Virtual power plant electric power-steam collaborative optimization method and system for textile industrial park

Abstract

The invention discloses a virtual power plant power-steam collaborative optimization method and system for a textile industry park, and the method comprises the steps: constructing a virtual power plant operator-user double-layer scheduling architecture, obtaining the related data of a virtual power plant operator and a textile industry user system according to the virtual power plant double-layer scheduling architecture, and carrying out the operation of the virtual power plant operator-user system; modeling the multi-energy-flow coupling equipment, inputting initial electricity, heat and steam purchasing and selling energy prices provided for industrial users by a virtual power plant operator, constructing a user layer optimization scheduling model by the users by taking the lowest total operation cost as an optimization target, and calculating income by the virtual power plant operator according to an energy purchasing and selling strategy fed back by a lower layer; and constructing a virtual power plant operator layer optimization scheduling model with the maximum income as an optimization target, and finally obtaining an electricity, heat and steam purchasing and selling strategy with the lowest user cost. According to the invention, production plan adjustment and multi-energy load transfer can be realized, so that the multi-energy load demand fluctuation of textile industry users is slowed down.

Description

Technical Field

[0001] The present invention belongs to the technical field of power dispatching, and in particular relates to a method and system for power-steam collaborative optimization of a virtual power plant in a textile industrial park. Background Art

[0002] As an important pillar industry of my country's economy, the textile industry has an economic scale in the industrial cluster area that is close to 50% of the entire industry. Under the background of energy conservation and emission reduction, the textile industry has shown significant room for optimization in terms of energy consumption. Although the supply-side resources are constantly enriched, the increased penetration rate of distributed energy has brought challenges such as randomness, intermittency and volatility of output, which has aggravated the difficulty of load regulation and resource allocation for textile industry users. In this context, virtual power plants rely on advanced information and communication technology and intelligent control technology to efficiently aggregate and optimize distributed energy. As a bridge between the power system and distributed energy, it has a dual role in the energy management of the textile industrial park: on the one hand, it realizes energy optimization scheduling by coordinating the energy consumption behavior of textile industry users. On the other hand, the textile industry users and virtual power plant operators aggregated within it jointly participate in the power market for bidding. Virtual power plant operators formulate scientific energy purchase and sale strategies to guide users to participate in demand response, thereby realizing the advantages of multi-energy complementarity, which is of great significance to guiding its industrial users to use energy reasonably. Through this model, virtual power plants can bring more flexible and efficient energy management solutions to the textile industry and even the entire energy market.

[0003] CN117787075A discloses a method, device and storage medium for predicting power load based on machine learning, which mainly includes steps such as data preprocessing, prediction model establishment, data feature set division, model training, etc. The power demand for the next day and the next 15 minutes is predicted by establishing a CatBoost model and a long short-term memory network model. The prediction method can effectively improve the prediction accuracy and reliability of future power load. With the increasing application of virtual power plants, power demand prediction is an indispensable and important part. In addition, power consumption structure, economic level and electricity price mechanism are also important factors affecting power load. Power load prediction can be carried out according to different power demand and modeled in virtual power plants, which is very important for power operation safety. This patent mainly focuses on steps such as data preprocessing and model establishment, and does not fully consider the correlation and dynamic changes between different power demands. At the same time, it fails to optimize the energy consumption characteristics of a specific industry, ignoring the complexity and diversity of the industry in power and steam demand, so the optimization effect is not good.

[0004] CN116221690A discloses a user-side thermal-electric decoupling system based on time-of-use electricity price differences. This technical solution organically combines an electric heat storage furnace, a cogeneration unit and a steam solid heat storage device through a control system, and achieves the same power generation capacity as a battery storage system with an investment cost lower than 25% of the battery storage system, forming a cheap and efficient electric energy heat storage power generation plus exhaust steam utilization solution, forming a comprehensive energy system with the goal of achieving the lowest energy cost. The system converts the electricity in the off-peak period into thermal energy and stores it, outputs the stored thermal energy in the peak period to drive the cogeneration unit to generate electricity, and uses the exhaust steam of the generator set for heating. When the exhaust steam volume is greater than the heat load, the excess steam is stored in the steam heat storage device and used for heating in other periods. A good return on investment is achieved and the diversified development of the "energy storage equipment" form in the new power grid concept of "source, network, load, storage" is promoted. This patent fails to fully consider the equivalent energy storage characteristics of steam based on the relationship between steam flow and temperature, and there are still deficiencies in the optimization of the steam energy consumption characteristic structure, so the optimization effect is not good. Summary of the invention

[0005] In order to solve the problem of poor optimization effect in the prior art, the present invention provides a virtual power plant electricity-steam collaborative optimization method and system for a textile industrial park.

[0006] The present invention adopts the following technical solution.

[0007] The present invention discloses, on one hand, a virtual power plant power-steam collaborative optimization method for a textile industrial park, comprising:

[0008] Step 1: Construct a virtual power plant operator-user two-layer dispatching architecture; the two-layer dispatching architecture includes an upper layer representing the virtual power plant operator and a lower layer representing the textile industry user;

[0009] Step 2: Based on the two-layer scheduling architecture, construct an upper-layer multi-energy flow coupling device model and a lower-layer multi-energy flow coupling device model;

[0010] Step 3: Based on the two-layer scheduling architecture and the lower-layer multi-energy flow coupling device model, a lower-layer optimization scheduling model is constructed;

[0011] Step 4: construct an upper-layer optimization scheduling model based on the two-layer scheduling architecture, the upper-layer multi-energy flow coupling device model and the lower-layer optimization scheduling model, and then construct a virtual power plant operator-user two-layer scheduling model;

[0012] Step 5: Collect relevant data of virtual power plant operators and textile industry users, solve the two-layer scheduling model, and obtain the electricity-steam scheduling results of virtual power plant operators and textile industry users.

[0013] More preferably,

[0014] In step 1, the upper layer includes a virtual power plant operator, the lower layer includes no less than one textile industry user, and each textile industry user is associated with the virtual power plant operator in the upper layer.

[0015] More preferably,

[0016] In step 2, the upper multi-energy flow coupling device model includes an upper electrode type industrial steam boiler model, an upper steam accumulator model, an upper heat pump model, an upper cogeneration unit model, an upper power storage device model and an upper heat storage device model;

[0017] The lower-layer multi-energy flow coupling equipment model includes a lower-layer gas boiler model, a lower-layer condensing machine model, a lower-layer electrode-type industrial steam boiler model, a lower-layer steam accumulator model, a lower-layer electricity storage equipment model and a lower-layer heat storage equipment model.

[0018] More preferably,

[0019] The upper heat pump model is as follows:

[0020]

[0021] in, Indicates the heating power of the heat pump; Indicates the heating efficiency of the heat pump; Indicates the power consumption of the heat pump; Indicates the upper limit of the heat pump heating power.

[0022] More preferably,

[0023] The upper cogeneration unit model is as follows:

[0024]

[0025] in, Represents the electric power output of the cogeneration unit at time t; V t VPPO represents the volume of natural gas consumed by the cogeneration unit at time t; It indicates the power generation efficiency of the cogeneration unit; represents the thermal power output of the cogeneration unit at time t; It represents the heating efficiency of the cogeneration unit; β represents the lower calorific value of natural gas.

[0026] More preferably,

[0027] The lower electrode type industrial steam boiler model is as follows:

[0028]

[0029] in, represents the steam energy output by the electrode-type industrial steam boiler of user i at time t; η ESB Indicates the heat generation efficiency of electrode-type industrial steam boiler; represents the electric power input to the electrode-type industrial steam boiler of user i at time t; Δt represents the time interval between two sampling times; represents the steam flow rate output by the electrode-type industrial steam boiler of user i at time t; h ESB Indicates the enthalpy value of steam output from electrode-type industrial steam boiler; represents the steam energy purchased by the electrode-type industrial steam boiler of user i at time t; It represents the steam energy inputted into the condenser by the electrode-type industrial steam boiler of user i at time t; represents the steam energy output from the electrode-type industrial steam boiler of user i to the steam accumulator at time t; where l is an integer, and l∈[1, N ir3 ],N ir3 Indicates the total number of ironing process production lines; It represents the steam energy and steam flow rate directly delivered to the l-th ironing process production line by the user i electrode industrial steam boiler at time t; represents the steam energy sold by the electrode-type industrial steam boiler of user i at time t; represents the steam flow rate sold by the electrode-type industrial steam boiler of user i at time t; h ESB→VPPO Enthalpy of steam sold for electric steam boilers; The upper limit of the electric power input to the user's i-electrode industrial steam boiler; It is the upper limit of steam flow rate output by the user's i-electrode industrial steam boiler.

[0030] More preferably,

[0031] In step 3, the lower-layer optimization scheduling model includes a lower-layer objective function, a lower-layer purchase and sale constraint, and a lower-layer power balance constraint;

[0032] The lower-level purchase and sale constraints include the lower-level purchase and sale of electricity power constraints, the lower-level purchase and sale of heat power constraints, and the lower-level purchase and sale of steam power constraints.

[0033] More preferably,

[0034] The lower objective function C u for:

[0035]

[0036] Among them, C e,i represents the cost of purchasing and selling electricity from the virtual power plant operator to textile industry user i; C h,irepresents the cost of purchasing and selling thermal energy from the virtual power plant operator to textile industry user i; C s,i represents the cost of purchasing and selling steam from the virtual power plant operator to textile industry user i; represents the cost of purchasing and selling natural gas to the virtual power plant operator by textile industry user i; min[·] means taking the minimum value of the formula in brackets.

[0037] More preferably,

[0038] In step 4, the upper-layer optimization scheduling model includes an upper-layer objective function, an upper-layer energy price constraint, a multi-energy transaction volume constraint between the upper and lower layers, and an upper-layer power balance constraint;

[0039] The upper energy price constraints include the upper purchase and sale of lower electricity energy price constraints, the upper purchase and sale of lower thermal energy price constraints, and the upper purchase and sale of lower steam energy price constraints;

[0040] The multi-energy trading volume constraints between upper and lower layers include the electric energy trading volume constraints between upper and lower layers, the thermal energy trading volume constraints between upper and lower layers, and the steam energy trading volume constraints between upper and lower layers.

[0041] More preferably,

[0042] The upper objective function B VPPO for:

[0043]

[0044] Among them, F e,t F represents the revenue earned by the virtual power plant operator from purchasing and selling user electricity; h,t F represents the revenue earned by the virtual power plant operator from purchasing and selling thermal energy from users; s,t It represents the revenue of the virtual power plant operator from purchasing and selling steam from users; represents the revenue from transactions between the virtual power plant operator and the external grid; represents the gas purchase cost of the virtual power plant operator; max[·] means taking the maximum value of the formula in brackets.

[0045] More preferably,

[0046] The upper layer power balance constraint is:

[0047]

[0048] in, represents the power purchased by the virtual power plant operator from the grid at time t; It represents the electric power output of the cogeneration unit at time t; represents the discharge power of the electric energy storage of the virtual power plant operator at time t, represents the power purchased by the virtual power plant operator from users at time t; Indicates the power consumption of the heat pump; represents the power of the electrode-type industrial steam boiler input electric energy of the virtual power plant operator at time t; represents the charging power of the electric energy storage of the virtual power plant operator at time t; represents the electric power sold by the virtual power plant operator to the user at time t; represents the electric power sold to the grid by the virtual power plant operator at time t; Indicates the heating power of the heat pump; represents the thermal power output of the cogeneration unit at time t; represents the heat release power of the thermal energy storage of the virtual power plant operator at time t; represents the power of thermal energy purchased by the virtual power plant operator from users at time t; represents the power of thermal energy charged into the thermal storage of the virtual power plant operator at time t; represents the thermal power sold by the virtual power plant operator to the user at time t; represents the steam output power of the electrode-type industrial steam boiler of the virtual power plant operator at time t; represents the steam power purchased by the virtual power plant operator from the user at time t; represents the steam flow rate released from the steam accumulator of the virtual power plant operator at time t; h VPPOSAdis Indicates the enthalpy value of high-temperature steam released from the steam accumulator; represents the steam power sold by the virtual power plant operator to the user at time t; represents the steam flow rate charged into the steam accumulator of the virtual power plant operator at time t; h VPPOSAcha Indicates the enthalpy value of high-temperature steam charged into the steam accumulator.

[0049] More preferably,

[0050] In step 4, the virtual power plant operator-user two-layer scheduling model includes an upper-layer multi-energy flow coupling equipment model, a lower-layer multi-energy flow coupling equipment model, an upper-layer optimization scheduling model and a lower-layer optimization scheduling model.

[0051] On the other hand, the present invention discloses a virtual power plant power-steam collaborative optimization system for a textile industrial park based on a virtual power plant power-steam collaborative optimization method for a textile industrial park, comprising: a two-layer scheduling architecture construction module, an upper and lower layer multi-energy flow coupling equipment model construction module, a lower layer optimization scheduling model construction module, a two-layer scheduling model construction module, and a two-layer scheduling model solving module.

[0052] The two-layer scheduling architecture building module builds a virtual power plant operator-user two-layer scheduling architecture; the two-layer scheduling architecture includes an upper layer representing the virtual power plant operator and a lower layer representing the textile industry user;

[0053] The upper and lower layer multi-energy flow coupling device model construction module constructs an upper layer multi-energy flow coupling device model and a lower layer multi-energy flow coupling device model based on the two-layer scheduling architecture;

[0054] The lower-layer optimization scheduling model construction module constructs a lower-layer optimization scheduling model based on the two-layer scheduling architecture and the lower-layer multi-energy flow coupling device model;

[0055] The two-layer scheduling model construction module constructs an upper-layer optimization scheduling model based on the two-layer scheduling architecture, the upper-layer multi-energy flow coupling device model and the lower-layer optimization scheduling model, and then constructs a virtual power plant operator-user two-layer scheduling model;

[0056] The two-layer scheduling model solving module collects relevant data of virtual power plant operators and relevant data of textile industry users, solves the two-layer scheduling model, and obtains the power-steam scheduling results of virtual power plant operators and textile industry users.

[0057] On the other hand, the present application discloses an electronic device, including a processor and a storage medium; the characteristics are:

[0058] The storage medium is used to store instructions;

[0059] The processor is used to operate according to the instructions to execute the virtual power plant electricity-steam collaborative optimization method of the aforementioned industrial park.

[0060] The present application also discloses a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the electricity-steam collaborative optimization method of the virtual power plant of the textile industrial park.

[0061] The beneficial effect of the present invention is that, compared with the prior art, the virtual power plant electricity-steam collaborative optimization method and system for textile industrial parks formed by the present invention improves the revenue of virtual power plant operators under real-time energy prices, reduces the overall economic cost of virtual power plants, and effectively improves the overall economic efficiency of virtual power plants. At the same time, under the guidance of the real-time energy prices sold by virtual power plants, users adjust their energy consumption behaviors and shift energy purchase time to valley periods, realize production plan adjustments and multi-energy load transfers, and slow down the fluctuations in multi-energy load demand of textile industry users.

[0062] In addition, the present invention also fully considers the complexity and diversity of the industry's electricity and steam demands, and has the outstanding advantage of good optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1A schematic diagram of a flow chart of a virtual power plant electricity-steam collaborative optimization method for a textile industrial park;

[0064] Figure 2 A schematic diagram of a solution process of a virtual power plant operator-user dual-layer scheduling model provided by an embodiment of the present invention;

[0065] Figure 3 The price of energy purchase and sale by a virtual power plant operator provided in one embodiment of the present invention;

[0066] Figure 4 A virtual power plant operator purchases and sells electrical power according to an embodiment of the present invention;

[0067] Figure 5 The user purchases and sells electric power provided by one embodiment of the present invention;

[0068] Figure 6 A virtual power plant operator buying and selling thermal power provided by an embodiment of the present invention;

[0069] Figure 7 A user purchase and sale of thermal power provided by an embodiment of the present invention;

[0070] Figure 8 A virtual power plant operator purchases and sells steam power according to an embodiment of the present invention;

[0071] Fig. 9 An embodiment of the present invention provides a method for users to purchase and sell steam power. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0073] This application discloses a virtual power plant power-steam collaborative optimization method for a textile industrial park, see Attachment Figure 1 ,include:

[0074] Step 1: Construct a virtual power plant operator-user two-layer dispatching architecture; the two-layer dispatching architecture includes an upper layer representing the virtual power plant operator and a lower layer representing the textile industry user;

[0075] The upper layer includes a virtual power plant operator, the lower layer includes no less than one textile industry user, and each textile industry user is associated with the virtual power plant operator in the upper layer.

[0076] Step 2: Based on the two-layer scheduling architecture, construct an upper-layer multi-energy flow coupling device model and a lower-layer multi-energy flow coupling device model;

[0077] The upper multi-energy flow coupling equipment model includes an upper electrode type industrial steam boiler model, an upper steam accumulator model, an upper heat pump model, an upper cogeneration unit model, an upper power storage equipment model and an upper heat storage equipment model;

[0078] The upper heat pump model is as follows:

[0079]

[0080] in, Indicates the heating power of the heat pump; Indicates the heating efficiency of the heat pump; Indicates the power consumption of the heat pump; Indicates the upper limit of the heat pump heating power.

[0081] The upper cogeneration unit model is as follows:

[0082]

[0083] in, Represents the electric power output of the cogeneration unit at time t; V t VPPO represents the volume of natural gas consumed by the cogeneration unit at time t; It indicates the power generation efficiency of the cogeneration unit; represents the thermal power output of the cogeneration unit at time t; It represents the heating efficiency of the cogeneration unit; β represents the lower calorific value of natural gas.

[0084] The lower-layer multi-energy flow coupling equipment model includes a lower-layer gas boiler model, a lower-layer condensing machine model, a lower-layer electrode-type industrial steam boiler model, a lower-layer steam accumulator model, a lower-layer electricity storage equipment model and a lower-layer heat storage equipment model.

[0085] The lower electrode industrial steam boiler model is as follows:

[0086]

[0087] in, represents the steam energy output by the electrode-type industrial steam boiler of user i at time t; η ESB Indicates the heat generation efficiency of electrode-type industrial steam boiler; represents the electric power input to the electrode-type industrial steam boiler of user i at time t; Δt represents the time interval between two sampling times; represents the steam flow rate output by the electrode-type industrial steam boiler of user i at time t; h ESB Indicates the enthalpy value of steam output from electrode-type industrial steam boiler; represents the steam energy purchased by the electrode-type industrial steam boiler of user i at time t; It represents the steam energy inputted into the condenser by the electrode-type industrial steam boiler of user i at time t; represents the steam energy output from the electrode-type industrial steam boiler of user i to the steam accumulator at time t; where l is an integer, and l∈[1, N ir3 ],N ir3 Indicates the total number of ironing process production lines; It represents the steam energy and steam flow rate directly delivered to the l-th ironing process production line by the user i electrode industrial steam boiler at time t; represents the steam energy sold by the electrode-type industrial steam boiler of user i at time t; represents the steam flow rate sold by the electrode-type industrial steam boiler of user i at time t; h ESB→VPPO Enthalpy of steam sold for electric steam boilers; The upper limit of the electric power input to the user's i-electrode industrial steam boiler; It is the upper limit of steam flow rate output by the user's i-electrode industrial steam boiler.

[0088] Step 3: Based on the two-layer scheduling architecture and the lower-layer multi-energy flow coupling device model, a lower-layer optimization scheduling model is constructed;

[0089] The lower-layer optimization scheduling model includes a lower-layer objective function, a lower-layer purchase and sale constraint, and a lower-layer power balance constraint;

[0090] The lower objective function C u for:

[0091]

[0092] Among them, C e,i represents the cost of purchasing and selling electricity from the virtual power plant operator to textile industry user i; C h,i represents the cost of purchasing and selling thermal energy from the virtual power plant operator to textile industry user i; C s,i represents the cost of purchasing and selling steam from the virtual power plant operator to textile industry user i; represents the cost of purchasing and selling natural gas to the virtual power plant operator by textile industry user i; min[·] means taking the minimum value of the formula in brackets.

[0093] The lower-level purchase and sale constraints include the lower-level purchase and sale of electricity power constraints, the lower-level purchase and sale of heat power constraints, and the lower-level purchase and sale of steam power constraints.

[0094] Step 4: construct an upper-layer optimization scheduling model based on the two-layer scheduling architecture, the upper-layer multi-energy flow coupling device model and the lower-layer optimization scheduling model, and then construct a virtual power plant operator-user two-layer scheduling model;

[0095] The upper-layer optimization scheduling model includes an upper-layer objective function, an upper-layer energy price constraint, a multi-energy transaction volume constraint between the upper and lower layers, and an upper-layer power balance constraint;

[0096] The upper objective function B VPPO for:

[0097]

[0098] Among them, F e,t F represents the revenue earned by the virtual power plant operator from purchasing and selling user electricity; h,t F represents the revenue earned by the virtual power plant operator from purchasing and selling thermal energy from users; s,t It represents the revenue of the virtual power plant operator from purchasing and selling steam from users; represents the revenue from transactions between the virtual power plant operator and the external grid; represents the gas purchase cost of the virtual power plant operator; max[·] means taking the maximum value of the formula in brackets.

[0099] The upper energy price constraints include the upper purchase and sale of lower electricity energy price constraints, the upper purchase and sale of lower thermal energy price constraints, and the upper purchase and sale of lower steam energy price constraints;

[0100] The multi-energy trading volume constraints between upper and lower layers include the electric energy trading volume constraints between upper and lower layers, the thermal energy trading volume constraints between upper and lower layers, and the steam energy trading volume constraints between upper and lower layers.

[0101] The upper layer power balance constraint is:

[0102]

[0103] in, represents the power purchased by the virtual power plant operator from the grid at time t; It represents the electric power output of the cogeneration unit at time t; represents the discharge power of the electric energy storage of the virtual power plant operator at time t, represents the power purchased by the virtual power plant operator from users at time t; Indicates the power consumption of the heat pump; represents the power of the electrode-type industrial steam boiler input electric energy of the virtual power plant operator at time t; represents the charging power of the electric energy storage of the virtual power plant operator at time t; represents the electric power sold by the virtual power plant operator to the user at time t; represents the electric power sold to the grid by the virtual power plant operator at time t; Indicates the heating power of the heat pump; represents the thermal power output of the cogeneration unit at time t; represents the heat release power of the thermal energy storage of the virtual power plant operator at time t; represents the power of thermal energy purchased by the virtual power plant operator from users at time t; represents the power of thermal energy charged into the thermal storage of the virtual power plant operator at time t; represents the thermal power sold by the virtual power plant operator to the user at time t; represents the steam output power of the electrode-type industrial steam boiler of the virtual power plant operator at time t; represents the steam power purchased by the virtual power plant operator from the user at time t; represents the steam flow rate released from the steam accumulator of the virtual power plant operator at time t; h VPPOSAdis Indicates the enthalpy value of high-temperature steam released from the steam accumulator; represents the steam power sold by the virtual power plant operator to the user at time t; represents the steam flow rate charged into the steam accumulator of the virtual power plant operator at time t; h VPPOSAcha Indicates the enthalpy value of high-temperature steam charged into the steam accumulator.

[0104] The virtual power plant operator-user two-layer scheduling model includes an upper-layer multi-energy flow coupling equipment model, a lower-layer multi-energy flow coupling equipment model, an upper-layer optimization scheduling model and a lower-layer optimization scheduling model.

[0105] Step 5: Collect relevant data of virtual power plant operators and textile industry users, solve the two-layer scheduling model, and obtain the electricity-steam scheduling results of virtual power plant operators and textile industry users.

[0106] The virtual power plant operator-related data include all data related to the virtual power plant operator used in the aforementioned formula or constraint calculation process; specifically, the virtual power plant operator's electrical load, cooling load, heating load, upper-level energy acquisition parameters and upper-level multi-energy coupling equipment operating parameters; the upper-level energy acquisition parameters include natural gas prices, the virtual power plant operator's gas purchase volume, and the virtual power plant operator's purchase and sale of electricity, heat and steam prices with the power grid; the upper-level multi-energy coupling equipment operating parameters include electrode-type industrial steam boiler operating parameters, steam accumulator operating parameters, heat pump operating parameters, electricity storage equipment operating parameters and heat storage equipment operating parameters.

[0107] The textile industry user-related data include all data related to textile industry users used in the aforementioned formula or constraint calculation process; specifically, including the textile industry users' electric load, cooling load, heating load, lower-level energy acquisition parameters, lower-level multi-energy coupling equipment operating parameters and lower-level production process operating parameters; wherein the lower-level energy acquisition parameters include the electric power output of distributed photovoltaics, the electricity price, power and price of electricity purchased and sold from the virtual power plant operator, the power and price of heat energy purchased and sold from the user to the virtual power plant operator, the power and price of steam purchased and sold from the virtual power plant operator, the user's natural gas consumption, the lower calorific value of natural gas and the price of natural gas; the lower-level multi-energy coupling equipment operating data include gas boiler operating parameters, electrode industrial steam boiler operating parameters, condensing machine operating parameters, steam accumulator operating parameters, electricity storage equipment operating parameters and heat storage equipment operating parameters; the lower-level production process operating parameters include textile process operating parameters, printing and dyeing process operating parameters, and ironing process operating parameters.

[0108] Preferably, solving the virtual power plant operator-user two-tier dispatching model can be implemented by using a mathematical programming method, or by calling the commercial solver BARON to solve the above model based on the EMP algorithm package in a GAMS environment to obtain the electricity-steam dispatching result.

[0109] Those skilled in the art should know that solving the model with mathematical programming methods mainly relies on the basic principles of nonlinear optimization, including defining the objective function and constraints, and solving the problem using techniques such as branch and bound method, tangent plane method and local optimization. Specifically, the branch and bound method effectively searches for feasible solutions by constructing a solution space tree, while the tangent plane method simplifies the problem by linearizing nonlinear constraints. In addition, heuristic and meta-heuristic algorithms such as genetic algorithms and simulated annealing are used to explore the solution space, especially when facing complex nonlinear or non-smooth objective functions.

[0110] Preferably, when calling the commercial solver BARON for solving, refer to Figure 2, solve the virtual power plant operator-user two-layer scheduling model. First, the initial purchase and sale electricity, heat, and steam energy prices provided by the virtual power plant operator to the user are substituted into the lower-level multi-energy flow coupling equipment model and the lower-level optimization scheduling model. Then the lower-level user calls the commercial solver BARON to solve, obtains its own operating cost, and feeds back the purchase and sale strategy to the upper-level VPPO. At this time, the cost of industrial users is the lowest, and the output user's actual purchase and sale of electricity, heat, steam, steam purchase volume and production plan data of the virtual power plant operator are substituted into the upper-level multi-energy flow coupling equipment model and the upper-level optimization scheduling model of the upper-level VPPO. At the same time, the VPPO initial composite data and the initial cost coefficients of each upper-level energy supply equipment are also substituted into the upper-level multi-energy flow coupling equipment model and the upper-level optimization scheduling model. Then the upper-level VPPO calls the commercial solver BARON to solve, obtain the maximum VPPO benefit result, and then obtain the optimized purchase and sale energy strategy.

[0111] The present application also discloses a virtual power plant power-steam collaborative optimization system for a textile industrial park based on a virtual power plant power-steam collaborative optimization method for a textile industrial park, including a two-layer scheduling architecture construction module, an upper and lower layer multi-energy flow coupling equipment model construction module, a lower layer optimization scheduling model construction module, a two-layer scheduling model construction module and a two-layer scheduling model solving module.

[0112] The two-layer scheduling architecture building module builds a virtual power plant operator-user two-layer scheduling architecture; the two-layer scheduling architecture includes an upper layer representing the virtual power plant operator and a lower layer representing the textile industry user;

[0113] The upper layer includes a virtual power plant operator, the lower layer includes no less than one textile industry user, and each textile industry user is associated with the virtual power plant operator in the upper layer.

[0114] The upper and lower layer multi-energy flow coupling device model construction module constructs an upper layer multi-energy flow coupling device model and a lower layer multi-energy flow coupling device model based on the two-layer scheduling architecture;

[0115] The upper multi-energy flow coupling equipment model includes an upper electrode type industrial steam boiler model, an upper steam accumulator model, an upper heat pump model, an upper cogeneration unit model, an upper power storage equipment model and an upper heat storage equipment model;

[0116] The upper heat pump model is as follows:

[0117]

[0118] in, Indicates the heating power of the heat pump; Indicates the heating efficiency of the heat pump; Indicates the power consumption of the heat pump; Indicates the upper limit of the heat pump heating power.

[0119] The upper cogeneration unit model is as follows:

[0120]

[0121] in, Represents the electric power output of the cogeneration unit at time t; V t VPPO represents the volume of natural gas consumed by the cogeneration unit at time t; It indicates the power generation efficiency of the cogeneration unit; represents the thermal power output of the cogeneration unit at time t; It represents the heating efficiency of the cogeneration unit; β represents the lower calorific value of natural gas.

[0122] The lower-layer multi-energy flow coupling equipment model includes a lower-layer gas boiler model, a lower-layer condensing machine model, a lower-layer electrode-type industrial steam boiler model, a lower-layer steam accumulator model, a lower-layer electricity storage equipment model and a lower-layer heat storage equipment model.

[0123] The lower electrode industrial steam boiler model is as follows:

[0124]

[0125] in, represents the steam energy output by the electrode-type industrial steam boiler of user i at time t; η ESB Indicates the heat generation efficiency of electrode-type industrial steam boiler; represents the electric power input to the electrode-type industrial steam boiler of user i at time t; Δt represents the time interval between two sampling times; represents the steam flow rate output by the electrode-type industrial steam boiler of user i at time t; h ESB Indicates the enthalpy value of steam output from electrode-type industrial steam boiler; represents the steam energy purchased by the electrode-type industrial steam boiler of user i at time t; It represents the steam energy inputted into the condenser by the electrode-type industrial steam boiler of user i at time t; represents the steam energy output from the electrode-type industrial steam boiler of user i to the steam accumulator at time t; where l is an integer, and l∈[1, N ir3 ],N ir3 Indicates the total number of ironing process production lines; It represents the steam energy and steam flow rate directly delivered to the l-th ironing process production line by the user i electrode industrial steam boiler at time t; represents the steam energy sold by the electrode-type industrial steam boiler of user i at time t; represents the steam flow rate sold by the electrode-type industrial steam boiler of user i at time t; h ESB→VPPO Enthalpy of steam sold for electric steam boilers; The upper limit of the electric power input to the user's i-electrode industrial steam boiler; It is the upper limit of steam flow rate output by the user's i-electrode industrial steam boiler.

[0126] The lower-layer optimization scheduling model construction module constructs a lower-layer optimization scheduling model based on the two-layer scheduling architecture and the lower-layer multi-energy flow coupling device model;

[0127] The lower-layer optimization scheduling model includes a lower-layer objective function, a lower-layer purchase and sale constraint, and a lower-layer power balance constraint;

[0128] The lower objective function C u for:

[0129]

[0130] Among them, C e,i represents the cost of purchasing and selling electricity from the virtual power plant operator to textile industry user i; C h,i represents the cost of purchasing and selling thermal energy from the virtual power plant operator to textile industry user i; C s,i represents the cost of purchasing and selling steam from the virtual power plant operator to textile industry user i; represents the cost of purchasing and selling natural gas to the virtual power plant operator by textile industry user i; min[·] means taking the minimum value of the formula in brackets.

[0131] The lower-level purchase and sale constraints include the lower-level purchase and sale of electricity power constraints, the lower-level purchase and sale of heat power constraints, and the lower-level purchase and sale of steam power constraints.

[0132] The two-layer scheduling model construction module constructs an upper-layer optimization scheduling model based on the two-layer scheduling architecture, the upper-layer multi-energy flow coupling device model and the lower-layer optimization scheduling model, and then constructs a virtual power plant operator-user two-layer scheduling model;

[0133] The upper-layer optimization scheduling model includes an upper-layer objective function, an upper-layer energy price constraint, a multi-energy transaction volume constraint between the upper and lower layers, and an upper-layer power balance constraint;

[0134] The upper objective function B VPPO for:

[0135]

[0136] Among them, F e,t F represents the revenue earned by the virtual power plant operator from purchasing and selling user electricity; h,t F represents the revenue of the virtual power plant operator from purchasing and selling thermal energy from users; s,tIt represents the revenue of the virtual power plant operator from purchasing and selling steam from users; represents the revenue from transactions between the virtual power plant operator and the external grid; represents the gas purchase cost of the virtual power plant operator; max[·] means taking the maximum value of the formula in brackets.

[0137] The upper energy price constraints include the upper purchase and sale of lower electricity energy price constraints, the upper purchase and sale of lower thermal energy price constraints, and the upper purchase and sale of lower steam energy price constraints;

[0138] The multi-energy trading volume constraints between upper and lower layers include the electric energy trading volume constraints between upper and lower layers, the thermal energy trading volume constraints between upper and lower layers, and the steam energy trading volume constraints between upper and lower layers.

[0139] The upper layer power balance constraint is:

[0140]

[0141] in, represents the power purchased by the virtual power plant operator from the grid at time t; It represents the electric power output of the cogeneration unit at time t; represents the discharge power of the electric energy storage of the virtual power plant operator at time t, represents the power purchased by the virtual power plant operator from users at time t; Indicates the power consumption of the heat pump; represents the power of the electrode-type industrial steam boiler input electric energy of the virtual power plant operator at time t; represents the charging power of the electric energy storage of the virtual power plant operator at time t; represents the electric power sold by the virtual power plant operator to the user at time t; represents the electric power sold to the grid by the virtual power plant operator at time t; Indicates the heating power of the heat pump; represents the thermal power output of the cogeneration unit at time t; represents the heat release power of the thermal energy storage of the virtual power plant operator at time t; represents the power of thermal energy purchased by the virtual power plant operator from users at time t; represents the power of thermal energy charged into the thermal storage of the virtual power plant operator at time t; represents the thermal power sold by the virtual power plant operator to the user at time t; represents the steam output power of the electrode-type industrial steam boiler of the virtual power plant operator at time t; represents the steam power purchased by the virtual power plant operator from the user at time t; represents the steam flow rate released from the steam accumulator of the virtual power plant operator at time t; h VPPOSAdisIndicates the enthalpy value of high-temperature steam released from the steam accumulator; represents the steam power sold by the virtual power plant operator to the user at time t; represents the steam flow rate charged into the steam accumulator of the virtual power plant operator at time t; h VPPOSAcha Indicates the enthalpy value of high-temperature steam charged into the steam accumulator.

[0142] The virtual power plant operator-user two-layer scheduling model includes an upper-layer multi-energy flow coupling equipment model, a lower-layer multi-energy flow coupling equipment model, an upper-layer optimization scheduling model and a lower-layer optimization scheduling model.

[0143] The two-layer scheduling model solving module collects relevant data of virtual power plant operators and relevant data of textile industry users, solves the two-layer scheduling model, and obtains the power-steam scheduling results of virtual power plant operators and textile industry users.

[0144] Example 1

[0145] A virtual power plant power-steam collaborative optimization method for a textile industrial park, comprising:

[0146] Step 1: Construct a two-layer scheduling architecture of virtual power plant operators and users. The two-layer scheduling architecture includes an upper layer and a lower layer, where the upper layer is the virtual power plant operator VPPO, namely Virtual Power Plant Operators, and the lower layer is the textile industry users.

[0147] In step 1 of the present invention, the overall scheduling of virtual power plants is studied.

[0148] The upper layer includes a virtual power plant operator, the lower layer includes no less than one textile industry user, and each textile industry user is associated with the virtual power plant operator in the upper layer, that is, each textile industry user has an energy purchase and sale relationship with the virtual power plant operator in the upper layer.

[0149] When the discharge of the photovoltaic power generation system and the supporting energy storage equipment cannot meet the electricity demand of the textile industry users in the lower layer, the textile industry users can purchase and sell electricity with the virtual power plant operator to achieve a dynamic balance of energy supply and demand. When the photovoltaic power generation capacity of the textile industry users in the lower layer exceeds the immediate demand, the remaining electricity can be stored in their own energy storage equipment and the surplus electricity can be sold to the virtual power plant operator in the upper layer. The virtual power plant operator sets the internal purchase and sale energy prices for each time period, and its income mainly comes from the energy trading volume conducted by the textile industry users through the virtual power plant operator in different time periods. In addition, the textile industry users provide the virtual power plant operator with their strategy information on the purchase and sale of electricity, heat and steam, and their purchase and sale energy power is affected by the time-of-use electricity price set by the virtual power plant operator.

[0150] Step 2: According to the two-layer scheduling architecture of the virtual power plant, the upper-layer multi-energy flow coupling equipment model and the lower-layer multi-energy flow coupling equipment model are modeled. The upper-layer multi-energy flow coupling equipment corresponding to the virtual power plant operator includes: electrode-type industrial steam boilers, steam regenerators, heat pumps, power storage equipment and heat storage equipment; the lower-layer multi-energy flow coupling equipment model corresponding to the textile industry users includes: gas boilers, condensing machines, electrode-type industrial steam boilers, steam regenerators, power storage equipment and heat storage equipment.

[0151] In the present invention, the electric and thermal energy storage modeling of industrial users is consistent with the electric and thermal storage equipment of the virtual power plant operator.

[0152] The upper-level multi-energy flow coupling equipment model corresponding to the virtual power plant operator and the lower-level multi-energy flow coupling equipment model corresponding to the textile industry users are studied.

[0153] 2.1 Study the upper-layer multi-energy flow coupling equipment model corresponding to the virtual power plant operator; the upper-layer multi-energy flow coupling equipment model includes an upper-layer heat pump model, an upper-layer cogeneration unit model, an upper-layer electrode-type industrial steam boiler model, an upper-layer steam accumulator model, an upper-layer power storage equipment model and an upper-layer heat storage equipment model;

[0154] 2.1.1 Upper heat pump model

[0155] The heat pump is used as a conversion device for electric energy and cold and hot energy through electric drive to improve the utilization rate of primary energy. This project uses the heat pump as a heating device, and its mathematical model can be expressed as:

[0156]

[0157] Where: They represent the power consumption and heating power of the heat pump respectively; Indicates the heating efficiency of the heat pump; Indicates the upper limit of the heat pump heating power.

[0158] 2.1.2 Upper cogeneration unit model

[0159] As an important energy coupling device, cogeneration is widely used in industrial parks. It generates electricity and heat energy by burning natural gas. The cogeneration unit of this project uses an internal combustion engine with a fixed heat-to-electricity ratio. The mathematical model is as follows:

[0160]

[0161] Where: They represent the electricity generation power and heating power of the cogeneration unit respectively; Respectively represent the electrical power and thermal power output of the cogeneration unit at time t; V t VPPOrepresents the volume of natural gas consumed by the cogeneration unit at time t; β represents the lower calorific value of natural gas, which is 9.97kW×h / m 3 .

[0162] 2.1.3 Upper electrode industrial steam boiler model

[0163] The electricity-steam energy conversion constraint of the electrode-type industrial steam boiler and the high-temperature steam flow constraint output by the electrode-type industrial steam boiler can be expressed as:

[0164]

[0165] Where: P represents the steam energy output by the electrode-type industrial steam boiler at time t; t ESB Indicates ESB at time t

[0166] The electric power input of the electrode type industrial steam boiler; η represents the heat generation efficiency of the electrode type industrial steam boiler; represents the steam flow rate output by the electrode-type industrial steam boiler at time t; h ESB Indicates the enthalpy value of steam output from an electrode-type industrial steam boiler.

[0167] 2.1.4 Upper steam accumulator model

[0168] Since the virtual power plant operator can only buy and sell high-temperature steam with the user, the steam released by the steam accumulator is also high-temperature steam, so the steam accumulator model can be expressed as:

[0169]

[0170] Where: All are 0-1 variables. When the steam accumulator is charged with steam, otherwise it is not charged with steam. When the steam accumulator releases steam, otherwise it does not release steam; Indicates the maximum charging and discharging steam flow rate of the steam accumulator; represents the remaining capacity of the steam accumulator at time t, Respectively represent the minimum and maximum values ​​of the remaining capacity of the steam accumulator; S1 VPPOSA , represent the remaining capacity of the steam accumulator at the initial moment and at the end of the optimization period, respectively; They represent the steam flow rate charged into and discharged from the steam accumulator of the electrode-type industrial steam boiler at time t; h VPPOSAcha 、h VPPOSAdis They respectively represent the enthalpy values ​​of charging and discharging high-temperature steam in the steam accumulator.

[0171] The first three items / formulas in the upper steam accumulator model are only effective when purchasing or selling electricity.

[0172] The constraints on electricity and heat storage equipment, heat pump units and cogeneration units are not discussed here.

[0173] 2.1.5 Upper-layer electricity storage device model and upper-layer heat storage device model

[0174] It should be made clear that the electrical and thermal energy storage devices of VPPO cannot be charged and discharged at the same time.

[0175] The upper-level power storage device model is as follows:

[0176]

[0177] Where: Indicates the remaining capacity of the power storage device at time t, in kWh; They represent the charging and discharging power of the power storage device at time t respectively; They represent the charging and discharging efficiency of the power storage device respectively; All are 0-1 variables. At time t, the VPPO energy storage is charged, otherwise the energy storage is not charged; When t, the energy storage of VPPO is discharged, otherwise the energy storage is not discharged; Respectively represent the maximum charging and discharging power of the power storage device; Respectively represent the upper and lower limits of the remaining capacity of the power storage device; Indicates the remaining capacity of the electric energy storage device at the initial moment of the optimization cycle; Indicates the remaining capacity of the energy storage device at the end of the optimization cycle.

[0178] The upper heat storage equipment model is as follows:

[0179]

[0180] Where: They respectively represent the remaining capacity of the heat storage device at time t, and the unit is kWh; , They represent the charging and discharging power of the heat storage device at time t respectively; They represent the charging and discharging efficiency of the heat storage device respectively; All are 0-1 variables. When t, the thermal energy storage of VPPO is charged, otherwise the thermal energy storage is not charged; When , the thermal energy storage of VPPO releases heat at time t, otherwise the thermal energy storage does not release heat; Respectively represent the maximum charging and discharging power of the electric storage device and the heat storage device; Respectively represent the upper and lower limits of the remaining capacity of the heat storage device; represents the remaining capacity of the thermal energy storage device at the initial moment of the optimization cycle; Indicates the remaining capacity of the thermal energy storage device at the end of the optimization period.

[0181] 2.2 Study the lower-level multi-energy flow coupling equipment model corresponding to textile industry users;

[0182] The lower-layer multi-energy flow coupling equipment model includes a lower-layer gas boiler model, a lower-layer electrode-type industrial steam boiler model, a lower-layer condenser model, a lower-layer steam accumulator model, a lower-layer electricity storage equipment model and a lower-layer heat storage equipment model.

[0183] 2.2.1 Lower gas boiler model

[0184] Gas boilers produce heat energy by consuming natural gas to meet the heat demand of textile industry users. The mathematical model can be expressed as:

[0185]

[0186] Where: represents the heating power of user i's gas boiler at time t, in kW; η GB represents the heat generation efficiency of the gas boiler at time t; It represents the natural gas consumption of the gas boiler of user i at time t, and its unit is m 3 ; β represents the lower calorific value of natural gas, which is 9.97kW·h / m 3 .

[0187] 2.2.2 Lower electrode industrial steam boiler model

[0188] Part of the steam sold by the user is provided by the electrode-type industrial steam boiler, and all the steam purchased by the user enters the outlet of the electrode-type industrial steam boiler. Therefore, the modeling of the electrode-type industrial steam boiler of the textile industry user i can be expressed as:

[0189]

[0190] Where i is an integer, i∈[1,N], and N is the total number of users in the textile industry; represents the steam energy output by the electrode-type industrial steam boiler of user i at time t; η ESB Indicates the heat generation efficiency of electrode-type industrial steam boiler; represents the power consumption of the electrode-type industrial steam boiler input to user i at time t; Δt represents the time interval between two sampling times; represents the steam flow rate output by the electrode-type industrial steam boiler of user i at time t; h ESB Indicates the enthalpy value of steam output from electrode-type industrial steam boiler; represents the steam energy purchased by the electrode-type industrial steam boiler of user i at time t; It represents the steam energy output from the user i-electrode industrial steam boiler to the condenser at time t; represents the steam energy output from the electrode-type industrial steam boiler of user i to the steam accumulator at time t; where l is an integer, and l∈[1, N ir3 ],N ir3 Indicates the total number of ironing process production lines; It represents the steam energy and steam flow rate directly delivered to the l-th ironing process production line by the user i electrode industrial steam boiler at time t; represents the steam energy sold by user i’s electrode-type industrial steam boiler to the virtual power plant operator at time t; h represents the steam flow rate output from the electrode-type industrial steam boiler of user i to the steam accumulator at time t; SAcha Indicates the steam enthalpy output from the electric steam boiler to the steam accumulator; represents the steam flow rate output from the user i electrode industrial steam boiler to the condenser at time t; h CONcha Indicates the steam enthalpy output from the electric steam boiler to the condenser; represents the steam flow rate directly delivered by the electrode-type industrial steam boiler of user i to the lth ironing process production line at time t; h ESB _ ir3 It indicates the enthalpy of steam delivered by the electric steam boiler to the ironing process production line; represents the steam flow rate sold by the electrode-type industrial steam boiler of user i to the virtual power plant operator at time t; h ESB→VPPO enthalpy of steam sold to the virtual power plant operator for the electric steam boiler; The upper limit of the electric power input to the user's i-electrode industrial steam boiler; It is the upper limit of steam flow rate output by the user's i-electrode industrial steam boiler.

[0191] 2.2.3 Lower condenser model

[0192] In the present invention, the production process of the textile industry user includes weaving, printing and dyeing, and ironing processes.

[0193] The energy balance constraint of the condenser input, the relationship between the steam energy delivered by the condenser of each process and the electric power consumed by the condenser, the output steam energy balance constraint, the enthalpy constraint of the steam delivered to each process, and the output steam flow constraint can be expressed as:

[0194]

[0195]

[0196] Where: m, n, l are all integers, representing the production line indexes of textile, printing and dyeing, and ironing processes, respectively. te1 、N pd2 、N ir3 Respectively represent the number of production lines for textile, printing and dyeing, and ironing processes; represents the total energy input to the condenser at time t, kJ; represents the steam energy input to the condenser at time t; They represent the electric power required by the condenser to supply steam to the production lines of textile, printing and dyeing, and ironing processes at time t respectively;

[0197] They represent the power consumption efficiency and heating efficiency of the condensing unit respectively; They represent the steam energy, steam flow rate and steam enthalpy delivered by the condenser to the mth textile production line at time t respectively; They represent the steam energy, steam flow rate and steam enthalpy delivered by the condenser to the nth printing and dyeing production line at time t respectively; They represent the steam energy, steam flow rate and steam enthalpy delivered by the condenser to the mth textile production line at time t respectively; Indicates the maximum output steam flow of the condenser.

[0198] 2.2.4 Lower steam accumulator model

[0199] Part of the steam sold by textile industry users is provided by steam accumulators, so the steam accumulator modeling of textile industry user i can be expressed as:

[0200]

[0201] Where: It represents the maximum steam flow rate charged into the steam accumulator of textile industry user i at time t; All are 0-1 variables. When the steam accumulator of textile industry user i is charged with steam, otherwise it is not charged with steam. When , the steam accumulator of textile industry user i releases steam, otherwise it does not release steam; It represents the maximum steam charging and discharging flow rate of the steam accumulator of textile industry user i; They represent the steam flow rate and steam enthalpy value delivered by the steam accumulator of textile industry user i to the mth textile production line at time t respectively; They represent the steam flow rate and steam enthalpy value delivered by the steam accumulator of textile industry user i to the nth printing and dyeing production line at time t respectively; They represent the steam flow rate and steam enthalpy value delivered to the l-th ironing production line by the steam accumulator of textile industry user i at time t respectively; represents the remaining capacity of the steam accumulator of textile industry user i at time t, They represent the minimum and maximum values ​​of the remaining capacity of the steam accumulator of textile industry user i respectively; represents the remaining capacity of the steam accumulator of textile industry user i at the initial moment and at the end of the optimization period; h SAcha It represents the maximum enthalpy of steam charged into the steam accumulator of textile industry user i at time t; h SA→VPPO They represent the steam flow and steam enthalpy sold by the steam accumulator of textile industry user i at time t. Since the user sells constant temperature steam, h SA→VPPO is also a constant value.

[0202] The steam sold by the user is the sum of the steam sold by the electrode-type industrial steam boiler and the steam accumulator, which can be expressed as:

[0203]

[0204]

[0205] Where: It indicates the amount of steam sold by the user's ESB to the VPPO; represents the steam energy sold by the steam accumulator of textile industry user i at time t; Represents the total steam energy sold by textile industry user i at time t.

[0206] The condenser steam temperature boundary of textile industry user i is modeled as:

[0207]

[0208] T s_te1 , T s_pd2 , T s_ir3 Indicates the rated steam temperature of each production process, represents the steam temperature delivered by the condenser to the mth textile production line, °C, It represents the steam temperature delivered by the condenser to each production line of the nth printing and dyeing production process at time t, ℃; It represents the steam temperature delivered by the condenser to each production line of the first ironing production process at time t, ℃; is a 0-1 variable, It indicates that the textile production line is running, otherwise it indicates that the textile production line is not running; is a 0-1 variable, It indicates that the printing and dyeing production line is running, otherwise it indicates that the printing and dyeing production line is not running; is a 0-1 variable, Indicates that the ironing production line is running, otherwise it means that the ironing production line is not running; They represent the steam pressures delivered by the condenser to the mth textile, nth printing and dyeing, and lth ironing production line at time t, in MPa respectively; a, b, and c represent the exponential parameters of the Antoine equation, which are 9.3876, 3826.36, and 227.68 respectively; k1, k2, k3, k4, k5, and k6 represent the fitting numerical coefficients of the steam enthalpy-temperature relationship, and are 2.326, 0.071533, 13.08426, 0.1, 0.808956, and 1075.181444 respectively.

[0209] Step 3: Based on the two-layer scheduling architecture and the lower-layer multi-energy flow coupling device model, a lower-layer optimization scheduling model is constructed;

[0210] The lower-level users build a user-level optimization scheduling model with the lowest total operating cost as the optimization goal.

[0211] The lower-layer optimization scheduling model includes a lower-layer objective function, a lower-layer purchase and sale constraint, and a lower-layer power balance constraint;

[0212] In step 3 of the present invention, the textile industry user layer, ie, the lower layer optimization scheduling model is studied.

[0213] 3.1 Lower layer objective function

[0214] The lower layer objective function C of the user layer optimization scheduling model u , as shown below:

[0215]

[0216] Where: C e,i , C h,i , C s,i , Respectively represent the cost of purchasing electricity, heat, steam and natural gas from the virtual power plant operator by textile industry user i, RMB; min[·] means taking the minimum value of the formula in brackets.

[0217] The cost C of purchasing and selling electricity from the virtual power plant operator to textile industry user i e,i Calculated as follows:

[0218]

[0219] Where T represents the planning period; represents the electricity price sold by the virtual power plant operator to users at time t, RMB / kWh; represents the electric power purchased by user i from the virtual power plant operator at time t, kW; represents the price at which the virtual power plant operator purchases electricity from users at time t, RMB / kWh; Represents the thermal power sold by user i to the virtual power plant operator at time t, kW.

[0220] The cost C of purchasing and selling thermal energy from the virtual power plant operator to textile industry user i h,i Calculated as follows:

[0221]

[0222] represents the price of thermal energy sold by the virtual power plant operator to users at time t, RMB / kWh; It represents the thermal power purchased by user i from the virtual power plant operator at time t, kW; represents the price at which the virtual power plant operator purchases thermal energy from users at time t, RMB / kWh; Represents the thermal power sold by user i to the virtual power plant operator at time t, kW.

[0223] Cost C of purchasing and selling steam from the virtual power plant operator to textile industry user i s,i Calculated as follows:

[0224]

[0225] in, They represent the steam prices purchased and sold by the virtual power plant operator to users at time t, RMB / kJ; They respectively represent the steam power purchased or sold by user i from the virtual power plant operator at time t, kJ / h.

[0226] Cost of purchasing and selling natural gas from virtual power plant operators to textile industry users Calculated as follows:

[0227]

[0228] λ gas represents the price of natural gas at time t, yuan / m 3 ; represents the natural gas consumption of user i’s gas boiler at time t, m 3 .

[0229] 3.2 Lower-level purchase and sale constraints

[0230] Construct the lower-level purchase and sale constraints only when the textile industry user is in the purchase / sale state.

[0231] Textile industry users cannot be in the buying and selling state at the same time, so the lower-level buying and selling constraints are constructed to constrain the maximum transaction volume of users' purchase and sale of electricity, heat, and steam.

[0232] The lower-level purchase and sale constraints include the lower-level purchase and sale of electricity power constraints, the lower-level purchase and sale of heat power constraints, and the lower-level purchase and sale of steam power constraints.

[0233] The power constraints for purchasing and selling electricity at the lower level are:

[0234]

[0235] Where: e,i,t is a 0-1 variable, indicating the state of electricity purchase and sale of textile industry user i during period t, ω e,i,t =1, it means the user sells electricity, otherwise the user buys electricity, P e,max It represents the maximum value of electricity purchase and sale between the virtual power plant operator and the user at each moment.

[0236] The lower layer heat purchase and sale power constraints are:

[0237]

[0238] Where: h,i,t is a 0-1 variable, indicating the buying and selling status of textile industry user i in period t, ω h,i,t =1, it means the user sells hot, otherwise the user buys hot; P h,max It represents the maximum value of heat purchase and sale between the virtual power plant operator and the user at each moment.

[0239] The power constraints of steam purchase and sale at the lower level are:

[0240]

[0241] Where: s,i,t are all 0-1 variables, indicating the state of steam purchase and sale of textile industry user i during period t, ω s,i,t =1, it means the user sells gas, otherwise the user buys gas; Q s,max Represents the maximum value of steam purchase and sale between the virtual power plant operator and the user at each moment.

[0242] 3.3 Lower-layer power balance constraints

[0243] The lower-level model is based on textile industry users. The electric energy input includes its own distributed photovoltaic output, electric energy storage discharge and electricity purchase from virtual power plant operators. The electric energy output includes electricity sale to virtual power plant operators, basic electricity load, electric energy storage charging, electrode-type industrial steam boiler power consumption, condensing machine power consumption and electricity demand of each production process. The thermal energy input of textile industry users includes heat purchase from virtual power plant operators, gas boiler heating and thermal energy storage heat release. The thermal energy output includes heat sale to virtual power plant operators, basic heat load, thermal energy storage heat charging and heat demand of each production process. The steam energy input of textile industry users includes steam output supplied by condensers to each process, steam output directly supplied by electrode-type industrial steam boilers to the ironing process and steam energy released by steam accumulators to each process. The steam energy output is the steam demand of each production process.

[0244]

[0245] Where: represents the distributed photovoltaic power of textile industry user i at time t; represents the discharge power of the electric energy storage of user i at time t; It represents the basic electrical load such as factory lighting of user i at time t; represents the charging power of the electric energy storage of user i at time t; It represents the electric power input to the electrode-type industrial steam boiler of user i at time t, kW; They represent the electricity demand per unit time of each production process of user i, where the electricity demand is the required amount of electricity; represents the heat release power of the electric energy storage of user i at time t, represents the charging power of the electric energy storage of user i at time t; represents the basic heat load of user i at time t; They represent the heat demand per unit time of each production process of user i; They represent the steam demand per unit time of each production process of user i.

[0246] Step 4: Based on the two-layer scheduling architecture, the upper-layer multi-energy flow coupling equipment model and the lower-layer optimization scheduling model, an upper-layer optimization scheduling model is constructed, and then a virtual power plant operator-user two-layer scheduling model is constructed.

[0247] The upper-layer optimization scheduling model includes an upper-layer objective function, an upper-layer energy price constraint, a multi-energy transaction volume constraint between the upper and lower layers, and an upper-layer power balance constraint;

[0248] In step 4 of the present invention, the actual amount of electricity, heat, steam, steam purchased from the virtual power plant operator by the user output in step 3, the steam purchase amount and production plan are transmitted to the upper layer, and the upper-layer virtual power plant operator calculates the profit according to the energy purchase and sale strategy fed back from the lower layer, and inputs the amount of electric and hot steam purchased from the user to the virtual power plant operator, so as to iterate and construct an optimization scheduling model for the virtual power plant operator layer with maximizing profit as the optimization goal, and finally obtains the strategy for purchasing electricity, heat and steam with the lowest user cost.

[0249] In step 4 of the present invention, the virtual power plant operator-level optimization scheduling model is studied.

[0250] 4.1 Upper-level objective function

[0251] Based on the virtual power plant operator as the upper leader specifying the various energy prices of electricity, heat, and steam in each period, guiding the electricity, heat, and steam purchase and sale strategies of textile industry users, the objective function B is constructed. VPPO , as shown below:

[0252]

[0253] Where: F e,t 、F h,t 、F s,t , They represent the revenue of the virtual power plant operator from purchasing electricity, heat and steam from users, the revenue from trading between the virtual power plant operator and the external power grid, and the gas purchasing cost of the virtual power plant operator, RMB; max[·] means taking the minimum value of the formula in brackets.

[0254]

[0255] Where: They represent the electricity and heat prices purchased and sold by the virtual power plant operator to users at time t, RMB / kWh; They represent the prices of the virtual power plant operator for purchasing and selling power grid at time t, RMB / kWh; They represent the virtual power plant operators at time t.

[0256] Steam price for buyers and sellers, RMB / kJ; They represent the electricity and heat power purchased and sold by the virtual power plant operator to users at time t, kW; They represent the steam power purchased and sold by the virtual power plant operator to users at time t, kJ / h; represents the power purchased and sold by the virtual power plant operator at time t, kW; gas 、V t VPPO They represent the price of natural gas at time t, yuan / m 3 and gas purchase volume of virtual power plant operators, m 3 .

[0257] The electricity, thermal power and steam energy purchased and sold by the virtual power plant operator can be expressed as:

[0258]

[0259] Where: They represent the electric power and thermal power purchased and sold by user i to the virtual power plant operator at time t, kW; They respectively represent the steam flow rate and steam enthalpy value purchased by user i from the virtual power plant operator at time t. They respectively represent the steam power purchased or sold by user i from the virtual power plant operator at time t, kJ / h.

[0260] 4.2 Upper energy price constraints

[0261] The energy price constraints for the virtual power plant operator in purchasing and selling electricity, heat and steam to textile industry users are shown in the formula. In order to avoid the continuous high prices of multi-energy sold by the virtual power plant operator, average value constraints are set for the prices of electricity, heat and steam sold by the virtual power plant operator.

[0262] The upper energy price constraints include the upper purchase and sale of lower electricity energy price constraints, the upper purchase and sale of lower thermal energy price constraints, and the upper purchase and sale of lower steam energy price constraints;

[0263] The price constraint for the upper layer to purchase and sell lower layer electricity is:

[0264]

[0265] Where: It indicates the lower limit of the price that the virtual power plant operator buys the electricity from the textile industry users, and the grid electricity selling price is used as the upper limit of the price that the virtual power plant operator sells the electricity from the textile industry users; Represents the average price of electricity sold by virtual power plant operators.

[0266] The price constraint for the upper layer to purchase and sell the lower layer thermal energy is:

[0267]

[0268] Where: They represent the upper and lower limits of the price at which the virtual power plant operator buys and sells thermal energy from textile industry users; Represents the average price at which virtual power plant operators sell thermal energy.

[0269] The price constraint for upper-level purchase and sale of lower-level steam energy is:

[0270]

[0271] Where: They represent the upper and lower limits of the price that the virtual power plant operator can buy and sell steam from textile industry users; Represents the average price at which virtual power plant operators sell steam.

[0272] 4.3 Constraints on multi-energy transaction volume between upper and lower layers

[0273] The multi-energy trading volume constraints between upper and lower layers include the electric energy trading volume constraints between upper and lower layers, the thermal energy trading volume constraints between upper and lower layers, and the steam energy trading volume constraints between upper and lower layers.

[0274] The power transaction volume constraints between upper and lower layers are:

[0275]

[0276] Where: Represents the maximum electric power traded between the virtual power plant operator and the grid in each period.

[0277] The heat energy trading volume constraint between the upper and lower floors is:

[0278]

[0279] Where: Represents the maximum thermal power traded between the virtual power plant operator and users in each period.

[0280] The steam energy trading volume constraint between the upper and lower layers is:

[0281]

[0282] Where: Represents the maximum steam power traded between the virtual power plant operator and users in each period.

[0283] 4.4 Upper-layer power balance constraints

[0284] The upper model is based on the virtual power plant operator. The input of electric energy includes the purchase of electricity from the public power grid, the generation of electricity from the cogeneration unit, the discharge of electric energy storage, and the purchase of electricity from the textile industry users. The output of electric energy includes the sale of electricity to the power grid, the consumption of electricity by heat pumps, the consumption of electricity by electrode-type industrial steam boilers, the charging of electric energy storage, and the sale of electricity to the textile industry users. The heat input of the virtual power plant operator includes the heating by heat pumps, the heating by the cogeneration unit, the heat release of heat energy storage, and the purchase of heat from the textile industry users. The heat output includes the charging of heat energy storage and the sale of heat by the virtual power plant operator to the textile industry users. The steam energy input of the virtual power plant operator includes the generation of steam by electrode-type industrial steam boilers, the discharge of steam from the steam accumulator, and the purchase of steam from the textile industry users. The steam energy output includes the charging of steam into the steam accumulator and the sale of steam to the textile industry users. The upper power balance can be expressed as:

[0285]

[0286] in, represents the power purchased by the virtual power plant operator from the grid at time t; It represents the electric power output of the cogeneration unit at time t; represents the discharge power of the electric energy storage of the virtual power plant operator at time t, represents the power purchased by the virtual power plant operator from users at time t; Indicates the power consumption of the heat pump; represents the power of the electrode-type industrial steam boiler input electric energy of the virtual power plant operator at time t; represents the charging power of the electric energy storage of the virtual power plant operator at time t; represents the electric power sold by the virtual power plant operator to the user at time t; represents the electric power sold to the grid by the virtual power plant operator at time t; Indicates the heating power of the heat pump; represents the thermal power output of the cogeneration unit at time t; represents the heat release power of the thermal energy storage of the virtual power plant operator at time t; represents the power of thermal energy purchased by the virtual power plant operator from users at time t; represents the power of thermal energy charged into the thermal storage of the virtual power plant operator at time t; represents the thermal power sold by the virtual power plant operator to the user at time t; represents the steam output power of the electrode-type industrial steam boiler of the virtual power plant operator at time t; represents the steam power purchased by the virtual power plant operator from the user at time t; represents the steam flow rate released from the steam accumulator of the virtual power plant operator at time t; h VPPOSAdis Indicates the enthalpy value of high-temperature steam released from the steam accumulator; represents the steam power sold by the virtual power plant operator to the user at time t; represents the steam flow rate charged into the steam accumulator of the virtual power plant operator at time t; h VPPOSAcha Indicates the enthalpy value of high-temperature steam charged into the steam accumulator.

[0287] Step 5: Collect relevant data of virtual power plant operators and textile industry users, solve the two-layer scheduling model, and obtain the electricity-steam scheduling results of virtual power plant operators and textile industry users.

[0288] The virtual power plant operator-related data include all data related to the virtual power plant operator used in the aforementioned formula or constraint calculation process; specifically, the virtual power plant operator's electrical load, cooling load, heating load, upper-level energy acquisition parameters and upper-level multi-energy coupling equipment operating parameters; the upper-level energy acquisition parameters include natural gas prices, the virtual power plant operator's gas purchase volume, and the virtual power plant operator's purchase and sale of electricity, heat and steam prices with the power grid; the upper-level multi-energy coupling equipment operating parameters include electrode-type industrial steam boiler operating parameters, steam accumulator operating parameters, heat pump operating parameters, electricity storage equipment operating parameters and heat storage equipment operating parameters.

[0289] The textile industry user-related data include all data related to textile industry users used in the aforementioned formula or constraint calculation process; specifically, including the textile industry users' electric load, cooling load, heating load, lower-level energy acquisition parameters, lower-level multi-energy coupling equipment operating parameters and lower-level production process operating parameters; wherein the lower-level energy acquisition parameters include the electric power output of distributed photovoltaics, the electricity price, power and price of electricity purchased and sold from the virtual power plant operator, the power and price of heat energy purchased and sold from the user to the virtual power plant operator, the power and price of steam purchased and sold from the virtual power plant operator, the user's natural gas consumption, the lower calorific value of natural gas and the price of natural gas; the lower-level multi-energy coupling equipment operating data include gas boiler operating parameters, electrode industrial steam boiler operating parameters, condensing machine operating parameters, steam accumulator operating parameters, electricity storage equipment operating parameters and heat storage equipment operating parameters; the lower-level production process operating parameters include textile process operating parameters, printing and dyeing process operating parameters, and ironing process operating parameters.

[0290] Preferably, solving the virtual power plant operator-user two-tier dispatching model can be implemented by using a mathematical programming method, or by calling the commercial solver BARON to solve the above model based on the EMP algorithm package in a GAMS environment to obtain the electricity-steam dispatching result.

[0291] Those skilled in the art should know that solving the model with mathematical programming methods mainly relies on the basic principles of nonlinear optimization, including defining the objective function and constraints, and solving the problem using techniques such as branch and bound method, tangent plane method and local optimization. Specifically, the branch and bound method effectively searches for feasible solutions by constructing a solution space tree, while the tangent plane method simplifies the problem by linearizing nonlinear constraints. In addition, heuristic and meta-heuristic algorithms such as genetic algorithms and simulated annealing are used to explore the solution space, especially when facing complex nonlinear or non-smooth objective functions.

[0292] Example 2

[0293] One embodiment of the present invention, referring to Figure 3-9 , a virtual power plant power-steam collaborative optimization method for textile industrial parks is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through comparative experiments.

[0294] The present invention selects the daily operation task volume and production equipment parameters of a textile industrial park in Changzhou. The price at which the virtual power plant operator sells electricity to the external power grid is set to 0.5 times the purchase price. The natural gas price purchased by the virtual power plant operator is 3.7 yuan / m 3 , the low calorific value is 9.97kWh / m 3 .

[0295] To verify the effectiveness of the two-layer approach, two scenarios are set up for comparative analysis based on various energy prices. The scenarios are as follows:

[0296] S1: The price of electricity, heat and steam energy sold by the virtual power plant operator to users is the time-of-use energy price;

[0297] S2: The price at which the virtual power plant operator sells electricity, heat and steam energy to users is the real-time energy price.

[0298] In scenario S1, multiple energy prices adopt time-of-use energy prices. The specific time division intervals are shown in Table 1. Taking into account the currently widely implemented time-of-use electricity prices, time-of-use energy prices are based on the extension of time-of-use electricity prices. Multi-energy prices are divided into peak, flat and valley periods to guide users' energy consumption behavior; in scenario S2, multiple energy prices adopt real-time energy prices, and the energy prices at each moment are generally not equal.

[0299] Table 1 The time interval division of time-of-use energy price

[0300]

[0301] The revenue of the virtual power plant operator within the industrial park, the total daily operating cost of textile industry users and the overall economic cost of the industrial park under different scenarios are shown in Table 2.

[0302] Table 2 Comparison of virtual power plant operator revenue, total operating costs of textile industry users and overall economic costs of virtual power plants under different scenarios

[0303] Table 2Comparison ofVPPO profits,total operating costs ofindustrialusers and overall economic cost ofvirtual power plant under different scenarios

[0304]

[0305] Comparing the virtual power plant operator revenue, total daily operating cost of textile industry users and overall economic cost of virtual power plants in scenarios S1 and S2 in Table 2, compared with scenario S1, the virtual power plant operator revenue in scenario S2 increased by 87.5%, the total operating cost of textile industry users increased by 1.51%, and the overall economic cost of virtual power plants decreased by 4.13%. This is because compared with scenario S1, the multi-energy price of virtual power plant operators in scenario S2 is basically higher than that in S1, which increases the revenue of virtual power plant operators and the total operating cost of textile industry users; at the same time, in scenario S2, the amount of energy purchased by textile industry users during peak energy price periods is reduced, and the amount of energy purchased during valley energy price periods is increased, which reduces the overall economic cost of virtual power plants. This shows that the proposed method of participating in the electricity-steam collaborative optimization of virtual power plants for industrial parks under the guidance of energy prices in this project has effectively improved the overall economy of virtual power plants and the energy utilization efficiency of virtual power plants.

[0306] Table 3 Comparison of daily operating costs of textile industry users under different scenarios

[0307] Table 3Comparison of daily operating costs of industrial users under different scenarios

[0308]

[0309] Comparing the operating costs of the three textile industry users under scenarios S1 and S2 in Table 3, compared with scenario S1, the operating costs of the three textile industry users in scenario S2 increased by 0.646%, 1.658%, and 1.926%, respectively. This is because the distributed photovoltaic output of textile industry user 1 is the highest, and the steam accumulator capacity configured is the highest, making the cost increase rate of textile industry user 1 the smallest; while the distributed photovoltaic outputs of textile industry users 2 and 3 are basically the same, and the steam accumulator capacity configured by user 2 is slightly higher than that of user 3, making the economy of textile industry user 2 slightly higher than that of user 3. This shows that the installed capacity of new energy and the capacity of steam accumulator configured by textile industry users affect the operating costs of textile industry users. The higher the capacity, the lower the operating cost of textile industry users.

[0310] In scenarios S1 and S2, the virtual power plant operator sets the price for purchasing and selling multi-energy for textile industry users as follows: Figure 3 shown. Figure 4 and Figure 5The figures show the purchase and sale of electricity by virtual power plant operators and textile industry users in the S1 and S2 scenarios, respectively. As can be seen from the figure, the electricity sales of textile industry users are mostly concentrated in the period of 8:00-16:00 when the photovoltaic output is high. During the peak period of 11:00-19:00, compared with the S1 scenario, the electricity price sold by the virtual power plant operator in the S2 scenario is higher, so the sales volume of the virtual power plant operator is lower, and the purchase volume of users is lower. Among them, textile industry user 3 still purchases a large amount of electricity to produce steam during the period of 11:00-13:00 to complete the production task due to the small capacity of the steam accumulator; and during the valley period of 1:00-5:00, compared with the S1 scenario, the electricity price sold by the virtual power plant operator in the S2 scenario is lower, so the purchase volume of users is higher, and the sales volume of the virtual power plant operator is lower. Among them, textile industry user 1 purchases a large amount of electricity for production during this period. It can be seen that the double-layer optimization scheduling method under the time-of-use energy price proposed in this project can effectively prompt textile industry users to adjust their electricity purchase plans.

[0311] Figure 6 and Figure 7 The figures show the purchase and sale of heat energy by virtual power plant operators and textile industry users in scenarios S1 and S2, respectively. By comparison, the total amount of heat sold by virtual power plant operators in scenarios S1 and S2 does not change much, while the amount of heat purchased by virtual power plant operators in scenario S2 is significantly reduced. This is because the peak electricity price is higher in scenario S2, and virtual power plant operators choose to use cogeneration units to produce electricity and generate a lot of heat at the same time. Therefore, the amount of heat purchased by virtual power plant operators in scenario S2 is lower. At the same time, based on Figure 5 The electricity prices sold by virtual power plant operators in China lead to the shift of industrial production planning time. Figure 7 In the middle scenario S2, the heat purchase amount of textile industry users 1 and 3 also shifts compared with scenario S1, and is concentrated in the off-peak period.

[0312] Figure 8 and Fig. 9 The following are the steam purchase and sale situations of virtual power plant operators and textile industry users in scenarios S1 and S2 respectively. Figure 8 and 9 Compared with scenario S1, the steam sales volume of the virtual power plant operator in the period from 11:00 to 19:00 in scenario S2 is significantly reduced. This is because the steam price surges along with the electricity price during this period, and the steam purchase volume of textile industry users decreases; at the same time, the steam sales volume of textile industry user 1 increases, and the steam sales volume of textile industry user 2 increases. This is because the steam accumulator capacity of textile industry user 2 is small, and the steam accumulator capacity of textile industry user 1 is large, so its economic benefits can be increased through "valley charging and high discharge".

[0313] After the optimization scheduling of electricity, heat and steam of the virtual power plant operator in scenario S2, the electricity, heat and steam of the virtual power plant operator all meet the multi-energy power balance constraint. Photovoltaic power generation is the main source of electricity within the virtual power plant. The main sources of electric power for the virtual power plant operator are the power grid and cogeneration units. The virtual power plant operator purchases power from the power grid during the valley period of electricity prices, and purchases gas to use cogeneration units for auxiliary power supply during the peak period; the power consumption of the virtual power plant operator is mainly for the virtual power plant operator to sell electricity to textile industry users. The electrode-type industrial steam boilers and heat pumps of the virtual power plant operator are concentrated in the valley period of electricity prices. The electric storage is charged during the valley period of electricity prices and discharged during the peak period, thereby improving the power revenue of the virtual power plant operator. The heat source of the virtual power plant operator is mostly heat pump heating and purchase of heat from textile industry users during the valley period of electricity prices. Energy substitution is achieved through gas-to-heat conversion of cogeneration units during the peak period of electricity prices. During the valley period of electricity prices, the virtual power plant operator uses electrode-type industrial steam boilers to produce steam, and purchases steam from textile industry users to charge the resulting steam into the steam accumulator; during the peak period of electricity prices, the steam accumulator releases steam and sells steam to textile industry users.

[0314] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0315] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0316] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0317] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages-such as Smalltalk, C++, etc., and conventional procedural programming languages-such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network-including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0318] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A virtual power plant electricity-steam collaborative optimization method for a textile industrial park, characterized in that: include: Step 1: Build a two-layer dispatching architecture of virtual power plant operators and users; The two-layer dispatching architecture includes an upper layer representing virtual power plant operators and a lower layer representing textile industry users; Step 2: Based on the two-layer scheduling architecture, construct an upper-layer multi-energy flow coupling device model and a lower-layer multi-energy flow coupling device model; Step 3: Based on the two-layer scheduling architecture and the lower-layer multi-energy flow coupling device model, a lower-layer optimization scheduling model is constructed; Step 4: construct an upper-layer optimization scheduling model based on the two-layer scheduling architecture, the upper-layer multi-energy flow coupling device model and the lower-layer optimization scheduling model, and then construct a virtual power plant operator-user two-layer scheduling model; Step 5: Collect relevant data of virtual power plant operators and textile industry users, solve the two-layer scheduling model, and obtain the electricity-steam scheduling results of virtual power plant operators and textile industry users.

2. The virtual power plant electricity-steam collaborative optimization method for a textile industrial park according to claim 1 is characterized in that: In step 1, the upper layer includes a virtual power plant operator, the lower layer includes no less than one textile industry user, and each textile industry user is associated with the virtual power plant operator in the upper layer.

3. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 1, characterized in that: In step 2, the upper multi-energy flow coupling device model includes an upper electrode type industrial steam boiler model, an upper steam accumulator model, an upper heat pump model, an upper cogeneration unit model, an upper power storage device model and an upper heat storage device model; The lower-layer multi-energy flow coupling equipment model includes a lower-layer gas boiler model, a lower-layer condensing machine model, a lower-layer electrode-type industrial steam boiler model, a lower-layer steam accumulator model, a lower-layer electricity storage equipment model and a lower-layer heat storage equipment model.

4. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 3 is characterized in that: The upper heat pump model is as follows: in, Indicates the heating power of the heat pump; Indicates the heating efficiency of the heat pump; Indicates the power consumption of the heat pump; Indicates the upper limit of the heat pump heating power.

5. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 3, characterized in that: The upper cogeneration unit model is as follows: in, Represents the electric power output of the cogeneration unit at time t; V t VPPO represents the volume of natural gas consumed by the cogeneration unit at time t; It indicates the power generation efficiency of the cogeneration unit; represents the thermal power output of the cogeneration unit at time t; It represents the heating efficiency of the cogeneration unit; β represents the lower calorific value of natural gas.

6. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 3, characterized in that: The lower electrode type industrial steam boiler model is as follows: in, represents the steam energy output by the electrode-type industrial steam boiler of user i at time t; η ESB Indicates the heat generation efficiency of electrode-type industrial steam boiler; represents the electric power input to the electrode-type industrial steam boiler of user i at time t; Δt represents the time interval between two sampling times; represents the steam flow rate output by the electrode-type industrial steam boiler of user i at time t; h ESB Indicates the enthalpy value of steam output from electrode-type industrial steam boiler; represents the steam energy purchased by the electrode-type industrial steam boiler of user i at time t; It represents the steam energy inputted into the condenser by the user i-electrode industrial steam boiler at time t; represents the steam energy output from the electrode-type industrial steam boiler of user i to the steam accumulator at time t; where l is an integer, and l∈[1, N ir3 ],N ir3 Indicates the total number of ironing process production lines; It represents the steam energy and steam flow rate directly delivered to the l-th ironing process production line by the user i electrode industrial steam boiler at time t; represents the steam energy sold by the electrode-type industrial steam boiler of user i at time t; represents the steam flow rate sold by the electrode-type industrial steam boiler of user i at time t; h ESB→VPPO Enthalpy of steam sold for electric steam boilers; The upper limit of the electric power input to the user's i-electrode industrial steam boiler; It is the upper limit of steam flow rate output by the user's i-electrode industrial steam boiler.

7. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 1, characterized in that: In step 3, the lower-layer optimization scheduling model includes a lower-layer objective function, a lower-layer purchase and sale constraint, and a lower-layer power balance constraint; The lower-level purchase and sale constraints include the lower-level purchase and sale of electricity power constraints, the lower-level purchase and sale of heat power constraints, and the lower-level purchase and sale of steam power constraints.

8. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 7, characterized in that: The lower objective function C u for: Among them, C e,i represents the cost of purchasing and selling electricity from the virtual power plant operator to textile industry user i; C h,i represents the cost of purchasing and selling thermal energy from the virtual power plant operator to textile industry user i; C s,i represents the cost of purchasing and selling steam from the virtual power plant operator to textile industry user i; represents the cost of purchasing and selling natural gas to the virtual power plant operator by textile industry user i; min[·] means taking the minimum value of the formula in brackets.

9. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 1, characterized in that: In step 4, the upper-layer optimization scheduling model includes an upper-layer objective function, an upper-layer energy price constraint, a multi-energy transaction volume constraint between the upper and lower layers, and an upper-layer power balance constraint; The upper energy price constraints include the upper purchase and sale of lower electricity energy price constraints, the upper purchase and sale of lower thermal energy price constraints, and the upper purchase and sale of lower steam energy price constraints; The multi-energy trading volume constraints between upper and lower layers include the electric energy trading volume constraints between upper and lower layers, the thermal energy trading volume constraints between upper and lower layers, and the steam energy trading volume constraints between upper and lower layers.

10. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 9, characterized in that: The upper objective function B VPPO for: Among them, F e,t F represents the revenue earned by the virtual power plant operator from purchasing and selling user electricity; h,t F represents the revenue earned by the virtual power plant operator from purchasing and selling thermal energy from users; s,t It represents the revenue of the virtual power plant operator from purchasing and selling steam from users; represents the revenue from transactions between the virtual power plant operator and the external grid; represents the gas purchase cost of the virtual power plant operator; max[·] means taking the maximum value of the formula in brackets.

11. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 9, characterized in that: The upper layer power balance constraint is: in, represents the power purchased by the virtual power plant operator from the grid at time t; It represents the electric power output of the cogeneration unit at time t; represents the discharge power of the electric energy storage of the virtual power plant operator at time t, represents the power purchased by the virtual power plant operator from users at time t; Indicates the power consumption of the heat pump; represents the power of the electrode-type industrial steam boiler input electric energy of the virtual power plant operator at time t; represents the charging power of the electric energy storage of the virtual power plant operator at time t; represents the electric power sold by the virtual power plant operator to the user at time t; represents the electric power sold to the grid by the virtual power plant operator at time t; Indicates the heating power of the heat pump; represents the thermal power output of the cogeneration unit at time t; represents the heat release power of the thermal energy storage of the virtual power plant operator at time t; represents the power of thermal energy purchased by the virtual power plant operator from users at time t; represents the power of thermal energy charged into the thermal storage of the virtual power plant operator at time t; represents the thermal power sold by the virtual power plant operator to the user at time t; represents the steam output power of the electrode-type industrial steam boiler of the virtual power plant operator at time t; represents the steam power purchased by the virtual power plant operator from the user at time t; represents the steam flow rate released from the steam accumulator of the virtual power plant operator at time t; h VPPOSAdis Indicates the enthalpy value of high-temperature steam released from the steam accumulator; represents the steam power sold by the virtual power plant operator to the user at time t; represents the steam flow rate charged into the steam accumulator of the virtual power plant operator at time t; h VPPOSAcha Indicates the enthalpy value of high-temperature steam charged into the steam accumulator.

12. The method for power-steam collaborative optimization of a virtual power plant in a textile industrial park according to claim 1, characterized in that: In step 4, the virtual power plant operator-user two-layer scheduling model includes an upper-layer multi-energy flow coupling equipment model, a lower-layer multi-energy flow coupling equipment model, an upper-layer optimization scheduling model and a lower-layer optimization scheduling model.

13. A virtual power plant power-steam collaborative optimization system for a textile industrial park using the virtual power plant power-steam collaborative optimization method for a textile industrial park as described in any one of claims 1 to 12, characterized in that: It includes a two-layer scheduling architecture building module, an upper and lower layer multi-energy flow coupling equipment model building module, a lower layer optimization scheduling model building module, a two-layer scheduling model building module and a two-layer scheduling model solving module: The two-layer scheduling architecture building module builds a virtual power plant operator-user two-layer scheduling architecture; the two-layer scheduling architecture includes an upper layer representing the virtual power plant operator and a lower layer representing the textile industry user; The upper and lower layer multi-energy flow coupling device model construction module constructs an upper layer multi-energy flow coupling device model and a lower layer multi-energy flow coupling device model based on the two-layer scheduling architecture; The lower-layer optimization scheduling model construction module constructs a lower-layer optimization scheduling model based on the two-layer scheduling architecture and the lower-layer multi-energy flow coupling device model; The two-layer scheduling model construction module constructs an upper-layer optimization scheduling model based on the two-layer scheduling architecture, the upper-layer multi-energy flow coupling device model and the lower-layer optimization scheduling model, and then constructs a virtual power plant operator-user two-layer scheduling model; The two-layer scheduling model solving module collects relevant data of virtual power plant operators and relevant data of textile industry users, solves the two-layer scheduling model, and obtains the power-steam scheduling results of virtual power plant operators and textile industry users.

14. An electronic device, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the virtual power plant electricity-steam collaborative optimization method for the textile industrial park according to claims 1-12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the virtual power plant electricity-steam collaborative optimization method for the textile industrial park described in claims 1-12.

Images