Topological structure of port shore power direct current system with super capacity, optimization scheduling method and device, medium and equipment

Abstract

The invention discloses a port shore power direct-current system topological structure with a super capacitor. The port shore power direct-current system topological structure comprises a port power grid, a wind power unit, a photovoltaic power generation unit, a direct-current bus, a super capacitor bank, a ship power storage unit and a shore bridge feedback load. The port power grid, the wind power unit, the photovoltaic power generation unit and the super capacitor bank are respectively connected to the direct current bus through a first bidirectional AC / DC converter, a unidirectional AC / DC converter, a non-isolated DC / DC converter and an isolated DC / DC converter; the DC bus is connected to the ship power storage unit and the shore bridge feedback load through the bidirectional DC / DC converter and the second bidirectional AC / DC converter. The invention further discloses a seaport integrated energy system optimal scheduling method considering the ship demand response under the multi-time scale. According to the method, firstly, an electricity-heat-hydrogen coupling system model is established, and then optimization scheduling is carried out through day-ahead and intra-day optimization scheduling models. The ship docking power utilization efficiency can be effectively improved, the loss is reduced, the on-site absorption of port new energy is promoted, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to a port shore power direct current system topology structure containing supercapacitor, an optimization dispatching method, a device, a medium and equipment, and belongs to the technical field of port shore power. Background Art

[0002] As the global climate change problem becomes increasingly serious, the environmental protection issues of ports, as distribution centers for transport ships, have attracted widespread attention. At present, ships usually use their own fuel generators to provide electricity during berthing, resulting in a large amount of exhaust gas emissions and causing serious pollution to the environment. In order to solve this problem, ship shore power technology came into being, which reduces pollutant emissions by supplying power to moored ships from shore power sources. However, most of the existing shore power systems are AC distribution systems, which have problems such as many power conversion levels and large energy losses. Therefore, it is necessary to develop the topology of shore power DC distribution and utilization systems to reduce the number of new energy conversion levels and increase the local consumption rate of new energy.

[0003] Supercapacitors can store and release energy, store surplus electric energy when new energy is abundant, and transmit energy to the DC bus when new energy is scarce, reducing the impact of ship load on the power grid, thereby smoothing the fluctuation of new energy output and improving the power supply stability of the shore power system. In addition, the supercapacitor energy storage and the new energy interface to the ship power storage in this system only need to go through two stages of converters. Compared with the traditional AC distribution system, the number of power conversion stages is reduced, and the power supply efficiency and new energy consumption rate are greatly improved. Based on these advantages, the port shore power DC system containing supercapacitors can better adapt to the volatility of new energy, improve the stability and reliability of the system, reduce energy waste, and improve energy utilization efficiency, which meets the requirements of energy conservation, emission reduction and sustainable development.

[0004] At the same time, there are many challenges in optimizing scheduling. First, the output of new energy sources such as photovoltaics and wind power is affected by factors such as weather and seasons, and has significant uncertainty. This puts high demands on the accurate prediction of new energy output in order to reasonably arrange the power supply of supercapacitor energy storage and ship loads. Secondly, the ship load demand is highly volatile due to the arrival time of the ship, operating conditions, etc., and the optimized scheduling needs to have the ability to quickly respond to changes in load demand to ensure the stable operation of the system. In addition, the shore power DC distribution and utilization system contains multiple ports, such as new energy interfaces, supercapacitor energy storage interfaces, and ship power storage interfaces. The coordinated control of these ports is the key to optimizing the power flow path, reducing power loss, and increasing the local consumption rate of new energy. At the same time, the management of supercapacitor energy storage is crucial, and its charging and discharging needs to be reasonably controlled to avoid overcharging and over-discharging, and the impact of the number of charging and discharging on the life of the supercapacitor should be considered. Furthermore, in the peak and valley time-of-use electricity price scenario, the power grid purchase and sale and supercapacitor energy storage charging and discharging plans are formulated according to the real-time electricity price and the power of the new energy interface to achieve the optimal economic operation of the system, while meeting the charging and load needs of the ship and reducing the overall load peak and valley difference, which poses a higher challenge to the optimization scheduling strategy. Finally, when the port loses power, ensuring that the DC bus voltage is stable in a short time is an important guarantee for system reliability. The optimized scheduling strategy needs to be able to quickly switch the working state to deal with such situations. Therefore, it is necessary to propose a system optimization scheduling method to address the problems of the peak-to-valley difference on a long time scale caused by the difference in power consumption of ships at the port, and the power fluctuation on a short time scale caused by impact loads such as gantry cranes and cranes. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a port shore power DC system topology structure with super capacity, an optimization scheduling method, a device, a medium and equipment, aiming to solve the problems of low energy utilization efficiency, insufficient power supply stability, insufficient efficiency and flexibility of interconnection of various interfaces, and insufficient optimization of energy management in the existing port shore power system, thereby effectively improving the power consumption efficiency of ships when docked, reducing losses, promoting the on-site consumption of new energy in ports, reducing carbon emissions after ships docked, and reducing environmental maintenance costs.

[0006] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0007] On the one hand, the present invention proposes a port shore power DC system topology structure with supercapacitor, including: a port power grid, a wind power unit, a photovoltaic power generation unit, a DC bus, a supercapacitor group, a ship power storage unit and a shore bridge feedback load; The port power grid, wind power unit, photovoltaic power generation unit and supercapacitor group are respectively connected to the DC bus through a first bidirectional AC / DC converter, a unidirectional AC / DC converter, a non-isolated DC / DC converter and an isolated DC / DC converter; The DC bus is connected to the ship power storage unit and the shore bridge feedback load through a bidirectional DC / DC converter and a second bidirectional AC / DC converter respectively.

[0008] Furthermore, the first bidirectional AC / DC converter, the unidirectional AC / DC converter and the second bidirectional AC / DC converter all adopt ANPC type three-level AC / DC converters.

[0009] Furthermore, the non-isolated DC / DC converter adopts a Boost converter.

[0010] Furthermore, the isolated DC / DC converter adopts a DAB converter.

[0011] Furthermore, the bidirectional DC / DC converter adopts a four-switch Buck-Boost converter.

[0012] In a second aspect, the present invention proposes a method for optimizing the scheduling of a seaport integrated energy system under multiple time scales taking into account the ship demand response, which is applied to the port shore power DC system topology with overcapacity described in the first aspect, and the method comprises: Obtaining the initial energy value of the first energy storage device of the seaport integrated energy system model, as well as the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model; Inputting the initial energy value of the first energy storage device of the seaport integrated energy system model, the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model into a pre-built day-ahead optimization scheduling model including day-ahead optimization constraints, and generating a day-ahead scheduling output plan for each unit; Obtain the initial energy of the second energy storage device after being updated at a fixed time period within the day, as well as the new energy forecast value and load forecast value of the seaport integrated energy system model; The output plan of each unit scheduled on the day before, the initial energy of the second energy storage device updated at a fixed time period within the day, and the new energy forecast value and load forecast value of the seaport comprehensive energy system model are input into a pre-constructed intraday optimization model including intraday optimization constraints to generate an output plan for the intraday target time scale.

[0013] Furthermore, the first energy storage device includes a battery, a hydrogen storage tank and a heat storage tank; the second energy storage device includes the battery and a supercapacitor; the new energy prediction value includes a wind power prediction value and a photovoltaic prediction value; the load data and the load in the load prediction value include shore power load and thermal load; the thermal load is the thermal energy demand that needs to be met during the operation of the seaport.

[0014] Furthermore, the seaport integrated energy system model includes: a battery model, a heat storage tank model, an electric boiler model, a supercapacitor model, a hydrogen fuel cell model, an electrolyzer model and a hydrogen storage tank model; The battery model is: ; ; Where: and They are t Moment and t- 1: The remaining battery power at the moment; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the battery self-discharge rate; The heat storage tank model is: ; ; Where: and They are t Moment and t- 1. The residual heat of the heat storage device at moment 1; and are heat storage power and heat release power respectively; It is the charging and discharging heat power; and are heat storage efficiency and heat release efficiency respectively; is the heat loss coefficient; The electric boiler model is: ; Where: is the output thermal power; is the input electrical power; is the heat production efficiency; The supercapacitor model is: ; ; Where: and They are t Moment and t- The remaining power of the supercapacitor at moment 1; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the self-discharge rate of the supercapacitor; The hydrogen fuel cell model is: ; Where: is the total power obtained by the fuel cell from the hydrogen storage tank; and are the output electrical power and output thermal power respectively; is the hydrogen consumption rate; is the calorific value of natural gas, is the battery discharge efficiency; The electrolytic cell model is: ; Where: is the electrical power; is the power used for hydrogen production; is the power used to generate heat; is the hydrogen production efficiency; is the hydrogen production rate; The hydrogen storage tank model is: ; Where: is Avogadro's constant; is the temperature in Kelvin; is the volume of the hydrogen storage tank; is the hydrogen storage capacity; , is the proportionality coefficient; is the initial hydrogen storage capacity; is in storage state; is the maximum pressure; is the power of the hydrogen storage tank.

[0015] Furthermore, the day-ahead optimization scheduling model is: ; Where: is the objective function of the day-ahead optimization scheduling model, Cost of purchasing and selling electricity; for the cost of carbon emissions; For operation and maintenance costs; reimbursement costs for demand response; ; Where: for t The unit price of electricity purchased at any time; The power purchased; The unit price of electricity sold; For the power sold; Carbon emissions come from power generation in the main grid, and the cost of carbon emissions is: ; Where: is the unit carbon price; is the CO2 emission factor of the power grid; ; Where: , , , , and They are the unit maintenance costs of batteries, supercapacitors, electric boilers, hydrogen fuel cells, electrolyzers and heat storage tanks; ; Where: is the unit compensation coefficient of shore power load, , They are the transferable load and curtailable load of the shore power system respectively; T is the scheduling period, t Indicates the time, t ∈ T ; The day-ahead optimization constraints include: The electric power balance constraint is expressed as: ; Where: for t Total shore power load at the port at any given moment, for t Wind power generation at any given moment, for t Photovoltaic power generation at all times; The thermal power balance constraint is expressed as: ; Where: for t Harbor heat load at all times; The output power constraint of the upper grid is expressed as: ; Where: The maximum output power of the upper power grid; The thermal storage tank constraint is expressed as: ; Where: The maximum value of the heat storage tank charging and discharging power; for tThe remaining heat in the heat storage tank at all times; and The upper and lower limits of the heat storage tank capacity; Indicates the heat storage tank passes through After hours of operation, the heat returns to the initial value; The battery constraint is expressed as: ; Where: The maximum value of the battery charging and discharging power; for t Always check the remaining battery power; and The upper and lower limits of the battery power; Indicates that the battery has passed After running for hours, the battery returns to its initial value.

[0016] Furthermore, the t The total shore power load of the port at the moment is expressed as: ; Where: is the ship's electrical power, is the power of shore-based equipment, is the power of shore-based equipment, i and m is the serial number of the shore-based load equipment and the total number of shore-based load equipment, The first j Large ship power load, is the power load of the j-th small and medium-sized ship after the incentive-based demand response; ; In the formula, , They are the self-elasticity coefficient and the cross-elasticity coefficient, respectively, which are used to measure the dependence of ship power consumption on the growth rate of electricity prices at a certain time and adjacent times; , are electricity price and electricity price change respectively; This is the electricity load of large ships before price-based demand response; ; In the formula, The power load of small and medium-sized ships before and after the incentive demand response. , They are transferable load and curtailable load respectively.

[0017] Furthermore, the intraday optimization model includes: Intraday optimization objective function , expressed as: ; Where: It is the penalty fee for equipment output adjustment, expressed as: ; The equipment output adjustment penalty fee is caused by the day-ahead and day-intraday power deviation of each equipment. The specific deviations are as follows: ; Where: , , , , They are the output adjustment amounts for electricity purchase and sales, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , They are the penalty coefficients for electricity purchase and sale, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , , They are the daily electric power of electricity purchase, electricity sales, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; Intraday optimization constraints include: The supercapacitor constraint is expressed as: ; Where: is the maximum value of the supercapacitor charging and discharging power, is the daily charging power of the supercapacitor at time t, is the daily discharge power of the supercapacitor at time t; The remaining power of the supercapacitor in a day; and The upper and lower limits of the supercapacitor power; Indicates that the supercapacitor has passed After running for hours, the power will be restored to the initial value; The electric power balance constraint is expressed as: ; Where: , , , They are the daily wind turbine, photovoltaic, shore power load forecast data and the power consumption of electric boilers.

[0018] In a third aspect, the present invention proposes a device for optimizing and dispatching a comprehensive energy system of a seaport taking into account the response of ship demand under multiple time scales, which is applied to the topological structure of the port shore power DC system with supercapacity described in the first aspect, and the device comprises: A first acquisition module is used to obtain the initial energy value of the first energy storage device of the seaport integrated energy system model, as well as the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model; A day-ahead dispatch optimization module, used to input the initial energy value of the first energy storage device of the seaport integrated energy system model, the new energy forecast value of the seaport integrated energy system model, energy storage and conversion equipment parameters, load data and electricity price parameters into a pre-built day-ahead optimization dispatch model including day-ahead optimization constraints, and generate a day-ahead dispatch output plan for each unit; A second acquisition module is used to obtain the initial energy of the second energy storage device after being updated at a fixed time period within the day and the new energy forecast value and load forecast value of the seaport comprehensive energy system model; The intraday scheduling optimization module is used to input the output plan of each unit scheduled on the previous day, the initial energy of the second energy storage device updated at a fixed time period during the day, and the new energy forecast value and load forecast value of the seaport comprehensive energy system model into a pre-constructed intraday optimization model including intraday optimization constraints, so as to generate an output plan at the intraday target time scale.

[0019] In a fourth aspect, the present invention proposes a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to execute the method of the second aspect.

[0020] In a fifth aspect, the present invention provides a computer device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method of the second aspect.

[0021] The beneficial effects achieved by the present invention are: The topological structure of the present invention has many advantages. First, by adopting a DC busbar architecture, the number of power conversion levels from the new energy interface to the ship interface is greatly reduced. The energy storage from the supercapacitor and each new energy interface to the ship power storage only needs to pass through two stages of converters, thereby significantly improving the power supply efficiency and the new energy consumption rate. Second, the topological structure can effectively adapt to the intermittent and random characteristics of renewable energy sources such as solar energy and wind energy. The configured appropriate energy storage system can smooth the fluctuation of new energy output, greatly improving the stability of shore power system power supply. Third, by replacing traditional city power supply with new energy power generation (photovoltaic power generation and wind power generation), carbon emissions are effectively reduced, which is of great significance to energy conservation and emission reduction and the construction of green ports. Fourth, it can fully meet the power demand of berthing ships under different working conditions, including ship power storage and feedback loads such as shore bridges, and can also realize the absorption and bidirectional transmission of electric energy generated by feedback loads such as shore bridges. It has excellent performance in terms of power supply efficiency improvement, new energy utilization, environmental protection, functional satisfaction and system stability, and has extremely high application value and promotion potential.

[0022] The optimization scheduling method of the present invention performs optimization scheduling through the day-ahead stage and the intra-day stage, and has the advantages of high energy storage efficiency, fast response speed, flexible optimization scheduling, less energy loss, and high system reliability. It significantly improves the energy utilization efficiency and the overall performance of the system and meets the diversified energy needs of modern ports. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the topological structure of the port shore power DC system based on supercapacitor; Figure 2 It is a schematic diagram of the topology of the Boost converter; Figure 3 It is a schematic diagram of the two-level DAB series-parallel switching structure; Figure 4 This is a schematic diagram of a single transformer three-level DAB structure; Figure 5 It is a schematic diagram of the dual-transformer three-level DAB structure; Figure 6 Schematic diagram of the topology of a four-switch Buck-Boost converter; Figure 7 It is a schematic diagram of the topology structure of a three-level ANPC inverter; Figure 8 The two-stage scheduling relationship diagram for optimal scheduling; Fig. 9 The flowchart for solving the two-stage optimal scheduling problem is shown in Figure 2.

[0024] In the figure: 1. DC bus, 2. Wind power generation unit, 3. Photovoltaic power generation unit, 4. Supercapacitor group, 5. Shore crane regenerative load interface, 6. Ship power storage unit, 7. Boost converter, 8. DAB converter, 9. Four-switch Buck-Boost converter, 10. ANPC type three-level AC / DC converter. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0026] Embodiment 1, as Figure 1 As shown, this embodiment introduces a port shore power DC system topology with supercapacitor, including a DC bus 1, a wind power generation unit 2, a photovoltaic power generation unit 3, a supercapacitor group 4, a shore crane feedback load 5 and a ship power storage unit 6.

[0027] The photovoltaic power generation unit 3 and the wind power generation unit 2 are connected to the DC bus 1 through a primary DC converter to achieve efficient transmission of electric energy. The supercapacitor group 4 is connected to the DC bus 1 through a high-efficiency, high-power-density bidirectional DC converter to store and release electric energy and balance the power supply and demand of the system.

[0028] The quay crane regenerative load 5 and the ship power storage unit 6 are connected to the DC bus 1 through the DC converter to realize the power supply of port machinery and moored ships. The system optimizes the operation control strategy and adjusts the working state of each converter in real time, thereby improving the local consumption rate of new energy.

[0029] The port power grid, wind power unit, photovoltaic power generation unit and supercapacitor group are respectively connected to the DC bus through a first bidirectional AC / DC converter, a unidirectional AC / DC converter, a non-isolated DC / DC converter and an isolated DC / DC converter; the DC bus is respectively connected to the ship power storage unit and the shore crane feedback load through a bidirectional DC / DC converter and a second bidirectional AC / DC converter.

[0030] Specifically, the first bidirectional AC / DC converter, the unidirectional AC / DC converter and the second bidirectional AC / DC converter all adopt ANPC type three-level AC / DC converters, and their circuit structures adopt a three-level ANPC type inverter topology structure 10.

[0031] The non-isolated DC / DC converter adopts a Boost converter, and its circuit structure adopts a Boost converter topology structure 7.

[0032] The isolated DC / DC converter adopts a DAB converter, and the DAB converter circuit structure 8 adopts one of a two-level DAB series-parallel switching structure, a single-transformer three-level DAB structure, or a dual-transformer three-level DAB structure.

[0033] The bidirectional DC / DC converter adopts a four-switch Buck-Boost converter, and its circuit structure adopts a four-switch Buck-Boost converter topology structure 9.

[0034] like Figure 2 As shown, the Boost converter topology 7 includes a filter inductor L , switch tube S ,diode D 、Filter capacitor C The inductor L is connected between the input power supply and the switch to store energy. The switch S is a MOSFET transistor, which regulates the energy storage and release of the inductor by controlling its on and off. The diode D is connected between the switch and the output capacitor to prevent reverse current from flowing back to the inductor. The output capacitor C is connected between the diode and the load to smooth the output voltage.

[0035] like Figure 3 As shown in the figure, the two-level DAB series-parallel switching structure includes a DC bus, two DAB converters and a supercapacitor group. The series or parallel switching of the DAB converters is realized by controlling the switch to achieve efficient energy transmission between different DC bus voltages. The bidirectional energy conversion characteristics of the DAB converter are used to maintain power stability through rapid charging and discharging of the supercapacitor when the load changes.

[0036] like Figure 4 As shown in the figure, the single-transformer three-level DAB structure includes a DC bus, a transformer, and multiple switches (S1-S 12 ), diodes (D1-D4) and supercapacitors. The DC bus provides input, the transformer adjusts the voltage through the transformation ratio, the switch controls the energy flow to achieve three-level output, the diode is used for rectification, and the supercapacitors store and release energy. Through the high-frequency switching of the switch, the energy can flow bidirectionally between the DC bus and the supercapacitors, improving the voltage level and power density of the system.

[0037] like Figure 5 As shown in the figure, the dual-transformer three-level DAB structure includes a DC bus, two transformers (T1, T2), and multiple switches (S1-S 10), inductor and supercapacitor group. The DC bus provides input, the transformer adjusts the voltage through the transformation ratio, the inductor is used for energy storage and conversion, the switch controls the energy flow to achieve three-level output, and the supercapacitor group stores and releases energy. Through the high-frequency switching of the switch and the voltage conversion of the transformer, the bidirectional flow of energy between the DC bus and the supercapacitor group is realized, which improves the voltage level and power density of the system.

[0038] Figure 3 , Figure 4 and Figure 5 For the three DAB topologies, a comprehensive evaluation should be conducted from the aspects of R&D cost, conversion efficiency and reliability to select the best solution.

[0039] like Figure 6 As shown, the four-switch Buck-Boost converter topology structure 9 includes an input power supply E, two capacitors C1 and C2, an inductor L, four switch devices S1, S2, S3 and S4, and a load resistor R. Among them, S1 and S2 form a Buck unit, and S3 and S4 form a Boost unit. The input power supply E provides a DC input voltage; the capacitor C is used for filtering and energy storage, and smoothing the input and output voltages; the inductor is used as an energy storage element to smooth current changes during the switching process. The switch devices S1, S2, S3 and S4 are composed of power semiconductor devices, which control the on and off of the circuit to achieve voltage conversion. It realizes the step-down (Buck mode) or step-up (Boost mode) conversion of the input voltage by alternately controlling the switch state, thereby maintaining a stable output voltage.

[0040] like Figure 7 As shown, the three-level ANPC inverter topology structure 10 includes a DC bus, a capacitor, an inductor, a switch device, an anti-parallel diode and an output capacitor. The DC bus provides input voltage, the capacitor divides and stabilizes the voltage, and the midpoint is used as a reference point. The switch device and the anti-parallel diode are combined to realize the conversion of DC to AC, and are filtered by the inductor and the output capacitor to finally output smooth three-phase AC.

[0041] Embodiment 2: This embodiment provides a method for optimizing and scheduling a seaport integrated energy system under multiple time scales taking into account the ship demand response, which is applied to the port shore power DC system topology structure with overcapacity. The method includes: Obtaining the initial energy value of the first energy storage device of the seaport integrated energy system model, as well as the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model; Inputting the initial energy value of the first energy storage device of the seaport integrated energy system model, the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model into a pre-built day-ahead optimization scheduling model including day-ahead optimization constraints, and generating a day-ahead scheduling output plan for each unit; Obtain the initial energy of the second energy storage device after being updated at a fixed time period within the day, as well as the new energy forecast value and load forecast value of the seaport integrated energy system model; The output plan of each unit scheduled on the day before, the initial energy of the second energy storage device updated at a fixed time period within the day, and the new energy forecast value and load forecast value of the seaport comprehensive energy system model are input into a pre-constructed intraday optimization model including intraday optimization constraints to generate an output plan for the intraday target time scale.

[0042] The first energy storage device includes a battery, a hydrogen storage tank and a heat storage tank; the second energy storage device includes the battery and a supercapacitor; the new energy forecast value includes a wind power forecast value and a photovoltaic forecast value; the load data and the load in the load forecast value include shore power load and thermal load; wherein the thermal load refers to the thermal energy demand that needs to be met during the operation of the seaport, including but not limited to thermal energy demand for heating, heating, process production, etc. In the seaport integrated energy system, most of the thermal load is supplied by electric boilers, and a small part is supplied by hydrogen fuel cells and electrolyzers.

[0043] The seaport integrated energy system model includes: a battery model, a heat storage tank model, an electric boiler model, a supercapacitor model, a hydrogen fuel cell model, an electrolyzer model and a hydrogen storage tank model; The battery model is: ; ; Where: and They are Moment and The remaining battery power at all times; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the battery self-discharge rate; The heat storage tank model is: ; ; Where: and They are Moment and The residual heat of the heat storage device at all times; and are heat storage power and heat release power respectively; It is the charging and discharging heat power; and are heat storage efficiency and heat release efficiency respectively; is the heat loss coefficient; The electric boiler model is: ; Where: is the output thermal power; is the input electrical power; is the heat production efficiency; The supercapacitor model is: ; ; Where: and They are Moment and The remaining power of the supercapacitor at all times; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the self-discharge rate of the supercapacitor; The hydrogen fuel cell model is: ; Where: is the total power obtained by the fuel cell from the hydrogen storage tank; and are the output electrical power and output thermal power respectively; is the hydrogen consumption rate; is the calorific value of natural gas, is the battery discharge efficiency; for t Wind power generation at each moment; for t Photovoltaic power generation at all times.

[0044] The electrolytic cell model is: ; Where: is the electrical power; is the power used for hydrogen production; is the power used to generate heat; is the hydrogen production efficiency; is the hydrogen production rate.

[0045] The hydrogen storage tank model is: ; Where: is Avogadro's constant; is the temperature in Kelvin; is the volume of the hydrogen storage tank; is the hydrogen storage capacity; , is the proportionality coefficient; is the initial hydrogen storage capacity; is in storage state; is the maximum pressure, is the power of the hydrogen storage tank, is the hydrogen consumption rate.

[0046] The day-ahead optimization scheduling model is: ; Where: is the objective function of the day-ahead optimization scheduling model, Cost of purchasing and selling electricity; for the cost of carbon emissions; For operation and maintenance costs; reimbursement costs for demand response; ; Where: for The unit price of electricity purchased at any time; The power purchased; The unit price of electricity sold; For the power sold; Carbon emissions come from power generation in the main grid, and the cost of carbon emissions is: ; Where: is the unit carbon price; is the CO2 emission factor for the power grid; ; Where: , , , , and They are the unit maintenance costs of batteries, supercapacitors, electric boilers, hydrogen fuel cells, electrolyzers and heat storage tanks; ; Where: is the unit compensation coefficient of shore power load, , They are the transferable load and curtailable load of the shore power system ("transferable load" refers to the load in the system that can be temporarily interrupted or postponed without affecting the normal use of users; "curtailable load" refers to the load in the power system that can be reduced or shut down to cope with power shortages or power system emergencies.); T is the scheduling period, t Indicates the time, t ∈ T ; The day-ahead optimization constraints include: The electric power balance constraint is expressed as: ; Where: for Total shore power load at the port at any given moment, for t Wind power generation at any given moment, for t Photovoltaic power generation at all times.

[0047] The thermal power balance constraint is expressed as: ; Where: for Harbor heat load at all times; The output power constraint of the upper grid is expressed as: ; Where: The maximum output power of the upper power grid; The heat storage tank constraint is expressed as: ; Where: The maximum value of the heat storage tank charging and discharging power; for The remaining heat in the heat storage tank at all times; and The upper and lower limits of the heat storage tank capacity; Indicates the heat storage tank passes through After hours of operation, the heat returns to the initial value; The battery constraint is expressed as: ; Where: The maximum value of the battery charging and discharging power; for Always check the remaining battery power; and The upper and lower limits of the battery power; Indicates that the battery has passed After running for hours, the battery returns to its initial value.

[0048] Said The total shore power load of the port at the moment is expressed as: ; Where: is the ship's electrical power, is the power of shore-based equipment, is the power of shore-based equipment, i and m It is the serial number of the shore-based load equipment and the total number of shore-based load equipment.

[0049] The intraday optimization model includes: Intraday optimization objective function , expressed as: ; Where: It is the penalty fee for equipment output adjustment, expressed as: ; The equipment output adjustment penalty fee is caused by the day-ahead and day-intraday power deviation of each equipment. The specific deviations are as follows: ; Where: , , , , They are the output adjustment amounts for electricity purchase and sales, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , They are the penalty coefficients for electricity purchase and sale, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , , They are the daily electric power of electricity purchase, electricity sales, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; Intraday optimization constraints include: The supercapacitor constraint is expressed as: ; Where: is the maximum value of the supercapacitor charging and discharging power, is the charging power of the supercapacitor at time t, is the discharge power of the supercapacitor at time t; The remaining power of the supercapacitor in a day; and The upper and lower limits of the supercapacitor power; Indicates that the supercapacitor has passed After running for hours, the power will be restored to the initial value; The electric power balance constraint is expressed as: ; Where: , , , They are the daily wind turbine, photovoltaic, shore power load forecast data and the power consumption of electric boilers.

[0050] Embodiment 3: This embodiment provides a method for optimizing and scheduling a seaport integrated energy system under multiple time scales considering ship demand response, which specifically includes the following steps: Step S1, establishing a comprehensive energy system model of the seaport, including establishing models of batteries, heat storage tanks, electric boilers, supercapacitors, hydrogen fuel cells, electrolyzers and hydrogen storage tanks; Step S2, establishing a ship demand response model; Step S3, establishing a day-ahead scheduling model; Step S4, establishing an intraday scheduling model; Step S5, solving the model and performing optimal scheduling based on the results.

[0051] The model established in the above step S1 is as follows: (1) Battery model: Selected storage The battery is a lithium battery, and its output model is: ; ; Where: and They are Moment and The remaining battery power at all times; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the battery self-discharge rate.

[0052] (2) Heat storage tank model The heat storage device is phase change heat storage, and its model is as follows: ; ; Where: and They are Moment and The residual heat of the heat storage device at all times; and are heat storage power and heat release power respectively; It is the charging and discharging heat power; and are heat storage efficiency and heat release efficiency respectively; is the heat loss coefficient.

[0053] (3) Electric boiler model: Electric boilers can convert surplus wind power, photovoltaic power or low-cost electricity into thermal energy. The model is as follows: ; Where: is the output thermal power; is the input electrical power; For heat production efficiency.

[0054] (4) Supercapacitor model: Supercapacitors can achieve fast charging and discharging to reduce power fluctuations caused by impact loads in the harbor. Its output model is: ; ; Where: and They are Moment and The remaining power of the supercapacitor at all times; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the self-discharge rate of the supercapacitor.

[0055] (5) Hydrogen fuel cell model: Hydrogen fuel cells convert hydrogen into electrical energy and thermal energy, and their output model is: ; Where: is the total power obtained by the fuel cell from the hydrogen storage tank; and are the output electrical power and output thermal power respectively; is the hydrogen consumption rate; is the calorific value of natural gas.

[0056] (6) Electrolyzer model: The electricity-heat-hydrogen relationship is: ; Where: is the electrical power; is the power used for hydrogen production; is the power used to generate heat; is the hydrogen production efficiency; is the hydrogen production rate.

[0057] (7) Hydrogen storage tank model: The hydrogen storage tank can store a large amount of hydrogen produced by water electrolysis, thereby reducing the peak-to-valley difference of the harbor. The model is as follows: ; Where: is Avogadro's constant; is the temperature in Kelvin; is the volume of the hydrogen storage tank; is the hydrogen storage capacity; , is the proportionality coefficient; is the initial hydrogen storage capacity; is in storage state; is the maximum pressure.

[0058] In the above step S2, first, define the ship The status in Hong Kong is , Indicates that the ship is in port. Indicates that the ship has left the port. In a single scheduling cycle, the ship's status in the port is: ; In the formula ： Ship arrival time ， Time of port call ， Departure time ， Scheduling cycle 。

[0059] Then, according to the different power of ships, they are divided into large ships, medium-sized ships and small ships. According to the different electricity consumption characteristics of ships, the demand response behavior of ships arriving at the port is mainly divided into price-based demand response and incentive-based demand response.

[0060] Large ships have high power consumption and are more sensitive to changes in electricity prices. Therefore, price-based demand response is considered: ; In the formula, , They are the self-elasticity coefficient and the cross-elasticity coefficient, respectively, which are used to measure the dependence of ship power consumption on the growth rate of electricity prices at a certain time and adjacent times; , are electricity price and electricity price change respectively; , They are respectively the ship electricity load before and after price-based demand response.

[0061] Small and medium-sized ships have lower power consumption and thus consume less electricity than large ships. They are more sensitive to direct economic incentives, so incentive-based demand response is considered: ; In the formula, , are the ship power load before and after the incentive demand response, , They are transferable load and curtailable load respectively.

[0062] Afterwards, the demand response compensation cost is determined as: ; Where: It is the unit compensation coefficient of shore power load.

[0063] Finally, since the shore power load is composed of ship load and shore-based load, the ship load is the power load after price-based demand response and incentive-based demand response, and the constant power load remains unchanged during the port, the shore-based load is composed of intermittent working equipment such as gantry cranes and cranes, and the power changes continuously over time. The calculation method is as follows: ; Where: is the total power consumption after ship demand response, is the total number of ships, is the total shore power load power, is the power of shore-based equipment, is the total number of shore-based equipment.

[0064] In the above step S3: First, establish the day-ahead optimization objective function: ; Where: Cost of purchasing and selling electricity; for the cost of carbon emissions; For operation and maintenance costs; Reimbursement costs for demand response.

[0065] Among them, the cost of electricity purchase and sale is the purchase and sale of electricity from the main power grid by the seaport according to the time-of-use electricity price mechanism, that is: ; Where: for The unit price of electricity purchased at any time; The power purchased; The unit price of electricity sold; For selling electricity.

[0066] Carbon emissions come from power generation in the main grid, and the cost of carbon emissions is: ; Where: is the unit carbon price; is the carbon dioxide emission factor of the power grid.

[0067] Operation and maintenance costs include the maintenance costs of batteries, supercapacitors, electric boilers, hydrogen fuel cells, electrolyzers, and heat storage tanks, namely: ; Where: , , , , and They are the unit maintenance costs of batteries, supercapacitors, electric boilers, hydrogen fuel cells, electrolyzers and heat storage tanks.

[0068] Then, set the day-ahead optimization constraints, including the following constraints: (1) Electric power balance constraints: ; Where: for Total shore power load at the port at all times.

[0069] (2) Thermal power balance constraints: ; Where: for Harbor heat load at all times.

[0070] (3) Output power constraints of the upper grid: The transmission power of the power grid transmission line needs to be within a safe range and must meet the following requirements: ; Where: It is the maximum output power of the upper power grid.

[0071] (4) Heat storage tank constraints: The charging and discharging power of the heat storage tank and the stored heat must be within a safe range, and the states at the beginning and end of the scheduling cycle must be equal, which must meet the following requirements: ; Where: The maximum value of the heat storage tank charging and discharging power; for The remaining heat in the heat storage tank at all times; and The upper and lower limits of the heat storage tank capacity; Indicates the heat storage tank passes through After hours of operation, the heat returns to the initial value.

[0072] (5) Battery constraints: The battery charging and discharging power and stored electric energy must be within a safe range, and the states at the beginning and end of the dispatch cycle must be the same, which must meet the following requirements: ; Where: The maximum value of the battery charging and discharging power; for Always check the remaining battery power; and The upper and lower limits of the battery power; Indicates that the battery has passed After running for hours, the battery returns to its initial value.

[0073] In the above step S4: First, establish the intraday optimization objective function: ; Where: The penalty fee for equipment output adjustment is: ; The penalty fee for equipment output adjustment is caused by the power deviation of each equipment from the day before to the day after. The specific deviation is as follows: ; Where: , , , , They are the output adjustment of electricity purchase and sale, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , They are the penalty coefficients for electricity purchase and sale, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , , They are the daily electrical power of electricity purchase, electricity sales, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells.

[0074] Then, determine the intraday optimization constraints. Since the constraints involved in wind turbines, photovoltaic units, electric boilers, batteries, hydrogen storage tanks, hydrogen fuel cells and other equipment are exactly the same as those formulated for day-ahead scheduling, they will not be repeated here. In the optimization scheduling process of a shorter time scale within the day, it is necessary to consider the charging and discharging of supercapacitors to smooth the power fluctuations of the port. The constraints are as follows: (1) Supercapacitor constraints: The supercapacitor charging and discharging power and stored electrical energy must be within a safe range, and the states at the beginning and end of the scheduling cycle must be equal. The following conditions must be met: ; Where: The maximum value of the supercapacitor charging and discharging power; The remaining power of the supercapacitor in a day; and The upper and lower limits of the supercapacitor power; Indicates that the supercapacitor has passed After running for hours, the battery returns to its initial value.

[0075] (2) Electric power balance constraints: ; Where: , , , They are the daily wind turbine, photovoltaic, shore power load forecast data and electric boiler power consumption data.

[0076] In the above step S5, the two-stage optimization scheduling solution process is as follows: 1. Day-ahead long-time scale optimization scheduling: 1.1 Data preprocessing: energy storage device initialization, including the initial value of battery energy, the initial value of hydrogen storage tank energy, and the initial value of heat storage tank energy; Process input data, including wind and solar power forecast values, equipment parameters, shore power load and thermal load, electricity price parameters, etc.

[0077] 1.2 Input data: Obtain data such as wind and solar power forecast values, equipment parameters, shore power load, thermal load, and electricity price parameters.

[0078] 1.3 Day-ahead scheduling strategy: Based on the input data, a day-ahead optimization scheduling model is developed.

[0079] 1.4 Output plan of each unit: Generate a 24-hour output plan based on the day-ahead optimization scheduling model.

[0080] 2. Optimized scheduling of intraday rolling time scale: 2.1 Update data: Data is updated every 15 minutes, including the initial value of battery energy, initial value of supercapacitor energy, wind and solar load forecast data, etc.

[0081] 2.2 Intraday scheduling model: Based on the updated data, the day-ahead optimization scheduling model is used for intraday optimization.

[0082] 2.3 Intraday dispatch reference value: Use the day-ahead dispatch reference value for comparison and adjustment.

[0083] 2.4 Optimal output plan for each unit: During the scheduling cycle, the optimal output plan for each unit is generated and an optimization plan for the next period is formulated.

[0084] 2.5 Check the end of the scheduling cycle: Check whether the scheduling cycle has ended. If not, obtain the latest prediction value and use it as the initial value for the next moment, and perform optimization in a loop.

[0085] 2.6 Output the optimal scheduling results: When the scheduling cycle ends, the final optimization results are output, including the 4-hour output plan and the 15-minute time scale output plan.

[0086] like Figure 8 As shown in the figure, during the day-ahead scheduling, the forecast data of the next day is used as the basis, with 1 hour as the time scale and 24 hours as the scheduling cycle for long-term optimization; during the day-ahead scheduling, the time scale of 15 minutes and 4 hours as the scheduling cycle are used for short-term rolling optimization.

[0087] like Figure 8 and Fig. 9 As shown, the optimization scheduling method proposed in the present invention includes day-ahead long-time scale optimization scheduling and intraday rolling time scale optimization scheduling.

[0088] The day-ahead long-time scale optimization scheduling includes the following steps: First, prepare for data preprocessing and input data. Initialize the energy storage device, obtain the initial energy values ​​of the battery, hydrogen storage tank, and heat storage tank, and process various input data such as wind and solar forecast values, energy storage and conversion equipment parameters, shore power load and heat load, and electricity price parameters. Ensure that all forecast data and parameters are complete so that the scheduling model can fully consider uncertainty factors. Next, based on the processed input data, establish a day-ahead optimization scheduling model and formulate the optimal scheduling strategy for the next day. Finally, based on the optimization scheduling model, generate a detailed 24-hour output plan to guide the operation of each unit the next day.

[0089] Intraday rolling time scale optimization scheduling includes the following steps: First, the data needs to be updated every 15 minutes to ensure that the dispatch model can respond to changes in the system status in real time, including updating the initial value of battery energy, the initial value of supercapacitor energy, and the wind and solar load forecast data. Based on the latest data, the day-ahead optimization dispatch model is applied for rolling optimization to meet real-time operation requirements. Combined with the day-ahead dispatch reference value, the current dispatch is corrected and optimized. During the dispatch cycle, the optimal output plan for each unit is generated, and the optimization plan for the next period is formulated.

[0090] At the end of each scheduling cycle, it is necessary to check whether the predetermined goal has been achieved. If the scheduling cycle has not ended, the latest forecast data is obtained and the optimization scheduling of the next period is continued. When the scheduling cycle ends, the final optimization results are output, including the 4-hour output plan and the 15-minute time scale output plan. This process ensures that the system can perform effective energy management and optimization scheduling in an environment of uncertainty and real-time changes.

[0091] Embodiment 4 is based on the same inventive concept as Embodiment 2. This embodiment introduces a device for optimizing and dispatching a comprehensive energy system of a seaport taking into account the response of ship demand under multiple time scales, which is applied to the topological structure of the port shore power DC system with super capacity. The device comprises: A first acquisition module is used to obtain the initial energy value of the first energy storage device of the seaport integrated energy system and the new energy prediction value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model; A day-ahead dispatch optimization module is used to input the initial energy value of the energy storage device of the seaport integrated energy system model, the new energy forecast value of the seaport integrated energy system model, energy storage and conversion equipment parameters, load data and electricity price parameters into a pre-built day-ahead optimization dispatch model including day-ahead optimization constraints, and generate a day-ahead dispatch output plan for each unit; A second acquisition module is used to obtain the initial energy of the second energy storage device after being updated at a fixed time period within the day and the new energy forecast value and load forecast value of the seaport comprehensive energy system model; The intraday scheduling optimization module is used to input the output plan of each unit scheduled on the previous day, the initial energy of the second energy storage device updated at a fixed time period during the day, and the new energy forecast value and load forecast value of the seaport comprehensive energy system model into a pre-constructed intraday optimization model including intraday optimization constraints, so as to generate an output plan at the intraday target time scale.

[0092] Example 5 is based on the same inventive concept as Example 2. This example introduces a computer-readable storage medium that stores one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, enable the computing device to execute the method described in Example 2.

[0093] Embodiment 6 is based on the same inventive concept as Embodiment 3. This embodiment introduces a computer device, including: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method described in Example 3.

[0094] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A port shore power DC system topology with supercapacitor, characterized in that: include: Port power grid, wind power unit, photovoltaic power generation unit, DC bus, super capacitor group, ship power storage unit and quay crane feedback load; The port power grid, wind power unit, photovoltaic power generation unit and supercapacitor group are respectively connected to the DC bus through a first bidirectional AC / DC converter, a unidirectional AC / DC converter, a non-isolated DC / DC converter and an isolated DC / DC converter; The DC bus is connected to the ship power storage unit and the shore bridge feedback load through a bidirectional DC / DC converter and a second bidirectional AC / DC converter respectively.

2. The topological structure of the port shore power DC system with supercapacitor according to claim 1 is characterized in that: The first bidirectional AC / DC converter, the unidirectional AC / DC converter and the second bidirectional AC / DC converter all adopt ANPC type three-level AC / DC converters.

3. The topological structure of the port shore power DC system with supercapacitor according to claim 1 is characterized in that: The non-isolated DC / DC converter adopts a Boost converter.

4. The topological structure of the port shore power DC system with supercapacitor according to claim 1 is characterized in that: The isolated DC / DC converter adopts a DAB converter.

5. The topological structure of the port shore power DC system with supercapacitor according to claim 1 is characterized in that: The bidirectional DC / DC converter adopts a four-switch Buck-Boost converter.

6. A method for optimizing the dispatching of a seaport integrated energy system considering the ship demand response under multiple time scales, characterized in that: Applied to the port shore power direct current system topology structure with super capacity as described in any one of claims 1 to 5, the method comprises: Obtaining the initial energy value of the first energy storage device of the seaport integrated energy system model, as well as the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model; Inputting the initial energy value of the first energy storage device of the seaport integrated energy system model, the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model into a pre-built day-ahead optimization scheduling model including day-ahead optimization constraints, and generating a day-ahead scheduling output plan for each unit; Obtain the initial energy of the second energy storage device after being updated at a fixed time period within the day, as well as the new energy forecast value and load forecast value of the seaport comprehensive energy system model; The output plan of each unit scheduled on the day before, the initial energy of the second energy storage device updated at a fixed time period within the day, and the new energy forecast value and load forecast value of the seaport comprehensive energy system model are input into a pre-constructed intraday optimization model including intraday optimization constraints to generate an output plan for the intraday target time scale.

7. The method for optimizing and dispatching a seaport integrated energy system considering ship demand response under multiple time scales according to claim 6 is characterized in that: The first energy storage device includes a battery, a hydrogen storage tank and a heat storage tank; the second energy storage device includes the battery and a supercapacitor; the new energy prediction value includes a wind power prediction value and a photovoltaic prediction value; the load data and the load in the load prediction value include shore power load and thermal load; the thermal load is the thermal energy demand that needs to be met during the operation of the seaport.

8. The method for optimizing and dispatching a seaport integrated energy system considering ship demand response under multiple time scales according to claim 7 is characterized in that: The seaport integrated energy system model includes: a battery model, a heat storage tank model, an electric boiler model, a supercapacitor model, a hydrogen fuel cell model, an electrolyzer model and a hydrogen storage tank model; The battery model is: ； ； Where: and They are t Moment and t- 1: The remaining battery power at the moment; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the battery self-discharge rate; The heat storage tank model is: ； ； Where: and They are t Moment and t- 1. The residual heat of the heat storage device at moment 1; and are heat storage power and heat release power respectively; It is the charging and discharging heat power; and are heat storage efficiency and heat release efficiency respectively; is the heat loss coefficient; The electric boiler model is: ； Where: is the output thermal power; is the input electrical power; is the heat production efficiency; The supercapacitor model is: ； ； Where: and They are t Moment and t- The remaining power of the supercapacitor at moment 1; and are charging power and discharging power respectively; is the charge and discharge power; and are charging efficiency and discharging efficiency respectively; is the self-discharge rate of the supercapacitor; The hydrogen fuel cell model is: ； Where: is the total power obtained by the fuel cell from the hydrogen storage tank; and are the output electrical power and output thermal power respectively; is the hydrogen consumption rate; is the calorific value of natural gas, is the battery discharge efficiency; The electrolytic cell model is: ； Where: is the electrical power; is the power used for hydrogen production; is the power used to generate heat; is the hydrogen production efficiency; is the hydrogen production rate; The hydrogen storage tank model is: ； Where: is Avogadro's constant; is the temperature in Kelvin; is the volume of the hydrogen storage tank; is the hydrogen storage capacity; , is the proportionality coefficient; is the initial hydrogen storage capacity; is the storage state; is the maximum pressure; is the power of the hydrogen storage tank.

9. The method for optimizing and dispatching a seaport integrated energy system considering ship demand response under multiple time scales according to claim 8 is characterized in that: The day-ahead optimization scheduling model is: ； Where: is the objective function of the day-ahead optimization scheduling model, Cost of purchasing and selling electricity; for the cost of carbon emissions; For operation and maintenance costs; reimbursement costs for demand response; ； Where: for t The unit price of electricity purchased at any time; The power purchased; The unit price of electricity sold; For the power sold; Carbon emissions come from power generation in the main grid, and the cost of carbon emissions is: ； Where: is the unit carbon price; is the CO2 emission factor of the power grid; ； Where: , , , , and They are the unit maintenance costs of batteries, supercapacitors, electric boilers, hydrogen fuel cells, electrolyzers and heat storage tanks; ； Where: is the unit compensation coefficient of shore power load, , They are the transferable load and curtailable load of the shore power system respectively; T is the scheduling period, t Indicates the time, t ∈ T ; The day-ahead optimization constraints include: The electric power balance constraint is expressed as: ； Where: for t Total shore power load at the port at any given moment, for t Wind power generation at any given moment, for t Photovoltaic power generation at all times; The thermal power balance constraint is expressed as: ； Where: for t Harbor heat load at all times; The output power constraint of the upper grid is expressed as: ； Where: The maximum output power of the upper power grid; The heat storage tank constraint is expressed as: ； Where: The maximum value of the heat storage tank charging and discharging power; for t The remaining heat in the heat storage tank at all times; and The upper and lower limits of the heat storage tank capacity; Indicates the heat storage tank passes through After hours of operation, the heat returns to the initial value; The battery constraint is expressed as: ； Where: The maximum value of the battery charging and discharging power; for t Always check the remaining battery power; and The upper and lower limits of the battery power; Indicates that the battery has passed After running for hours, the battery returns to its initial value.

10. The method for optimizing and dispatching a seaport integrated energy system considering ship demand response under multiple time scales according to claim 9 is characterized in that: Said t The total shore power load of the port at the moment is expressed as: ； Where: is the ship's electrical power, is the power of shore-based equipment, is the power of shore-based equipment, i and m is the serial number of the shore-based load equipment and the total number of shore-based load equipment, The first j Large ship power load, is the power load of the j-th small and medium-sized ship after the incentive-based demand response; ； In the formula, , They are the self-elasticity coefficient and the cross-elasticity coefficient, respectively, which are used to measure the dependence of ship power consumption on the growth rate of electricity prices at a certain time and adjacent times; , are electricity price and electricity price change respectively; This is the electricity load of large ships before price-based demand response; ； In the formula, The power load of small and medium-sized ships before and after the incentive demand response. , They are transferable load and curtailable load respectively.

11. The method for optimizing and dispatching a seaport integrated energy system considering ship demand response under multiple time scales according to claim 9 is characterized in that: The intraday optimization model includes: Intraday optimization objective function , expressed as: ； Where: It is the penalty fee for equipment output adjustment, expressed as: ； The equipment output adjustment penalty fee is caused by the day-ahead and day-intraday power deviation of each equipment. The specific deviations are as follows: ； Where: , , , , They are the output adjustment amounts for electricity purchase and sales, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , They are the penalty coefficients for electricity purchase and sale, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; , , , , , They are the daily electric power of electricity purchase, electricity sales, batteries, supercapacitors, electrolyzers, and hydrogen fuel cells; Intraday optimization constraints include: The supercapacitor constraint is expressed as: ； Where: is the maximum value of the supercapacitor charging and discharging power, is the daily charging power of the supercapacitor at time t, is the daily discharge power of the supercapacitor at time t; The remaining power of the supercapacitor in a day; and The upper and lower limits of the supercapacitor power; Indicates that the supercapacitor has passed After running for hours, the power will be restored to the initial value; The electric power balance constraint is expressed as: ； Where: , , , They are the daily wind turbine, photovoltaic, shore power load forecast data and the power consumption of electric boilers.

12. A device for optimizing and dispatching a comprehensive energy system in a seaport under multiple time scales considering the response of ship demand, characterized in that: The device is applied to the port shore power DC system topology structure with super capacity as described in any one of claims 1 to 5, comprising: A first acquisition module is used to obtain the initial energy value of the first energy storage device of the seaport integrated energy system model, as well as the new energy forecast value, energy storage and conversion equipment parameters, load data and electricity price parameters of the seaport integrated energy system model; A day-ahead dispatch optimization module, used to input the initial energy value of the first energy storage device of the seaport integrated energy system model, the new energy forecast value of the seaport integrated energy system model, energy storage and conversion equipment parameters, load data and electricity price parameters into a pre-built day-ahead optimization dispatch model including day-ahead optimization constraints, and generate a day-ahead dispatch output plan for each unit; A second acquisition module is used to obtain the initial energy of the second energy storage device after being updated at a fixed time period within the day and the new energy forecast value and load forecast value of the seaport comprehensive energy system model; The intraday scheduling optimization module is used to input the output plan of each unit scheduled on the previous day, the initial energy of the second energy storage device updated at a fixed time period during the day, and the new energy forecast value and load forecast value of the seaport comprehensive energy system model into a pre-constructed intraday optimization model including intraday optimization constraints, so as to generate an output plan at the intraday target time scale.

13. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions which, when executed by a computing device, cause the computing device to perform the method of any one of claims 6 to 11.

14. A computer device, characterized in that: include, One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in claims 6 to 11.

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