Campus integrated energy system optimization scheduling method and system

Abstract

The application discloses a kind of park integrated energy system double-layer optimization scheduling method and system, method includes: constructing park integrated energy system operation framework, modeling each equipment in operation framework;Establish the vehicle-to-grid interaction model considering the temporal and spatial distribution characteristics and charging and discharging behavior of electric vehicles, and the ice storage air conditioning load model;Set considering the PIES double-layer optimization scheduling strategy of vehicle-to-grid interaction and ice storage air conditioning coordination, define upper layer as PIES energy scheduling layer, lower layer as flexible load response layer;Solve electric vehicle optimization model and ice storage air conditioning optimization model, solve system operation cost optimization model, according to the solution result, each equipment in park integrated energy system is optimized and dispatched;The application has the advantages that: EV and IAC are deeply integrated, and collaborative scheduling is carried out, and double-layer optimization scheduling strategy is used, so that economic and low-carbon operation of system level is realized under the premise of guaranteeing user energy cost and comfort.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system operation and control, specifically to an optimized scheduling method and system for an integrated energy system in a park. Background Technology

[0002] Park-integrated energy systems (PIES) are becoming an important form of future zero-carbon parks due to their advantages such as energy cascade utilization and multi-energy coupling complementarity. To cope with the future penetration of extremely high proportions of renewable energy (such as wind and solar power), simple electricity / heat coupling is no longer sufficient to meet long-term energy balance requirements. Therefore, introducing hydrogen energy (P2G) and various types of energy storage devices to construct an "electricity-heat-hydrogen" multi-energy coupling system has become a mainstream trend. However, equipment such as electrolyzers and combined heat and power units often have physical characteristics such as strong ramp-up constraints, high start-up costs, and large response inertia. Faced with high-frequency, large-amplitude fluctuations in renewable energy generation, these devices cannot respond quickly and independently, resulting in the continued existence of wind and solar curtailment, or the need to configure extremely costly energy storage systems.

[0003] Therefore, tapping into the flexible load resources on the power side within the industrial park that possess rapid response capabilities is particularly crucial. Electric vehicles (EVs) possess mobile energy storage characteristics and can achieve bidirectional energy interaction through V2G (Vehicle-to-Grid) technology; ice storage air conditioning (IAC) utilizes peak-shaving and valley-filling characteristics and can serve as a large-capacity cold-electricity time-shifting resource. Existing technologies mostly focus on the regulation of single flexible loads (EVs only or IAC only), or treat EVs and air conditioning only as passive loads. For example, Chinese Patent Publication No. CN117039867A discloses a multi-objective joint optimization scheduling method for microgrids, which only regulates IAC. Although some research involves both, it lacks a precise characterization of the spatiotemporal travel patterns of EV users. EVs and IACs have significant complementarity in their operating characteristics (EVs have rapid power response, IACs have large-capacity energy time-shifting; the two also differ in their regulation periods), but existing technologies lack research on deep integration and coordinated scheduling of the two. In addition, existing scheduling models often adopt single-layer optimization, which often ignores the wishes of EV users and IAC as independent stakeholders, making it difficult to achieve system-level economic and low-carbon operation while ensuring users' energy costs and comfort. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the existing technology lacks a solution for deep integration and coordinated scheduling of EVs and IACs. Moreover, the existing scheduling models mostly adopt single-layer optimization, which often ignores the wishes of EV users and IACs as independent stakeholders, making it difficult to achieve system-level economic and low-carbon operation while ensuring users' energy costs and comfort.

[0005] This invention solves the above-mentioned technical problems through the following technical means: a two-layer optimized scheduling method for a park integrated energy system, comprising:

[0006] S1. Taking the integrated energy system of the park as the research object, construct the operation framework of the integrated energy system of the park, model each device in the operation framework, and determine the operation constraints of each device in the integrated energy system of the park and the system power balance conditions. S2. Establish a vehicle-to-grid interaction model that considers the spatiotemporal distribution characteristics and charging and discharging behavior of electric vehicles, as well as an ice storage air conditioning load model that takes into account multiple working modes such as refrigeration, ice storage, and ice melting. S3. Set up a PIES two-layer optimization scheduling strategy that considers vehicle-to-grid interaction and ice storage air conditioning coordination. Define the upper layer as the PIES energy scheduling layer and the lower layer as the flexible load response layer. In the flexible load response layer, establish electric vehicle optimization model and ice storage air conditioning optimization model based on vehicle-to-grid interaction model and ice storage air conditioning load model. In the PIES energy scheduling layer, establish system operating cost optimization model based on the modeling results of each device in the operation framework, vehicle-to-grid interaction model and ice storage air conditioning load model. S4. Obtain data from the park's integrated energy system, solve the electric vehicle optimization model and the ice storage air conditioning optimization model, and then, based on the obtained electric vehicle power data, ice storage air conditioning power data, and data of each device in the operating framework, solve the system operating cost optimization model, and optimize the scheduling of each device in the park's integrated energy system according to the solution results.

[0007] This invention provides a two-layer optimization scheduling method for a park integrated energy system that considers the synergy between V2G and ice storage air conditioning. By modeling the EV vehicle-to-grid interaction behavior considering its spatiotemporal distribution characteristics and the IAC (Integrated Air Conditioning) operation model with its cold energy storage characteristics, it fully leverages the natural complementary advantages of the rapid power response of EVs and the large-capacity energy shift of IACs on the time scale. This constructs a master-slave game-theoretic two-layer optimization scheduling architecture, achieving deep integration and collaborative scheduling of EVs and IACs. Furthermore, through this two-layer optimization scheduling, on the one hand, it establishes an electric vehicle optimization model and an ice storage air conditioning optimization model, fully considering the wishes of EV users and IACs as independent stakeholders; on the other hand, it establishes a system operating cost optimization model, achieving system-level economical and low-carbon operation while ensuring user energy costs and comfort.

[0008] Furthermore, the modeling of each device in the operating framework includes: Electrolytic cell model is In the formula, EL represents the electrolytic cell. for Hydrogen production capacity at any given moment; For EL electro-hydrogen conversion efficiency; for Energy consumption of EL at any given moment; and These are the lower and upper limits of EL energy consumption; A unified model is used to model electrical energy storage, hydrogen storage tanks, gas energy storage, and thermal energy storage, resulting in the following energy storage model:

[0009] In the formula, for Real-time energy storage devices The amount in; and for Real-time energy storage devices The charging power and discharging power; and For energy storage devices The charging efficiency and discharging efficiency; and For energy storage devices The lower and upper limits of capacity; and This represents the upper limit of charging and discharging power; and It serves as a marker for the charging and discharging states.

[0010] Furthermore, the modeling of each device in the operating framework also includes: The model of the hydrogen-doped cogeneration unit is In the formula, CHP represents a hydrogen-doped cogeneration unit. for t The hydrogen doping ratio of CHP at that time; and for t Constantly input the natural gas power and hydrogen power of CHP; and The lower heating value of natural gas and hydrogen; The calorific value of the mixture of natural gas and hydrogen; for t The output power of CHP at any given time; and for t The output electrical power and output thermal power of CHP at any given time; and For the electrical and thermal efficiency of CHP; and These are the lower and upper limits of natural gas ramping power. and These represent the lower and upper limits of hydrogen ramping power; and These represent the lower and upper limits of the thermoelectric adjustable ratio.

[0011] Furthermore, the modeling of each device in the operating framework also includes: The methane reactor model is In the formula, MR represents a methane reactor. for The power of MR in producing natural gas at any given time; For conversion efficiency; for The hydrogen power is constantly input into the MR; and The lower and upper limits of the hydrogen power input to MR; and The lower and upper limits of the hydrogen ramp power for input MR; Gas boiler model is In the formula, GB represents a gas-fired boiler. For GB in Heat production capacity at any given time; For GB in Power consumption of natural gas at any given time; The coefficient of performance is the GB thermal efficiency. and These are the lower and upper limits of GB natural gas power consumption. and These are the lower and upper limits of GB ramp power. Electric boiler model is In the formula, EB represents an electric boiler. For EB in Heat production capacity at any given time; For the thermal conversion efficiency of EB; For EB in The electrical power of EB at time t; and These are the lower and upper limits of the EB power, respectively; and These represent the lower and upper limits of EB ramp power.

[0012] Furthermore, the vehicle-to-everything (V2X) interaction model includes: Assuming the charging trigger condition for EV owners is

[0013] In the formula: For the first End time of the trip; For the first The charge level at the end of the trip; The charge required for the next journey; Energy consumption per 100 kilometers for EVs; For EV users The distance traveled on the trip; For EV battery capacity; To provide a safety margin of charge for the EV, the EV needs enough charging time for the next trip. Calculated as

[0014] In the formula, Charging power for EVs; The incentive price is related to the real-time price and is calculated as follows:

[0015] In the formula, for Incentive pricing for electricity at any given time; for Incentive rate at any given moment; Based on real-time electricity pricing, users' discharge decisions are determined by a utility function driven by multiple factors:

[0016] In the formula, and The distribution consists of electricity price sensitivity coefficient and SOC sensitivity coefficient. express The charge at time t, let the threshold of the utility function be . ,when At that time, EV users were willing to participate in EV discharge.

[0017] Furthermore, the ice storage air conditioning load model is expressed as follows:

[0018] In the formula, and In order to be in The energy consumption of cooling and ice storage at any given time; , and They are respectively The cooling capacity, ice storage capacity, and ice melting cooling capacity at any given time; , and These are respectively the flags for refrigeration, ice storage, and ice melting; and These are the lower and upper limits of electrical power consumption; Indicates the upper limit of ice storage power; This is the upper limit of the cooling capacity for ice melting; for The ice storage tank continuously stores ice. This represents the maximum amount of ice that can be stored. , , and These are the self-loss rate, refrigeration efficiency, ice storage efficiency, and ice melting cooling efficiency of the ice storage tank.

[0019] Furthermore, the electric vehicle optimization model includes: The objective function of an EV is defined as minimizing the total cost of charging and discharging.

[0020] In the formula, For charging costs; Cost of battery discharge losses; The discharge benefits of EVs participating in V2G; among which, Charging costs , Indicates EV in Charging power at any time Indicates the scheduling period. Indicates the power factor; Battery loss cost , Battery cycle life; Battery capacity; The cost of replacing the entire battery; This refers to the depth of battery discharge. Discharge benefits , express Incentive pricing for electricity at any given time; The constraints for the ordered charging and discharging of EVs are: , for Time of the first The battery capacity of the Taiwan EV This indicates the lower limit of the EV's charge capacity.

[0021] Furthermore, the ice storage air conditioning optimization model includes: The IAC aims to minimize electricity costs, and its objective function can be defined as follows:

[0022] In the formula, This refers to the electricity cost of IAC; the calculation method is as follows:

[0023] The constraints for IAC load dispatching are:

[0024] In the formula, For the first Taiwan IAC's ice melting cooling capacity, For the first Power consumption of Taiwan IAC.

[0025] Furthermore, the system operating cost optimization model includes: PIES aims to minimize system operating costs, and its objective function is defined as:

[0026] In the formula, For energy purchase costs; For equipment operating costs; For carbon trading costs; To account for the costs associated with abandoning wind and solar power, the calculations for each cost are as follows: Energy purchase cost , and They are respectively Real-time electricity and gas purchase prices; and They are respectively The power transmission capacity and gas transmission capacity of the upstream power grid at any given time; Equipment operating costs , Price coefficient; The hydrogen production power of EL; Tiered carbon trading costs , and These are actual carbon emissions and carbon trading allowances, respectively. The cost of tiered carbon trading is

[0027] In the formula: , and These are the carbon trading base price, penalty coefficient, and carbon emission range length; Abandoning wind and light costs , The penalty coefficient for abandoning scenic views; for Constantly curtailing wind and solar power; The constraints of the PIES scheduling layer are:

[0028] In the formula: and for The power output of wind turbines and photovoltaic systems at any given time; Charging power for EV clusters; Indicates that the EV cluster is in The total discharge power at any given moment; and The first EVs in Charge and discharge flags at specific times; For IAC clusters in Total operating power at any given time; For the first Taiwan IAC Operating power at any given time; , and They are respectively The electrical, heat and gas load at any given time, These represent the heat production power of EB, the heat production power of GB, the heat storage power, and the heat release power, respectively. These represent gas purchasing capacity and GS gas storage capacity, respectively. These represent the hydrogen production power of EL, the hydrogen storage power of HST, the hydrogen consumption power of MR, and the hydrogen release power of HST, respectively.

[0029] This invention also provides a system for implementing the two-layer optimized scheduling method for a park integrated energy system as described in any of the preceding claims, comprising: The first modeling module is used to construct the operation framework of the integrated energy system of the park, taking the integrated energy system of the park as the research object, model each device in the operation framework, and determine the operation constraints of each device in the integrated energy system of the park and the system power balance conditions. The second modeling module is used to establish a vehicle-to-grid interaction model that considers the spatiotemporal distribution characteristics and charging and discharging behavior of electric vehicles, as well as an ice storage air conditioning load model that takes into account multiple working modes such as refrigeration, ice storage, and ice melting. The optimization model building module is used to set up a PIES two-layer optimization scheduling strategy that considers vehicle-to-grid interaction and ice storage air conditioning coordination. The upper layer is defined as the PIES energy scheduling layer and the lower layer as the flexible load response layer. In the flexible load response layer, electric vehicle optimization model and ice storage air conditioning optimization model are established based on vehicle-to-grid interaction model and ice storage air conditioning load model. In the PIES energy scheduling layer, system operating cost optimization model is established based on the modeling results of each device in the operation framework, vehicle-to-grid interaction model and ice storage air conditioning load model. The optimization and solution module is used to acquire data from the park's integrated energy system, solve the electric vehicle optimization model and the ice storage air conditioning optimization model, and then, based on the obtained electric vehicle power data, ice storage air conditioning power data, and data of each device in the operating framework, solve the system operating cost optimization model, and optimize the scheduling of each device in the park's integrated energy system according to the solution results.

[0030] The advantages of this invention are: (1) This invention provides a two-layer optimization scheduling method for a park integrated energy system that considers the synergy between V2G and ice storage air conditioning. By modeling the EV vehicle-to-grid interaction behavior that considers the spatiotemporal distribution characteristics and the IAC operation model with cold energy storage characteristics, the natural complementary advantages of the EV's fast power response and the IAC's large-capacity energy time shift on the time scale are fully utilized to construct a master-slave game-theoretic two-layer optimization scheduling architecture, realizing the deep integration and collaborative scheduling of EV and IAC. Furthermore, through two-layer optimization scheduling, on the one hand, an electric vehicle optimization model and an ice storage air conditioning optimization model are established to fully consider the wishes of EV users and IAC as independent stakeholders. On the other hand, a system operation cost optimization model is established to achieve system-level economic and low-carbon operation while ensuring user energy costs and comfort.

[0031] (2) In the master-slave game-based two-layer optimization scheduling architecture of this invention, the upper-layer model aims to minimize the overall energy purchase, operation and maintenance, carbon trading, and wind and solar curtailment costs of the park, optimizes the output of energy conversion and storage units, and sets internal dynamic electricity prices; the lower-layer model responds to the price signal from the upper layer and optimizes the charging and discharging behavior of EVs and the cooling / ice storage status of IACs to minimize user energy costs. Through the above mechanism, this invention effectively solves the coordination and control problem of multiple links of source, load, and storage, while ensuring the interests and energy needs of users, significantly improving the system's ability to absorb wind and solar resources, reducing carbon emissions, and realizing the low-carbon, economical, and efficient operation of the park's energy system. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the operational architecture of a two-layer optimized scheduling method for a park integrated energy system disclosed in an embodiment of the present invention; Figure 2 This is a flowchart of EV charging and discharging in a two-layer optimized scheduling method for a park integrated energy system disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the optimization model solution process in a two-layer optimization scheduling method for a park integrated energy system disclosed in an embodiment of the present invention. Figures 4(a) to 4(d) are respectively the power balance diagram, heat power balance diagram, gas power balance diagram and hydrogen power balance diagram in a two-layer optimization scheduling method for a park integrated energy system disclosed in an embodiment of the present invention. Figure 5 This is a graph showing the daily charging and discharging power variation of an EV cluster in a two-layer optimized scheduling method for a park integrated energy system disclosed in an embodiment of the present invention. Figure 6 This is a cold load balance diagram in a two-layer optimized scheduling method for a park integrated energy system disclosed in an embodiment of the present invention; Figure 7This is a graph showing the daily power variation of the IAC cluster in a two-layer optimized scheduling method for a park integrated energy system disclosed in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1 Embodiment 1 of this invention aims to provide a two-layer optimization scheduling method for a park's integrated energy system, considering the synergy of V2G and ice storage / air conditioning. This invention utilizes precise modeling of EV vehicle-to-grid interaction behavior, taking into account spatiotemporal distribution characteristics, and an IAC (Infrastructure as a Charge) operation model with cold storage capabilities. It fully leverages the natural complementary advantages of the rapid power response of EVs and the large-capacity energy shift of IACs on a time scale, constructing a master-slave game-theoretic two-layer optimization scheduling architecture. In this architecture, the upper-layer model aims to minimize the overall energy purchase, operation and maintenance, carbon trading, and wind / solar curtailment costs of the park, optimizing the output of energy conversion and storage units and setting internal dynamic electricity prices. The lower-layer model responds to the upper-layer price signal, optimizing EV charging and discharging behavior and IAC cooling / ice storage status to minimize user energy costs. Through this mechanism, this invention effectively solves the coordination and control challenges of multiple links in the energy source, load, and storage systems. While ensuring user interests and energy demand, it significantly improves the system's ability to absorb wind and solar resources, reduces carbon emissions, and achieves low-carbon, economical, and efficient operation of the park's energy system. Based on this, the present invention provides a two-layer optimization scheduling method for a park integrated energy system that considers the synergy of V2G and ice storage air conditioning. This method constructs a physical architecture for the park integrated energy system including sources, grid, loads, and storage; establishes a two-layer optimization mathematical model; and utilizes price signals to guide two types of flexible loads, EVs and IACs, to participate in system regulation, thereby achieving economical and low-carbon operation of the system. Figure 1 As shown, the method of the present invention includes the following core steps: S1: Taking the integrated energy system of the park as the research object, a PIES operating framework is constructed, which includes an electrolyzer model, an energy storage model, a hydrogen-blended cogeneration unit model, a methane reactor model, a gas boiler model, and an electric boiler model. The operating constraints of each device and the system power balance conditions are determined.

[0035] 1) Electrolytic cell model Proton exchange membrane (PEM) hydrogen production technology exhibits good adaptability to power fluctuations, enabling rapid adjustment of hydrogen production even under conditions of significant fluctuations in renewable energy sources. The EL model is as follows: (1) In the formula: for Hydrogen production capacity at any given moment; For EL electro-hydrogen conversion efficiency; for Energy consumption of EL at any given moment; and These represent the lower and upper limits of EL energy consumption.

[0036] 2) Energy storage model All energy storage devices, including electrical energy storage, hydrogen storage tanks, gas energy storage, and thermal energy storage, are modeled uniformly and represented as follows: (2) In the formula: for Real-time energy storage devices The amount in; and for Real-time energy storage devices The charging power and discharging power; and For energy storage devices The charging efficiency and discharging efficiency; and For energy storage devices The lower and upper limits of capacity; and This represents the upper limit of charging and discharging power; and It serves as a marker for the charging and discharging states.

[0037] 3) Model of hydrogen-doped cogeneration unit The basic principle of a combined heat and power (CHP) system is to draw in and compress air, which is then mixed with fuel and burned in a combustion chamber to produce high-temperature, high-pressure gas that drives a turbine to generate electricity. Simultaneously, the high-temperature exhaust gas can be utilized through a waste heat recovery device, achieving combined heat and power generation. Its model is as follows: (3) In the formula: for t The hydrogen doping ratio of CHP at that time; and for t Constantly input the natural gas power and hydrogen power of CHP; and The lower heating value of natural gas and hydrogen; The calorific value of the mixture of natural gas and hydrogen; for t The output power of CHP at any given time; and for t The output electrical power and output thermal power of CHP at any given time; and For the electrical and thermal efficiency of CHP; and These are the lower and upper limits of natural gas ramping power. and These represent the lower and upper limits of hydrogen ramping power; and These represent the lower and upper limits of the thermoelectric adjustable ratio.

[0038] 4) Methane reactor model MR produces natural gas by reacting hydrogen generated in EL with carbonaceous compounds; its model can be represented as follows: (4) In the formula: for The power of MR in producing natural gas at any given time; For conversion efficiency; for The hydrogen power is constantly input into the MR; and The lower and upper limits of the hydrogen power input to MR; and The lower and upper limits of the hydrogen ramp power input for MR are given.

[0039] 5) Gas-fired boiler model GB heats water or generates steam by burning natural gas to meet heat load demands. Its model is represented as follows: (5) In the formula: For GB in Heat production capacity at any given time; For GB in Power consumption of natural gas at any given time; The value represents the GB thermal efficiency coefficient. and These are the lower and upper limits of GB natural gas power consumption. and These represent the lower and upper limits of GB ramp power.

[0040] 6) Electric boiler model EB converts electrical energy into heat energy by heating steam and then transfers that heat energy to the heat bus. Its model is represented as follows: (6) In the formula: For EB in Heat production capacity at any given time; For the thermal conversion efficiency of EB; For EB in The electrical power of EB at time t; and These are the lower and upper limits of the EB power, respectively; and These represent the lower and upper limits of EB ramp power.

[0041] S2: Establish a V2G model that considers the spatiotemporal distribution characteristics and charging and discharging behavior of EVs, and an IAC load model that takes into account multiple working modes such as cooling, ice storage, and ice melting. 1) EV load model Within a single industrial park, EV users are primarily concentrated in three areas: residential areas ( ), work area ( ) and commercial area ( EV users are more likely to travel to different destinations at different times and in different areas. For example, between 8:00 and 9:00 AM, most EV users travel from their residential areas to their workplaces. Therefore, dividing a day into 24 time periods, each lasting one hour, and... The spatial transition probability matrix for each time period is used as a... matrix This indicates that the matrix elements are represented as follows: Indicates the first During this time period, EV users from location Go to The probability of.

[0042] A mixture of Weiber distributions is used to simulate the first trip time of EV users. The probability density function of the first trip time is defined as follows: (7) In the formula: The proportion of different standard Weibull distributions in the mixed Weibull distribution; Let be the kernel function of the Weiber distribution. The driving time and parking time of EV users are related to their location. Fitting it with a log-normal distribution, the probability density function can be expressed as: (8) In the formula: or This refers to the driving or parking time of the EV. or For the expectations of the corresponding regions; or The standard deviation is given by Equation 7, which is a mixed Weiber distribution model of the first trip time, used to simulate the random time when a user first starts driving in a day; Equation 8 is a log-normal distribution model of driving time and parking time, used to describe the random characteristics of EV driving or parking time in different areas. Together, they form the basis of EV user spatiotemporal behavior, providing real travel data support for subsequent charging and discharging scheduling.

[0043] The charging and discharging process is treated as constant power charging and discharging. EV owners determine whether to charge or discharge based on the current state of charge (SBC) of the EV. Charging is only initiated when the EV's SBC falls below a certain level. This invention assumes that the triggering condition for EV owners to charge is... (9) In the formula: For the first End time of the trip; For the first The charge level at the end of the trip; The charge required for the next journey; Energy consumption per 100 kilometers for EVs; For EV users The distance traveled on the trip; For EV battery capacity; This provides a safety margin of charge for the EV. It also ensures sufficient charging time for the EV's next trip. It can be calculated as (10) In the formula: Charging power for EVs.

[0044] The incentive price is related to the real-time price and is calculated as follows: (11) In the formula: for Incentive pricing for electricity at any given time; for Incentive rate at any given moment; The electricity price is real-time. Based on this, the user's discharge decision is determined by a utility function driven by multiple factors: (12) In the formula: and The distribution consists of electricity price sensitivity coefficient and SOC sensitivity coefficient. express The charge at time t. Let the threshold of the utility function be . ,when At that time, EV users are willing to participate in EV discharge. Based on this, such as Figure 2As shown, EV users in the first The trip ended and the [number]th The charging and discharging behavior at the start of the next stroke can be summarized as follows: (1) When and Even if EV users maintain constant power charging during this parking period, they cannot reach the target EV charge level. In this case, EV users will not participate in V2G and will only maintain constant power charging.

[0045] (2) When and At that time, EV users will first charge to their full capacity. Then, based on the current incentive electricity price, decide whether to participate in V2G.

[0046] (3) When At that time, EV users will choose whether to participate in V2G based on the EV's charge level and the incentive electricity price, while ensuring the next driving distance.

[0047] In the From the end of the trip to the start The EV state of charge transition for the next trip is as follows: (13) In the formula: and Efficiency in charging and discharging EVs; and Power for charging and discharging EVs; and For EVs The charging and discharging flags at specific times.

[0048] 2) IAC load modeling An IAC (Integrated Cooling Container) has cooling, ice storage, and ice melting functions. The chiller and ice storage tank operate in parallel, each with its own independent cooling circuit. It can flexibly adjust the cooling capacity output of the chiller and ice storage tank independently according to load demand. The IAC model can be represented as follows: (14) In the formula, and In order to be in The energy consumption of cooling and ice storage at any given time; , and They are respectively The cooling capacity, ice storage capacity, and ice melting cooling capacity at any given time; , and These are respectively the flags for refrigeration, ice storage, and ice melting; and These are the lower and upper limits of electrical power consumption; Indicates the upper limit of ice storage power; This is the upper limit of the cooling capacity for ice melting; for The ice storage tank continuously stores ice. This represents the maximum amount of ice that can be stored. , , and These are the self-loss rate, refrigeration efficiency, ice storage efficiency, and ice melting cooling efficiency of the ice storage tank.

[0049] S3: A PIES two-layer optimization scheduling strategy considering V2G and IAC collaboration is proposed. The upper layer is defined as the PIES energy scheduling layer and the lower layer is the flexible load response layer. The upper and lower layers are connected through internal dynamic electricity price signals to guide flexible loads to participate in system regulation. 1) PIES Scheduling Layer PIES aims to minimize system operating costs, and its objective function is defined as: (15) In the formula: For energy purchase costs; For equipment operating costs; For carbon trading costs; Costs associated with abandoning wind and solar power. The calculations for each cost are as follows: (1) Energy purchase cost The energy purchase cost from the upper-level grid by PIES can be expressed as: (16) In the formula: and They are respectively Real-time electricity and gas purchase prices; and They are respectively The power transmission capacity and gas transmission capacity of the upstream power grid at any given time.

[0050] (2) Equipment operating costs For the hydrogen energy component, the operating and maintenance cost per unit power needs to be calculated: (17) In the formula: Price coefficient; This represents the hydrogen production power of EL.

[0051] (3) Tiered carbon trading costs Carbon Emissions Trading It can be calculated from the PIES carbon allowance and the actual carbon emissions of PIES, expressed as follows: (18) In the formula: and These are actual carbon emissions and carbon trading allowances, respectively. The tiered carbon trading cost is... (19) In the formula: , and These are the carbon trading base price, penalty coefficient, and carbon emission range length, respectively.

[0052] (4) Cost of abandoning wind and solar power (20) In the formula: The penalty coefficient for abandoning scenic views; for Constantly curtailing wind and solar power.

[0053] The constraints of the PIES scheduling layer include various power balance requirements: (twenty one) In the formula: and for The power output of wind turbines and photovoltaic systems at any given time; Charging power for EV clusters; Indicates that the EV cluster is in The total discharge power at any given moment; and The first EVs in Charge and discharge flags at specific times; For IAC clusters in Total operating power at any given time; For the first Taiwan IAC Operating power at any given time; , and They are respectively The electrical, heat and gas load at any given time, These represent the heat production power of EB, the heat production power of GB, the heat storage power, and the heat release power, respectively. These represent gas purchasing capacity and GS gas storage capacity, respectively. These represent the hydrogen production power of EL, the hydrogen storage power of HST, the hydrogen consumption power of MR, and the hydrogen release power of HST, respectively.

[0054] 2) Flexible load dispatching layer The objective function of an EV is defined as minimizing the total cost of charging and discharging. (twenty two) In the formula: For charging costs; Cost of battery discharge losses; The discharge benefits of EVs participating in V2G are calculated as follows: (1) Charging cost (twenty three) in, Indicates EV in Charging power at any time Indicates the scheduling period. Indicates the power factor; (2) Battery loss cost (twenty four) In the formula: Battery cycle life; Battery capacity; The cost of replacing the entire battery; This refers to the depth of battery discharge.

[0055] (3) Discharge benefits (25) in, express Incentive electricity prices at any time.

[0056] The constraints for ordered charging and discharging of EVs are mainly as follows: (26) In the formula: for Time of the first The battery capacity of the Taiwan EV This indicates the lower limit of the EV's charge capacity.

[0057] The IAC aims to minimize electricity costs, and its objective function can be defined as follows: (27) In the formula: The electricity cost for IAC can be calculated as follows: (28) The constraints for IAC load dispatching are: (29) In the formula: For the first Taiwan IAC's ice melting cooling capacity, For the first Power consumption of Taiwan IAC.

[0058] S4: The integrated energy system built in S1 serves as the energy supply side, while the EV and IAC established in S2 serve as the flexible load side. The two are bidirectionally coupled through power flow and electricity price information flow. This invention employs an iterative interactive approach to solve a two-layer optimization model. The specific solution process is as follows: The lower response layer receives the incentive price published by the upper layer. Under the premise of satisfying the EV load constraints, charging / discharging mutual exclusion constraints, and IAC ice storage tank capacity and melting rate constraints established in S2, it adjusts the charging / discharging power of the EVs and the operating parameters of the IAC, such as cooling, ice storage, and melting power, to minimize the total charging / discharging cost for users and the electricity cost for the IAC. The optimized load power is then fed back to the upper layer. The upper scheduling layer receives the load data fed back from the lower layer. Under the premise of satisfying the system power balance constraints and the operating constraints of each device established in S1, it optimizes and adjusts the output plans of each device in the S1 model. Specifically, it adjusts the power purchase, gas turbine output, electrolyzer power, and charging / discharging power parameters of various energy storage devices to minimize the total system operating cost, including energy purchase, operation and maintenance, carbon trading, and wind and solar curtailment costs. By iteratively updating the price and power strategies until the objective functions of the upper and lower layers converge, the optimal scheduling scheme that maximizes the interests of all parties is determined.

[0059] Specifically, firstly, based on historical wind and solar power output and load demand data, typical daily wind and solar power output curves and load curves are obtained; then, parameters such as generating units, EVs, IACs, electricity prices, and costs are imported; finally, with a step size of 1 hour, the Groubi 11.0.3 solver is called through MATLAB software for solution. For detailed solution process, please refer to [link / reference]. Figure 3 .

[0060] Figures 4(a) to 4(d) are schematic diagrams of the PIES operation results described in this invention, corresponding to the electric power balance diagram, thermal power balance diagram, gas power balance diagram and hydrogen power balance diagram, respectively. Figure 5 This is a graph showing the change in the charging and discharging power of the EV cluster within a day according to the present invention; Figure 6 This is the cooling load balance diagram of the present invention; Figure 7This is a graph showing the daily power variation of the IAC cluster according to this invention. The simulation results above demonstrate the optimal decision of the upper-level optimization model under the dual objectives of minimizing total operating costs and maximizing renewable energy consumption. On the one hand, off-peak electricity prices at night and surplus wind power provide cheap energy for various types of electrical loads; on the other hand, the model, through the coordinated scheduling of EVs, IACs, and electrolyzers—three main energy consumption "sponges"—converts electrical energy into chemical energy, cooling energy, and hydrogen energy, thereby minimizing wind curtailment. This result strongly demonstrates that the strategy proposed in this invention can effectively mitigate renewable energy fluctuations through the coordinated interaction of multiple types of flexible loads. It not only improves the wind power consumption rate but also achieves time-shifting and storage of energy between different categories through electricity-cooling and electricity-hydrogen energy conversion, enhancing the overall flexibility and resilience of PIES.

[0061] Through the above technical solutions, the two-layer optimized scheduling method for integrated energy systems in industrial parks provided by this invention has significant advantages compared with existing technologies. First, this invention achieves deep synergy and complementary utilization of multiple types of flexible load resources. By accurately modeling the spatiotemporal distribution and charging / discharging behavior of electric vehicles (EVs) and the multi-mode operation characteristics of ice storage air conditioning (IAC) in terms of cooling, ice storage, and ice melting, the complementary potential of the two in terms of time scale and functional characteristics is explored. EVs, as mobile energy storage units, possess millisecond-level rapid power response capabilities, primarily providing flexible adjustment during nighttime parking periods and daytime working hours; while IACs, as large-capacity time-shifting energy resources, convert off-peak electricity at night into cold energy storage through "peak shaving and valley filling," releasing it during the high-temperature afternoon hours in summer, effectively alleviating the pressure of peak load on the power grid. The synergistic effect of the two not only compensates for the limited adjustment capacity of a single load but also acts as a "shock absorber" for system power fluctuations, significantly improving the integrated energy system's ability to accommodate a high proportion of renewable energy. Second, the two-layer optimized scheduling architecture constructed by this invention effectively solves the problems of conflicting interests and coordination among multiple stakeholders. Unlike traditional single-layer optimization methods that treat flexible loads as passive resources, this invention treats PIES operators and flexible load users (EV owners and IAC users) as independent stakeholders. The upper-layer model focuses on the economical and low-carbon operation of the system, optimizing the output of multi-energy conversion and storage devices and guiding the lower layer with dynamic electricity price signals; the lower-layer model focuses on user-side interests, responding to price signals to minimize energy costs. This master-slave game-based mechanism ensures the overall efficiency of the system while fully respecting users' energy consumption intentions and economic interests, thereby greatly enhancing users' enthusiasm for participating in demand response. Finally, this invention is highly effective in improving economic efficiency, environmental benefits, and system operational flexibility. Simulation results show that using the method described in this invention, the wind and solar energy absorption rate of the park can be increased by approximately 4.14%, effectively reducing clean energy waste; the total system operating cost and tiered carbon trading cost are reduced by approximately 3.40% and 10.42%, respectively, verifying its low-carbon economic efficiency. Meanwhile, for users, EV users experienced an 8.90% reduction in travel costs, and IAC electricity costs decreased significantly by 22.91%, achieving a win-win situation for all parties. Furthermore, this method effectively smoothed out net load fluctuations, reduced peak-valley differences, created a stable operating environment for hydrogen-to-thermal energy conversion equipment, extended equipment lifespan, and enhanced the overall resilience and operational stability of the park's energy system.

[0062] Example 2 Based on Embodiment 1, Embodiment 2 of the present invention also provides a system for implementing the two-layer optimization scheduling method for a park integrated energy system described in Embodiment 1, comprising: The first modeling module is used to construct the operation framework of the integrated energy system of the park, taking the integrated energy system of the park as the research object, model each device in the operation framework, and determine the operation constraints of each device in the integrated energy system of the park and the system power balance conditions. The second modeling module is used to establish a vehicle-to-grid interaction model that considers the spatiotemporal distribution characteristics and charging and discharging behavior of electric vehicles, as well as an ice storage air conditioning load model that takes into account multiple working modes such as refrigeration, ice storage, and ice melting. The optimization model building module is used to set up a PIES two-layer optimization scheduling strategy that considers vehicle-to-grid interaction and ice storage air conditioning coordination. The upper layer is defined as the PIES energy scheduling layer and the lower layer as the flexible load response layer. In the flexible load response layer, electric vehicle optimization model and ice storage air conditioning optimization model are established based on vehicle-to-grid interaction model and ice storage air conditioning load model. In the PIES energy scheduling layer, system operating cost optimization model is established based on the modeling results of each device in the operation framework, vehicle-to-grid interaction model and ice storage air conditioning load model. The optimization and solution module is used to acquire data from the park's integrated energy system, solve the electric vehicle optimization model and the ice storage air conditioning optimization model, and then, based on the obtained electric vehicle power data, ice storage air conditioning power data, and data of each device in the operating framework, solve the system operating cost optimization model, and optimize the scheduling of each device in the park's integrated energy system according to the solution results.

[0063] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-layer optimized scheduling method for a park's integrated energy system, characterized in that, include: S1. Taking the integrated energy system of the park as the research object, construct the operation framework of the integrated energy system of the park, model each device in the operation framework, and determine the operation constraints of each device in the integrated energy system of the park and the system power balance conditions. S2. Establish a vehicle-to-grid interaction model that considers the spatiotemporal distribution characteristics and charging and discharging behavior of electric vehicles, as well as an ice storage air conditioning load model that takes into account multiple working modes such as refrigeration, ice storage, and ice melting. S3. Set up a PIES two-layer optimization scheduling strategy that considers vehicle-to-grid interaction and ice storage air conditioning coordination. Define the upper layer as the PIES energy scheduling layer and the lower layer as the flexible load response layer. In the flexible load response layer, establish electric vehicle optimization model and ice storage air conditioning optimization model based on vehicle-to-grid interaction model and ice storage air conditioning load model. In the PIES energy scheduling layer, establish system operating cost optimization model based on the modeling results of each device in the operation framework, vehicle-to-grid interaction model and ice storage air conditioning load model. S4. Obtain data from the park's integrated energy system, solve the electric vehicle optimization model and the ice storage air conditioning optimization model, and then, based on the obtained electric vehicle power data, ice storage air conditioning power data, and data of each device in the operating framework, solve the system operating cost optimization model, and optimize the scheduling of each device in the park's integrated energy system according to the solution results.

2. The two-layer optimized scheduling method for a park integrated energy system according to claim 1, characterized in that, The modeling of each device in the operating framework includes: Electrolytic cell model is In the formula, EL represents the electrolytic cell. for Hydrogen production capacity at any given moment; For EL electro-hydrogen conversion efficiency; for Energy consumption of EL at any given moment; and These are the lower and upper limits of EL energy consumption; A unified model is used to model electrical energy storage, hydrogen storage tanks, gas energy storage, and thermal energy storage, resulting in the following energy storage model: In the formula, for Real-time energy storage devices The amount in; and for Real-time energy storage devices The charging power and discharging power; and For energy storage devices The charging efficiency and discharging efficiency; and For energy storage devices The lower and upper limits of capacity; and This represents the upper limit of charging and discharging power; and It serves as a marker for the charging and discharging states.

3. The two-layer optimized scheduling method for a park integrated energy system according to claim 2, characterized in that, The modeling of each device in the operating framework also includes: The model of the hydrogen-doped cogeneration unit is In the formula, CHP represents a hydrogen-doped cogeneration unit. for t The hydrogen doping ratio of CHP at that time; and for t Constantly input the natural gas power and hydrogen power of CHP; and The lower heating value of natural gas and hydrogen; The calorific value of the mixture of natural gas and hydrogen; for t The output power of CHP at any given time; and for t The output electrical power and output thermal power of CHP at any given time; and For the electrical and thermal efficiency of CHP; and These are the lower and upper limits of natural gas ramping power. and These represent the lower and upper limits of hydrogen ramping power; and These represent the lower and upper limits of the thermoelectric adjustable ratio.

4. The two-layer optimized scheduling method for a park integrated energy system according to claim 3, characterized in that, The modeling of each device in the operating framework also includes: The methane reactor model is In the formula, MR represents a methane reactor. for The power of MR in producing natural gas at any given time; For conversion efficiency; for The hydrogen power is constantly input into the MR; and The lower and upper limits of the hydrogen power input to MR; and The lower and upper limits of the hydrogen ramp power for input MR; Gas boiler model is In the formula, GB represents a gas-fired boiler. For GB in Heat production capacity at any given time; For GB in Power consumption of natural gas at any given time; The coefficient of performance is the GB thermal efficiency. and These are the lower and upper limits of GB natural gas power consumption. and These are the lower and upper limits of GB ramp power. Electric boiler model is In the formula, EB represents an electric boiler. For EB in Heat production capacity at any given time; For the thermal conversion efficiency of EB; For EB in The electrical power of EB at time t; and These are the lower and upper limits of the EB power, respectively; and These represent the lower and upper limits of EB ramp power.

5. The two-layer optimized scheduling method for a park integrated energy system according to claim 4, characterized in that, The vehicle-to-everything (V2X) interaction model includes: Assuming the charging trigger condition for EV owners is In the formula: For the first End time of the trip; For the first The charge level at the end of the trip; The charge required for the next journey; Energy consumption per 100 kilometers for EVs; For EV users The distance traveled on the trip; For EV battery capacity; To provide a safety margin of charge for the EV, the EV needs enough charging time for the next trip. Calculated as In the formula, Charging power for EVs; The incentive price is related to the real-time price and is calculated as follows: In the formula, for Incentive pricing for electricity at any given time; for Incentive rate at any given moment; Based on real-time electricity pricing, users' discharge decisions are determined by a utility function driven by multiple factors: In the formula, and The distribution consists of electricity price sensitivity coefficient and SOC sensitivity coefficient. express The charge at time t, let the threshold of the utility function be . ,when At that time, EV users were willing to participate in EV discharge.

6. The two-layer optimized scheduling method for a park integrated energy system according to claim 5, characterized in that, The ice storage air conditioning load model is expressed as follows: In the formula, and In order to be in The energy consumption of cooling and ice storage at any given time; , and They are respectively The cooling capacity, ice storage capacity, and ice melting cooling capacity at any given time; , and These are respectively the flags for refrigeration, ice storage, and ice melting; and These are the lower and upper limits of electrical power consumption; Indicates the upper limit of ice storage power; This is the upper limit of the cooling capacity for ice melting; for The ice storage tank continuously stores ice. This represents the maximum amount of ice that can be stored. , , and These are the self-loss rate, refrigeration efficiency, ice storage efficiency, and ice melting cooling efficiency of the ice storage tank.

7. The two-layer optimized scheduling method for a park integrated energy system according to claim 6, characterized in that, The electric vehicle optimization model includes: The objective function of an EV is defined as minimizing the total cost of charging and discharging. In the formula, For charging costs; Cost of battery discharge losses; The discharge benefits of EVs participating in V2G; among which, Charging costs ,in, Indicates EV in Charging power at any time Indicates the scheduling period. Indicates the power factor; Battery loss cost , Battery cycle life; Battery capacity; The cost of replacing the entire battery; This refers to the depth of battery discharge. Discharge benefits , express Incentive pricing for electricity at any given time; The constraints for the ordered charging and discharging of EVs are: , for Time of the first The battery capacity of the Taiwan EV This indicates the lower limit of the EV's charge capacity.

8. The two-layer optimized scheduling method for a park integrated energy system according to claim 7, characterized in that, The ice storage air conditioning optimization model includes: The IAC aims to minimize electricity costs, and its objective function can be defined as follows: In the formula, This refers to the electricity cost of IAC; the calculation method is as follows: The constraints for IAC load dispatching are: In the formula, For the first Taiwan IAC's ice melting cooling capacity, For the first Power consumption of Taiwan IAC.

9. A two-layer optimized scheduling method for a park integrated energy system according to claim 8, characterized in that, The system operating cost optimization model includes: PIES aims to minimize system operating costs, and its objective function is defined as: In the formula, For energy purchase costs; For equipment operating costs; For carbon trading costs; To account for the costs associated with abandoning wind and solar power, the calculations for each cost are as follows: Energy purchase cost , and They are respectively Real-time electricity and gas purchase prices; and They are respectively The power transmission capacity and gas transmission capacity of the upstream power grid at any given time; Equipment operating costs , Price coefficient; The hydrogen production power of EL; Tiered carbon trading costs , and These are actual carbon emissions and carbon trading allowances, respectively. The cost of tiered carbon trading is In the formula: , and These are the carbon trading base price, penalty coefficient, and carbon emission range length; Abandoning wind and light costs , The penalty coefficient for abandoning scenic views; for Constantly curtailing wind and solar power; The constraints of the PIES scheduling layer are: In the formula: and for The power output of wind turbines and photovoltaic systems at any given time; Charging power for EV clusters; Indicates that the EV cluster is in The total discharge power at any given moment; and The first EVs in Charge and discharge flags at specific times; For IAC clusters in Total operating power at any given time; For the first Taiwan IAC Operating power at any given time; , and They are respectively The electrical, heat and gas load at any given time, These represent the heat production power of EB, the heat production power of GB, the heat storage power, and the heat release power, respectively. These represent gas purchasing capacity and GS gas storage capacity, respectively. These represent the hydrogen production power of EL, the hydrogen storage power of HST, the hydrogen consumption power of MR, and the hydrogen release power of HST, respectively.

10. A system for implementing the two-layer optimal scheduling method for a park integrated energy system according to any one of claims 1-9, characterized in that, include: The first modeling module is used to construct the operation framework of the integrated energy system of the park, taking the integrated energy system of the park as the research object, model each device in the operation framework, and determine the operation constraints of each device in the integrated energy system of the park and the system power balance conditions. The second modeling module is used to establish a vehicle-to-grid interaction model that considers the spatiotemporal distribution characteristics and charging and discharging behavior of electric vehicles, as well as an ice storage air conditioning load model that takes into account multiple working modes such as refrigeration, ice storage, and ice melting. The optimization model building module is used to set up a PIES two-layer optimization scheduling strategy that considers vehicle-to-grid interaction and ice storage air conditioning coordination. The upper layer is defined as the PIES energy scheduling layer and the lower layer as the flexible load response layer. In the flexible load response layer, electric vehicle optimization model and ice storage air conditioning optimization model are established based on vehicle-to-grid interaction model and ice storage air conditioning load model. In the PIES energy scheduling layer, system operating cost optimization model is established based on the modeling results of each device in the operation framework, vehicle-to-grid interaction model and ice storage air conditioning load model. The optimization and solution module is used to acquire data from the park's integrated energy system, solve the electric vehicle optimization model and the ice storage air conditioning optimization model, and then, based on the obtained electric vehicle power data, ice storage air conditioning power data, and data of each device in the operating framework, solve the system operating cost optimization model, and optimize the scheduling of each device in the park's integrated energy system according to the solution results.

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