adjustable loads, charging and discharging behavior patterns of electric vehicles, battery performance data, and time-of-use electricity pricing data. The data is then preprocessed.

[0025] Furthermore, the specific method for step 2 is as follows:

[0026] First, based on the marginal cost and market price curves of competitors, a set of typical competitor pricing scenarios is generated using the Latin hypercube sampling method. Then, typical scenarios of typical competitor pricing are selected based on scenario reduction technology.

[0027] Furthermore, the specific steps of step 3 include:

[0028] (1) Construct the overall power balance function for the virtual power plant's external power purchase and sales:

[0029]

[0030] At time t, the total power transmitted from the virtual power plant to the grid is: Among them, the power transmitted to the grid by wind turbines, photovoltaic units, and energy storage facilities including electric vehicles are respectively Adjustable load participates in the regulation of electricity. Conversely, at time t, the power transmitted by the power grid to the virtual power plant is P. t gr,VPP The transmission power to energy storage facilities and loads is

[0031] (2) With the goal of minimizing the cost of the virtual power plant, multiple bidding methods are generated, and an internal coordination and optimization scheduling objective function for the virtual power plant is established:

[0032]

[0033] In the formula, P t VPPLet t be the power purchased and sold by the virtual power plant in the power grid at time t, which is obtained from the model operation. It is positive when purchasing electricity and negative when selling electricity. Let P be the charging and discharging power of the energy storage facility at time t. t abd Let be the amount of wind and solar power curtailed at time t. The curves for both are fitted from historical data under typical conditions. The electricity purchase and sale price, energy storage facility charging and discharging cost, load regulation cost, and wind and solar curtailment cost at time t are respectively... The criteria are determined based on market research and the specific circumstances of the aggregation entities.

[0034] (3) Analyze the characteristics of various entities within the virtual power plant and determine the constraints:

[0035] ① Output constraints of wind and solar power units

[0036]

[0037]

[0038] In the formula, the power generation of wind power and photovoltaic units at time t is... and The values ​​are all within the upper and lower limits of the unit's active power generation, and the generated power includes the power allocated to the grid. and Power delivered to internal loads and Power delivered to the energy storage unit and Power allocation is derived from model execution.

[0039] ②Power output constraints for electric vehicles.

[0040]

[0041]

[0042] In the formula, and Represents the charge / discharge state of an electric vehicle, with a constant value of 1; S t P represents the State of Charge (SOC) value of the electric vehicle battery at time period t. The SOC value of the battery at each moment is determined by the previous time period, and the battery's initial state of charge each day is the same as the state of charge at the end of the previous day. t POP E(P) represents the planned charging and discharging power of the electric vehicle at time t, derived from fitting historical charging and discharging data. t EV,c ) and E(P t EV,dc η represents the expected actual charging and discharging power of the electric vehicle, set according to market demand. c and η dcP represents the battery's discharge and charge efficiency. t BEV,dc,u ,P t BEV,dc,d ,P t BEV,dc,r These represent the up-and-down and spinning reserve power provided by the electric vehicle to the grid at time t, respectively, derived from the historical behavior patterns of the electric vehicle.

[0043] ③ Charge and discharge constraints of energy storage facilities

[0044]

[0045] and , representing the charging and discharging power of the energy storage facility at time t, respectively, obtained through model optimization. The capacity of the energy storage facility at time t has upper and lower limits. and η c and η dc , where represents the charging and discharging efficiency of the energy storage facility at time t.

[0046] ④ Adjustable load regulation power constraint.

[0047]

[0048] At time t, the total load power of the virtual power plant Power transferred from the power grid to the virtual power plant Internal wind turbine power generation Energy storage power supply The above data was obtained through model optimization. and These represent the power adjustments for increasing and decreasing the load, determined by a coefficient. and control; That is the limit of regulation.

[0049] Furthermore, the specific steps of step 4 include:

[0050] (1) The following function is constructed with the goal of maximizing day-ahead profit by combining day-ahead energy and day-ahead frequency regulation market.

[0051]

[0052] The parameters of various power generation entities and energy storage facilities that can be aggregated in the virtual power plant constitute set I, and the aggregated electricity load parameters constitute set K; where h is an expected scenario in the typical scenario set H, under this scenario, the clearing price of the day-ahead service market frequency regulation capacity, the clearing price of the day-ahead service market frequency regulation mileage, and the system marginal clearing price of the day-ahead energy market are respectively... Under the above conditions, and This represents the up / down frequency regulation capacity of the i-th unit accepted by independent system operators in the current frequency regulation market. and D represents the frequency up / down mileage of the i-th generator unit accepted by independent system operators in the current service market; i,t,h This represents the reported power volume of the i-th generating unit accepted by an independent system operator in the current trading market; l k,t,h The curve representing the application for the kth load accepted by an independent system operator in the current trading market;

[0053] (2) Virtual power plants participating in electricity spot market bidding belong to the upper-level trading model, and its constraints include:

[0054] ① Power transmission constraints in the power grid.

[0055]

[0056] At time t, the virtual power plant's electricity sales in the day-ahead energy market Electricity Purchase The difference should be less than the maximum bidding volume in the market.

[0057] ② Virtual power plant mileage adjustment constraints

[0058]

[0059] In the current service market scenario h, the frequency regulation mileage of the i-th generating unit at time t. and It should be greater than its frequency modulation power. and in and It should be less than the frequency regulation power it reported. α i It is the frequency regulation mileage multiplier, used to calculate the actual mileage of the generating unit for every 1MW increase / decrease in frequency regulation capacity. α i It is calculated by comparing the total mileage and total capacity of all frequency modulation resources in the previous cycle.

[0060] ③ Virtual power plant capacity adjustment constraints

[0061]

[0062] The sum of the frequency regulation power and the purchased / sold power reported by the generating unit in the day-ahead service market is less than the maximum charging and discharging power of the virtual power plant in the day-ahead market. and

[0063] Furthermore, the specific steps of step 5 include:

[0064] (1) The dispatching agency and the power trading center are followers in the game. They conduct day-ahead joint market clearing based on power demand and the bidding situation of each entity. The function is established with the goal of minimizing the total power purchase cost:

[0065]

[0066] U represents the collection of conventional generating units. S u,t,h , These are the bids for unit u in the electric energy market and ancillary services market for frequency regulation capacity and mileage at time t and under scenario h, respectively. These are, at time t, in scenario h, the mileage quote for subject i during charging and discharging in the electric energy market. These are the pricing quotes for frequency modulation capacity and mileage in the ancillary services market, respectively. D u,t,h This refers to the electricity volume reported by conventional generating units that has been accepted by independent system operators in the energy market recently. and This refers to the up / down frequency modulation capacity reported by conventional generating units and accepted by independent system operators in the frequency modulation market. and This refers to the frequency up / down mileage of generator units accepted by independent system operators in the current service market.

[0067] (2) The above objective function must meet five constraints, including supply and demand balance and safety constraints of various units.

[0068] ①Power balance constraints in the electricity market

[0069]

[0070] L t Let t be the system load at time t.

[0071] ② Power constraints of conventional units

[0072]

[0073] The output P of unit u at time t u,t,h Subject to constraints. g,min and P g,max These represent the maximum and minimum output of the unit, respectively.

[0074] ③ System frequency regulation capacity constraints

[0075]

[0076] Virtual power plant wins bid for up / down frequency regulation capacity at time t Up / down frequency regulation capacity compared to conventional units The sum of these values ​​represents the system's up / down frequency modulation capacity.

[0077] ④ System frequency regulation mileage constraints

[0078]

[0079] Virtual power plant winning bid for frequency regulation mileage at time t The up / down frequency regulation mileage won in the bid for conventional units The sum of these is the system's up / down frequency modulation mileage.

[0080] ⑤ Network security constraints

[0081] f min ≤f≤f max

[0082] The power flow of the line at time t is limited by the transmission capacity of the line.

[0083] Advantages and beneficial effects of the present invention:

[0084] This invention addresses the problem of insufficient accuracy in current electricity spot market settlements due to a lack of consideration for the impact of virtual power plant participation. It proposes a two-tiered bidding system and method for the virtual power plant spot market that considers settlement price changes. Based on generated classic scenarios, it fully considers the multi-entity characteristics of virtual power plant aggregation and the mutual influence between virtual power plants and other market participants. It takes into account the changes in spot market settlement prices after virtual power plant participation, thereby improving the accuracy of the two-tiered bidding system for the virtual power plant spot market and facilitating the optimization of methods for virtual power plant participation in the electricity spot market's two-tiered bidding process. Attached Figure Description

[0085] Figure 1 This is a structural diagram of the virtual power plant spot market two-tier bidding system that takes into account changes in settlement prices, as described in this invention.

[0086] Figure 2 This is a flowchart of the virtual power plant spot market two-tier bidding method that takes into account changes in settlement prices, as described in this invention.

[0087] Figure 3 This is the improved wiring diagram of the IEEE 30-node system of the present invention;

[0088] Figure 4 This is a diagram showing the charging and discharging power of each component of the present invention;

[0089] Figure 5 This is the optimized load diagram within the virtual power plant of this invention;

[0090] Figure 6 This is a schematic diagram showing the winning bids of the various entities involved in this invention in the day-to-day energy market;

[0091] Figure 7 This is a chart showing the optimal pricing of the VPP of this invention in various markets;

[0092] Figure 8 This is the optimal sales volume chart of the VPP of this invention in various markets. Detailed Implementation

[0093] The present invention will be further described in detail below with reference to the accompanying drawings:

[0094] A two-tiered bidding system for a virtual power plant spot market that takes into account changes in settlement prices, such as Figure 1 As shown, it includes: identity authentication module, information entry module, database module, data processing module, scene generation module, internal optimization module, external optimization module, result verification module, and result output module;

[0095] The identity authentication module is used to authenticate the identity of the virtual power plant aggregator, confirm the login to the system, and input the virtual power plant aggregator equipment information.

[0096] The output of the identity authentication module is connected to the information input module, and is used to input the electricity price, electricity volume and corresponding time information according to the market type and declaration mode selected by the virtual power plant.

[0097] The output end of the database module is connected to the information entry module. Historical data is randomly extracted from the database, including information on market competitors, competitor quotations, load conditions, and market prices, and then output to the information entry module.

[0098] The output of the information input module is processed by a data processing module to preprocess the input electricity price, electricity volume and corresponding time information and randomly extracted historical data from the database to obtain data with consistent dimensions.

[0099] The output of the data processing module is connected to the scene generation module. It is used to randomly extract historical data from the preprocessed database, perform Latin hypercube sampling, generate a scene set, reduce the scene to generate a classic scene, and use the classic scene set for subsequent internal optimization of the virtual power plant.

[0100] The output of the scenario generation module is connected to the internal optimization module. The internal optimization module is used to coordinate and optimize the internal resources of the virtual power plant based on classic scenarios, so as to minimize the internal cost of the virtual power plant and generate a variety of bidding methods.

[0101] The output of the internal optimization module is connected to the information input module, and the generated bidding method and the internal power dispatching status of the virtual power plant aggregation equipment are re-inputted into the information input module and the data processing module for data noise reduction.

[0102] The outputs of the data processing module and the scenario generation module are respectively connected to the external optimization module. The output of the external scheduling module is connected to the result verification module. The external optimization module is used to simulate the bidding situation of various entities in the day-ahead joint market based on the classic scenario set and fully consider the interaction between the virtual power plant and the market and other market entities. It outputs the winning bid situation of each entity in the day-ahead energy market and the winning bid situation of the day-ahead frequency regulation ancillary service market mileage to the result verification module.

[0103] The output of the result verification module is connected to the result output module to obtain the final virtual power plant's winning bid volume, output, and benefits in various markets.

[0104] A two-tiered bidding method for the virtual power plant spot market that takes into account changes in settlement prices, such as... Figure 2 As shown, it includes the following steps:

[0105] Step 1: Collect and preprocess virtual power plant data, historical electricity market transaction data, and competitor bidding data;

[0106] The specific method for step 1 is as follows:

[0107] Data on power generation from wind turbines and photovoltaic units, power consumption data from adjustable loads, charging and discharging behavior patterns of electric vehicles, battery performance, and time-of-use pricing are collected and preprocessed.

[0108] Step 2: Based on the competitor marginal cost and market price curves collected in Step 1, generate a set of scenarios and classic scenarios;

[0109] The specific method for step 2 is as follows:

[0110] First, based on the marginal cost and market price curves of competitors, a set of typical competitor pricing scenarios is generated using the Latin hypercube sampling method. Then, typical scenarios of typical competitor pricing are selected based on scenario reduction technology.

[0111] Step 3: Based on classic scenarios, construct an optimized scheduling model for the internal entities of the virtual power plant, generate multiple bidding methods, and provide data reference for the next stage of optimization;

[0112] The specific steps of step 3 include:

[0113] (1) Construct the overall power balance function for the virtual power plant's external power purchase and sales:

[0114]

[0115] At time t, the total power transmitted from the virtual power plant to the grid is P. t VPP,grAmong them, the power transmitted to the grid by wind turbines, photovoltaic units, and energy storage facilities, including electric vehicles, were respectively Adjustable load participates in the regulation of electricity. Conversely, at time t, the power transmitted by the power grid to the virtual power plant is P. t gr,VPP The transmission power to energy storage facilities and loads is

[0116] (2) With the goal of minimizing the cost of the virtual power plant, multiple bidding methods are generated, and an internal coordination and optimization scheduling objective function for the virtual power plant is established:

[0117]

[0118] In the formula, P t VPP Let t be the power purchased and sold by the virtual power plant in the power grid at time t, which is obtained from the model operation. It is positive when purchasing electricity and negative when selling electricity. Let P be the charging and discharging power of the energy storage facility at time t. t abd Let be the amount of wind and solar power curtailed at time t. The curves for both are fitted from historical data under typical conditions. The electricity purchase and sale price, energy storage facility charging and discharging cost, load regulation cost, and wind and solar curtailment cost at time t are respectively... It is determined by market research and the specific circumstances of the aggregation entities.

[0119] (3) Analyze the characteristics of various entities within the virtual power plant and determine the constraints:

[0120] a. Output constraints of wind and solar power units

[0121]

[0122]

[0123] In the formula, the power generation of wind power and photovoltaic units at time t is... and The values ​​are all within the upper and lower limits of the unit's active power generation, and the generated power includes the power allocated to the grid. and Power delivered to internal loads and Power delivered to the energy storage unit and Power allocation is derived from model execution.

[0124] b. Output constraints of electric vehicles.

[0125]

[0126]

[0127] In the formula, and Represents the charge / discharge state of an electric vehicle, with a constant value of 1; S t P represents the State of Charge (SOC) value of the electric vehicle battery at time period t. The SOC value of the battery at each moment is determined by the previous time period, and the battery's initial state of charge each day is the same as the state of charge at the end of the previous day. t POP E(P) represents the planned charging and discharging power of the electric vehicle at time t, derived from fitting historical charging and discharging data. t EV,c ) and E(P t EV,dc η represents the expected actual charging and discharging power of the electric vehicle, set according to market demand. c and η dc P represents the battery's discharge and charge efficiency. t BEV,dc,u ,P t BEV,dc,d ,P t BEV,dc,r These represent the up-and-down and spinning reserve power provided by the electric vehicle to the grid at time t, respectively, derived from the historical behavior patterns of the electric vehicle.

[0128] c. Charge and discharge constraints of energy storage facilities

[0129]

[0130] and , representing the charging and discharging power of the energy storage facility at time t, respectively, obtained through model optimization. The capacity of the energy storage facility at time t has upper and lower limits. and η c and η dc , where represents the charging and discharging efficiency of the energy storage facility at time t.

[0131] d. Adjustable load regulation power constraint.

[0132]

[0133] At time t, the total load power of the virtual power plant Power transferred from the power grid to the virtual power plant Internal wind turbine power generation Energy storage power supply The above data was obtained through model optimization. and These represent the power adjustments for increasing and decreasing the load, determined by a coefficient. and control; That is the limit of regulation.

[0134] Step 4: Construct the upper-level model in the external optimization model, namely the virtual power plant participating in the electricity spot market bidding model;

[0135] The specific steps of step 4 include:

[0136] Factors influencing the trend of the day-ahead market price expectation curve include: the day-ahead market price expectation curve under different scenarios and the pricing methods of competitors. Based on this, one should adjust one's own pricing method to achieve the goal of virtual power plants participating in the spot retail market.

[0137] (1) The following function is constructed with the goal of maximizing day-ahead profit by combining day-ahead energy and day-ahead frequency regulation market.

[0138]

[0139] The parameters of various power generation entities and energy storage facilities that can be aggregated in a virtual power plant constitute set I, and the aggregated electricity load parameters constitute set K. Here, h is an expected scenario in the typical scenario set H, where the clearing price of the day-ahead service market frequency regulation capacity, the clearing price of the day-ahead service market frequency regulation mileage, and the system marginal clearing price of the day-ahead energy market within time t are respectively... Under the above conditions, and This represents the up / down frequency regulation capacity of the i-th unit accepted by independent system operators in the current frequency regulation market. and D represents the frequency up / down mileage of the i-th generator unit accepted by independent system operators in the current service market; i,t,h This represents the reported power volume of the i-th generating unit accepted by an independent system operator in the current trading market; l k,t,h This represents the application curve for the kth load accepted by an independent system operator in the current trading market.

[0140] (2) Virtual power plants participating in electricity spot market bidding belong to the upper-level trading model, and the relevant constraints include the following three points.

[0141] (1) Power transmission constraints of the power grid.

[0142]

[0143] At time t, the virtual power plant's electricity sales in the day-ahead energy market Electricity Purchase The difference should be less than the maximum bidding volume in the market.

[0144] (2) Virtual power plant mileage adjustment constraints

[0145]

[0146] In the current service market scenario h, the frequency regulation mileage of the i-th generating unit at time t. and It should be greater than its frequency modulation power. and in and It should be less than the frequency regulation power it reported. α i It is the frequency regulation mileage multiplier, used to calculate the actual mileage of the generating unit for every 1MW increase / decrease in frequency regulation capacity. α i It is calculated by comparing the total mileage and total capacity of all frequency modulation resources in the previous cycle.

[0147] (3) Virtual power plant capacity adjustment constraints

[0148]

[0149] The sum of the frequency regulation power and the purchased / sold power reported by the generating unit in the day-ahead service market is less than the maximum charging and discharging power of the virtual power plant in the day-ahead market. and

[0150] Step 5: Construct the lower-level model in the external optimization model, namely the joint clearing model of the day-ahead energy market and the day-ahead frequency regulation market, and calculate the probability-weighted average of all electricity price forecast scenarios and the day-ahead market clearing price;

[0151] The specific steps of step 5 include:

[0152] (1) The dispatching agency and the power trading center are followers in the game. They conduct day-ahead joint market clearing based on power demand and the bidding situation of each entity, and establish a function with the goal of minimizing the total power purchase cost.

[0153]

[0154] U represents the collection of conventional generating units. S u,t,h , These are the bids for unit u in the electric energy market and ancillary services market for frequency regulation capacity and mileage at time t and under scenario h, respectively. These are, at time t, in scenario h, the mileage quote for subject i during charging and discharging in the electric energy market. These are the pricing quotes for frequency modulation capacity and mileage in the ancillary services market, respectively. D u,t,h This refers to the electricity volume reported by conventional generating units that has been accepted by independent system operators in the energy market recently. and This refers to the up / down frequency modulation capacity reported by conventional generating units and accepted by independent system operators in the frequency modulation market. and This refers to the frequency up / down mileage of generator units accepted by independent system operators in the current service market.

[0155] (2) The above objective function must meet five constraints, including supply and demand balance and safety constraints of various units.

[0156] ①Power balance constraints in the electricity market

[0157]

[0158] L t Let t be the system load at time t.

[0159] ② Power constraints of conventional units

[0160]

[0161] The output P of unit u at time t u,t,h Subject to constraints. g,min and P g,max These represent the maximum and minimum output of the unit, respectively.

[0162] ③ System frequency regulation capacity constraints

[0163]

[0164] Virtual power plant wins bid for up / down frequency regulation capacity at time t Up / down frequency regulation capacity compared to conventional units The sum of these values ​​represents the system's up / down frequency modulation capacity.

[0165] ④ System frequency regulation mileage constraints

[0166]

[0167] Virtual power plant winning bid for frequency regulation mileage at time t The up / down frequency regulation mileage won in the bid for conventional units The sum of these is the system's up / down frequency modulation mileage.

[0168] ⑤ Network security constraints

[0169] f min ≤f≤f max

[0170] The power flow of the line at time t is limited by the transmission capacity of the line.

[0171] Step 6: Combine the upper and lower layer models from Steps 4 and 5 for optimization, and calculate the total benefits of the virtual power plant under different methods.

[0172] In this embodiment, based on the above functions, a more accurate two-layer bidding system for the virtual power plant spot market is constructed. Based on a nonlinear, mixed-integer optimization problem-solving method, Matlab is used as the tool to solve the method that maximizes the total output benefit.

[0173] The invention will be further illustrated below with specific examples:

[0174] This invention selects a virtual power plant that aggregates wind power, photovoltaic units, energy storage facilities, and residential users in a certain area, and constructs a system based on IEEE 30 nodes as a case study. The overall wiring configuration is as follows: Figure 3 As shown, the characteristic parameters of the generating units are listed in Appendix 1, and the virtual power plant is connected to node 25. Daily load, wind and solar turbine output, and electricity market price forecasts are shown in Appendix 2-4. Energy storage facility performance is shown in Table 5.

[0175] 1. After inputting the above data into the system, the system first performs a virtual power plant operation to minimize operating costs and optimize operation, then outputs the charging and discharging power of each component, such as... Figure 4 As shown, the load within the virtual power plant is as follows: Figure 5 As shown in Appendix 6, the output system's up and down frequency regulation mileage multipliers are shown in Appendix 6, and the unit's frequency regulation market price information is shown in Appendix 7.

[0176] 2. The system will re-input the above output data for external optimization of the virtual power plant. The system simulates the bidding situation of various market participants in the day-ahead joint market, and outputs the winning bids for each participant in the day-ahead energy market, the winning bids for mileage in the day-ahead frequency regulation ancillary services market, etc. Figure 6 As shown in the diagram. In this case, the virtual power plant primarily participates in frequency regulation ancillary services.

[0177] 3. Based on the above optimizations, multiple scenarios meeting expectations were output. The scenario maximizing the benefits of the virtual power plant was identified as the optimal bidding method, provided to aggregators for reference. The optimal bid and optimal quantity for the virtual power plant in each market are as follows: Figure 7 and Figure 8 As shown, at different times, the virtual power plant reports different electricity prices based on market demand.

[0178] Based on the output results, in this example, the final method for the virtual power plant is that the discharge price is always higher than the charging price, the frequency regulation capacity price fluctuates along a straight line, and the frequency regulation mileage price remains unchanged. Furthermore, the virtual power plant charges its internal energy storage device during periods of low load and discharges it to the outside during periods of high load. This invention can be adapted to various types of power systems. Considering the impact of virtual power plant participation in the electricity market on spot price settlement, the accuracy of virtual power plant participation in the two-tier bidding process in the electricity market is optimized. By inputting relevant parameters and optimizing operation, a spot market bidding method that maximizes the benefits of virtual power plants is found.

[0179] Appendix 1 Parameters of Conventional Generating Units

[0180]

[0181] Note: P g,max P g,min D g,max The unit is MW. K1, K2, and K3 are the cost characteristic coefficients of conventional units, with the unit being yuan / MW.

[0182] Appendix 2: Daily Load Information (MWh)

[0183]

[0184] Appendix 3: Power output of wind and solar turbines (MWh)

[0185]

[0186]

[0187] Appendix: Electricity Price Forecast 4 Days Ago (RMB / MWh)

[0188]

[0189] Appendix 5 Energy Storage Facility Parameters

[0190]

[0191] Appendix 6: System Up- and Down-Frequency Modulation Mileage Multipliers

[0192] T α T α 1 4.0 13 1.5 2 4.2 14 1.8 3 4.5 15 2.3 4 4.3 16 2.7 5 4.0 17 2.9 6 3.9 18 3.1 7 3.2 19 3.2 8 3.0 20 3.3 9 2.8 21 3.5 10 2.4 22 3.5 11 2.1 23 3.7 12 1.6 24 3.9

[0193] Appendix 7: Market Price Information for Unit Frequency Regulation

[0194] parameter G1 G2 G3 G4 G5 G6 Virtual power plant Frequency modulation capacity price / yuan 110 100 80 70 65 60 95 FM mileage quote / yuan 4.5 5.8 6.0 5.0 4.5 4.3 3.0

[0195] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A two-tier bidding system that considers the impact of virtual power plants on spot market settlement, characterized in that: include: The module includes an identity authentication module, an information entry module, a database module, a data processing module, a scene generation module, an internal optimization module, an external optimization module, a result verification module, and a result output module. The identity authentication module is used to authenticate the identity of the virtual power plant aggregator, confirm the login to the system, and input the virtual power plant aggregator equipment information. The output of the identity authentication module is connected to the information input module, and is used to input the electricity price, electricity volume and corresponding time information according to the market type and declaration mode selected by the virtual power plant. The output end of the database module is connected to the information entry module, and historical data is randomly extracted from the database and output to the information entry module. The output of the information input module is processed by a data processing module to preprocess the input electricity price, electricity volume and corresponding time information and randomly extracted historical data from the database to obtain data with consistent dimensions. The output of the data processing module is connected to the scene generation module. It is used to randomly extract historical data from the preprocessed database, perform Latin hypercube sampling, generate a scene set, and then reduce the scene to generate a classic scene. The output of the scenario generation module is connected to the internal optimization module. The internal optimization module is used to coordinate and optimize the internal resources of the virtual power plant based on classic scenarios, so as to minimize the internal cost of the virtual power plant and generate a variety of bidding methods. The output of the internal optimization module is connected to the information input module, and the generated bidding method and the internal power dispatching status of the virtual power plant aggregation equipment are re-inputted into the information input module and the data processing module for data noise reduction. The outputs of the data processing module and the scenario generation module are respectively connected to the external optimization module. The output of the external optimization module is connected to the result verification module. The external optimization module is used to simulate the bidding situation of various entities in the day-ahead joint market based on the classic scenario set and fully consider the interaction between the virtual power plant and the market and other market entities. It outputs the winning bids of each entity in the day-ahead energy market and the winning bids of the day-ahead frequency regulation ancillary service market mileage to the result verification module. The output of the result verification module is connected to the result output module to obtain the final virtual power plant's winning bid volume, output, and benefits in various markets; The bidding method of the two-tier bidding system that considers the impact of virtual power plants on spot market settlement includes the following steps: Step 1: Collect and preprocess virtual power plant data, historical electricity market transaction data, and competitor bidding data; Step 2: Based on the competitor marginal cost and market price curves collected in Step 1, generate a set of scenarios and classic scenarios; Step 3: Based on classic scenarios, construct an optimized scheduling model for the internal entities of a virtual power plant and generate multiple bidding methods; Step 4: Construct the upper-level model in the external optimization model, namely the virtual power plant participating in the electricity spot market bidding model; Step 5: Construct the lower-level model in the external optimization model, namely the joint clearing model of the day-ahead energy market and the day-ahead frequency regulation market, and calculate the probability-weighted average of all electricity price forecast scenarios and the day-ahead market clearing price; Step 6: Combine the upper and lower layer models from Steps 4 and 5 for optimization, and calculate the total benefits of the virtual power plant under different methods; The specific method for step 2 is as follows: First, based on the marginal cost and market price curves of competitors, a typical competitor pricing scenario set is generated using the Latin hypercube sampling method. Then, typical scenarios of typical competitor pricing are selected based on scenario reduction technology. The specific steps of step 3 include: (1) Construct the overall power balance function for the virtual power plant's external power purchase and sales: At time t, the total power transmitted from the virtual power plant to the grid is P. t VPP,gr Among them, the power transmitted to the grid by wind turbines, photovoltaic units, and energy storage facilities, including electric vehicles, were respectively Adjustable load participates in regulating power consumption. Conversely, at time t, the power transmitted by the power grid to the virtual power plant is P. t gr,VPP The transmission power to energy storage facilities and loads is (2) With the goal of minimizing the cost of the virtual power plant, multiple bidding methods are generated, and an objective function for internal coordination and optimization scheduling of the virtual power plant is established: In the formula, P t VPP Let t be the power purchased and sold by the virtual power plant in the power grid at time t, which is obtained from the model operation. It is positive when purchasing electricity and negative when selling electricity. Let P be the charging and discharging power of the energy storage facility at time t. t abd Let be the amount of wind and solar power curtailed at time t. The curves for both are fitted from historical data under typical conditions. The electricity purchase and sale price, energy storage facility charging and discharging cost, load regulation cost, and wind and solar curtailment cost at time t are respectively... The criteria are determined based on market research and the specific circumstances of the aggregation entities. (3) Analyze the characteristics of various entities within the virtual power plant and determine the constraints: ① Output constraints of wind and solar power units In the formula, the power generation of wind power and photovoltaic units at time t is... and The values ​​are all within the upper and lower limits of the unit's active power generation, and the generated power includes the power allocated to the grid. and Power delivered to internal loads and Power delivered to the energy storage unit and Power allocation is derived from model execution; ② Output constraints of electric vehicles: In the formula, and Represents the charge / discharge state of an electric vehicle, with a constant value of 1; S t P represents the State of Charge (SOC) value of the electric vehicle battery at time period t. The SOC value of the battery at each moment is determined by the previous time period, and the battery's initial state of charge each day is the same as the state of charge at the end of the previous day. t POP E(P) represents the planned charging and discharging power of the electric vehicle at time t, derived from fitting historical charging and discharging data. t EV,c ) and E(P t EV ,dc η represents the expected actual charging power and discharge power of the electric vehicle, respectively, set according to market demand. c and η dc These represent the charging and discharging efficiencies of the battery, respectively; P t BEV,dc,u ,P t BEV,dc,d ,P t BEV,dc,r These represent the power supplied by the electric vehicle to the grid at time t, including the up-down and spinning reserves, respectively, derived from the historical behavior patterns of the electric vehicle. ③ Charge and discharge constraints of energy storage facilities: and , representing the charging and discharging power of the energy storage facility at time t, respectively, are derived from model optimization. Let t be the capacity of the energy storage facility, with upper and lower limits. and η c and η dc , respectively, represent the charging and discharging efficiencies of the energy storage facility at time t; ④ Adjustable load regulation power constraint: At time t, the total load power of the virtual power plant Power transferred from the power grid to the virtual power plant Internal wind turbine power generation Energy storage power supply The above data was obtained through model optimization. and These represent the power adjustments for increasing and decreasing the load, determined by a coefficient. and control; It is the limit of regulation; The specific method for step 1 is as follows: The data collected includes: power generation data from wind turbines and photovoltaic units, power consumption data from adjustable loads, charging and discharging behavior patterns of electric vehicles, battery performance, and time-of-use electricity price data, and the data is preprocessed. The specific steps of step 4 include: (1) The following function is constructed with the objective of maximizing day-ahead profit by combining day-ahead energy and day-ahead frequency regulation market; The parameters of various power generation entities and energy storage facilities that can be aggregated in the virtual power plant constitute set I, and the aggregated electricity load parameters constitute set K; where h is an expected scenario in the typical scenario set H, under this scenario, the clearing price of the day-ahead service market frequency regulation capacity, the clearing price of the day-ahead service market frequency regulation mileage, and the system marginal clearing price of the day-ahead energy market are respectively... Under the above conditions, and This represents the up / down frequency regulation capacity of the i-th unit accepted by independent system operators in the current frequency regulation market. and D represents the up / down frequency regulation mileage of the i-th unit accepted by independent system operators in the current service market; i,t,h This represents the reported power volume of the i-th generating unit accepted by an independent system operator in the current trading market; l k,t,h The curve representing the application for the kth load accepted by an independent system operator in the current trading market; (2) Virtual power plants participating in electricity spot market bidding belong to the upper-level trading model, and its constraints include: ① Power transmission constraints of the power grid: At time t, the virtual power plant's electricity sales in the day-ahead energy market Electricity Purchase The difference should be less than the maximum bidding volume in the market. ② Virtual power plant mileage adjustment constraints In the current service market scenario h, the frequency regulation mileage of the i-th generating unit at time t. and It should be greater than its frequency modulation power. and in and It should be less than the frequency modulation power it reports; α i It is the frequency regulation mileage multiplier, used to calculate the actual mileage of the generating unit for every 1MW increase / decrease in frequency regulation capacity; α i It is calculated by comparing the total mileage and total capacity of all frequency modulation resources in the previous cycle; ③ Virtual power plant capacity adjustment constraints The sum of the frequency regulation power and the purchased / sold power reported by the generating unit in the day-ahead service market is less than the maximum charging and discharging power of the virtual power plant in the day-ahead market. and The specific steps of step 5 include: (1) The dispatching agency and the power trading center are followers in the game. They conduct day-ahead joint market clearing based on power demand and the bidding situation of each entity. The function is established with the goal of minimizing the total power purchase cost: U represents the collection of conventional generating units; S u,t,h , These are the bids for unit u in the electric energy market and ancillary services market for frequency regulation capacity and mileage at time t and under scenario h, respectively. These are, at time t, in scenario h, the mileage price of subject i during charging and discharging in the electric energy market. These are the pricing quotes for frequency modulation capacity and mileage in the ancillary services market, respectively; D u,t,h This refers to the electricity volume reported by conventional generating units that has been accepted by independent system operators in the energy market recently. and This refers to the up / down frequency modulation capacity reported by conventional generating units and accepted by independent system operators in the frequency modulation market. and This refers to the frequency up / down mileage of generator units currently accepted by independent system operators in the service market; (2) The above objective function must satisfy the supply and demand balance and the safety constraints of various units: ①Power balance constraints in the electricity market L t Let t be the magnitude of the system load. ② Power constraints of conventional units The output P of unit u at time t u,t,h Subject to constraints; P g,min and P g,max These are the maximum and minimum output of the unit, respectively; ③ System frequency regulation capacity constraints Virtual power plant wins bid for up / down frequency regulation capacity at time t Up / down frequency regulation capacity compared to conventional units The sum of these values ​​represents the system's up / down frequency modulation capacity. ④ System frequency regulation mileage constraints Virtual power plant winning bid for frequency regulation mileage at time t The up / down frequency regulation mileage won in the bid for conventional units The sum of these is the system's up / down frequency modulation mileage. ⑤ Network security constraints f min ≤f≤f max The power flow of the line at time t is limited by the transmission capacity of the line.

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