A method for constructing a shared energy storage virtual power plant double-layer optimization model

Abstract

The application discloses a kind of shared energy storage virtual power plant double-layer optimization model construction method, specifically includes the following steps: S01: the minimum model of annual operation cost of upper layer shared energy storage power station is established;S02: the minimum model of annual operation cost of lower layer based on chance constrained programming multi virtual power plant system is established;S03: after solving lower layer model, optimization result is passed to upper layer model, and the best scheduling plan of model is obtained by joint iteration solution, this method considers wind power, photovoltaic uncertainty and the optimization scheduling problem of containing shared energy storage service combined cooling heating and power type multi virtual power plant system, the double-layer optimization model of two different interest subjects is established respectively, the minimum of upper layer model is taken as the goal of annual operation cost of shared energy storage power station, and the configuration of shared energy storage power station is optimized;Lower layer model is responsible for solving the optimization operation problem of combined cooling heating and power type multi virtual power plant system, can effectively consume system renewable energy, reduce the operation cost of system, realize the win-win of both sides interests.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method for constructing a two-layer optimization model for a shared energy storage virtual power plant. Background Technology

[0002] In recent years, the rapid development of the global economy has been accompanied by a continuous increase in energy demand. Problems such as energy shortages and environmental pollution caused by traditional energy power generation have become increasingly prominent. Building a new power system has become a necessary trend in energy development. Renewable energy, represented by wind power and photovoltaics, plays an important role in energy transition due to its advantages such as cleanliness, economy, and recyclability. However, renewable energy also has disadvantages such as geographical dispersion, serious curtailment of wind and solar power, and randomness in power output.

[0003] A virtual power plant is a power supply coordination and management system that uses advanced information and communication technologies and software systems to aggregate and coordinate energy storage systems, controllable loads, electric vehicles, etc. It participates in the electricity market and grid operation as a special power plant. It can aggregate different types of distributed energy and utilize the complementary output between different types of energy to effectively promote local energy consumption and supply and demand complementarity between individuals. Summary of the Invention

[0004] To overcome the aforementioned problems, the present invention aims to provide a method for constructing a two-layer optimization model for shared energy storage virtual power plants. This method considers the uncertainties of wind power and photovoltaic power, as well as the optimization scheduling problem of a multi-virtual power plant system with combined cooling, heating, and power (CCHP) including shared energy storage services. Two two-layer optimization models with different stakeholders are established. The upper-layer model aims to minimize the annual operating cost of the shared energy storage power station and optimizes the relevant configurations of the shared energy storage power station. The lower-layer model is responsible for solving the optimization operation problem of the CCHP multi-virtual power plant system and analyzes the impact of wind power and photovoltaic power output fluctuations on the spinning reserve capacity margin of each virtual power plant. In addition, for the service fee pricing method of shared energy storage power stations, a tiered service fee pricing model based on a reward and punishment mechanism is proposed, which can reduce the operating costs of both the upper and lower layer stakeholders.

[0005] The technical solution adopted in this invention is as follows: the virtual power plant is a multi-virtual power plant system that considers the uncertainties of wind power and photovoltaic power and includes shared energy storage services, specifically including the following steps:

[0006] S01: Establish a model to minimize the annual operating cost of the upper-level shared energy storage power station;

[0007] S02: Establish a lower-level model for minimizing the annual operating cost of a multi-virtual power plant system based on opportunity-constrained programming;

[0008] S03: After solving the lower-level model, the optimization results are passed to the upper-level model, and the optimal scheduling plan of the model is obtained through joint iterative solution;

[0009] The objective function for minimizing the model in step S01 is:

[0010]

[0011] Where: F1—Annual operating cost of shared energy storage power station; W—Number of typical days; D w —Number of days corresponding to each typical day; C1 —Average daily investment and maintenance costs of the shared energy storage power station; C2 —Losses caused by charging and discharging activities on each typical day; C3 —Service fee revenue paid by each virtual power plant to the shared energy storage power station on each typical day; C4 —Revenue obtained by the shared energy storage power station for providing spinning reserve capacity to each virtual power plant on each typical day.

[0012]

[0013] In the formula: η1, η2—power cost and capacity cost of the shared energy storage power station; P emax —Maximum charging and discharging power of shared energy storage power stations; E emax —Maximum capacity of shared energy storage power stations; N ess —Daily maintenance cost of shared energy storage power stations; T ess —Expected usage days of the shared energy storage power station;

[0014]

[0015] In the formula: N—number of virtual power plants; T—scheduling period, which is taken as 24 in this paper; —Energy storage unit charge / discharge power loss cost coefficient; P e,b,i (t) — The power discharged by the shared energy storage station used by the i-th virtual power plant during time period t; P e,s,i (t)——The power of the i-th virtual power plant charging the shared energy storage station during time period t;

[0016] The service fee revenue is calculated using a tiered service fee pricing model. Based on a uniform pricing, the service fee payable by the virtual power plant is calculated in intervals according to the amount of electricity exchanged between the virtual power plant and the shared energy storage power station within a scheduling period. The calculation model is as follows:

[0017]

[0018]

[0019] In the formula: C fw,i (t)——Service fee paid by the i-th virtual power plant to the shared energy storage power station during time period t; —Base service fee; k —Reward coefficient; —Penalty coefficient; P e,i (t)——Power of the interaction between the i-th virtual power plant and the shared energy storage power station during time period t; L——Length of the interaction power interval;

[0020]

[0021] In the formula: P ress,i (t) represents the spinning reserve capacity provided by the shared energy storage power station to the i-th virtual power plant during time period t; —Alternative pricing;

[0022] The objective function for minimizing the model in step S02 is:

[0023]

[0024] In the formula: C gridg —The total cost of electricity purchased from the grid by each virtual power plant on each typical day; C fuel —The total gas cost for each virtual power plant unit on each typical day; C rc —The total cost of configuring spinning reserve capacity for each virtual power plant system on each typical day; C qi —Total penalty cost for power curtailment at each virtual power plant on each typical day;

[0025] Cost of purchasing electricity from the grid:

[0026]

[0027] Where: θ1(t) — the electricity price sold by the power grid during time period t; P gridg,i (t)——The amount of electricity purchased from the grid by the i-th virtual power plant during time period t;

[0028] Unit gas cost:

[0029]

[0030] Where: θ2—price per unit volume of natural gas; P GT,i (t) — Output power of the gas turbine in the i-th virtual power plant during time period t; Q GB,i (t) — Output thermal power of the gas-fired boiler in the i-th virtual power plant during time period t; η GT η GB —Power output efficiency of gas turbines and gas boilers; L NG —Gas calorific value;

[0031] Backup costs:

[0032]

[0033] In the formula: θ3—reserve cost; RGT,i (t)——Spinning reserve capacity provided by the gas turbine of the i-th virtual power plant during time period t;

[0034] Fees for power curtailment:

[0035]

[0036] In the formula: θ4 — is the unit price of the power curtailment penalty; P qi,i (t) represents the amount of electricity wasted by the i-th virtual power plant during time period t.

[0037] Furthermore, the constraints in step S01 include the continuity of the state of charge of the shared energy storage power station, the charging and discharging constraints of the shared energy storage power station, and the charging and discharging power balance constraints of the shared energy storage power station.

[0038] The continuity constraint of the state of charge of the shared energy storage power station is as follows:

[0039]

[0040] In the formula: E(t), E(t-1) — the amount of electricity stored in the shared energy storage power station during time periods t and t-1; η ch η dc —Charging and discharging efficiency of shared energy storage power stations; P CH (t), P DC (t)——Charging and discharging power of the shared energy storage power station during time period t; E0, E end —The initial power capacity and the power capacity after one operating cycle of the shared energy storage power station;

[0041] The charging and discharging constraints of the shared energy storage power station are as follows:

[0042]

[0043] In the formula: U CH (t), U DC (t)——Charging and discharging status of the shared energy storage power station;

[0044] The power balance constraint for the shared energy storage power station is that, within a scheduling period, the sum of the charging and discharging power of the shared energy storage power station is equal to the sum of the charging and discharging power of each virtual power plant using the shared energy storage power station, i.e.:

[0045]

[0046] Furthermore, the constraints in step S02 include the power balance constraints of each virtual power plant (electricity, cooling, and heating), the power interaction constraints between the virtual power plant and the shared energy storage power station, the waste heat balance constraints of the waste heat boiler, the output constraints of each piece of equipment in the virtual power plant, the power purchase constraints from the grid, and the spinning reserve constraints.

[0047] The power balance constraints for electricity, cooling, and heating in each virtual power plant are as follows:

[0048] P GT,t (t)+P grdg,t (t)+P e,b,i (t)+E(P RE,i (t))=P ER,i (t)+P qi,i (t)+P e,s,i (t)+P L,i (t)

[0049] P ER,i (t)η ER +Q AC,i (t)=P cool,i (t)

[0050] Q GB,i (t)+P HE,i (t)=P heat,i (t)

[0051] In the formula: E(P) RE,i (t))——Expected combined wind and solar power output of the i-th virtual power plant; P ER,i (t) — Power consumed by the electric chiller; P L,i (t) — the electrical load power of the i-th virtual power plant during time period t; η ER —The conversion efficiency of the electric chiller; Q AC,i (t) — Output power of the absorption chiller of the i-th virtual power plant during time period t; P cool,i (t) — The cooling load power of the i-th virtual power plant during time period t; P HE,i (t)——The thermal power output of the heat exchanger of the i-th virtual power plant during time period t; P heat,i (t)——The heat load power of the i-th virtual power plant in time period t;

[0052] The power constraint for the interaction between the virtual power plant and the shared energy storage power station is:

[0053]

[0054] In the formula: —The maximum charging and discharging power for interaction between the virtual power plant and the shared energy storage power station; U e,b,i U e,s,i —Charging and discharging status bits of the virtual power plant;

[0055]

[0056] The above formula restricts the equivalent charging and discharging power of each virtual power plant within a time period from exceeding the maximum charging and discharging power of the shared energy storage power station, and ensures that the sum of charging of virtual power plants equals the sum of discharging within a scheduling cycle.

[0057] The waste heat balance constraint of the waste heat boiler is:

[0058]

[0059] In the formula: η HE —Efficiency of the heat exchanger; η AC —Energy efficiency ratio of absorption chillers; γ GT —Thermoelectric ratio of a gas turbine; η WH —Efficiency of waste heat boilers;

[0060] The output constraints for each piece of equipment in the virtual power plant are as follows:

[0061]

[0062] In the formula: P GTmin P ERmin Q ACmin Q GBmin P HEmin —Lower output limits for gas turbines, electric chillers, absorption chillers, gas boilers, and heat exchangers; P GTmax P ERmax Q ACmax Q GBmax P HEmax — Output limits for gas turbines, electric chillers, absorption chillers, gas boilers, and heat exchangers;

[0063] The constraint on purchasing electricity from the grid is:

[0064] 0≤P gridg,i (t)≤P gridgmax

[0065] In the formula: P gridgmax —Maximum power capacity to be purchased from the power grid;

[0066] Considering that gas turbines and shared energy storage power stations together provide spinning reserve capacity for a virtual power plant, the spinning reserve constraint can be described as follows:

[0067] P GT,i (t)+R GT,i (t)≤P GTmax

[0068]

[0069] The rotational reserve constraint is modeled as a probabilistic constraint satisfying a certain confidence level, expressed as:

[0070] Pr{P ress,i (t)+R GT,i (t)≥E(P RE,i (t))-P WT,i (t)-P PV,i (t)}≥α

[0071] Furthermore, the rotational spare constraint is an uncertain constraint, which is converted into a deterministic constraint using a chance constraint transformation, and finally converted into a mixed-integer linear programming model. The specific steps are as follows:

[0072] Step 1: Introduce 0-1 variables

[0073]

[0074] Step 2: The form of linear constraints is as follows:

[0075]

[0076] In the formula: i c,t =0,1,…,N c,t M — a very large positive number.

[0077] Furthermore, the specific steps for constructing the lower-level model in step S02 are as follows:

[0078] Step 1: Construct a stochastic model of the virtual power plant's output;

[0079] Step 2: Discretize the random variables and perform sequence operations based on sequence operation theory;

[0080] Step 3: Obtain the probabilistic sequence and expected value of the random variable;

[0081] Step 4: Constructing a virtual power plant model for combined cooling, heating and power (CCHP);

[0082] Step 5: Generate a virtual power plant optimization scheduling model with opportunity constraints;

[0083] Step 6: Transform the probabilistic constraints into deterministic constraints to obtain a solution model for mixed-integer linear structures.

[0084] Furthermore, step S03 utilizes MATLAB to call CPLEX for solving.

[0085] The beneficial effects of this invention are:

[0086] This invention discloses a method for constructing a two-layer optimization model for a shared energy storage virtual power plant. The method takes a shared energy storage power station as the main body in the upper layer, optimizes relevant configurations, and proposes a tiered pricing method based on a reward and punishment mechanism for the unit service fee. The lower layer takes a combined cooling, heating and power (CCHP) type multi-virtual power plant system as the main body, fully considers the spinning reserve capacity requirements of each virtual power plant in response to the fluctuations in wind power and photovoltaic output, and applies sequence theory to quantify random variables using probabilistic sequences to construct an economic operation model based on chance-constrained programming.

[0087] This invention provides a method for constructing a two-layer optimization model for a shared energy storage virtual power plant. The method proposes a chance-constrained programming model based on sequence operation theory for the virtual power plant, which can reasonably describe the impact of wind power and photovoltaic output fluctuations on the system's spinning reserve capacity margin. As the system's reliability increases, the reserve cost will also increase. In actual operation, a reasonable choice can be made between reliability and economy.

[0088] This invention provides a method for constructing a two-layer optimization model for shared energy storage virtual power plants. Compared to virtual power plants investing in and constructing their own energy storage power stations, each virtual power plant uses shared energy storage services provided by a third party by paying service fees. This method can fully utilize the complementarity of electricity consumption behavior among virtual power plants, effectively absorb renewable energy in the system, reduce system operating costs, and achieve economical operation.

[0089] This invention provides a method for constructing a two-layer optimization model for shared energy storage virtual power plants. Compared to a uniform pricing method, the service fee unit price of shared energy storage power stations adopts the tiered service fee pricing method based on a reward and punishment mechanism proposed in this paper, which can simultaneously reduce the operating costs of shared energy storage power stations and combined cooling, heating and power (CCHP) multi-virtual power plant systems, achieving a win-win situation for both parties. Attached Figure Description

[0090] Figure 1 A flowchart illustrating a method for constructing a two-layer optimization model for a shared energy storage virtual power plant proposed in this invention;

[0091] Figure 2 A method for constructing a two-layer optimization model of a shared energy storage virtual power plant is proposed for the invention. The virtual power plant spinning reserve configuration diagram under different confidence levels is shown.

[0092] Figure 3 A method for constructing a two-layer optimization model for a shared energy storage virtual power plant is proposed for the invention. A typical day in spring is shown in the virtual power plant's load balance curve.

[0093] Figure 4 A method for constructing a two-layer optimization model for a shared energy storage virtual power plant is proposed for the invention. A typical day in spring is shown in the virtual power plant's two-stage load balance curve.

[0094] Figure 5A method for constructing a two-layer optimization model for a shared energy storage virtual power plant is proposed for the invention. A typical day in spring is shown in the virtual power plant's three-phase load balance curve.

[0095] Figure 6 A method for constructing a two-layer optimization model for a shared energy storage virtual power plant is proposed for the invention. The graph shows the electricity generation and charging / discharging power of a shared energy storage power station on a typical day in spring.

[0096] Figure 7 A method for constructing a two-layer optimization model for a shared energy storage virtual power plant was proposed for the invention. The virtual power plant uses typical daily source-load data from spring 1-3.

[0097] Figure 8 A method for constructing a two-layer optimization model for a shared energy storage virtual power plant is proposed for the invention. The virtual power plant uses typical summer day source-load data from 1-3 days.

[0098] Figure 9 A method for constructing a two-layer optimization model for a shared energy storage virtual power plant is proposed for the invention. Typical daily source-load data of the virtual power plant in autumn 1-3 are also provided.

[0099] Figure 10 This invention proposes a method for constructing a two-layer optimization model for a shared energy storage virtual power plant, based on typical winter day source-load data from virtual power plants 1-3. Detailed Implementation

[0100] The specific embodiments of the present invention are described below with reference to the accompanying drawings and examples:

[0101] It should be noted that the structures, proportions, sizes, etc. illustrated in the accompanying drawings of this specification are only used to complement the content disclosed in the specification, so that those skilled in the art can understand and read them, and are not intended to limit the conditions under which the present invention can be implemented. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0102] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0103] like Figures 1-10 As shown, this invention illustrates a specific implementation method: The invention discloses a method for constructing a two-layer optimization model for a shared energy storage virtual power plant. The virtual power plant is a multi-virtual power plant system considering the uncertainties of wind power and photovoltaic power, and including shared energy storage services. The method specifically includes the following steps:

[0104] S01: Establish a model to minimize the annual operating cost of the upper-level shared energy storage power station;

[0105] S02: Establish a lower-level model for minimizing the annual operating cost of a multi-virtual power plant system based on opportunity-constrained programming;

[0106] S03: After solving the lower-level model, the optimization results are passed to the upper-level model, and the optimal scheduling plan of the model is obtained through joint iterative solution.

[0107] In this invention, such as Figure 1 As shown, this method considers the uncertainties of wind power and photovoltaic power, as well as the optimal scheduling problem of a multi-virtual power plant system with combined cooling, heating and power (CCHP) and shared energy storage services. It establishes two two-layer optimization models for different stakeholders. The upper-layer model aims to minimize the annual operating cost of the shared energy storage power station and optimizes its configuration. The lower-layer model solves the optimal operation problem of the CCHP multi-virtual power plant system and analyzes the impact of wind power and photovoltaic output fluctuations on the spinning reserve capacity margin of each virtual power plant. Furthermore, it proposes a tiered service fee pricing model based on a reward and punishment mechanism for the service fee pricing of shared energy storage power stations, which can reduce the operating costs of both the upper and lower layers.

[0108] The objective function for minimizing the model in step S01 is:

[0109]

[0110] Where: F1—Annual operating cost of shared energy storage power station; W—Number of typical days; D w —Number of days corresponding to each typical day; C1 —Average daily investment and maintenance costs of the shared energy storage power station; C2 —Losses caused by charging and discharging activities on each typical day; C3 —Service fee revenue paid by each virtual power plant to the shared energy storage power station on each typical day; C4 —Revenue obtained by the shared energy storage power station for providing spinning reserve capacity to each virtual power plant on each typical day.

[0111]

[0112] In the formula: η1, η2—power cost and capacity cost of the shared energy storage power station; P emax —Maximum charging and discharging power of shared energy storage power stations; E emax —Maximum capacity of shared energy storage power stations; N ess —Daily maintenance cost of shared energy storage power stations; T ess —Expected usage days of the shared energy storage power station;

[0113]

[0114] In the formula: N—number of virtual power plants; T—scheduling period, which is taken as 24 in this paper; —Energy storage unit charge / discharge power loss cost coefficient; P e,b,i (t) — The power discharged by the shared energy storage station used by the i-th virtual power plant during time period t; P e,s,i (t)——The power of the i-th virtual power plant charging the shared energy storage station during time period t;

[0115] The service fee revenue is calculated using a tiered service fee pricing model. Based on a uniform pricing structure, the service fee is calculated in tiers according to the amount of electricity exchanged between the virtual power plant and the shared energy storage station within a scheduling period. When the amount of electricity exchanged with the shared energy storage station is low, a reward coefficient is introduced to incentivize user participation by reducing the unit service fee. Conversely, the unit service fee is increased. Electricity exceeding the specified tier is settled at an additional service fee. The calculation model is as follows:

[0116]

[0117]

[0118] In the formula: C fw,i (t)——Service fee paid by the i-th virtual power plant to the shared energy storage power station during time period t; —Base service fee; κ —Reward coefficient; —Penalty coefficient; P e,i (t)——Power of the interaction between the i-th virtual power plant and the shared energy storage power station during time period t; L——Length of the interaction power interval;

[0119]

[0120] In the formula: P ress,i (t) represents the spinning reserve capacity provided by the shared energy storage power station to the i-th virtual power plant during time period t; —Alternative pricing;

[0121] The objective function for minimizing the model in step S02 is:

[0122]

[0123] In the formula: C gridg —The total cost of electricity purchased from the grid by each virtual power plant on each typical day; C fuel —The total gas cost for each virtual power plant unit on each typical day; C rc —The total cost of configuring spinning reserve capacity for each virtual power plant system on each typical day; C qi —Total penalty cost for power curtailment at each virtual power plant on each typical day;

[0124] Cost of purchasing electricity from the grid:

[0125]

[0126] Where: θ1(t) — the electricity price sold by the power grid during time period t; P gridg,i (t)——The amount of electricity purchased from the grid by the i-th virtual power plant during time period t;

[0127] Unit gas cost:

[0128]

[0129] Where: θ2—price per unit volume of natural gas; P GT,i (t) — Output power of the gas turbine in the i-th virtual power plant during time period t; Q GB,i (t) — Output thermal power of the gas-fired boiler in the i-th virtual power plant during time period t; η GT η GB —Power output efficiency of gas turbines and gas boilers; L NG —Gas calorific value;

[0130] Backup costs:

[0131]

[0132] In the formula: θ3—reserve cost; R GT,i (t)——Spinning reserve capacity provided by the gas turbine of the i-th virtual power plant during time period t;

[0133] Fees for power curtailment:

[0134]

[0135] In the formula: θ4 — is the unit price of the power curtailment penalty; P qi,i (t) represents the amount of electricity wasted by the i-th virtual power plant during time period t.

[0136] Furthermore, the constraints in step S01 include the continuity of the state of charge of the shared energy storage power station, the charging and discharging constraints of the shared energy storage power station, and the charging and discharging power balance constraints of the shared energy storage power station.

[0137] The continuity constraint of the state of charge of the shared energy storage power station is as follows:

[0138]

[0139] In the formula: E(t), E(t-1) — the amount of electricity stored in the shared energy storage power station during time periods t and t-1; η ch η dc —Charging and discharging efficiency of shared energy storage power stations; P CH (t), P DC(t)——Charging and discharging power of the shared energy storage power station during time period t; E0, E end —The initial power capacity and the power capacity after one operating cycle of the shared energy storage power station;

[0140] The charging and discharging constraints of the shared energy storage power station are as follows:

[0141]

[0142] In the formula: U CH (t), U DC (t)——Charging and discharging status of the shared energy storage power station;

[0143] The power balance constraint for the shared energy storage power station is that, within a scheduling period, the sum of the charging and discharging power of the shared energy storage power station is equal to the sum of the charging and discharging power of each virtual power plant using the shared energy storage power station, i.e.:

[0144]

[0145] Furthermore, the constraints in step S02 include the power balance constraints of each virtual power plant (electricity, cooling, and heating), the power interaction constraints between the virtual power plant and the shared energy storage power station, the waste heat balance constraints of the waste heat boiler, the output constraints of each piece of equipment in the virtual power plant, the power purchase constraints from the grid, and the spinning reserve constraints.

[0146] The power balance constraints for electricity, cooling, and heating in each virtual power plant are as follows:

[0147] P GT，t (t)+P grdg,t (t)+P e，b，i (t)+E(P RE,i (t))=P ER,i (t)+P qi,i (t)+P e，s，i (t)+P L，i (t)

[0148] P ER，i (t)η ER +q AC，i (t)=P cool，i (t)

[0149] Q GB，i (t)+P HE，i (t)=P heat，i (t)

[0150] In the formula: E(P) RE，i (t))——Expected combined wind and solar power output of the i-th virtual power plant; P ER，i (t) — Power consumed by the electric chiller; P L，i (t) — the electrical load power of the i-th virtual power plant during time period t; ηER —The conversion efficiency of the electric chiller; Q AC，i (t) — Output power of the absorption chiller of the i-th virtual power plant during time period t; P cool，i (t) — The cooling load power of the i-th virtual power plant during time period t; P HE,i (t)——The thermal power output of the heat exchanger of the i-th virtual power plant during time period t; P heat,i (t)——The heat load power of the i-th virtual power plant in time period t;

[0151] The power constraint for the interaction between the virtual power plant and the shared energy storage power station is:

[0152]

[0153] In the formula: —The maximum charging and discharging power for interaction between the virtual power plant and the shared energy storage power station; U e，b,i U e,s,i —Charging and discharging status bits of the virtual power plant;

[0154]

[0155] The above formula restricts the equivalent charging and discharging power of each virtual power plant within a time period from exceeding the maximum charging and discharging power of the shared energy storage power station, and ensures that the sum of charging of virtual power plants equals the sum of discharging within a scheduling cycle.

[0156] The waste heat balance constraint of the waste heat boiler is:

[0157]

[0158] In the formula: η HE —Efficiency of the heat exchanger; η AC —Energy efficiency ratio of absorption chillers; γ GT —Thermoelectric ratio of a gas turbine; η WH —Efficiency of waste heat boilers;

[0159] The output constraints for each piece of equipment in the virtual power plant are as follows:

[0160]

[0161] In the formula: P GTmin P ERmin Q ACmin Q GBmin P HEmin —Lower output limits for gas turbines, electric chillers, absorption chillers, gas boilers, and heat exchangers; P GTmax P ERmax Q ACmax Q GBmax PHEmax — Output limits for gas turbines, electric chillers, absorption chillers, gas boilers, and heat exchangers;

[0162] The constraint on purchasing electricity from the grid is:

[0163] 0≤P gridg,i (t)≤P gridgmax

[0164] In the formula: P gridgmax —Maximum power capacity to be purchased from the power grid;

[0165] Considering that gas turbines and shared energy storage power stations together provide spinning reserve capacity for a virtual power plant, the spinning reserve constraint can be described as follows:

[0166] P GT,i (t)+R GT,i (t)≤P GTmax

[0167]

[0168] The rotational reserve constraint is modeled as a probabilistic constraint satisfying a certain confidence level, expressed as:

[0169] Pr{P ress，i (t)+R GT，i (t)≥E(P RE，i (t))-P WT，i (t)-P PV,i (t)}≥α

[0170] Furthermore, the rotational spare constraint is an uncertain constraint, which is converted into a deterministic constraint using a chance constraint transformation, and finally converted into a mixed-integer linear programming model. The specific steps are as follows:

[0171] Step 1: Introduce 0-1 variables

[0172]

[0173] The above formula shows that: during time period t, when the spinning reserve capacity R of the i-th virtual power plant system... GT，i (t)+P ress，i (t) is not less than the expected value of combined wind and solar power output E(P) RE，i (t) and the i-th sequence of wind and light c，t Each element outputs i c，t When the difference in q is a 0-1 variable We take 1 for the wind-solar combined output and 0 for the solar combined output. From the probabilistic sequence of wind-solar combined output, we know that the wind-solar combined output i in time period t... c，t The probability corresponding to q is c(i) c，tTherefore, the above formula can be equivalent to:

[0174]

[0175] The above formula shows that, within any given time period, the confidence level that the spinning reserve capacity can meet the demand for all possible output values ​​of the combined wind and solar power is greater than or equal to α; in the above formula, Since the variables are 0-1, their expressions cannot be solved using the methods for mixed-integer linear programming.

[0176] Step 2: The form of linear constraints is as follows:

[0177]

[0178] In the formula: i c，t =0,1,...,N c，t M — a very large positive number.

[0179] The above formula shows that when the reserve capacity can meet the deviation caused by the fluctuation of wind and solar power output, i.e., R GT，i (t)+P ress，i (t)-E(P RE，i (t))+i c，t When q > 0, we can obtain Where ε is a very small number, and because It is a 0-1 variable, so at this time Conversely, when the rotating reserve capacity cannot meet the deviation caused by fluctuations in wind and solar power output, i.e., R... GT，i (t)+P ress，i (t)-E(P RE，i (t))+ic ，t When q < 0, we can obtain Where ε is a very small negative number, at this time Thus, the chance constraint has been transformed into a mixed-integer linear programming model that is easy to solve.

[0180] Furthermore, the specific steps for constructing the lower-level model in step S02 are as follows:

[0181] Step 1: Construct a stochastic model of the virtual power plant's output;

[0182] Step 2: Discretize the random variables and perform sequence operations based on sequence operation theory;

[0183] Step 3: Obtain the probabilistic sequence and expected value of the random variable;

[0184] Step 4: Constructing a virtual power plant model for combined cooling, heating and power (CCHP);

[0185] Step 5: Generate a virtual power plant optimization scheduling model with opportunity constraints;

[0186] Step 6: Transform the probabilistic constraints into deterministic constraints to obtain a solution model for mixed-integer linear structures.

[0187] Furthermore, step S03 utilizes MATLAB to call CPLEX for solving.

[0188] Example

[0189] The simulation results were obtained using a computational simulation system. The embodiment consisted of three combined cooling, heating, and power (CCHP) virtual power plants and one shared energy storage station. Each virtual power plant was directly connected to the shared energy storage station, and there was no power exchange between the virtual power plants. Wind turbine and photovoltaic module parameters are shown in Table 1. The common discretization step size was 50kW. The expected values ​​of wind and solar power output, and the predicted values ​​of electricity, cooling, and heating loads for typical days in each season for the virtual power plants are shown in Table 1. Figure 7-10 The relevant parameter settings of the equipment in the virtual power plant are shown in Table 2, and the time-of-use electricity price of the power grid is shown in Table 3.

[0190] Table 1 Parameters of Wind Turbines and Photovoltaic Modules in the Virtual Power Plant

[0191]

[0192] Table 2 Virtual Power Plant Equipment Parameters

[0193]

[0194]

[0195] Table 3 Time-of-use Electricity Prices

[0196]

[0197] The initial capacity of the shared energy storage power station is 20% of its maximum capacity. The maximum and minimum states of charge are 10% and 90% of the maximum capacity, respectively. The base price for the virtual power plant to use the shared energy storage service is 0.45 yuan / (kW·h). The reward and penalty coefficients are 0.05 and 0.1, respectively. The interaction power range length is 550kW. The unit price of natural gas purchased by the virtual power plant is 2.4 yuan / m3. The capacity cost of the shared energy storage power station is based on the average winning bid price of lithium iron phosphate batteries in a certain energy storage project, which is 1897 yuan / (kW·h). The power cost is 1000 yuan / (kW·h). The operation and maintenance cost is 72 yuan / (year·kW). The service life of the shared energy storage power station is 8 years.

[0198] Taking a typical day in spring as an example, Figure 2The curves showing the variation of the required spinning reserve capacity for three virtual power plants at different confidence levels are presented, with three confidence levels set at 1, 0.95, and 0.9. At a confidence level of 1, the required spinning reserve capacity must meet all possible deviations caused by the indirect output of distributed generation in any given time period. In this case, the required spinning reserve capacity for the virtual power plant is the largest, and the system's operating cost increases accordingly. During the periods [0:00-08:00] and [18:00-24:00], virtual power plant 2 does not output renewable energy. In this case, the required spinning reserve capacity at a confidence level of 0.95 is similar to that at a confidence level of 1. During the period [08:00-18:00], virtual power plant 2 outputs photovoltaic power. In this case, the required spinning reserve capacity at a confidence level of 0.95 is less than that at a confidence level of 1. This indicates that distributed generation with different characteristics is complementary and can reduce output fluctuations to some extent, thereby reducing the required spinning reserve capacity of the system.

[0199] To investigate the changes in system backup cost and operating cost under different confidence levels, eight different confidence levels were set for simulation. The results are shown in Table 4. The results show that as the confidence level decreases, the system backup cost and operating cost also decrease. However, compared to other confidence levels, the backup cost and operating cost increase significantly at confidence levels of 1 and 0.99. This is because when the confidence level is 1, the system's risk tolerance is maximized, requiring the maximum possible spinning reserve capacity. At this point, the backup cost and operating cost will surge. Therefore, setting a reasonable confidence level and making a trade-off between economy and reliability is crucial.

[0200] Table 4 System Costs at Different Confidence Levels

[0201]

[0202] To balance the system's economy and reliability, this paper sets the system to operate at a confidence level of 0.9.

[0203] Taking a typical day in spring as an example, the optimized scheduling results of a combined cooling, heating and power (CCHP) multi-virtual power plant system using shared energy storage services are as follows: Figures 3-5 As shown.

[0204] Depend on Figure 3 It can be seen that the wind and solar power output of virtual power plant 1 can meet the demand of electricity load at any time. For the surplus electricity, virtual power plant 1 will give priority to storing it in the shared energy storage station for other virtual power plants to use when the output of renewable energy is insufficient. However, due to the influence of the service fee reward and punishment mechanism, a higher service fee must be paid for the part of the electricity that exceeds the second interval. Therefore, virtual power plant 1 chooses to discard this part of the electricity.

[0205] Depend on Figure 4 It can be seen that the photovoltaic output of virtual power plant 2 is 0 during the periods of [00:00-08:00] and [18:00-24:00]. At this time, virtual power plant 2 prioritizes the discharge of shared energy storage power station to meet the electricity load demand. During these two periods, the discharge power of virtual power plant 2 using shared energy storage reaches the upper limit of the second interval. At this time, virtual power plant 2 does not choose to continue to use shared energy storage service at the price of the third interval. Instead, during the period of [00:00-08:00], it chooses to purchase electricity from the grid at the off-peak electricity price. When the amount of electricity purchased from the grid reaches the upper limit, it finally chooses to generate electricity from gas turbines to meet the load demand. During the period of [18:00-24:00], the grid electricity price increases. At this time, virtual power plant 2 uses gas turbines with a relatively low unit output price to generate electricity, thus achieving optimal economic efficiency. During the period from 08:00 to 18:00, virtual power plant 2 is equipped with photovoltaic output. At this time, virtual power plant 2 prioritizes the absorption of photovoltaic output and prioritizes the exchange of power with the shared energy storage power station to achieve power balance during periods of insufficient or excessive power.

[0206] Depend on Figure 5 It can be seen that during the period [00:00-03:00], the electricity load demand of virtual power plant 3 is relatively low. At this time, virtual power plant 3 stores the surplus wind power output to the shared energy storage station after meeting the electricity load. During the period [05:00-24:00], the wind power output of virtual power plant 3 is insufficient to meet the electricity load, so the shared energy storage station is used to discharge to meet the load demand. Among them, the period [17:00-19:00] is the peak electricity consumption period, and the wind power output is relatively low. At this time, the discharge power of the shared energy storage station reaches its peak.

[0207] Figure 6This chart shows the power and charge / discharge power of a shared energy storage station on a typical spring day. A positive power value indicates the station is charging, while a negative value indicates it is discharging. Considering the charging and discharging power of the shared energy storage station used by the three virtual power plants, it can be seen that during the period [00:00-03:00], the amount of electricity charged by each virtual power plant to the shared energy storage station exceeds the amount of electricity discharged, indicating the shared energy storage station is in a charging state with its capacity reaching 0.227Eessmax. During the period [04:00-06:00], the charging and discharging power of the shared energy storage station is equal for each virtual power plant, resulting in zero charging and discharging power and unchanged capacity. During the period [07:00-09:00], the increased electricity demand of each virtual power plant leads to insufficient renewable energy output to meet the demand. During this period, the virtual power plants use the shared energy storage station to discharge to maintain power balance, reducing the capacity to a low value of 0.122Eessmax. During the period from 09:00 to 16:00, the photovoltaic output of Virtual Power Plant 2 increased, and the overall distributed energy output exceeded the load demand. The shared energy storage station was charging, with the power output reaching a maximum of 6536.70kW during the period from 15:00 to 16:00. During the period from 16:00 to 24:00, the shared energy storage station was discharging, and the power output of the shared energy storage station showed an overall downward trend, decreasing from the maximum value to the initial state to ensure normal operation in the next cycle.

[0208] To analyze the economic advantages that shared energy storage services bring to the system, four scenarios are set up for comparison to verify the effectiveness of the two-layer optimization model:

[0209] Scenario 1: A combined cooling, heating and power (CCHP) multi-virtual power plant system uses shared energy storage services provided by a third party, with service fees using a tiered pricing method based on a reward and punishment mechanism.

[0210] Scenario 2: A combined cooling, heating and power (CCHP) multi-virtual power plant system uses a shared energy storage service provided by a third party, with the service fee set at a uniform price.

[0211] Scenario 3: Independent configuration of energy storage power stations in multi-virtual power plant systems with combined cooling, heating and power (CCHP) capabilities.

[0212] Scenario 4: A combined cooling, heating and power (CCHP) multi-virtual power plant system without an energy storage station.

[0213] In scenario 3, without a third-party shared energy storage service, each virtual power plant independently configures its energy storage station based on fluctuations in renewable energy output and load demand. In this case, the optimization objective is:

[0214]

[0215]

[0216] Among them, P emax,i —The maximum charging and discharging power of the energy storage station configured in the i-th virtual power plant; E emax,i —The maximum capacity of the energy storage station configured in the i-th virtual power plant; N ess,i —The daily operation and maintenance cost of configuring an energy storage station in the i-th virtual power plant.

[0217] Table 5 shows the operation results of the combined cooling, heating and power (CCHP) multi-virtual power plant system in four scenarios, and Table 6 shows the operation results of the shared energy storage power station in scenarios 1 and 2.

[0218] Table 5 Analysis of the Operation Results of the Multi-Virtual Power Plant System in Four Scenarios

[0219]

[0220] Table 6 Analysis of the Operation Results of Shared Energy Storage Power Stations in Scenario 1 and Scenario 2

[0221]

[0222] The results from scenarios 1 and 2 show that, from the perspective of the multi-virtual power plant system, due to the influence of the tiered pricing method, each virtual power plant considers optimal economic efficiency. After the interaction power with the shared energy storage power station reaches the upper limit of the second tier, most of them no longer choose to continue using the shared energy storage power station at a higher service fee price. Instead, they choose to purchase electricity from the grid or use gas turbines to generate electricity to meet load demand. Therefore, virtual power plants with a large output of renewable energy will cause a certain amount of wind and solar curtailment. However, since the service fee per unit of interaction power in the first tier is lower than the base price, the service fee paid by the virtual power plant to the shared energy storage power station is significantly reduced. Therefore, the total operating cost of the multi-virtual power plant system is still reduced compared to the uniform pricing.

[0223] From the perspective of shared energy storage power stations, due to the impact of tiered pricing, each virtual power plant reduced its interaction with shared energy storage power stations to some extent at various times, resulting in a decrease of 4,371.52 yuan in service fee revenue for shared energy storage power stations. However, due to the reduction in interaction, the required capacity and maximum charging and discharging power of shared energy storage power stations were reduced, and their investment, operation and maintenance, and power loss costs decreased by 7,595.22 yuan. Therefore, overall, the revenue of shared energy storage power stations has still improved.

[0224] In summary, compared to a uniform service fee pricing method, a tiered pricing method based on a reward and punishment mechanism can effectively reduce the operating costs of multi-virtual power plant systems and improve the revenue of shared energy storage power stations.

[0225] In Scenario 3, each virtual power plant chooses to invest in and construct its own energy storage power station, with usage rights belonging to the individual plant. The operational results show that, compared to the high cost of configuring a large-capacity energy storage power station, each virtual power plant opted to discard most of its excess renewable energy output. The energy storage power station absorbs a small portion of the electricity and engages in limited power exchange with the virtual power plant. Simultaneously, due to the ineffective utilization of renewable energy, the virtual power plants need to additionally use gas turbines to generate electricity to meet load demands. This results in a 56.64% increase in gas costs compared to Scenario 1, and a 10.09% increase in total cost.

[0226] The results of Scenario 4 show that, due to the lack of energy storage stations in each virtual power plant, the surplus renewable energy output cannot be effectively utilized and can only be curtailed, incurring high penalty fees. The amount of curtailed electricity is also the highest among the four scenarios, with a renewable energy utilization rate of only 75.05%. Furthermore, each virtual power plant needs to rely heavily on gas turbines to meet load demands, resulting in higher gas costs compared to Scenario 1 and Scenario 3.

[0227] The comparative analysis of the four scenarios above shows that building energy storage power stations can effectively absorb renewable energy. However, compared to third-party investment in shared energy storage power stations, the operating costs of self-investing in energy storage power stations are higher. Therefore, by using the charging and discharging services of third-party shared energy storage power stations, the complementary nature of the electricity consumption behavior of various virtual power plants at different scheduling periods can be fully utilized. This significantly reduces the operating costs of multi-virtual power plant systems and shared energy storage power stations while ensuring effective absorption of renewable energy. Furthermore, the service fee pricing method proposed in this paper can achieve a win-win situation for both multi-virtual power plant systems and shared energy storage power stations.

[0228] This invention discloses a method for constructing a two-layer optimization model for a shared energy storage virtual power plant. The method takes a shared energy storage power station as the main body in the upper layer, optimizes relevant configurations, and proposes a tiered pricing method based on a reward and punishment mechanism for the unit service fee. The lower layer takes a combined cooling, heating and power (CCHP) type multi-virtual power plant system as the main body, fully considers the spinning reserve capacity requirements of each virtual power plant in response to the fluctuations in wind power and photovoltaic output, and applies sequence theory to quantify random variables using probabilistic sequences to construct an economic operation model based on chance-constrained programming.

[0229] This model proposes a chance-constrained programming model based on sequence operation theory for the virtual power plant entity. It can reasonably describe the impact of wind power and photovoltaic output fluctuations on the system's spinning reserve capacity margin. As the system's reliability increases, the reserve cost will also increase. In actual operation, a reasonable choice can be made between reliability and economy.

[0230] Compared to virtual power plants investing in and building their own energy storage stations, this model allows each virtual power plant to use shared energy storage services provided by a third party by paying service fees. This fully leverages the complementarity of electricity consumption behaviors among virtual power plants, effectively absorbs renewable energy from the system, reduces system operating costs, and achieves economical operation.

[0231] Compared to a uniform pricing method, the shared energy storage power station service fee model adopts a tiered service fee pricing method based on a reward and punishment mechanism proposed in this paper, which can simultaneously reduce the operating costs of shared energy storage power stations and combined cooling, heating and power (CCHP) multi-virtual power plant systems, achieving a win-win situation for both parties.

[0232] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

[0233] Many other changes and modifications can be made without departing from the concept and scope of this invention. It should be understood that this invention is not limited to the specific embodiments, and the scope of this invention is defined by the appended claims.

Claims

1. A method for constructing a two-layer optimization model for a shared energy storage virtual power plant, characterized in that, The virtual power plant is a multi-virtual power plant system that considers the uncertainties of wind power and photovoltaic power, and includes shared energy storage services for combined cooling, heating and power. Specifically, it includes the following steps: S01: Establish a model to minimize the annual operating cost of the upper-level shared energy storage power station; S02: Establish a lower-level model for minimizing the annual operating cost of a multi-virtual power plant system based on opportunity-constrained programming; S03: After solving the lower-level model, the optimization results are passed to the upper-level model, and the optimal scheduling plan of the model is obtained through joint iterative solution; The objective function for minimizing the model in step S01 is: Where: F1—Annual operating cost of shared energy storage power station; W—Number of typical days; D w —Number of days corresponding to each typical day; C1 —Average daily investment and maintenance costs of the shared energy storage power station; C2 —Losses caused by charging and discharging activities on each typical day; C3 —Service fee revenue paid by each virtual power plant to the shared energy storage power station on each typical day; C4 —Revenue obtained by the shared energy storage power station for providing spinning reserve capacity to each virtual power plant on each typical day. In the formula: η1, η2—power cost and capacity cost of the shared energy storage power station; P emax —Maximum charging and discharging power of shared energy storage power stations; E emax —Maximum capacity of shared energy storage power stations; N ess —Daily maintenance cost of shared energy storage power stations; T ess —Expected usage days of the shared energy storage power station; In the formula: N—number of virtual power plants; T—scheduling period, which is taken as 24 in this paper; —Energy storage unit charge / discharge power loss cost coefficient; P e，b，i (t) — The power discharged by the shared energy storage station used by the i-th virtual power plant during time period t; P e，s，i (t)——The power of the i-th virtual power plant charging the shared energy storage station during time period t; The service fee revenue is calculated using a tiered service fee pricing model. Based on a uniform pricing, the service fee payable by the virtual power plant is calculated in intervals according to the amount of electricity exchanged between the virtual power plant and the shared energy storage power station within a scheduling period. The calculation model is as follows: In the formula: C fw，i (t)——Service fee paid by the i-th virtual power plant to the shared energy storage power station during time period t; —Base service fee; κ —Reward coefficient; —Penalty coefficient; P e，i (t)——Power of the interaction between the i-th virtual power plant and the shared energy storage power station during time period t; L——Length of the interaction power interval; In the formula: P ress，i (t) represents the spinning reserve capacity provided by the shared energy storage power station to the i-th virtual power plant during time period t; —Alternative pricing; The objective function for minimizing the model in step S02 is: In the formula: C gridg —The total cost of electricity purchased from the grid by each virtual power plant on each typical day; C fuel —The total gas cost for each virtual power plant unit on each typical day; C rc —The total cost of configuring spinning reserve capacity for each virtual power plant system on each typical day; C qi —Total cost of power curtailment penalties for each virtual power plant on each typical day; Cost of purchasing electricity from the grid: Where: θ1(t) — the electricity price sold by the power grid during time period t; P gridg，i (t)——The amount of electricity purchased from the grid by the i-th virtual power plant during time period t; Unit gas cost: Where: θ2—price per unit volume of natural gas; P GT，i (t) — Output power of the gas turbine in the i-th virtual power plant during time period t; Q GB，i (t) — Output thermal power of the gas-fired boiler in the i-th virtual power plant during time period t; η GT η GB —Power output efficiency of gas turbines and gas boilers; L NG —Gas calorific value; Backup costs: In the formula: θ3—reserve cost; R GT，i (t)——Spinning reserve capacity provided by the gas turbine of the i-th virtual power plant during time period t; Fees for power curtailment: In the formula: θ4 — is the unit price of the power curtailment penalty; P qi，i (t) represents the amount of electricity wasted by the i-th virtual power plant during time period t.

2. The method for constructing a two-layer optimization model for a shared energy storage virtual power plant according to claim 1, characterized in that, The constraints in step S01 include the continuity of the state of charge of the shared energy storage power station, the charging and discharging constraints of the shared energy storage power station, and the charging and discharging power balance constraints of the shared energy storage power station. The continuity constraint of the state of charge of the shared energy storage power station is as follows: In the formula: E(t), E(t-1) — the amount of electricity stored in the shared energy storage power station during time periods t and t-1; η ch η dc —Charging and discharging efficiency of shared energy storage power stations; P CH (t), P DC (t)——Charging and discharging power of the shared energy storage power station during time period t; E0, E end —The initial power and the power after one operating cycle of the shared energy storage power station; The charging and discharging constraints of the shared energy storage power station are as follows: In the formula: U CH (t), U DC (t)——Charging and discharging status of the shared energy storage power station; The power balance constraint for the shared energy storage power station is that, within a scheduling period, the sum of the charging and discharging power of the shared energy storage power station is equal to the sum of the charging and discharging power of each virtual power plant using the shared energy storage power station, i.e.:

3. The method for constructing a two-layer optimization model for a shared energy storage virtual power plant according to claim 1, characterized in that, The constraints in step S02 include the power balance constraints of each virtual power plant (electricity, cooling, and heating), the power interaction constraints between the virtual power plant and the shared energy storage power station, the waste heat balance constraints of the waste heat boiler, the output constraints of each piece of equipment in the virtual power plant, the power purchase constraints from the grid, and the spinning reserve constraints. The power balance constraints for electricity, cooling, and heating in each virtual power plant are as follows: P GT，t (t)+P grdg，t (t)+P e，b，i (t)+E(P RE，i (t))=P ER，i (t)+P qi，i (t)+P e，s，i (t)+P L，i (t) P ER，i (t)η ER +Q AC，i (t)=P cool，i (t) Q GB，i (t)+P HE，i (t)=P heat，i (t) In the formula: E(P) RE，i (t))——Expected combined wind and solar power output of the i-th virtual power plant; P ER，i (t) — Power consumed by the electric chiller; P L，i (t)——The electrical load power of the i-th virtual power plant in time period t; η ER —The conversion efficiency of the electric chiller; Q AC，i (t) — Output power of the absorption chiller of the i-th virtual power plant during time period t; P cool，i (t)——The cooling load power of the i-th virtual power plant in time period t; P HE，i (t) — The thermal power output of the heat exchanger of the i-th virtual power plant during time period t; P heat，i (t)——The heat load power of the i-th virtual power plant in time period t; The power constraint for the interaction between the virtual power plant and the shared energy storage power station is: In the formula: —The maximum charging and discharging power for interaction between the virtual power plant and the shared energy storage power station; U e，b，i U e，s，i —Charging and discharging status bits of the virtual power plant; The above formula restricts the equivalent charging and discharging power of each virtual power plant within a time period from exceeding the maximum charging and discharging power of the shared energy storage power station, and ensures that the sum of charging of virtual power plants equals the sum of discharging within a scheduling cycle. The waste heat balance constraint of the waste heat boiler is: In the formula: η HE —Efficiency of the heat exchanger; η AC —Energy efficiency ratio of absorption chillers; γ GT —Thermoelectric ratio of a gas turbine; η WH —Efficiency of waste heat boilers; The output constraints for each piece of equipment in the virtual power plant are as follows: In the formula: P GTmin P ERmin Q ACmin Q GBmin、 P HEmin —Lower output limits for gas turbines, electric chillers, absorption chillers, gas boilers, and heat exchangers; P GTmax P ERmax Q ACmax Q GBmax P HEmax — Output limits for gas turbines, electric chillers, absorption chillers, gas boilers, and heat exchangers; The constraint on purchasing electricity from the grid is: 0≤P gridg，i (t)≤P gridgma x In the formula: P gridgmax —Maximum power capacity to be purchased from the power grid; Considering that gas turbines and shared energy storage power stations together provide spinning reserve capacity for a virtual power plant, the spinning reserve constraint can be described as follows: P GT，i (t)+R GT，i (t)≤P GTmax The rotational reserve constraint is modeled as a probabilistic constraint satisfying a certain confidence level, expressed as: Pr{P ress，i (t)+R GT，i (t)≥E(P RE，i (t))-P WT，i (t)-P PV，i (t)}≥α 。 4. The method for constructing a two-layer optimization model for a shared energy storage virtual power plant according to claim 3, characterized in that, The rotational spare constraint is an uncertain constraint. It is transformed into a deterministic constraint using a chance constraint transformation, and finally transformed into a mixed-integer linear programming model. The specific steps are as follows: Step 1: Introduce 0-1 variables Step 2: The form of linear constraints is as follows: In the formula: i c，t =0,1,...,N c，t M — a very large positive number.

5. The method for constructing a two-layer optimization model for a shared energy storage virtual power plant according to claim 1, characterized in that, The specific steps for constructing the lower-level model in step S02 are as follows: Step 1: Construct a stochastic model of the virtual power plant's output; Step 2: Discretize the random variables and perform sequence operations based on sequence operation theory; Step 3: Obtain the probabilistic sequence and expected value of the random variable; Step 4: Constructing a virtual power plant model for combined cooling, heating and power (CCHP); Step 5: Generate a virtual power plant optimization scheduling model with opportunity constraints; Step 6: Transform the probabilistic constraints into deterministic constraints to obtain a solution model for mixed-integer linear structures.

6. The method for constructing a two-layer optimization model for a shared energy storage virtual power plant according to claim 1, characterized in that, Step S03 uses MATLAB to call CPLEX to solve the problem.

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