Virtual power plant resource optimization scheduling method and system considering source load uncertainty

Abstract

The invention provides a virtual power plant resource optimization scheduling method and system considering source load uncertainty. The method specifically comprises the following steps: establishing a wind and light related output scene by using a coupling function theory, an average influence value method and an agglomerated hierarchical clustering algorithm; establishing a virtual power plant flexibility demand model considering dual uncertainty of wind and light output and load demand; establishing a load response model and a flexibility supply model of the multi-element flexibility resources of the virtual power plant; establishing a probability mass function and a cumulative distribution function based on the flexibility demand of the source load historical data; establishing a flexibility quantitative index and a flexibility supply excitation mathematical model which simultaneously consider predictable and unpredictable flexibility requirements; and establishing a master-slave game model of the virtual power plant operator and the load aggregator, and solving the clearing electricity price of the virtual power plant to obtain a flexible resource response strategy. According to the method, by optimizing flexible resource configuration, renewable energy consumption is improved, and economic and low-carbon operation of the virtual power plant is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of demand response optimization, and specifically relates to a virtual power plant resource optimization scheduling method and system taking into account source and load uncertainty. Background Art

[0002] The flexibility of the new power system is the interaction of sources, grids, loads and storage. In virtual power plants with a high proportion of renewable energy, the output of traditional units is limited, and flexible resources are needed to match the random fluctuations of sources and loads through demand response mechanisms. With the diversification of flexible resources in the power market, the power market structure is gradually transformed from a vertically integrated structure to an interactive and competitive structure.

[0003] Hydrogen production by water electrolysis is widely involved in power system response because it can quickly track the output of renewable energy. The hydrogen plus carbon dioxide synthesis methanol system solves some of the hydrogen problems while participating in the response, but ignores the research on the flexibility analysis of the synthesis methanol system. The current power system flexibility quantitative indicators focus on quantifying the foreseeable lack of flexibility demand, ignoring the problem of flexibility redundancy, which reflects the adequacy of power supply when the power system encounters unforeseen flexibility demand. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention intends to provide a virtual power plant resource optimization scheduling method that takes into account source and load uncertainty, so as to solve the problem of multi-flexibility resource optimization scheduling of virtual power plants with a high proportion of renewable energy, and to build a technical foundation for the development of virtual power plants with a high proportion of renewable energy.

[0005] In order to solve the above problems existing in the prior art, the technical solution adopted by the present invention is to provide a virtual power plant resource optimization scheduling method considering source and load uncertainty, which specifically includes the following steps:

[0006] S1, based on the historical data of wind power generation and photovoltaic power generation, the coupling function theory (Copula theory) is used to characterize the synergistic and complementary correlation between wind power generation and photovoltaic power generation in the same area, and a wind-solar output correlation set is generated at the same time. Then, the average influence value method is used to reduce the dimension of the wind-solar output correlation set, and the agglomerative hierarchical clustering algorithm is used to cluster the wind-solar output correlation set to obtain the required wind power generation and photovoltaic power generation scenarios;

[0007] S2, based on the uncertainty of wind and photovoltaic power generation and the uncertainty of load demand, establish a virtual power plant flexibility demand model for the time scale of interest;

[0008] S3, based on the characteristics of demand-side flexibility resources, establish demand response models and flexibility supply models for hydrogen production and methanol synthesis systems, active load resources, electric vehicles and HVAC;

[0009] S4, based on the historical data of wind power generation, photovoltaic power generation and load demand, calculate the flexibility demand at each historical moment under the time scale of interest, and establish the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant;

[0010] S5, based on the cumulative distribution function, establish the virtual power plant flexibility deficiency and redundancy probability index and flexibility sufficiency index, and establish a flexibility supply incentive mathematical model;

[0011] S6, based on the master-slave game framework, establishes a master-slave game model between virtual power plant operators and load aggregators, solves the clearing electricity price that satisfies the optimal benefit function of virtual power plant operators and load aggregators, and obtains the response strategy of flexible resources.

[0012] The step S1 specifically includes:

[0013] Kernel density estimation is used to calculate the probability density and distribution function of historical data of wind and solar power generation; Frank coupling function is used to describe the joint distribution of wind and solar power output, and a specified number of random values ​​of marginal distribution function are generated; by inverting the random values ​​of marginal distribution function, G groups of wind and solar power output sets with spatiotemporal correlation are obtained; the average influence value method is used to calculate the individual influence of each time dimension of the wind and solar power output collection on the average contour coefficient, and the time dimension with less influence is eliminated from the original wind and solar power output collection; the Euclidean distance between each sample of the new wind and solar power output collection is calculated, and each sample is clustered by linking the sum of squared deviations to construct a hierarchical clustering tree structure of the new wind and solar power output collection; the constructed tree structure is divided, and the wind power and photovoltaic scenes are respectively aggregated into a specified number of clusters, and cluster labels are added to each sample, and the center point of each cluster cluster is used as the reduced wind and solar power output scene G1 as the required wind power generation and photovoltaic power generation scenes.

[0014] The step S2 specifically includes:

[0015] According to the wind and solar power output scenario G1, considering the wind and solar power output and load demand with the maximum fluctuation error, the quantitative model of the virtual power plant flexibility demand at the time scale of interest is expressed as:

[0016]

[0017] Where: F t load,U,xu and F t load,D,xu are the upward and downward flexibility requirements of load at time t respectively; Pt load is the load demand forecast value at time t; is the maximum error coefficient of load at time t; F t W,U,xu and F t W,D,xu They are the upward and downward flexibility requirements of wind power generation in the wind and solar power output scenario G1 at time t; P t W is the wind power generation prediction value of the wind and solar power output scenario G1 at time t; t W F is the maximum error coefficient of wind power generation in the wind and solar power output scenario G1 at time t; t PV,U,xu and F t PV,D,xu They are the upward and downward flexibility demands of photovoltaic power generation in the wind and solar power output scenario G1 at time t; P t PV is the predicted value of photovoltaic power generation in the wind and solar power output scenario G1 at time t; t PV It is the maximum error coefficient of photovoltaic power generation in the wind and solar power output scenario G1 at time t.

[0018] The step S3 specifically includes:

[0019] Step S3.1, establish a demand response model and flexibility supply model for the electric hydrogen production and methanol synthesis system:

[0020] Energy consumption of hydrogen production by electrolysis of water in industrial-grade electric hydrogen production and methanol synthesis system Total energy consumption t MSS The main part is to combine multiple electrolyzers to produce hydrogen to meet the hydrogen demand, and the remaining energy consumption is the energy consumption of synthesizing methanol P t Me , Hydrogen compressor energy consumption Energy consumption of CO2 compressor The energy consumption relationship is expressed as:

[0021]

[0022] Where: is the energy consumption coefficient of hydrogen production from electricity; and The hydrogen produced by electrolysis of water at time t directly participates in the synthesis of methanol and the flow rate of hydrogen charged into the hydrogen storage tank; and They are the start, standby and shutdown status flags, which are Boolean values; is the maximum power of the electrolyzer; and are the energy consumption coefficients of the reaction tower feed compressor; kMe is the energy consumption coefficient of methanol synthesis in the reaction tower; and They are the methanol reaction progress and minimum reaction progress of the reaction tower at time t respectively; is the carbon dioxide flow rate produced in the reaction at time t; and are the molar masses of hydrogen and carbon dioxide, respectively; and are the carbon dioxide pressures at the inlet and outlet, respectively; and are the hydrogen pressures at the inlet and outlet, respectively; R is a thermodynamic constant; T is room temperature; η c is the compressor efficiency;

[0023] The relationship between the feed flow rate of hydrogen and carbon dioxide catalytic synthesis of methanol system and the methanol yield and reaction progress is expressed as:

[0024]

[0025] Where: is the methanol yield at time t; is the hydrogen flow rate released from the hydrogen storage tank at time t; and Respectively represent the raw material ratio; is the daily methanol production; γ Me is the ratio of methanol reaction progress to feedstock flow rate;

[0026] The maximum low-load operating time limit of the model and the minimum downtime limit of the electrolyzer are expressed as:

[0027]

[0028] Where: It is the low load operation flag of the synthetic methanol reaction tower, which is a Boolean value; and The maximum operating time at low load and the minimum downtime of the proton exchange membrane electrolyzer are respectively;

[0029] The hydrogen storage tank model is expressed as:

[0030]

[0031] Where: is the hydrogen storage capacity in the hydrogen storage tank at time t; and are the minimum / maximum capacity of the hydrogen storage tank respectively; and They are charge and discharge status flags, which are Boolean values; is the flow rate of hydrogen released from the hydrogen storage tank at time t; and are the maximum flow rates for filling and releasing hydrogen into the hydrogen storage tank, respectively;

[0032] Minimum methanol yield at time t It is expressed as:

[0033]

[0034] Where: is the maximum value of the methanol yield that can be reduced at time t, specifically expressed as:

[0035]

[0036] Where: and are the maximum / minimum methanol yield, respectively;

[0037] Hydrogen storage capacity of the hydrogen storage tank at time t It is expressed as:

[0038]

[0039] The minimum power consumption of the system for synthesizing methanol by electrolysis of water to produce hydrogen at time t is expressed as:

[0040]

[0041] Where: The power consumption when the minimum methanol yield is considered at time t but there is insufficient hydrogen in the hydrogen storage tank; The power consumption at time t when the minimum methanol yield is considered and there is sufficient hydrogen in the hydrogen storage tank;

[0042] The upward flexibility supply F of the synthetic methanol system at time t t MSS,U,gong , expressed as:

[0043]

[0044] Maximum methanol yield at time t It is expressed as:

[0045]

[0046] Where: is the maximum value of the methanol yield at time t, which is specifically expressed as:

[0047]

[0048] The free capacity of the hydrogen storage tank at time t It is expressed as:

[0049]

[0050] The maximum power consumption of the system for synthesizing methanol by electrolysis of water to produce hydrogen at time t is expressed as:

[0051]

[0052] Where: The power consumption when the maximum methanol yield is considered at time t and the hydrogen storage tank is filled with hydrogen; The power consumption at time t when the maximum methanol yield is considered and the electrolyzer is running at full load; is the upper limit of the load variation range of the proton exchange membrane electrolyzer;

[0053] The downward flexible supply F of the synthetic methanol system at time t t MSS,D,gong , expressed as:

[0054]

[0055] Step S3.2: Establish a demand response model and flexibility supply model for active load resources:

[0056] Active loads include translatable loads and interruptible loads, expressed as:

[0057]

[0058] Where: P t py is the load power that can be translated at time t; is the power of the mth type of interruptible load at time t; P t load is the load demand power at time t; py and is the proportion of the movable load and the mth type interruptible load in the total load; and are the minimum and maximum power that can be translated at time t respectively; Climbing for maximum translatable power; is the maximum interruptible power of the mth category at time t; It is the maximum interruptible power ramp;

[0059] Can translate loads upward and downward flexibility supply F t py,U,gong and F t py,D,gong It is expressed as:

[0060]

[0061] Interruptible load supply up and down flexibility F tzd,m,U,gong and F t zd,m,D,gong It is expressed as:

[0062]

[0063] Step S3.3: Establishing a demand response model and flexibility supply model for electric vehicles:

[0064] The charging and discharging working model of a single electric vehicle is expressed as:

[0065]

[0066] Where: is the battery capacity of the i-th electric car in period t; η EV is the charging / discharging efficiency of the i-th electric vehicle in period t; is the charging power of the i-th electric vehicle in period t; is the discharge power of the i-th electric vehicle in period t; is the distance traveled by the i-th electric vehicle in period t; S i,t It is the electric power consumed by the i-th electric vehicle per unit driving distance in period t. and are the maximum charging / discharging power of the i-th electric vehicle in period t respectively; and are the charging / discharging status of the i-th electric vehicle in period t, Boolean values; is the maximum battery capacity of the i-th electric vehicle; and They are the minimum / maximum limits of battery power respectively;

[0067] Upward and downward flexibility of electric vehicle cluster supply F t EV,U,gong and F t EV,D,gong It is expressed as:

[0068]

[0069]

[0070] Where: is the battery power of the electric vehicle cluster at time t; and They are the minimum and maximum charging and discharging power of the electric vehicle cluster at time t respectively; and They are the minimum and maximum values ​​of the virtual battery power of the electric vehicle cluster at time t respectively.

[0071] Step S3.4: Establish HVAC demand response model and flexibility supply model:

[0072] The relationship between HVAC power consumption and temperature is expressed as:

[0073]

[0074] Where: and T t out are the indoor temperature and outdoor temperature of the jth HVAC at time t; and are the parameters related to the thermal capacity, thermal resistance and energy efficiency coefficient of the jth HVAC; is the power consumption of the jth HVAC at time t; is the switch state, Boolean value; and are the minimum and maximum power consumption of the jth HVAC, respectively; and are the minimum and maximum values ​​of the comfortable temperature range respectively.

[0075] Upward and downward flexibility of HVAC cluster supply F t HVAC,U,gong and F t HVAC,D,gong It is expressed as:

[0076]

[0077] Where: P t HVAC is the power consumption of the HVAC cluster at time t; and are the minimum and maximum power consumption of the HVAC cluster at time t, which is affected by the comfortable temperature range and the indoor and outdoor temperatures at each time, and is expressed as:

[0078]

[0079] The step S4 specifically includes:

[0080] Step S4.1, calculate the upward / downward flexibility demand at each moment in the time scale of interest through the historical data set;

[0081] Step S4.2: Assuming that there are N moments with upward flexibility demand and M moments with downward flexibility demand in the historical data set, the probability of upward / downward flexibility demand at each moment is and The probability mass function of upward / downward flexibility demand is constructed as:

[0082]

[0083] Where: and are the i-th value of upward / downward flexibility demand in the historical data set; n i and m i Take the upward / downward flexibility requirements in the historical data set as and The number of ; τ is the time scale of interest;

[0084] Step S4.3, the continuous approximate cumulative distribution function is expressed as:

[0085]

[0086] Where: P U,xu and P D,xu They are respectively the upward / downward flexibility requirements for the cumulative probability in the optimal scheduling.

[0087] The step S5 specifically includes:

[0088] Step S5.1: Establish inflexibility deficiency and redundancy probability indicators:

[0089] Probability CDF of insufficient upward / downward flexibility or redundant interval at time t t U / D , expressed as:

[0090]

[0091] Where: CDF t U / D is positive, indicating the probability of flexibility redundancy, CDF t U / D is negative, indicating the opposite of the probability of insufficient flexibility;

[0092] Step S5.2: Establish flexibility margin index:

[0093] fleP t U / D =P t U / D,gong -P t U / D,xu (37)

[0094] Where: flep t U / D is the upward / downward flexibility margin at time t within the scheduling period, where a positive value indicates flexibility redundancy and a negative value indicates insufficiency;

[0095] Step S5.3: Establish a mathematical model for flexibility supply incentives:

[0096]

[0097] Where: δ1 and δ2 are the equalization coefficients; λ U,p and λ D,p are upward / downward flexibility probability compensation coefficients; U,red and λ D,red They are the upward / downward flexibility margin compensation coefficients respectively.

[0098] The step S6 specifically includes:

[0099] Step S6.1: Establishing a virtual power plant operator benefit model:

[0100] Clearing electricity price The constraints are:

[0101]

[0102] Where: and Indicates the minimum / maximum value of the internal electricity price of the virtual power plant at time t.

[0103] The virtual power plant operator benefit function includes the power supply income C NE , Cost and income of electricity purchased and sold to external parties C grid , Virtual power plant daily reward and penalty carbon trading cost C C , expressed as:

[0104] maxI VPPO =C NE -C grid -C C (40)

[0105]

[0106] Where: P t Y is the load power at time t after demand response; and are the purchase / sale electricity prices of the virtual power plant to the grid at time t; P t buy and P t sell They are the amount of electricity purchased / sold by the virtual power plant from the power grid at time t;

[0107] Step S6.2: Establishing the load aggregator benefit model:

[0108] The load aggregator benefit function includes the smoothing net load incentive C jing , interrupt load excitation C zd , shift loads to save electricity costs C py、Electric vehicle response cost C EV 、HVAC response stimulus C HVAC 、Electricity cost of methanol park C MSS , Flexible supply incentives C CDF , expressed as:

[0109]

[0110] C jing =M-ασ(P t PV +P t W -P t Y ) (44)

[0111]

[0112] Where: M and α are net load incentive coefficients; P t Y为 The load demand after each part participates in the virtual power plant response at time t; * is a constant between the maximum and minimum electricity prices; a zd and b zd are the interruption load penalty coefficients; bat is the charging and discharging cost of electric vehicles; η EV is the charging efficiency of electric vehicles; P t base is the basic power of HVAC at time t; The power-on flag of electrolytic cell m at time t, which is a Boolean value;

[0113] Step S6.3: Based on the master-slave game framework, establish a set of flexible resource response strategies:

[0114] The model is expressed as:

[0115]

[0116] In the formula, {VPPO∪LA} is the set of participants in the master-slave game; is the internal electricity price strategy set of the virtual power plant; is the flexible load power strategy set; {δ EV (P t EV )} is the electric vehicle charging and discharging power strategy set; {δ HVAC (P t HVAC )} is the HVAC power strategy set; {δ MSS (P t MSS )} is the methanol park power consumption strategy set; {IVPPO ,I LA} is the master-slave game benefit function set.

[0117] Furthermore, the present invention provides a virtual power plant resource optimization scheduling system considering source and load uncertainty, comprising:

[0118] Renewable energy scenario establishment unit, used to establish wind power generation and photovoltaic power generation scenarios: using coupling function theory to characterize the synergistic and complementary correlation between wind power generation and photovoltaic power generation in the same area, and generating a wind-solar output correlation set, then using the average influence value method to reduce the dimension of the wind-solar output correlation set, and using the agglomerative hierarchical clustering algorithm to cluster the wind-solar output correlation set, to obtain the required wind power generation and photovoltaic power generation scenarios;

[0119] A virtual power plant flexibility demand model building unit is used to build a virtual power plant flexibility demand model: based on the uncertainty of wind power generation and photovoltaic power generation and the uncertainty of load demand, a virtual power plant flexibility demand model of the concerned time scale is built;

[0120] The demand response model and flexibility supply model establishment unit is used to establish the demand response model and flexibility supply model: according to the characteristics of the demand-side flexibility resources, the demand response model and flexibility supply model of the electric hydrogen synthesis methanol system, active load resources, electric vehicles and HVAC are established;

[0121] A probability mass function and cumulative distribution function establishment unit is used to establish the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant: based on the historical data of wind power generation, photovoltaic power generation and load demand, the flexibility demand at each historical moment under the time scale of interest is calculated, and the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant are established;

[0122] The flexibility quantitative index establishment unit is used to establish flexibility quantitative indicators that take into account foreseeable and unforeseeable flexibility needs: based on the cumulative distribution function, establish virtual power plant flexibility deficiency and redundancy probability indicators and flexibility margin indicators, and establish a flexibility supply incentive mathematical model;

[0123] The virtual power plant clearing unit is used to formulate the clearing electricity price and flexibility resource response strategy: a master-slave game model between virtual power plant operators and load aggregators is established to solve the clearing electricity price that satisfies the optimal benefit function of virtual power plant operators and load aggregators, and obtain the response strategy of flexibility resources.

[0124] The present invention has the following advantages:

[0125] In view of the characteristics of each participant in a virtual power plant with a high proportion of renewable energy, the present invention adopts the coupling function theory to generate a collection of wind and solar power outputs, adopts the average influence value method to reduce the dimension of the collection, and then obtains the typical scenario by the agglomerative hierarchical algorithm, and then obtains the flexibility demand of the virtual power plant from the typical wind and solar scenario and the predicted load output; then, considering the characteristics of multiple flexibility resources, a flexibility supply model in which multiple flexibility resources participate in the response of the virtual power plant is established; secondly, a flexibility quantitative index that considers both foreseeable and unforeseen flexibility demands is proposed, and a flexibility supply incentive mathematical model is established; finally, a master-slave game framework is used to solve the model and determine the clearing electricity price of the virtual power plant, and it is explained that the flexibility margin of the virtual power plant is significantly increased after considering the flexibility supply incentive, and the hydrogen production and methanol synthesis system saves the hydrogen storage and transportation costs while realizing the low-carbon economic operation of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0126] Figure 1 This is a flow chart of a virtual power plant resource optimization scheduling method considering source and load uncertainty of the present invention;

[0127] Figure 2a and Figure 2b A wind power generation and photovoltaic power generation output correlation set diagram shown in an embodiment of the present invention;

[0128] Figure 3 A diagram of a virtual power plant clearing electricity price according to an embodiment of the present invention;

[0129] Figure 4 A response strategy diagram of a methanol synthesis system using hydrogen from electricity according to an embodiment of the present invention;

[0130] Figure 5 An active load response strategy diagram is shown for an embodiment of the present invention;

[0131] Figure 6 An electric vehicle cluster response strategy diagram shown in an embodiment of the present invention;

[0132] Figure 7 A HVAC cluster response strategy diagram is shown for an embodiment of the present invention;

[0133] Figure 8a and Figure 8b A diagram of flexibility demand and supply of a virtual power plant shown for an embodiment of the present invention;

[0134] Fig. 9 This is a structural block diagram of a virtual power plant resource optimization scheduling system taking into account source and load uncertainty according to an embodiment of the present invention. DETAILED DESCRIPTION

[0135] The technical solution of the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings. Figure 1The present invention provides a virtual power plant resource optimization scheduling method considering source and load uncertainty, comprising the steps of:

[0136] S1, based on the historical data of wind power generation and photovoltaic power generation, the coupling function theory is used to characterize the synergistic and complementary correlation of wind power generation and photovoltaic power generation in the same area, and the wind-solar output correlation set is generated at the same time. Then, the average influence value method is used to reduce the dimension of the wind-solar output correlation set, and the agglomerative hierarchical clustering algorithm is used to cluster the wind-solar output correlation set to obtain the required wind power generation and photovoltaic power generation scenarios;

[0137] The above step S1 specifically includes:

[0138] Kernel density estimation is used to calculate the probability density and distribution function of historical data of wind and solar power generation; the Frank coupling function is used to describe the joint distribution of wind and solar power output, and a specified number of random values ​​of the marginal distribution function are generated; by inverting the random values ​​of the marginal distribution function, a set of G wind and solar power outputs with spatiotemporal correlation is obtained; the average influence value method is used to calculate the individual influence of each time dimension of the wind and solar power output collection on the average contour coefficient, and the time dimension with less influence is eliminated from the original wind and solar power output collection; the Euclidean distance between each sample of the new wind and solar power output collection is calculated, and the samples are clustered by the sum of squared deviations and links to construct a hierarchical clustering tree structure of the new wind and solar power output collection; the constructed tree structure is divided, and the wind power and photovoltaic scenes are respectively aggregated into a specified number of clusters, and a cluster label is added to each sample, and the center point of each cluster cluster is used as the reduced scene G1.

[0139] S2, based on the uncertainty of wind and photovoltaic power generation and the uncertainty of load demand, establish a virtual power plant flexibility demand model for the time scale of interest;

[0140] The above step S2 specifically includes:

[0141] According to the wind and solar power output scenario G1, the quantitative model of the virtual power plant flexibility demand considering the wind and solar power output and load demand with the maximum fluctuation error is expressed as:

[0142]

[0143] Where: F t load,U,xu and F t load,D,xu are the upward and downward flexibility requirements of load at time t respectively; P t load is the load demand forecast value at time t; is the maximum error coefficient of load at time t; F t W,U,xu and F t W,D,xuThey are the upward and downward flexibility requirements of wind power generation in the wind and solar power output scenario G1 at time t; P t W is the wind power generation prediction value of the wind and solar power output scenario G1 at time t; t W F is the maximum error coefficient of wind power generation in the wind and solar power output scenario G1 at time t; t PV,U,xu and F t PV,D,xu They are the upward and downward flexibility demands of photovoltaic power generation in the wind and solar power output scenario G1 at time t; P t PV is the predicted value of photovoltaic power generation in the wind and solar power output scenario G1 at time t; t PV It is the maximum error coefficient of photovoltaic power generation in the wind and solar power output scenario G1 at time t.

[0144] S3, based on the characteristics of demand-side flexibility resources, establish demand response models and flexibility supply models for hydrogen production and methanol synthesis systems, active load resources, electric vehicles and HVAC;

[0145] The above step S3 specifically includes:

[0146] 1) Electricity-generated hydrogen-to-methanol synthesis system

[0147] Energy consumption of hydrogen production by electrolysis of water in industrial-grade electric hydrogen production and methanol synthesis system About P t MSS The main part is to combine multiple electrolyzers to produce hydrogen to meet the hydrogen demand, and the remaining energy consumption is the energy consumption of synthesizing methanol P t Me , Hydrogen compressor energy consumption Energy consumption of CO2 compressor It is expressed as:

[0148]

[0149] Where: is the energy consumption coefficient of hydrogen production from electricity; and The hydrogen produced by electrolysis of water at time t directly participates in the synthesis of methanol and the flow rate of hydrogen charged into the hydrogen storage tank; and They are the start, standby and shutdown status flags, which are Boolean values; is the maximum power of the electrolyzer; and are the energy consumption coefficients of the reaction tower feed compressor; k Me is the energy consumption coefficient of methanol synthesis in the reaction tower; and They are the methanol reaction progress and minimum reaction progress of the reaction tower at time t respectively; is the carbon dioxide flow rate produced in the reaction at time t; and are the molar masses of hydrogen and carbon dioxide, respectively; and are the carbon dioxide pressures at the inlet and outlet, respectively; and are the hydrogen pressures at the inlet and outlet, respectively; R is a thermodynamic constant; T is room temperature; η c is the compressor efficiency.

[0150] The relationship between the feed flow rate of hydrogen and carbon dioxide catalytic synthesis of methanol system and the methanol yield and reaction progress is expressed as:

[0151]

[0152] Where: is the methanol yield at time t; is the hydrogen flow rate released from the hydrogen storage tank at time t; and Respectively represent the raw material ratio; is the daily methanol production; γ Me is the ratio of methanol reaction progress to feedstock flow rate;

[0153] The maximum low-load operating time limit of the model and the minimum downtime limit of the electrolyzer are expressed as:

[0154]

[0155] Where: It is the low load operation flag of the synthetic methanol reaction tower, which is a Boolean value; and The maximum operating time at low load and the minimum downtime of the proton exchange membrane electrolyzer are respectively;

[0156] The hydrogen storage tank model is expressed as:

[0157]

[0158] Where: is the hydrogen storage capacity in the hydrogen storage tank at time t; and are the minimum / maximum capacity of the hydrogen storage tank respectively; and They are respectively the charge and discharge status flags, which are Boolean quantities; is the flow rate of hydrogen released from the hydrogen storage tank at time t; and are the maximum flow rates for filling and releasing hydrogen into the hydrogen storage tank, respectively;

[0159] Minimum methanol yield at time t It is expressed as:

[0160]

[0161] Where: is the maximum value of the methanol yield that can be reduced at time t, specifically expressed as:

[0162]

[0163] Where: and are the maximum / minimum methanol yield, respectively;

[0164] Hydrogen storage capacity of the hydrogen storage tank at time t It is expressed as:

[0165]

[0166] The minimum power consumption of the system for synthesizing methanol by electrolysis of water to produce hydrogen at time t is expressed as:

[0167]

[0168] Where: The power consumption when the minimum methanol yield is considered at time t but there is insufficient hydrogen in the hydrogen storage tank; It is the power consumption when the minimum methanol yield is considered at time t and there is sufficient hydrogen in the hydrogen storage tank.

[0169] The upward flexibility supply F of the synthetic methanol system at time t t MSS,U,gong , expressed as:

[0170]

[0171] Maximum methanol yield at time t It is expressed as:

[0172]

[0173] Where: is the maximum value of the methanol yield at time t, which is specifically expressed as:

[0174]

[0175] The free capacity of the hydrogen storage tank at time t It is expressed as:

[0176]

[0177] The maximum power consumption of the system for synthesizing methanol by electrolysis of water to produce hydrogen at time t is expressed as:

[0178]

[0179] Where: The power consumption when the maximum methanol yield is considered at time t and the hydrogen storage tank is filled with hydrogen; The power consumption at time t when the maximum methanol yield is considered and the electrolyzer is running at full load; It is the upper limit of the load variation range of the proton exchange membrane electrolyzer.

[0180] The downward flexible supply F of the synthetic methanol system at time t t MSS,D,gong , expressed as:

[0181]

[0182] 2) Active load resources

[0183] Active loads include translatable loads and interruptible loads, expressed as:

[0184]

[0185] Where: P t py is the load power that can be translated at time t; is the power of the mth type of interruptible load at time t; P t load is the load demand power at time t; py and is the proportion of the movable load and the mth type interruptible load in the total load; and are the minimum and maximum power that can be translated at time t respectively; Climbing for maximum translatable power; is the maximum interruptible power of the mth category at time t; This is the maximum interruptible power ramp.

[0186] Can translate loads upward and downward flexibility supply F t py,U,gong and F t py,D,gong It is expressed as:

[0187]

[0188] Interruptible load supply up and down flexibility F t zd,m,U,gong and F t zd,m,D,gong It is expressed as:

[0189]

[0190] 3) Electric vehicles

[0191] The charging and discharging working model of a single electric vehicle is expressed as:

[0192]

[0193] Where: is the battery capacity of the i-th electric car in period t; η EV is the charging / discharging efficiency of the i-th electric vehicle in period t; is the charging power of the i-th electric vehicle in period t; is the discharge power of the i-th electric vehicle in period t; is the distance traveled by the i-th electric vehicle in period t; S i,t It is the electric power consumed by the i-th electric vehicle per unit driving distance in period t. and are the maximum charging / discharging power of the i-th electric vehicle in period t respectively; and are the charging / discharging status of the i-th electric vehicle in period t, Boolean values; is the maximum battery capacity of the i-th electric vehicle; and Respectively, they are the minimum / maximum limits of battery power.

[0194] Upward and downward flexibility of electric vehicle cluster supply F t EV,U,gong and F t EV,D,gong It is expressed as:

[0195]

[0196] Where: is the battery power of the electric vehicle cluster at time t; and They are the minimum and maximum charging and discharging power of the electric vehicle cluster at time t respectively; and They are the minimum and maximum values ​​of the virtual battery power of the electric vehicle cluster at time t respectively.

[0197] 4) HVAC

[0198] The relationship between HVAC power consumption and temperature is expressed as:

[0199]

[0200] Where: and T t outare the indoor temperature and outdoor temperature of the jth HVAC at time t; and are the parameters related to the thermal capacity, thermal resistance and energy efficiency coefficient of the jth HVAC; is the power consumption of the jth HVAC at time t; is the switch state, Boolean value; and are the minimum and maximum power consumption of the jth HVAC, respectively; and are the minimum and maximum values ​​of the comfortable temperature range respectively.

[0201] Upward and downward flexibility of HVAC cluster supply F t HVAC,U,gong and F t HVAC,D,gong It is expressed as:

[0202]

[0203] Where: P t HVAC is the power consumption of the HVAC cluster at time t; and are the minimum and maximum power consumption of the HVAC cluster at time t, which is affected by the comfortable temperature range and the indoor and outdoor temperatures at each time, and is expressed as:

[0204]

[0205] S4, based on the historical data of wind power generation, photovoltaic power generation and load demand, calculate the flexibility demand at each historical moment under the time scale of interest, and establish the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant;

[0206] The above step S4 specifically includes:

[0207] 1) Calculate the upward / downward flexibility requirements at each moment in the time scale of interest using historical data sets;

[0208] 2) Assuming that there are N moments with upward flexibility demand and M moments with downward flexibility demand in the historical data set, the probability of upward / downward flexibility demand at each moment is and The probability mass function of upward / downward flexibility demand is constructed as:

[0209]

[0210] Where: and are the i-th value of upward / downward flexibility demand in the historical data set; ni and m i Take the upward / downward flexibility requirements in the historical data set as and The number of ; τ is the time scale of interest;

[0211] 3) The continuous approximate cumulative distribution function is expressed as:

[0212]

[0213] Where: P U,xu and P D,xu They are respectively the upward / downward flexibility requirements for the cumulative probability in the optimal scheduling.

[0214] S5, based on the cumulative distribution function, establish the virtual power plant flexibility deficiency and redundancy probability index and flexibility sufficiency index, and establish a flexibility supply incentive mathematical model;

[0215] The above step S5 specifically includes:

[0216] 1) Insufficient flexibility and redundant probability indicators

[0217] Probability CDF of insufficient upward / downward flexibility or redundant interval at time t t U / D , expressed as:

[0218]

[0219] Where: CDF t U / D is positive, indicating the probability of flexibility redundancy, CDF t U / D A negative value indicates the opposite of the probability of insufficient flexibility.

[0220] 2) Flexibility margin index

[0221] fleP t U / D =P t U / D,gong -P t U / D,xu (37)

[0222] Where: flep t U / D It is the upward / downward flexibility margin at time t within the scheduling period. A positive value indicates flexibility redundancy, while a negative value indicates insufficiency.

[0223] The flexibility supply incentive is expressed as:

[0224]

[0225] Where: δ1 and δ2 are the equalization coefficients; λ U,p and λ D,p are upward / downward flexibility probability compensation coefficients; U,red and λ D,red They are the upward / downward flexibility margin compensation coefficients respectively.

[0226] S6, based on the master-slave game framework, establishes a master-slave game model between virtual power plant operators and load aggregators, solves the clearing electricity price that satisfies the optimal benefit function of virtual power plant operators and load aggregators, and obtains the response strategy of flexible resources.

[0227] The above step S6 specifically includes:

[0228] 1) Virtual power plant operator benefit model

[0229] Clearing electricity price The constraints are:

[0230]

[0231] Where: and Indicates the minimum / maximum value of the internal electricity price of the virtual power plant at time t.

[0232] The virtual power plant operator benefit function includes the power supply income C NE , Cost and income of electricity purchased and sold to external parties C grid , Virtual power plant daily reward and penalty carbon trading cost C C , expressed as:

[0233] maxI VPPO =C NE -C grid -C C (40)

[0234]

[0235] Where: P t Y is the load power at time t after demand response; and are the purchase / sale electricity prices of the virtual power plant to the grid at time t; P t buy and P t sell They are respectively the amount of electricity purchased / sold by the virtual power plant to the power grid at time t.

[0236] 2) Load Aggregator Benefit Model

[0237] The load aggregator benefit function includes the smoothing net load incentive C jing, interrupt load excitation C zd , shift loads to save electricity costs C py 、Electric vehicle response cost C EV 、HVAC response stimulus C HVAC 、Electricity cost of methanol park C MSS , Flexible supply incentives C CDF , expressed as:

[0238]

[0239] C jing =M-ασ(P t PV +P t W -P t Y ) (44)

[0240]

[0241] Where: M and α are net load incentive coefficients; P t Y is the load demand after each part participates in the virtual power plant response at time t; * is a constant between the maximum and minimum electricity prices; a zd and b zd are the interruption load penalty coefficients; bat is the charging and discharging cost of electric vehicles; η EV is the charging efficiency of electric vehicles; P t base is the basic power of HVAC at time t; It is the power-on flag of electrolytic cell m at time t, which is a Boolean value.

[0242] 3) Master-slave game framework

[0243] The master-slave model is represented as:

[0244]

[0245] In the formula, {VPPO∪LA} is the set of participants in the master-slave game; is the internal electricity price strategy set of the virtual power plant; is the flexible load power strategy set; {δ EV (P t EV )} is the electric vehicle charging and discharging power strategy set; {δ HVAC (P t HVAC )} is the HVAC power strategy set; {δ MSS (P tMSS )} is the methanol park power consumption strategy set; {I VPPO ,I LA} is the set of benefit functions of the master-slave game.

[0246] As shown in Figure 2, the scenario obtained by clustering the wind and solar output correlation set established in step S1 using the annual power generation data of 4 wind power plants and 3 photovoltaic power stations in North China, Central China and Northwest China from 2019 to 2020 as the historical data of renewable output is shortened from 0.556826 seconds to 0.259239 seconds by the clustering algorithm after dimensionality reduction using the average influence algorithm. With the increase of raw data, the advantages will be more obvious.

[0247] Through the master-slave game model established in step 6, when the solution model reaches the Stackelberg equilibrium, the electricity price strategy of the virtual power plant operator will no longer change, and both parties in the game cannot change their strategies to increase the value of their benefit function. Finally, the clearing electricity price of the virtual power plant is obtained as follows: Figure 3 As shown in the figure, the clearing price of the virtual power plant is similar to the net load (the difference between the wind and solar power output forecast and the load forecast). The price is higher when the net load is larger, and lower when the net load is smaller. Thus, the price guides the flexible resources to shift from high-price periods to low-price periods. The response strategy of the flexible resources is as follows: Figure 4 As shown in Figure 7, the flexible resources aggregated by the load aggregator include the methanol synthesis system of hydrogen production from electricity, active load, electric vehicle group and HVAC group. The response of each flexible resource basically follows: during the period when the net load is small and the electricity price is low, the methanol synthesis system of hydrogen production from electricity consumes more electricity and the methanol synthesis rate is higher; in the active load, a large amount of load that can be shifted is moved into this period to reduce electricity costs, and a small amount of interruptible load is removed; the electric vehicle cluster charges at a large power, but does not participate in the response in some periods; the HVAC cluster increases the power consumption to obtain response compensation, and in some periods, the HVAC cluster maintains the room temperature at the set temperature, and a small amount of HVAC participates in the response. During the period when the net load is large and the electricity price is high in some periods, the methanol synthesis system of hydrogen production from electricity consumes less electricity, the methanol synthesis rate decreases, and the hydrogen storage generally increases first and then decreases; in the active load, a large amount of load that can be shifted is moved out of this period to reduce electricity costs, and a large amount of interruptible load is removed to obtain response compensation; in some periods, the electric vehicle cluster discharges at the maximum power to obtain benefits, and in some periods, the electric vehicle cluster does not charge or discharge; the HVAC cluster reduces the power consumption in most periods to obtain response compensation. The electricity price guided the flexible resources to achieve the "peak shaving" of the load curve, among which the "peak shaving" was particularly obvious, with the load peak dropping from 302.019MW to 281.401MW. At the same time, the net load variance changed from 616.4205 to 79.5322, avoiding the impact of the large fluctuation of the purchased power of the virtual power plant on the large power grid.

[0248] As shown in Figure 8, considering the virtual power plant's insufficient flexibility and redundancy probability index, flexibility margin index, and flexibility supply incentive mathematical model established in step 5, the flexibility supply incentive is added to the load aggregator benefit function of the master-slave game framework, which increases the flexibility margin inside the virtual power plant. Considering the flexibility index and mathematical incentive model proposed in this paper, the upward flexibility satisfaction probability index is reduced from 2.8366 to 0.93198, the downward flexibility satisfaction probability index is reduced from 7.908 to 1.9141, the upward flexibility redundancy is increased from 788.2152 to 987.105, and the downward flexibility redundancy is increased from 357.2482 to 510.1662. All indicators have been optimized, which significantly increases the flexibility margin of the virtual power plant and improves its ability to meet foreseeable and unforeseen flexibility needs.

[0249] like Fig. 9 As shown, a virtual power plant resource optimization scheduling system considering source and load uncertainty specifically includes:

[0250] A renewable energy scenario establishment unit is used to establish scenarios for wind power generation and photovoltaic power generation. It is characterized by using coupling function theory to characterize the synergistic and complementary correlation between wind power generation and photovoltaic power generation in the same area and generating a wind-solar output correlation set at the same time. Then, the average influence value method is used to reduce the dimension of the wind-solar output correlation set, and the agglomerative hierarchical clustering algorithm is used to cluster the wind-solar output correlation set to obtain the required wind power generation and photovoltaic power generation scenarios.

[0251] A virtual power plant flexibility demand model building unit is used to build a virtual power plant flexibility demand model, characterized in that a virtual power plant flexibility demand model of a concerned time scale is built according to the uncertainty of wind power generation and photovoltaic power generation and the uncertainty of load demand;

[0252] A demand response model and flexibility supply model establishment unit, used to establish a demand response model and a flexibility supply model, characterized in that, according to the characteristics of the demand-side flexibility resources, a demand response model and a flexibility supply model for the electric hydrogen production and methanol synthesis system, active load resources, electric vehicles and HVAC are established;

[0253] A probability mass function and cumulative distribution function establishing unit, used for establishing the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant, characterized in that the flexibility demand at each historical moment under the time scale of interest is calculated based on the historical data of wind power generation, photovoltaic power generation and load demand, and the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant are established;

[0254] A flexibility quantitative index establishment unit is used to establish a flexibility quantitative index that takes into account foreseeable and unforeseeable flexibility needs, characterized in that, based on the cumulative distribution function, a virtual power plant flexibility deficiency and redundancy probability index and a flexibility margin index are established, and a flexibility supply incentive mathematical model is established;

[0255] The virtual power plant clearing unit is used to formulate the clearing electricity price and flexibility resource response strategy. It is characterized by establishing a master-slave game model between the virtual power plant operator and the load aggregator, solving the clearing electricity price that satisfies the optimal benefit function of the virtual power plant operator and the load aggregator, and obtaining the flexibility resource response strategy.

[0256] The protection scope of the present invention is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the scope and spirit of the present invention. If these changes and modifications fall within the scope of the claims of the present invention and their equivalents, the present invention is intended to include these changes and modifications.

Claims

1. A virtual power plant resource optimization scheduling method considering source and load uncertainty, characterized in that: The specific steps include: S1, based on the historical data of wind power generation and photovoltaic power generation, the coupling function theory is used to characterize the synergistic and complementary correlation of wind power generation and photovoltaic power generation in the same area, and a wind-solar output correlation set is generated at the same time. Then, the average influence value method is used to reduce the dimension of the wind-solar output correlation set, and the agglomerative hierarchical clustering algorithm is used to cluster the wind-solar output correlation set to obtain the required wind power generation and photovoltaic power generation scenarios; S2, based on the uncertainty of wind and photovoltaic power generation and the uncertainty of load demand, establish a virtual power plant flexibility demand model for the time scale of interest; S3, based on the characteristics of demand-side flexibility resources, establish demand response models and flexibility supply models for hydrogen production and methanol synthesis systems, active load resources, electric vehicles and HVAC; S4, based on the historical data of wind power generation, photovoltaic power generation and load demand, calculate the flexibility demand at each historical moment under the time scale of interest, and establish the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant; S5, based on the cumulative distribution function, establish the virtual power plant flexibility deficiency and redundancy probability index and flexibility sufficiency index, and establish a flexibility supply incentive mathematical model; S6, based on the master-slave game framework, establishes a master-slave game model between virtual power plant operators and load aggregators, solves the clearing electricity price that satisfies the optimal benefit function of virtual power plant operators and load aggregators, and obtains the response strategy of flexible resources.

2. A virtual power plant resource optimization scheduling method considering source and load uncertainty as claimed in claim 1, characterized in that: The step S1 specifically includes: Kernel density estimation is used to calculate the probability density and distribution function of historical data of wind and solar power generation; Frank coupling function is used to describe the joint distribution of wind and solar power output, and a specified number of random values ​​of marginal distribution function are generated; by inverting the random values ​​of marginal distribution function, G groups of wind and solar power output sets with spatiotemporal correlation are obtained; the average influence value method is used to calculate the individual influence of each time dimension of the wind and solar power output collection on the average contour coefficient, and the time dimension with less influence is eliminated from the original wind and solar power output collection; the Euclidean distance between each sample of the new wind and solar power output collection is calculated, and each sample is clustered by linking the squared sum of deviations to construct a hierarchical clustering tree structure of the new wind and solar power output collection; the constructed tree structure is divided, and the wind power and photovoltaic scenes are respectively aggregated into a specified number of clusters, and cluster labels are added to each sample, and the center point of each cluster cluster is used as the reduced wind and solar power output scene G1 as the required wind power generation and photovoltaic power generation scenes.

3. A virtual power plant resource optimization scheduling method considering source and load uncertainty as claimed in claim 1, characterized in that: The step S2 specifically includes: According to the wind power generation and photovoltaic power generation scenarios obtained in step S1, the wind and photovoltaic output and load demand with the maximum fluctuation error are considered, and the virtual power plant flexibility demand quantitative model of the time scale of interest is established as follows: Where: F t load,U,xu and F t load,D,xu are the upward and downward flexibility requirements of load at time t respectively; P t load is the load demand forecast value at time t; is the maximum error coefficient of load at time t; F t W,U,xu and F t W,D,xu They are the upward and downward flexibility requirements of wind power generation in the wind and solar power output scenario G1 at time t; P t W is the wind power generation prediction value of the wind and solar power output scenario G1 at time t; t W F is the maximum error coefficient of wind power generation in the wind and solar power output scenario G1 at time t; t PV,U,xu and F t PV,D,xu They are the upward and downward flexibility demands of photovoltaic power generation in the wind and solar power output scenario G1 at time t; P t PV is the predicted value of photovoltaic power generation in the wind and solar power output scenario G1 at time t; It is the maximum error coefficient of photovoltaic power generation in the wind and solar power output scenario G1 at time t.

4. A virtual power plant resource optimization scheduling method considering source and load uncertainty as claimed in claim 1, characterized in that: The step S3 specifically includes: Step S3.1, establish a demand response model and flexibility supply model for the electric hydrogen production and methanol synthesis system: Energy consumption of hydrogen production by electrolysis of water in industrial-grade electric hydrogen production and methanol synthesis system Total energy consumption t MSS The main part is to combine multiple electrolyzers to produce hydrogen to meet the hydrogen demand, and the remaining energy consumption is the energy consumption of synthesizing methanol P t Me , Hydrogen compressor energy consumption Energy consumption of CO2 compressor The energy consumption relationship is expressed as: Where: is the energy consumption coefficient of hydrogen production from electricity; and The hydrogen produced by electrolysis of water at time t directly participates in the synthesis of methanol and the flow rate of hydrogen charged into the hydrogen storage tank; and They are the start, standby and shutdown status flags, which are Boolean values; is the maximum power of the electrolyzer; and are the energy consumption coefficients of the reaction tower feed compressor; k Me is the energy consumption coefficient of methanol synthesis in the reaction tower; and They are the methanol reaction progress and minimum reaction progress of the reaction tower at time t respectively; is the carbon dioxide flow rate produced in the reaction at time t; and are the molar masses of hydrogen and carbon dioxide, respectively; and are the carbon dioxide pressures at the inlet and outlet, respectively; and are the hydrogen pressures at the inlet and outlet, respectively; R is a thermodynamic constant; T is room temperature; η c is the compressor efficiency; The relationship between the feed flow rate of hydrogen and carbon dioxide catalytic synthesis of methanol system and the methanol yield and reaction progress is expressed as: Where: is the methanol yield at time t; is the hydrogen flow rate released from the hydrogen storage tank at time t; and Respectively represent the raw material ratio; is the daily methanol production; γ Me is the ratio of methanol reaction progress to feedstock flow rate; The maximum low-load operating time limit of the model and the minimum downtime limit of the electrolyzer are expressed as: Where: It is the low load operation flag of the synthetic methanol reaction tower, which is a Boolean value; and The maximum operating time at low load and the minimum downtime of the proton exchange membrane electrolyzer are respectively; The hydrogen storage tank model is expressed as: Where: is the hydrogen storage capacity in the hydrogen storage tank at time t; and are the minimum / maximum capacity of the hydrogen storage tank respectively; and They are charge and discharge status flags, which are Boolean values; is the flow rate of hydrogen released from the hydrogen storage tank at time t; and are the maximum flow rates for filling and releasing hydrogen into the hydrogen storage tank, respectively; Minimum methanol yield at time t It is expressed as: Where: is the maximum value of the methanol yield that can be reduced at time t, specifically expressed as: Where: and are the maximum / minimum methanol yield, respectively; Hydrogen storage capacity of the hydrogen storage tank at time t It is expressed as: The minimum power consumption of the system for synthesizing methanol by electrolysis of water to produce hydrogen at time t is expressed as: Where: The power consumption when the minimum methanol yield is considered at time t but there is insufficient hydrogen in the hydrogen storage tank; The power consumption at time t when the minimum methanol yield is considered and there is sufficient hydrogen in the hydrogen storage tank; The upward flexibility supply F of the synthetic methanol system at time t t MSS,U,gong , expressed as: Maximum methanol yield at time t It is expressed as: Where: is the maximum value of the methanol yield at time t, which is specifically expressed as: The free capacity of the hydrogen storage tank at time t It is expressed as: The maximum power consumption of the system for synthesizing methanol by electrolysis of water to produce hydrogen at time t is expressed as: Where: The power consumption when the maximum methanol yield is considered at time t and the hydrogen storage tank is filled with hydrogen; The power consumption at time t when the maximum methanol yield is considered and the electrolyzer is running at full load; is the upper limit of the load variation range of the proton exchange membrane electrolyzer; The downward flexible supply F of the synthetic methanol system at time t t MSS,D,gong , expressed as: Step S3.2: Establish a demand response model and flexibility supply model for active load resources: Active loads include translatable loads and interruptible loads, expressed as: Where: P t py is the load power that can be translated at time t; is the power of the mth type of interruptible load at time t; P t load is the load demand power at time t; py and is the proportion of the movable load and the mth type interruptible load in the total load; and are the minimum and maximum power that can be translated at time t respectively; Climbing for maximum translatable power; is the maximum interruptible power of the mth category at time t; It is the maximum interruptible power ramp; Can translate loads upward and downward flexibility supply F t py,U,gong and F t py,D,gong It is expressed as: Interruptible load supply up and down flexibility F t zd,m,U,gong and F t zd,m,D,gong It is expressed as: Step S3.3: Establishing a demand response model and flexibility supply model for electric vehicles: The charging and discharging working model of a single electric vehicle is expressed as: Where: is the battery capacity of the i-th electric car in period t; η EV is the charging / discharging efficiency of the i-th electric vehicle in period t; is the charging power of the i-th electric vehicle in period t; is the discharge power of the i-th electric vehicle in period t; is the distance traveled by the i-th electric vehicle in period t; S i,t is the electric power consumed by the i-th electric vehicle per unit driving distance in period t; and are the maximum charging / discharging power of the i-th electric vehicle in period t respectively; and are the charging / discharging status of the i-th electric vehicle in period t, Boolean values; is the maximum battery capacity of the i-th electric vehicle; and They are the minimum / maximum limits of battery power respectively; Upward and downward flexibility of electric vehicle cluster supply F t EV,U,gong and F t EV,D,gong It is expressed as: Where: is the battery power of the electric vehicle cluster at time t; and They are the minimum and maximum charging and discharging power of the electric vehicle cluster at time t respectively; and They are the minimum and maximum values ​​of the virtual battery power of the electric vehicle cluster at time t; Step S3.4: Establish HVAC demand response model and flexibility supply model: The relationship between HVAC power consumption and temperature is expressed as: Where: and T t out are the indoor temperature and outdoor temperature of the jth HVAC at time t; and are the parameters related to the thermal capacity, thermal resistance and energy efficiency coefficient of the jth HVAC; is the power consumption of the jth HVAC at time t; is the switch state, Boolean value; and are the minimum and maximum power consumption of the jth HVAC, respectively; and are the minimum and maximum values ​​of the comfortable temperature range respectively; Upward and downward flexibility of HVAC cluster supply F t HVAC,U,gong and F t HVAC,D,gong It is expressed as: Where: P t HVAC is the power consumption of the HVAC cluster at time t; and are the minimum and maximum power consumption of the HVAC cluster at time t, which is affected by the comfortable temperature range and the indoor and outdoor temperatures at each time, and is expressed as:

5. The method for optimizing resource scheduling of a virtual power plant considering source and load uncertainty according to claim 1, characterized in that: The step S4 specifically includes: Step S4.1, calculate the upward / downward flexibility demand at each moment in the time scale of interest through the historical data set; Step S4.2: Assuming that there are N moments with upward flexibility demand and M moments with downward flexibility demand in the historical data set, the probability of upward / downward flexibility demand at each moment is and The probability mass function of upward / downward flexibility demand is constructed as: Where: and are the i-th value of upward / downward flexibility demand in the historical data set; n i and m i Take the upward / downward flexibility requirements in the historical data set as and τ is the time scale of interest; Step S4.3, the continuous approximate cumulative distribution function is expressed as: Where: P U,xu and P D,xu They are respectively the upward / downward flexibility requirements for the cumulative probability in the optimal scheduling.

6. A virtual power plant resource optimization scheduling method considering source and load uncertainty as claimed in claim 1, characterized in that: The step S5 specifically includes: Step S5.1: Establish inflexibility deficiency and redundancy probability indicators: Probability CDF of insufficient upward / downward flexibility or redundant interval at time t t U / D , expressed as: Where: CDF t U / D is positive, indicating the probability of flexibility redundancy, CDF t U / D is negative, indicating the opposite of the probability of insufficient flexibility; Step S5.2: Establish flexibility margin index: fleP t U / D =P t U / D,gong -P t U / D,xu (37) Where: flep t U / D is the upward / downward flexibility margin at time t within the scheduling period, where a positive value indicates flexibility redundancy and a negative value indicates insufficiency; Step S5.3: Establish a mathematical model for flexibility supply incentives: Where: δ1 and δ2 are the equalization coefficients; λ U,p and λ D,p are upward / downward flexibility probability compensation coefficients; U,red and λ D,red They are the upward / downward flexibility margin compensation coefficients respectively.

7. A virtual power plant resource optimization scheduling method considering source and load uncertainty as claimed in claim 1, characterized in that: The step S6 specifically includes: Step S6.1: Establishing a virtual power plant operator benefit model: Clearing electricity price λ t VPP The constraints are: l t min ≤λ t VPP ≤λ t max (39) Where: and Indicates the minimum / maximum value of the internal electricity price of the virtual power plant at time t; The virtual power plant operator benefit function includes the power supply income C NE , Cost and income of electricity purchased and sold to external parties C grid , Virtual power plant daily reward and penalty carbon trading cost C C , expressed as: maxI VPPO =C NE -C grid -C C (40) Where: P t Y is the load power at time t after demand response; and are the purchase / sale electricity prices of the virtual power plant to the grid at time t; P t buy and P t sell They are the amount of electricity purchased / sold by the virtual power plant from the power grid at time t; Step S6.2: Establishing the load aggregator benefit model: The load aggregator benefit function includes the smoothing net load incentive C jing , interrupt load excitation C zd , shift loads to save electricity costs C py 、Electric vehicle response cost C EV 、HVAC response stimulus C HVAC 、Electricity cost of methanol park C MSS , Flexible supply incentives C CDF , expressed as: C jing =M-as(P t PV +P t W -P t Y ) (44) Where: M and α are net load incentive coefficients; P t Y is the load demand after each part participates in the virtual power plant response at time t; * is a constant between the maximum and minimum electricity prices; a zd and b zd are the interruption load penalty coefficients; bat is the charging and discharging cost of electric vehicles; η EV is the charging efficiency of electric vehicles; P t base is the basic power of HVAC at time t; The power-on flag of electrolytic cell m at time t, which is a Boolean value; Step S6.3: Based on the master-slave game framework, establish a set of flexible resource response strategies: The model is expressed as: Where, {VPPO∪LA} is the set of participants in the master-slave game; {ρ(λ t VPP )} is the internal electricity price strategy set of the virtual power plant; is the flexible load power strategy set; {δ EV (P t EV )} is the electric vehicle charging and discharging power strategy set; {δ HVAC (P t HVAC )} is the HVAC power strategy set; {δ MSS (P t MSS )} is the methanol park power consumption strategy set; {I VPPO ,I LA } is the set of benefit functions of the master-slave game.

8. A virtual power plant resource optimization scheduling method considering source and load uncertainty according to any one of claims 1 to 7, characterized in that: It also includes a virtual power plant resource optimization scheduling system that takes into account source and load uncertainty, specifically including: 1) a renewable energy scenario establishment unit, used to establish wind power generation and photovoltaic power generation scenarios; 2) a virtual power plant flexibility demand model establishment unit, used to establish a virtual power plant flexibility demand model; 3) a demand response model and flexibility supply model establishment unit, used to establish a demand response model and a flexibility supply model; 4) a probability mass function and cumulative distribution function establishment unit, used to establish a probability mass function and cumulative distribution function of the virtual power plant flexibility demand; 5) a flexibility quantification index establishment unit, used to establish flexibility quantification indicators that take into account foreseeable and unforeseen flexibility demands; 6) a virtual power plant clearing unit, used to formulate clearing electricity prices and flexibility resource response strategies.

9. A virtual power plant resource optimization scheduling system considering source and load uncertainty as claimed in claim 8, characterized in that: The renewable energy scenario establishment unit uses coupling function theory to characterize the synergistic and complementary correlation between wind power generation and photovoltaic power generation in the same area and generates a wind-solar output correlation set at the same time, then uses the average influence value method to reduce the dimension of the wind-solar output correlation set, and clusters the wind-solar output correlation set by an agglomerative hierarchical clustering algorithm to obtain the required wind power generation and photovoltaic power generation scenarios; The virtual power plant flexibility demand model establishment unit establishes a virtual power plant flexibility demand model for a time scale of interest based on the uncertainty of wind power generation and photovoltaic power generation and the uncertainty of load demand; The demand response model and flexibility supply model establishment unit establishes demand response models and flexibility supply models for the electric hydrogen production and methanol synthesis system, active load resources, electric vehicles, and HVAC according to the characteristics of the demand-side flexibility resources; The probability mass function and cumulative distribution function establishing unit calculates the flexibility demand at each historical moment under the time scale of interest based on the historical data of wind power generation, photovoltaic power generation and load demand, and establishes the probability mass function and cumulative distribution function of the flexibility demand of the virtual power plant; The flexibility quantitative index establishing unit establishes the virtual power plant flexibility deficiency and redundancy probability index and flexibility sufficiency index according to the cumulative distribution function, and establishes a flexibility supply incentive mathematical model; The virtual power plant clearing unit establishes a master-slave game model between the virtual power plant operator and the load aggregator, solves the clearing electricity price that satisfies the optimal benefit function of the virtual power plant operator and the load aggregator, and obtains the response strategy of the flexibility resources.

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