A low-carbon economic dispatching method and system for a hybrid energy storage virtual power plant considering multiple uncertainties

Abstract

The application discloses a kind of considering multiple uncertainties mixed energy storage virtual power plant low-carbon economic dispatching method and system, it is related to electric power system optimization scheduling field, including: design pumped storage-flywheel-electrochemical mixed energy storage layered collaborative control mechanism, establish carbon capture-electricity to gas equipment dynamic collaborative operation model and green certificate-carbon trading dynamic coupling model, construct day-ahead-intra-day two-stage rolling optimization framework, develop mixed energy storage capacity optimization configuration model.The application can fully tap the complementary characteristics of multi-time scale energy storage and the collaborative potential of low-carbon technology, realize the dual promotion of virtual power plant economy and environmental protection.

Description

Technical Field

[0001] This invention relates to the field of power system optimization dispatch and renewable energy consumption technology, and more specifically to a low-carbon economic dispatch method and system that considers multiple uncertainties and includes a virtual power plant with hybrid energy storage. Background Technology

[0002] Virtual power plants (VPPs), as advanced energy management platforms integrating distributed renewable energy, energy storage systems, and flexible loads, play a crucial role in enhancing grid flexibility and promoting low-carbon transformation. However, the strong random volatility caused by high-proportion wind and solar grid connections, the coupling of multiple uncertainties between source and load, and insufficient synergy in carbon reduction technologies have become core bottlenecks restricting the economical and environmentally friendly operation of VPPs.

[0003] The current virtual power plant dispatch faces three major challenges: 1) Low efficiency in smoothing wind and solar power fluctuations: Traditional single energy storage technologies are limited by time scale constraints. For example, pumped storage has a slow response speed (minutes) and limited electrochemical energy storage capacity, making it difficult to balance instantaneous power fluctuations and long-term energy balance, resulting in high wind and solar curtailment rates; 2) Difficulty in coordinating multiple uncertainties: Wind and solar power output prediction deviations, load fluctuations, and green certificate / carbon trading market price disturbances are superimposed, and existing models have not established a market-source-load dynamic coupling mechanism, resulting in inaccurate energy storage configuration and trading strategies; 3) Isolated operation of low-carbon technologies: Carbon capture systems rely on high-carbon power supply from the grid to indirectly increase emissions, power-to-gas (P2G) equipment lacks stable green power drive, and the quantitative contribution of green certificate trading to direct carbon emission reduction has not been effectively captured.

[0004] Therefore, there is an urgent need to build an optimized framework that integrates "frequency-division energy storage regulation, carbon cycle coordination, and market revenue closed loop" to overcome multiple uncertainties and achieve synergistic improvement in the economic efficiency and environmental protection of virtual power plants. Summary of the Invention

[0005] In view of this, the present invention provides a low-carbon economic dispatch method and system for virtual power plants with hybrid energy storage that takes into account multiple uncertainties, thereby solving the problems existing in the background technology.

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

[0007] A low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties includes the following steps:

[0008] S1. Construct a virtual power plant system structure. The virtual power plant system includes a wind power generation unit, a photovoltaic power generation unit, a carbon capture power plant, an electricity-to-gas conversion device, a pumped storage unit, a flywheel energy storage unit, an electrochemical energy storage unit, and loads.

[0009] S2. Design a layered and synergistic regulation mechanism for pumped hydro storage-flywheel-electrochemical hybrid energy storage;

[0010] S3. Establish a dynamic collaborative operation model for carbon capture-electricity-to-gas equipment and a dynamic coupling model for green certificates-carbon trading to quantify the contribution of green certificate trading to direct carbon emission reduction.

[0011] S4. Construct a two-stage rolling optimization scheduling model for day-ahead and intraday periods, with the goal of maximizing net operating revenue, and coordinately optimize energy storage charging and discharging strategies, carbon capture energy consumption and capture strategies, power-to-gas equipment energy consumption and CO2 consumption strategies, and market trading strategies.

[0012] S5. Solve the two-stage rolling optimization scheduling model from day-ahead to day-intraday, output the optimal scheduling command, and control the operation of each unit in the virtual power plant system.

[0013] Optionally, S2 specifically includes the following steps:

[0014] S21. An improved adaptive noise complete set empirical mode decomposition method is used to decompose the wind power sequence into high-frequency, mid-frequency and low-frequency components.

[0015] S22. Distribute high-frequency components to flywheel energy storage units for instantaneous power regulation, distribute medium-frequency components to electrochemical energy storage units for minute-level power compensation, and distribute low-frequency components to pumped storage units or coordinate with the grid for long-term energy balance regulation.

[0016] S23. With the goal of minimizing the operating cost of the virtual power plant, the optimal configuration of the capacity of the hybrid energy storage system is achieved through iterative optimization.

[0017] Optionally, in S21, the specific steps of frequency decomposition are as follows:

[0018] First-stage decomposition: Perform ICEEMDAN decomposition on the original wind and solar power sequence to obtain the intrinsic mode function components;

[0019] Calculate the average instantaneous frequency of each intrinsic mode function component. and the overall mean d;

[0020] A first frequency division threshold is set, and eigenmode function components with an average instantaneous frequency higher than the first frequency division threshold are classified as high-frequency components P. high The eigenmode function components with an average instantaneous frequency lower than the first frequency division threshold are classified as low-frequency components P. low ;

[0021] By correcting the residuals, we ensure that the sum of the divided power is equal to the original power P. W :

[0022] P W =Phigh +P low +r k

[0023] In the formula: r k Represents the residual;

[0024] Second-stage decomposition: The P obtained in the first stage... high The ICEEMDAN decomposition is performed again, and the results are screened and reconstructed based on the corresponding average instantaneous frequency threshold. The IMF components with average instantaneous frequencies higher than the second frequency division threshold are superimposed, which correspond to flywheel energy storage. The IMF components with average instantaneous frequencies between the first and second frequency division thresholds are superimposed to obtain the mid-to-high frequency components, which correspond to electrochemical energy storage.

[0025] Optionally, in S23, the optimal configuration of the hybrid energy storage system capacity is achieved through iterative optimization, specifically as follows:

[0026] During the power smoothing phase, based on actual operating data on a 15-minute timescale, the ICEEMDAN method is used to decompose wind power fluctuations and photovoltaic power fluctuations. The cross-frequency threshold is set according to a preset offset rate to obtain the power component that needs to be regulated by hybrid energy storage.

[0027] In the economic optimization stage, with the goal of minimizing costs, the power components that require hybrid energy storage regulation are substituted into the objective function, which includes energy storage cost indicators, for iterative optimization.

[0028] Optional energy storage cost indicators include: energy storage equivalent daily cycle life, electrochemical energy storage operation and maintenance cost, and flywheel energy storage operation and maintenance cost.

[0029] Optionally, in S3, the expression for the dynamic coordinated operation model of the carbon capture-electricity-to-gas conversion equipment is:

[0030]

[0031] In the formula: k CS The fixed price per unit of CO2 stored, in yuan / t; The unit price for carbon trading is yuan / ton; Let t be the amount of CO2 sequestered by carbon at time t; Let t represent the CO2 emitted into the atmosphere by the virtual power plant during time period t; The CO2 emission allowance allocated to the virtual power plant during time period t; γ C The baseline CO2 emission allowance per unit of electricity is t / (MW·h); The CO2-related costs of the virtual power plant during time period t; CO2 purchased by P2G devices during time period t, t; P tG For the carbon capture plant's output, MW;

[0032] The expression for the dynamic coupling model of green certificates and carbon trading is:

[0033]

[0034] In the formula: k gre The unit price for green certificate trading; P t gre P represents the total grid-connected power of wind and solar power generation during time period t, in MW; t re The renewable energy consumption weight of the virtual power plant in time period t, in MW; Let t be the revenue obtained by the virtual power plant from participating in green certificate trading.

[0035] Optionally, in S3, the dynamic collaborative operation model of the carbon capture-electricity-to-gas conversion equipment satisfies the following constraints:

[0036] CO2 capture limits at carbon capture plants:

[0037]

[0038] In the formula: E represents the carbon emissions of the carbon capture plant during period t. G Carbon intensity of the carbon capture plant, t / MW;

[0039] Relationship between CO2 consumption and methane production in P2G plants:

[0040]

[0041] V t P2G =3.6η P2G P t P2G / H g

[0042] In the formula: Let t represent the amount of CO2 consumed by the P2G device during time period t. The amount of CO2 required to generate a unit volume of methane for a P2G plant, t / MW; η P2G For the conversion efficiency of P2G devices; H g The calorific value of natural gas is taken as 39 MJ / m³. 3 ;P t P2G Let V be the energy consumption of the P2G device during time period t, expressed in MW; t P2G Let m be the volume of methane generated by the P2G device during time period t. 3 .

[0043] Optionally, S4 specifically includes the following steps:

[0044] S41. During the day-ahead scheduling phase, based on the day-ahead forecast data of wind power, photovoltaic power and load, establish a source-storage-carbon collaborative optimization model, and formulate pumped storage start-up and shutdown plans, reservoir water level benchmarks, output plans for each unit and green certificate-carbon trading plans.

[0045] S42. During the intraday scheduling phase, based on the day-ahead plan, rolling optimization is performed by combining updated data of wind power, photovoltaic, and load ultra-short-term forecasts; the scheduling plan for the next 4 hours is solved with an execution time resolution of 15 minutes; at the same time, wind power, photovoltaic, and load forecast error models are introduced.

[0046] Optionally, in S4, the day-to-day two-stage rolling optimization scheduling model satisfies the following operational constraints: power balance constraints, wind and solar power output constraints, pumped storage reservoir capacity constraints, electrochemical energy storage charging and discharging power and SOC constraints, flywheel energy storage speed and energy constraints, upper and lower limits and ramping constraints of carbon capture power plant and carbon capture system output, P2G equipment operation constraints, and grid interconnection line power constraints.

[0047] The present invention also discloses a system for implementing the low-carbon economic dispatch method of virtual power plants with hybrid energy storage that considers multiple uncertainties as described in any of the above claims, comprising:

[0048] The system construction module is used to build the structure of the virtual power plant system. The virtual power plant system includes wind power generation units, photovoltaic power generation units, carbon capture power plants, power-to-gas equipment, pumped storage units, flywheel energy storage units, electrochemical energy storage units, and loads.

[0049] The hybrid energy storage collaborative control module is used to design a hierarchical collaborative regulation mechanism for pumped hydro storage-flywheel-electrochemical hybrid energy storage, and to realize hierarchical regulation and capacity optimization configuration of pumped hydro storage-flywheel-electrochemical hybrid energy storage based on ICEEMDAN frequency decomposition.

[0050] The carbon-green certificate coupling modeling module is used to establish a dynamic collaborative operation model of carbon capture-electricity-to-gas equipment and a dynamic coupling model of green certificate-carbon trading, and to quantify the contribution of green certificate trading to direct carbon emission reduction.

[0051] The two-stage optimization scheduling module is used to build a day-ahead and intraday two-stage rolling optimization scheduling model with the goal of maximizing net operating revenue, and to coordinate the optimization of energy storage charging and discharging strategies, carbon capture energy consumption and capture strategies, power-to-gas equipment energy consumption and CO2 consumption strategies, and market trading strategies.

[0052] The scheduling instruction generation and execution module is used to solve the day-to-day two-stage rolling optimization scheduling model, output the optimal scheduling instruction, and control the operation of each unit in the virtual power plant system.

[0053] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a low-carbon economic dispatch method and system that considers multiple uncertainties and includes a virtual power plant with hybrid energy storage, which has the following beneficial effects:

[0054] (1) This invention uses ICEEMDAN frequency decomposition technology to distribute renewable energy fluctuations according to frequency bands to flywheel, electrochemical, and pumped storage units with matched response characteristics for coordinated smoothing, which significantly improves the fluctuation smoothing efficiency and system flexibility of hybrid energy storage.

[0055] (2) By establishing a carbon capture-P2G collaborative operation model and a green certificate-carbon trading dynamic coupling model, the contribution of green certificates to direct carbon emission reduction is accurately quantified, and a two-stage rolling optimization framework of day-to-day and intraday is embedded to optimize energy storage charging and discharging, carbon capture energy consumption, P2G operation and market trading strategies in a coordinated manner with the goal of maximizing net operating income.

[0056] (3) This invention effectively solves the scheduling problem under the coupling of multiple uncertainties (wind power / solar power output, load forecasting, green certificate / carbon trading revenue), and achieves a synergistic improvement in economic efficiency and low carbon emissions. Examples show that, compared with the traditional scheduling mode, the net operating cost of the hybrid energy storage synergy scenario using this invention is reduced by 7.7%, carbon emissions are reduced by 13.9%, and the renewable energy consumption rate is increased by 12.4%. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0058] Figure 1 A flowchart of a low-carbon economic dispatch method for virtual power plants with hybrid energy storage that considers multiple uncertainties, provided by the present invention.

[0059] Figure 2 The VPP system structure provided by this invention;

[0060] Figure 3 The present invention provides a framework diagram for the capacity optimization configuration of the hybrid energy storage system. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] To address the problems of insufficient synergistic efficiency of hybrid energy storage, the impact of multi-source uncertainty coupling on dispatch robustness, and inadequate synergistic optimization of carbon emission reduction and economic benefits in existing technologies, this invention discloses a low-carbon economic dispatch method for virtual power plants incorporating hybrid energy storage that considers multiple uncertainties. Figure 1 As shown, it includes the following steps:

[0063] S1. Construct a virtual power plant system structure. The virtual power plant system includes a wind power generation unit, a photovoltaic power generation unit, a carbon capture power plant, a power-to-gas (P2G) device, a pumped storage unit, a flywheel energy storage unit, an electrochemical energy storage unit, and loads. See [link / reference] Figure 2 ;

[0064] S2. Design a hierarchical synergistic regulation mechanism for pumped hydro storage-flywheel-electrochemical hybrid energy storage (PFE-HES);

[0065] S3. Establish a dynamic collaborative operation model for carbon capture-electricity-to-gas (P2G) equipment and a dynamic coupling model for green certificates-carbon trading to quantify the contribution of green certificate trading to direct carbon emission reduction.

[0066] S4. Construct a two-stage rolling optimization scheduling model for day-ahead and intraday periods, with the goal of maximizing net operating revenue, and coordinately optimize energy storage charging and discharging strategies, carbon capture energy consumption and capture strategies, power-to-gas equipment energy consumption and CO2 consumption strategies, and market trading strategies.

[0067] S5. Solve the two-stage rolling optimization scheduling model from day-ahead to day-intraday, output the optimal scheduling command, and control the operation of each unit in the virtual power plant system.

[0068] Furthermore, in the technical solution of this embodiment, S2 specifically includes the following steps:

[0069] S21. The improved adaptive noise complete set empirical mode decomposition (ICEEMDAN) method is used to decompose the wind power sequence into high-frequency components, mid-frequency components and low-frequency components.

[0070] S22. Distribute high-frequency components to flywheel energy storage units for instantaneous power regulation and perform millisecond-level fluctuation smoothing; distribute medium-frequency components to electrochemical energy storage units for minute-level power compensation; and distribute low-frequency components to pumped hydro storage units or coordinate with the grid for long-term energy balance regulation to achieve day-ahead energy transfer.

[0071] S23. Aiming to minimize the operating cost of the virtual power plant, the optimal configuration of the hybrid energy storage system capacity is achieved through iterative optimization. Specifically, a two-layer framework is constructed: a power smoothing layer and an economic optimization layer. The power smoothing layer generates a preliminary energy storage power allocation scheme based on the ICEEMDAN frequency division results. The economic optimization layer aims to minimize the total cost and dynamically adjusts the pre-allocation plan through slack variables. The constraint deviation range is [-50, 50] MW; the penalty coefficient is dynamically set. Where γ0 is the baseline penalty coefficient, P W For wind power output, MW; P V It contributes power to photovoltaic power plants, in MW.

[0072] Furthermore, in S21, the specific steps of frequency decomposition are as follows:

[0073] The first stage of decomposition (initial frequency division) involves performing ICEEMDAN decomposition on the original wind and solar power sequence to obtain 8 intrinsic mode function (IMF) components.

[0074] Calculate the average instantaneous frequency of each intrinsic mode function component. and the overall mean d;

[0075] A first frequency division threshold is set, and eigenmode function components with an average instantaneous frequency higher than the first frequency division threshold are classified as high-frequency components P. high The eigenmode function components with an average instantaneous frequency lower than the first frequency division threshold are classified as low-frequency components P. low ;

[0076] By correcting the residuals, we ensure that the sum of the divided power is equal to the original power P. W :

[0077] P W =P high +P low +r k

[0078] In the formula: r k This represents the residual. Based on this, a closed-loop check of the residual can be performed, and the effectiveness of the decomposition can be verified through the power balance equation.

[0079] Second-stage decomposition (internal frequency division of hybrid energy storage): The P obtained in the first stage... high The ICEEMDAN decomposition is performed again, and the results are screened and reconstructed based on the corresponding average instantaneous frequency threshold. The IMF components with average instantaneous frequencies higher than the second frequency division threshold are superimposed, which correspond to flywheel energy storage. The IMF components with average instantaneous frequencies between the first and second frequency division thresholds are superimposed to obtain the mid-to-high frequency components, which correspond to electrochemical energy storage.

[0080] Furthermore, in S23, the optimal configuration of the hybrid energy storage system capacity is achieved through iterative optimization, specifically as follows:

[0081] During the power smoothing phase, based on actual operating data on a 15-minute timescale, the ICEEMDAN method is used to decompose wind power fluctuations and photovoltaic power fluctuations. The cross-frequency threshold is set according to a preset offset rate to obtain the power component that needs to be regulated by hybrid energy storage.

[0082] In the economic optimization stage, with the goal of minimizing costs, the power components that require hybrid energy storage regulation are substituted into the objective function, which includes energy storage cost indicators, for iterative optimization.

[0083] Among them, energy storage cost indicators include:

[0084] Equivalent daily cycle life model for electrochemical energy storage:

[0085]

[0086] Where: N0 is the total number of cycles the battery undergoes during a complete cycle of full discharge to depth and subsequent full charge, until it reaches its lifespan limit; D op Where N is the actual depth of discharge of the battery, x is the equivalent number of energy storage cycles, and k is the equivalent daily cycle life of the energy storage. p These are the parameters for curve fitting;

[0087] Electrochemical energy storage operation and maintenance costs

[0088]

[0089] In the formula: z is the annual interest rate; The unit capacity maintenance cost is expressed in yuan / MWh; E ESS The rated capacity of electrochemical energy storage is MW;

[0090] Flywheel energy storage operation and maintenance costs

[0091]

[0092] In the formula: The unit capacity maintenance cost is expressed in yuan / MWh. Maintenance cost per unit power, in yuan / MW; E FW The rated capacity of flywheel energy storage is MWh; P FW The rated power of electrochemical energy storage is MW.

[0093] Furthermore, within the technical solution of this embodiment, the expression for the dynamic coordinated operation model of the carbon capture-electricity-to-gas conversion equipment in S3 is:

[0094]

[0095] In the formula: k CS The fixed price per unit of CO2 stored, in yuan / t; The unit price for carbon trading is yuan / ton; Let t be the amount of CO2 sequestered by carbon at time t; Let t represent the CO2 emitted into the atmosphere by the virtual power plant during time period t; The CO2 emission allowance allocated to the virtual power plant during time period t; γ C The baseline CO2 emission allowance per unit of electricity is t / (MW·h); The CO2-related costs of the virtual power plant during time period t; CO2 purchased by P2G devices during time period t, t; P t G For the carbon capture plant's output, MW;

[0096] The expression for the dynamic coupling model of green certificates and carbon trading is:

[0097]

[0098] In the formula: k gre The unit price for green certificate trading; P t gre P represents the total grid-connected power of wind and solar power generation during time period t, in MW; t re The renewable energy consumption weight of the virtual power plant in time period t, in MW; Let t be the revenue obtained by the virtual power plant from participating in green certificate trading.

[0099] Furthermore, in S3, the dynamic collaborative operation model of the carbon capture-electricity-to-gas conversion equipment satisfies the following constraints:

[0100] CO2 capture limits at carbon capture plants:

[0101]

[0102] In the formula: E represents the carbon emissions of the carbon capture plant during period t. G Carbon intensity of the carbon capture plant, t / MW;

[0103] Relationship between CO2 consumption and methane production in P2G plants:

[0104]

[0105] V t P2G =3.6ηP2G P t P2G / H g

[0106] In the formula: Let t represent the amount of CO2 consumed by the P2G device during time period t. The amount of CO2 required to generate a unit volume of methane for a P2G plant, t / MW; η P2G For the conversion efficiency of P2G devices; H g The calorific value of natural gas is taken as 39 MJ / m³. 3 ;P t P2G Let V be the energy consumption of the P2G device during time period t, expressed in MW; t P2G Let m be the volume of methane generated by the P2G device during time period t. 3 .

[0107] Furthermore, in the technical solution of this embodiment, S4 specifically includes the following steps:

[0108] S41. During the day-ahead dispatch phase (1-hour time resolution), based on the day-ahead forecast data of wind power, photovoltaic power, and load, establish a source-storage-carbon collaborative optimization model, and formulate pumped storage start-up and shutdown plans, reservoir water level benchmarks, output plans for each unit, and green certificate-carbon trading plans; the objective function is to minimize the total operating cost C of the virtual power plant within the dispatch period. vpp :

[0109]

[0110] In the formula: Let be the fuel cost of the carbon capture plant at time t. The revenue obtained by the virtual power plant from participating in green certificate trading at time t. The total operating and maintenance cost of the system at time t. The penalty cost for the virtual power plant to curtail wind and solar power at time t. This represents the cost for the virtual power plant to purchase electricity from the main grid at time t. Let be the operating cost of the P2G device at time t. The revenue obtained by the virtual power plant from participating in carbon trading at time t;

[0111] S42. During the intraday scheduling phase (15-minute time resolution), based on the day-ahead plan and combined with updated forecasts for wind power, photovoltaic power, and ultra-short-term load (15min-4h), rolling optimization is performed; the scheduling plan for the next 4 hours (16 time periods in total) is solved, with an execution time resolution of 15 minutes; the objective function is the same as that of the day-ahead scheduling phase, but the start-up and shutdown costs of pumped storage are ignored, and the expression for the total system operation and maintenance cost is adjusted as follows:

[0112]

[0113] In the formula: To reduce the operation and maintenance costs of electrochemical energy storage, For wind power operation and maintenance costs, For the operation and maintenance costs of photovoltaic power generation, For flywheel energy storage operation and maintenance costs;

[0114] Meanwhile, wind power, photovoltaic, and load forecasting error models are introduced into the rolling optimization framework during the intraday scheduling phase:

[0115]

[0116] In the formula: and These represent the fluctuations in wind power, photovoltaic power generation, and load at time t, respectively. and Let P be the variance of the fluctuation values ​​of wind power, photovoltaic power generation, and load at time t; t EL' The load demand (MW) of the virtual power plant during time period t in the intraday dispatch phase; P t w,pre' and P t pv,pre' These represent the predicted power output (MW) of wind power and photovoltaic power generation during time period t, respectively, in the intraday dispatch phase; P t w,pre and P t pv,pre Here, P represents the predicted power output of wind power and photovoltaic power generation during time period t, in MW; t EL This represents the load demand of the virtual power plant during time period t.

[0117] The predicted values ​​of wind power and photovoltaic power generation and load change between the day-ahead and intraday stages. Assuming that the prediction errors of the day-ahead and intraday prediction curves of wind power and photovoltaic power generation, as well as the load prediction errors, are characterized by independent normal distributions with zero mean, adding the corresponding output changes to the day-ahead data gives the predicted power of wind power and photovoltaic power generation in time period t during the intraday stage.

[0118] Furthermore, in S4, the day-to-day two-stage rolling optimization scheduling model satisfies the following operational constraints:

[0119] Power balance constraints:

[0120] P t EL =P t G,N +P t W,N +Pt PV,N +P t ESS,N +P t PS,N +P t FW,N +P t grid

[0121] In the formula: P t EL P represents the load demand of the virtual power plant during time period t. t G,N Net output of carbon capture plant, MW; P t W,N Net grid-connected wind power, MW; P t PV,N Net grid-connected power of photovoltaic power, MW; P t ESS,N Net output of electrochemical energy storage, MW; P t PS,N For the net output power of pumped storage, MW; P t FW,N Net output of flywheel energy storage, MW; P t grid Power purchased from the grid, in MW;

[0122] Wind and solar power output constraints:

[0123] 0≤P t w ≤P t w，pre

[0124] 0≤P t pv ≤P t pv，pre

[0125] In the formula: P t w，pre and P t pv，pre Here, P represents the predicted power output of wind power and photovoltaic power generation during time period t, in MW; t w Let P be the power output of the wind farm at time t, in MW; t pv Let be the power output of the photovoltaic power plant at time t, in MW;

[0126] Pumped storage reservoir capacity constraints:

[0127]

[0128] In the formula: W t PSThe upper reservoir capacity of the pumped storage power station during time period t. and η represents the lower and upper limits of the reservoir's capacity, respectively. c η is the water loss rate. P and η G These are the average water volume and electricity conversion coefficients under pumping and power generation conditions, respectively; The power stored by a pumped-storage hydroelectric unit for pumping water during time period t, in MW; The pumped storage unit generates power in time period t, in MW; T is the last time period.

[0129] Electrochemical energy storage charge / discharge power and SOC constraint:

[0130]

[0131] In the formula: P t ESS,C Let be the charging power of the electrochemical energy storage at time t, in MW; This is the upper limit coefficient for a single charge of electrochemical energy storage. P represents the maximum value of the electrochemical energy storage power. t ESS,D Let be the discharge power of the electrochemical energy storage at time t, in MW; This is the upper limit coefficient for a single discharge of electrochemical energy storage;

[0132] Flywheel energy storage speed and energy constraints:

[0133] ω min ≤ω t ≤ω max

[0134]

[0135] In the formula: ω t ω is the angular velocity of the flywheel rotor. min ω max These are the minimum and maximum values ​​of the rotor angular velocity, respectively.

[0136] Output limits and ramp-up constraints for carbon capture power plants and carbon capture systems:

[0137]

[0138] P t A ≤P t CC ≤P t CC,max

[0139] P t CC,max =kCC E G P t G

[0140]

[0141] In the formula: The output range of the carbon capture plant is MW; ΔP G Maximum ramp limit for carbon capture plants, MW; P t CC,max Let k be the maximum operating energy consumption of the carbon capture system during time period t, expressed in MW; CC Carbon capture energy consumption per unit of CO2 captured, MW / t; E G The carbon emission intensity per unit of carbon capture unit, t / MW; ΔP CC The maximum ramp-up limit for the energy consumption of a carbon capture system is MW; Let t be the CO2 captured by the carbon capture system at time t; P be the carbon capture amount. t G For the output of carbon capture plants, MW; P t A The fixed energy consumption of the carbon capture system is expressed in MW and P. t CC For the output of the carbon capture system, MW; P t OP Energy consumption for carbon capture system operation, in MW;

[0142] P2G device operating constraints:

[0143]

[0144] In the formula: Let t represent the amount of CO2 consumed by the P2G device during time period t. η is the amount of CO2 required to generate a unit volume of methane by a P2G device. P2G For the conversion efficiency of P2G devices; P t P2G Let be the energy consumption of the P2G device at time t, in MW; The maximum energy consumption of a P2G device, in MW;

[0145] Power constraints on grid interconnects:

[0146]

[0147] In the formula: P t grid,buy and P t grid,sell These represent the interactive power, in MW, between the virtual power plant and the distribution network interconnection line during time period t; and These represent the maximum power (MW) that the virtual power plant can purchase and sell electricity to the tie line.

[0148] and Figure 1 Corresponding to the method described above, embodiments of the present invention also provide a low-carbon economic dispatch system incorporating a hybrid energy storage virtual power plant that considers multiple uncertainties, for use in... Figure 1 The specific implementation of the method, as provided in this embodiment of the invention, is a low-carbon economic dispatch system for virtual power plants with hybrid energy storage that considers multiple uncertainties. This system can be applied to computer terminals or various mobile devices, and specifically includes:

[0149] The system construction module is used to build the structure of the virtual power plant system. The virtual power plant system includes wind power generation units, photovoltaic power generation units, carbon capture power plants, power-to-gas equipment, pumped storage units, flywheel energy storage units, electrochemical energy storage units, and loads.

[0150] The hybrid energy storage collaborative control module is used to design a hierarchical collaborative regulation mechanism for pumped hydro storage-flywheel-electrochemical hybrid energy storage, and to realize hierarchical regulation and capacity optimization configuration of pumped hydro storage-flywheel-electrochemical hybrid energy storage based on ICEEMDAN frequency decomposition.

[0151] The carbon-green certificate coupling modeling module is used to establish a dynamic collaborative operation model of carbon capture-electricity-to-gas equipment and a dynamic coupling model of green certificate-carbon trading, and to quantify the contribution of green certificate trading to direct carbon emission reduction.

[0152] The two-stage optimization scheduling module is used to build a day-ahead and intraday two-stage rolling optimization scheduling model with the goal of maximizing net operating revenue, and to coordinate the optimization of energy storage charging and discharging strategies, carbon capture energy consumption and capture strategies, power-to-gas equipment energy consumption and CO2 consumption strategies, and market trading strategies.

[0153] The scheduling instruction generation and execution module is used to solve the day-to-day two-stage rolling optimization scheduling model, output the optimal scheduling instruction, and control the operation of each unit in the virtual power plant system.

[0154] On the other hand, see appendix Figure 3 As shown, this embodiment also discloses a capacity optimization configuration framework for a hybrid energy storage system, including:

[0155] (1) Two-stage optimization method:

[0156] -Power smoothing phase:

[0157] Input: Actual wind and solar power data on a 15-minute timescale;

[0158] The power sequence is decomposed into high-frequency and low-frequency components using the ICEEMDAN decomposition technique.

[0159] -Economic optimization stage:

[0160] With the goal of minimizing the total cost of hybrid energy storage, the capacity configuration of flywheel (FW) and electrochemical energy storage (ESS) is optimized;

[0161] Optimal capacity allocation is achieved through iterative calculations.

[0162] (2) Economic analysis of the optimization layer:

[0163] Robust Optimization Mechanism (HSRC): Introduces slack variables, allowing power deviation to be adjusted within the range of [-50, 50] MW. A dynamic penalty coefficient is designed, which increases with increasing wind and solar power.

[0164] Economic objective function:

[0165] By inputting decomposed data from the power smoothing phase, and aiming to minimize costs, the optimal capacity configuration scheme of flywheel and electrochemical energy storage is obtained through iterative optimization.

[0166] In summary, the low-carbon economic dispatch method and system for virtual power plants with hybrid energy storage proposed in this embodiment considers multiple uncertainties. It acquires historical data on wind and solar power output and load in the virtual power plant and preprocesses it for multiple uncertainties. A hierarchical collaborative control strategy for hybrid energy storage is designed, and power fluctuations are decomposed into high, medium, and low-frequency components using ICEEMDAN frequency division technology. A carbon capture-electricity-to-gas (P2G) collaborative model and a dynamic coupling mechanism between green certificates and carbon trading are constructed to quantify the contribution of green certificates to direct carbon emission reduction. A two-stage rolling optimization framework, considering both market returns and carbon emission targets, is designed to achieve coordinated updates of energy storage charging and discharging strategies and carbon management strategies, enabling dynamic optimization of dispatch schemes for different source-load scenarios. This embodiment not only significantly improves the renewable energy absorption rate (by 12.4% compared to the baseline scenario) and reduces net carbon emissions (up to 13.9%), but also eliminates the risk of mis-dispatch caused by wind and solar fluctuations.

[0167] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0168] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A low-carbon economic dispatch method considering multiple uncertainties, including a virtual power plant with hybrid energy storage, characterized in that, Includes the following steps: S1. Construct a virtual power plant system structure. The virtual power plant system includes a wind power generation unit, a photovoltaic power generation unit, a carbon capture power plant, an electricity-to-gas conversion device, a pumped storage unit, a flywheel energy storage unit, an electrochemical energy storage unit, and loads. S2. Design a layered and synergistic regulation mechanism for pumped hydro storage-flywheel-electrochemical hybrid energy storage; S3. Establish a dynamic collaborative operation model for carbon capture-electricity-to-gas equipment and a dynamic coupling model for green certificates-carbon trading to quantify the contribution of green certificate trading to direct carbon emission reduction. S4. Construct a two-stage rolling optimization scheduling model for day-ahead and intraday periods, with the goal of maximizing net operating revenue, and coordinately optimize energy storage charging and discharging strategies, carbon capture energy consumption and capture strategies, power-to-gas equipment energy consumption and CO2 consumption strategies, and market trading strategies. S5. Solve the two-stage rolling optimization scheduling model from day-ahead to intraday, output the optimal scheduling command, and control the operation of each unit in the virtual power plant system; In S3, the expression for the dynamic coordinated operation model of the carbon capture-electricity-to-gas conversion equipment is: In the formula: The fixed price per unit of CO2 stored, in yuan / t; The unit price for carbon trading is yuan / ton; In order to be in t The amount of CO2 sequestered by carbon at any given time, in tons (t). For virtual power plants t CO2 emitted into the atmosphere during a given period, in tons (t); For virtual power plants t The CO2 emission allowance allocated for a given period, in tons (t); For virtual power plants t CO2-related costs over a period of time; For P2G devices t CO2 purchased during the specified time period, t; The expression for the dynamic coupling model of green certificates and carbon trading is: In the formula: The unit price for green certificate transactions; For wind power and photovoltaic power generation t Total power connected to the internet during the time period, in MW; For virtual power plants t Renewable energy consumption weight for a given period, in MW; for t The revenue generated by the virtual power plant through green certificate trading.

2. The low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties according to claim 1, characterized in that, S2 specifically includes the following steps: S21. An improved adaptive noise complete set empirical mode decomposition method is used to decompose the wind power sequence into high-frequency, mid-frequency and low-frequency components. S22. Distribute high-frequency components to flywheel energy storage units for instantaneous power regulation, distribute medium-frequency components to electrochemical energy storage units for minute-level power compensation, and distribute low-frequency components to pumped storage units or coordinate with the grid for long-term energy balance regulation. S23. With the goal of minimizing the operating cost of the virtual power plant, the optimal configuration of the capacity of the hybrid energy storage system is achieved through iterative optimization.

3. The low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties according to claim 2, characterized in that, In S21, the specific steps of frequency decomposition are as follows: First-stage decomposition: Perform ICEEMDAN decomposition on the original wind and solar power sequence to obtain the intrinsic mode function components; Calculate the average instantaneous frequency of each intrinsic mode function component. and overall mean d ; A first frequency division threshold is set, and eigenmode function components with an average instantaneous frequency higher than the first frequency division threshold are classified as high-frequency components. The intrinsic mode function components with an average instantaneous frequency lower than the first frequency division threshold are classified as low-frequency components. ; By correcting the residuals, we ensure that the sum of the divided power equals the original power. : In the formula: Represents the residual; Second-stage decomposition: The results obtained in the first stage... The ICEEMDAN decomposition is performed again, and the results are screened and reconstructed based on the corresponding average instantaneous frequency threshold. The IMF components with average instantaneous frequencies higher than the second frequency division threshold are superimposed, which correspond to flywheel energy storage. The IMF components with average instantaneous frequencies between the first and second frequency division thresholds are superimposed to obtain the mid-to-high frequency components, which correspond to electrochemical energy storage.

4. The low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties according to claim 2, characterized in that, In S23, the optimal configuration of the hybrid energy storage system capacity is achieved through iterative optimization, specifically as follows: During the power smoothing phase, based on actual operating data on a 15-minute timescale, the ICEEMDAN method is used to decompose wind power fluctuations and photovoltaic power fluctuations. The cross-frequency threshold is set according to a preset offset rate to obtain the power component that needs to be regulated by hybrid energy storage. In the economic optimization stage, with the goal of minimizing costs, the power components that require hybrid energy storage regulation are substituted into the objective function, which includes energy storage cost indicators, for iterative optimization.

5. The low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties according to claim 4, characterized in that, Energy storage cost metrics include: equivalent daily cycle life of energy storage, operation and maintenance cost of electrochemical energy storage, and operation and maintenance cost of flywheel energy storage.

6. The low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties according to claim 1, characterized in that, In S3, the dynamic collaborative operation model of the carbon capture-electricity-to-gas conversion equipment satisfies the following constraints: CO2 capture limits at carbon capture plants: In the formula: For carbon capture plants t Carbon emissions per period, in tons (t); Let t be the amount of CO2 captured by the carbon capture system at time t; Relationship between CO2 consumption and methane production in P2G plants: In the formula: For P2G devices t The amount of CO2 consumed during a given period, in tons (t). The amount of CO2 required to generate a unit volume of methane for a P2G plant, in t / MW; For the conversion efficiency of P2G devices; The calorific value of natural gas is taken as 39 MJ / m³. 3 ; For P2G devices t Energy consumption per time period, in MW; For P2G devices t The volume of methane generated during the time period, m 3 .

7. The low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties according to claim 1, characterized in that, S4 specifically includes the following steps: S41. During the day-ahead scheduling phase, based on the day-ahead forecast data of wind power, photovoltaic power and load, establish a source-storage-carbon collaborative optimization model, and formulate pumped storage start-up and shutdown plans, reservoir water level benchmarks, output plans for each unit and green certificate-carbon trading plans. S42. During the intraday scheduling phase, based on the day-ahead plan, rolling optimization is performed by combining updated data of wind power, photovoltaic, and load ultra-short-term forecasts; the scheduling plan for the next 4 hours is solved with an execution time resolution of 15 minutes; at the same time, wind power, photovoltaic, and load forecast error models are introduced.

8. The low-carbon economic dispatch method for virtual power plants with hybrid energy storage considering multiple uncertainties according to claim 1, characterized in that, In S4, the day-ahead and intraday two-stage rolling optimization scheduling model satisfies the following operational constraints: power balance constraints, wind and solar power output constraints, pumped storage reservoir capacity constraints, electrochemical energy storage charging and discharging power and SOC constraints, flywheel energy storage speed and energy constraints, upper and lower limits and ramping constraints of carbon capture power plant and carbon capture system output, P2G equipment operation constraints, and grid interconnection line power constraints.

9. A system for implementing the low-carbon economic dispatch method for virtual power plants with hybrid energy storage, considering multiple uncertainties, as described in any one of claims 1-8, characterized in that, include: The system construction module is used to build the structure of the virtual power plant system. The virtual power plant system includes wind power generation units, photovoltaic power generation units, carbon capture power plants, power-to-gas equipment, pumped storage units, flywheel energy storage units, electrochemical energy storage units, and loads. The hybrid energy storage collaborative control module is used to design a hierarchical collaborative regulation mechanism for pumped hydro storage-flywheel-electrochemical hybrid energy storage, and to realize hierarchical regulation and capacity optimization configuration of pumped hydro storage-flywheel-electrochemical hybrid energy storage based on ICEEMDAN frequency decomposition. The carbon-green certificate coupling modeling module is used to establish a dynamic collaborative operation model of carbon capture-electricity-to-gas equipment and a dynamic coupling model of green certificate-carbon trading, and to quantify the contribution of green certificate trading to direct carbon emission reduction. The two-stage optimization scheduling module is used to build a day-ahead and intraday two-stage rolling optimization scheduling model with the goal of maximizing net operating revenue, and to coordinate the optimization of energy storage charging and discharging strategies, carbon capture energy consumption and capture strategies, power-to-gas equipment energy consumption and CO2 consumption strategies, and market trading strategies. The scheduling instruction generation and execution module is used to solve the day-to-day two-stage rolling optimization scheduling model, output the optimal scheduling instruction, and control the operation of each unit in the virtual power plant system.

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