Zero-carbon highway energy complementation control cloud platform

Abstract

The invention discloses a zero-carbon highway energy complementation control cloud platform, and relates to the field of energy scheduling control, and the cloud platform comprises a solar photovoltaic unit which is used for converting sunlight into electric energy through a photoelectric conversion technology based on a solar photovoltaic panel; the wind power generation unit is used for converting wind energy into electric energy through a wind energy conversion technology by utilizing a wind generating set; the geothermal energy utilization unit is used for obtaining heat energy through a ground source heat pump according to shallow-layer and middle-deep-layer geothermal resources; the energy storage unit is used for storing excess electric energy and heat energy through the battery energy storage module and the heat energy storage module; and the intelligent energy management unit is used for establishing an energy complementary model based on the photovoltaic output characteristics and the wind power output characteristics, constructing a wind-solar-ground storage collaborative operation strategy, and realizing optimal configuration and scheduling of energy. Various renewable energy technologies such as solar energy, wind energy and geothermal energy can be integrated, and efficient conversion and utilization of energy are achieved.

Description

Technical Field

[0001] The present invention relates to the field of energy scheduling control, and more specifically, to a zero-carbon highway energy complementary control cloud platform. Background Art

[0002] Solar photovoltaic technology converts sunlight directly into electricity by installing photovoltaic modules on the roofs of service areas, sunshades of parking lots, etc.; wind power generation technology uses wind energy to drive turbines to generate electricity; geothermal energy technology includes a ground source heat pump system that utilizes the constant temperature characteristics of shallow soil and a heating and power generation system that utilizes medium and deep geothermal resources; at the same time, new energy vehicle charging facilities are provided to charge passing electric vehicles.

[0003] Currently, some highway service areas have begun to attempt to introduce renewable energy systems, mainly by installing solar photovoltaic panels and wind turbines in service areas for clean energy transformation. These systems are usually equipped with basic energy storage devices (such as battery energy storage systems, compressed air energy storage, etc.) to store excess electricity and are monitored through a simple energy management system; at the same time, some service areas also use geothermal energy technology to provide heating and cooling services and set up charging piles in parking lots to charge electric vehicles, initially forming a small intelligent microgrid system.

[0004] However, the existing technical solutions still have many deficiencies: First, due to the lack of effective energy storage technology and intelligent energy management systems, it is difficult to solve the intermittency and uncertainty problems of renewable energy generation, resulting in a serious imbalance between energy supply and demand in service areas and unable to achieve stable energy supply; second, existing service areas generally lack a perfect energy management and scheduling mechanism, with low energy utilization efficiency and serious energy waste; finally, the existing systems fail to effectively integrate multiple renewable energies, with insufficient multi-energy complementarity and coordination, making it difficult to give full play to the advantages of various renewable energies and affecting the overall stability and self-sufficiency of energy supply in service areas. These problems seriously restrict the process of the transformation of highway service areas towards zero carbon. No effective solutions have been proposed for the problems in the related technologies. Summary of the Invention

[0005] In view of the problems in the related technologies, the present invention proposes a zero-carbon highway energy complementary control cloud platform, which has the advantage of being able to make full use of rich natural resources such as solar energy and wind energy along highways. Through the intelligent optimization configuration and coordinated scheduling of energy, it realizes the self-sufficiency of energy supply in service areas, and further solves the problems in the existing technology that lack effective energy storage technology and intelligent energy management systems, making it difficult to solve the intermittency and uncertainty problems of renewable energy generation, resulting in a serious imbalance between energy supply and demand in service areas and unable to achieve stable energy supply.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] According to one aspect of the present invention, there is provided a zero-carbon highway energy complementary control cloud platform, which includes:

[0008] A solar photovoltaic unit, configured to convert sunlight into electric energy based on solar photovoltaic panels through photovoltaic conversion technology;

[0009] A wind power generation unit, configured to convert wind energy into electric energy by using a wind turbine generator set through wind energy conversion technology;

[0010] A geothermal energy utilization unit, configured to obtain heat energy according to shallow and medium-deep geothermal resources through a ground source heat pump;

[0011] An energy storage unit, configured to store excess electric energy and heat energy through a battery energy storage module and a heat energy storage module;

[0012] An intelligent energy management unit, configured to establish an energy complementary model based on the output characteristics of photovoltaic power generation and wind power generation, and construct a collaborative operation strategy for wind, light, ground, and storage to achieve optimal allocation and scheduling of energy.

[0013] Further, when the intelligent energy management unit establishes an energy complementary model based on the output characteristics of photovoltaic power generation and wind power generation, and constructs a collaborative operation strategy for wind, light, ground, and storage to achieve optimal allocation and scheduling of energy, it includes:

[0014] According to the solar irradiance data and wind speed data, through a photovoltaic power generation estimation model and a wind power generation estimation model, obtain the output characteristics of the solar photovoltaic unit and the wind power generation unit, and combine with an energy complementary constraint set to establish an energy complementary model;

[0015] Based on the service area load data, establish a two-layer optimization model for seasonal time-of-use electricity prices, and determine the electricity price schemes for peak and valley periods in each season. Among them, the two-layer optimization model for seasonal time-of-use electricity prices includes an upper-layer optimization model and a lower-layer optimization model;

[0016] Utilize the heating and cooling capacity parameters of geothermal energy to optimize the energy complementary model, and combine with the electricity price schemes for peak and valley periods in each season to establish a collaborative operation strategy for wind, light, ground, and storage, and perform energy complementary scheduling of the cloud platform.

[0017] Further, according to the solar irradiance data and wind speed data, through a photovoltaic power generation estimation model and a wind power generation estimation model, obtain the output characteristics of the solar photovoltaic unit and the wind power generation unit, and combine with an energy complementary constraint set to establish an energy complementary model, which includes:

[0018] Based on the solar irradiance data collected by the meteorological station, the output characteristics of the solar photovoltaic unit are obtained through the photovoltaic power generation estimation model and the temperature loss correction coefficient;

[0019] According to the wind speed data collected by the meteorological station, the output characteristics of the wind power generation unit are analyzed by using the wind power generation estimation model and the wind speed correction coefficient;

[0020] Based on the capacity parameters of the solar photovoltaic unit, the wind power generation unit and the energy storage unit, an energy complementary constraint set is established, and an energy complementary model is constructed in combination with the output characteristics of the solar photovoltaic unit and the wind power generation unit. Among them, the energy complementary constraint set includes annual power balance constraint, monthly balance constraint and power supply reliability constraint.

[0021] Furthermore, the expression of the photovoltaic power generation estimation model is:

[0022] H T =H0×R+[R+(1-1 / H b )×(1+cosβ) / 2]

[0023] In the formula, H T is the solar irradiance of the photovoltaic power station; H0 is the solar irradiance area of the horizontal photovoltaic power station; R is the ratio of direct and oblique irradiance on the horizontal plane; H b is the diffuse irradiance on the horizontal plane; β is the module tilt angle;

[0024] The expression of the temperature loss correction coefficient is:

[0025] S=(T-η)×λ

[0026] In the formula, S is the actual power generation correction coefficient considering the temperature loss of the photovoltaic module; T is the actual working temperature of the photovoltaic module; η is the standard working temperature of the photovoltaic module; λ is the maximum power temperature coefficient of the photovoltaic module.

[0027] Furthermore, the expression of the wind power generation estimation model is:

[0028] P W =1 / 2×ρ×A×C p ×V 3 ×μ

[0029] In the formula, P W is the theoretical output power of the fan; ρ is the air density; A is the swept area of the wind wheel; C p is the wind energy utilization coefficient; V is the wind speed; μ is the mechanical transmission efficiency;

[0030] The expression of the wind speed correction coefficient is:

[0031] K=0 when V<V ci or V>V co

[0032] K = 1 when V r ≤ V ≤ V co

[0033] K = (V 3 - V ci 3 ) / (V r 3 - V ci 3 ) when V ci ≤ V < V r

[0034] Wherein, K is the wind speed correction coefficient; V is the measured wind speed; V ci is the cut-in wind speed; V co is the cut-out wind speed; V r is the rated wind speed.

[0035] Furthermore, the expression of the annual power balance constraint is:

[0036] E WG + E PG = E L

[0037] Wherein, E WG is the annual power generation of the wind power generation unit, E PG is the annual power generation of the solar photovoltaic power generation unit, E L is the annual power consumption of the load;

[0038] The expression of the monthly balance constraint is:

[0039] E WGi + E PGi - E Li ≥ E N

[0040] Wherein, E WGi is the power generation of the wind power generation unit in the i-th month; E PGi is the power generation of the solar photovoltaic power generation unit in the i-th month; E Li is the power consumption of the load in the i-th month; E N is the rated capacity of the energy storage unit;

[0041] The expression of the power supply reliability constraint is:

[0042] E WGk + E PGk + E N ≥ E Lk

[0043] Wherein, E WGkis the power generation of the wind power generation unit in the k-th month; E PGk is the power generation of the solar photovoltaic power generation unit in the k-th month; E Lk is the power consumption of the load in the k-th month; E N is the rated capacity of the energy storage unit.

[0044] Furthermore, based on the load data of the service area, a two-layer optimization model of seasonal time-of-use electricity price is established, and the electricity price schemes for peak and valley periods in each season are determined, including:

[0045] According to the annual load data of the service area, the output characteristics of the solar photovoltaic unit and the output characteristics of the wind power generation unit, through classified statistical analysis, the typical load characteristic parameters in each season are obtained. Among them, the typical load characteristic parameters in each season include the peak-valley difference rate and the renewable energy coverage rate;

[0046] Based on the upper-layer constraint conditions, the upper-layer optimization model is iteratively solved to obtain the initial time-of-use electricity price scheme;

[0047] According to the initial time-of-use electricity price scheme and the load response characteristics, a lower-layer optimization model is constructed to obtain the final time-of-use electricity price scheme.

[0048] Furthermore, the objective function expression of the two-layer optimization model of seasonal time-of-use electricity price is:

[0049]

[0050]

[0051] In the formula, C s is the total cost of the cloud platform after implementing the time-of-use electricity price; is the annual power generation cost of the generator set; is the planning power source investment cost; is the line investment cost; C u is the total user electricity cost; P T is the electricity price at time T, L T is the load demand at time T, and T is the time series.

[0052] Furthermore, use the heating and cooling capacity parameters of geothermal energy to optimize the energy complementary model, and combine the electricity price schemes for peak and valley periods in each season to establish a coordinated operation strategy for wind, light, geothermal, and storage, and conduct energy complementary scheduling of the cloud platform, including:

[0053] Based on the thermal parameters of the geothermal energy utilization unit, through the geothermal energy utilization estimation model, obtain the heating capacity parameters of geothermal energy;

[0054] According to the energy complementary model and the electricity price schemes for peak and valley periods in each season, combined with the time-of-use energy allocation rules, construct a coordinated operation strategy for wind, light, geothermal, and storage;

[0055] Based on the coordinated operation strategy of wind, solar, geothermal and energy storage, the output power and operating status of the solar photovoltaic unit, wind power generation unit, geothermal energy utilization unit and energy storage unit are adjusted in real time.

[0056] Furthermore, the expression of the geothermal energy utilization estimation model is:

[0057] Q H =COP×W

[0058] COP=T c / (T c -T e )

[0059] In the formula, Q H is the heating capacity of the geothermal energy utilization unit; COP is the performance coefficient of the ground source heat pump; W is the input power of the ground source heat pump; T c is the condensation temperature of the ground source heat pump; T e is the evaporation temperature of the ground source heat pump.

[0060] The beneficial effects of the present invention are as follows:

[0061] (1) By integrating various renewable energy technologies such as solar energy, wind energy and geothermal energy, and combining the real-time monitoring and scheduling of the intelligent energy management unit, the present invention can make full use of the rich natural resources such as solar energy and wind energy along the highway, and realize the efficient conversion and utilization of energy through technologies such as photovoltaic conversion and wind energy conversion, significantly reducing the dependence on fossil energy and achieving self-sufficiency in energy supply for the service area.

[0062] (2) By introducing a dynamic electricity price mechanism composed of the benchmark electricity price, the distributed renewable energy electricity price control quantity and the diesel generator electricity price control quantity, the present invention can autonomously adjust the electricity price according to the changes in the renewable energy access quantity and the generator output, effectively promoting the consumption and utilization of distributed renewable energy, reducing the output share of traditional energy sources such as diesel generators, thereby reducing carbon emissions and greenhouse gas emissions, and providing strong support for the green and low-carbon development of highway service areas.

[0063] (3) Based on the photovoltaic power generation estimation model, the wind power generation estimation model and the geothermal energy utilization estimation model, combined with the energy complementary constraint set and the seasonal time-of-use electricity price double-layer optimization model, the present invention realizes the intelligent optimized allocation and coordinated scheduling of energy, which can not only reduce the electricity purchase cost of users, but also obtain additional income by selling the excess electricity to the grid, while ensuring power supply reliability and improving the economic benefits and operation efficiency of the cloud platform.

[0064] (4) By establishing energy allocation rules for different time periods and formulating differentiated operation strategies according to the characteristics of peak and valley periods, the present invention has strong flexibility and adaptability, and can be customized and dynamically adjusted according to different geographical locations, climate conditions and specific energy consumption requirements of the service area, effectively improving the regional adaptability and practical value of the cloud platform, and providing a feasible technical solution for the construction of a zero-carbon energy system in highway service areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0066] Figure 1 is a schematic block diagram of a zero-carbon highway energy complementary control cloud platform according to an embodiment of the present invention;

[0067] Figure 2 is a schematic diagram of energy collection and distribution in a zero-carbon highway energy complementary control cloud platform according to an embodiment of the present invention;

[0068] Figure 3 is a schematic diagram of the working process of the intelligent energy management unit in a zero-carbon highway energy complementary control cloud platform according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] To further illustrate the embodiments, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used to explain the operation principle of the embodiments in conjunction with the relevant descriptions in the specification. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0070] According to an embodiment of the present invention, a zero-carbon highway energy complementary control cloud platform is provided.

[0071] Now, the present invention will be further described in conjunction with the drawings and specific embodiments. As Figure 1 shown, according to an embodiment of the present invention, a zero-carbon highway energy complementary control cloud platform is provided. The zero-carbon highway energy complementary control cloud platform includes:

[0072] A solar photovoltaic unit 1, configured to convert sunlight into electric energy based on solar photovoltaic panels through photovoltaic conversion technology;

[0073] The wind power generation unit 2 is used to utilize a wind power generation set and convert wind energy into electric energy through wind energy conversion technology;

[0074] The geothermal energy utilization unit 3 is used to obtain heat energy according to shallow and medium-deep geothermal resources through a ground source heat pump;

[0075] The energy storage unit 4 is used to store excess electric energy and heat energy through a battery energy storage module and a heat energy storage module;

[0076] The intelligent energy management unit 5 is used to establish an energy complementary model based on the output characteristics of photovoltaic power generation and wind power generation, and construct a collaborative operation strategy for wind-solar-geothermal-energy storage to achieve optimal allocation and scheduling of energy.

[0077] Specifically, the main purpose of the cloud platform is to collect and utilize the electric energy generated by renewable energy (solar energy and wind energy). As Figure 2 shown, through a DC / DC converter, the energy transmitted by branch 1 and branch 2 is converted into a voltage level suitable for use in the DC bus. Then, a part of the electric energy is converted into alternating current through an HFDC / AC inverter in branch 4 for wireless charging of electric vehicles or other AC loads; another part of the electric energy flows through the DKM switching device to branch 3 to supply power to monitoring devices, lighting, and secondary-side devices, and when the main power supply fails, the UPS will provide emergency power support; the entire cloud platform is managed and controlled through a monitoring integrated machine to ensure the effective utilization and distribution of energy.

[0078] In one embodiment, when the intelligent energy management unit 5 monitors and manages the production, storage, and consumption of energy to achieve optimal allocation and scheduling of energy, it includes:

[0079] S1. According to the solar irradiance data and wind speed data, through a photovoltaic power generation estimation model and a wind power generation estimation model, obtain the output characteristics of the solar photovoltaic unit and the wind power generation unit, and establish an energy complementary model in combination with the energy complementary constraint set;

[0080] S2. Based on the service area load data, establish a two-layer optimization model for seasonal time-of-use electricity prices to determine the electricity price schemes for peak and valley periods in each season, where the two-layer optimization model for seasonal time-of-use electricity prices includes an upper-layer optimization model and a lower-layer optimization model;

[0081] Specifically, by establishing a two - layer optimization model for seasonal time - of - use electricity prices, the present invention introduces a dynamic electricity price mechanism, which consists of three parts: the base electricity price, the electricity price control quantity of distributed renewable energy, and the electricity price control quantity of diesel generators. The base electricity price is a set of specific constant values, that is, the fixed time - of - use electricity price implemented by the system. The electricity price control quantity of distributed renewable energy and the electricity price control quantity of diesel generators are adjusted autonomously with the change of the renewable energy access quantity and the output of diesel generators. The purpose is to promote the consumption of distributed renewable energy by dynamically adjusting the electricity price, reduce the output share of units such as diesel generators, and achieve the minimization of the user's electricity purchase cost and the maximization of the total system revenue.

[0082] S3. According to the energy complementary model and the electricity price schemes for peak - valley periods in each season, establish an optimal energy complementary scheduling strategy through an intelligent scheduling control algorithm.

[0083] Specifically, as Figure 3 shown, the intelligent energy management unit 5 schedules the use of energy according to the user's demand and the energy production situation. When there is sufficient sunlight, the solar photovoltaic unit 1 and the wind power generation unit 2 are preferentially used to meet the user's electricity demand. If the photovoltaic power generation is higher than the user's electricity demand, the remaining electricity is used to charge the energy storage battery or fed into the power grid. When the photovoltaic power generation cannot meet the total electricity demand of the user, power supply is carried out according to the state of the energy storage battery and the current time - of - use electricity price period. If it is in the flat - valley period of the electricity price, the large power grid is preferentially used to supplement electricity for the user. If it is in the peak period of the electricity price, the energy storage battery is used as the primary backup power supply to supply the user's electricity load demand, and when the electricity is insufficient, the power grid supplements the electricity.

[0084] Specifically, in the present invention, the geothermal energy utilization unit 4 is used to meet the heating and cooling demands of users. The circulating medium is sent into the heat storage tank for storage to meet the user's domestic hot water and heating demands. When the user's heat demand cannot be met, a heat pump system is used as an auxiliary heating device to ensure the stability of heat energy supply.

[0085] In one embodiment, according to the solar irradiance data and wind speed data, through the photovoltaic power generation estimation model and the wind power generation estimation model, the output characteristics of the solar photovoltaic unit and the wind power generation unit are obtained, and combined with the energy complementary constraint set, the established energy complementary model includes:

[0086] S11. Based on the solar irradiance data collected by the meteorological station, through the photovoltaic power generation estimation model and the temperature loss correction coefficient, the output characteristics of the solar photovoltaic unit are obtained;

[0087] Specifically, first, install a solar radiation measurement station at the highway service area, collect the horizontal plane solar irradiance data through a pyranometer, and record the module temperature at the same time. Collect the hourly solar irradiance data, grid peak-valley-flat electricity prices, and demand-side hourly load data throughout the year, and analyze the data to determine the energy demand pattern and renewable energy generation pattern of the cloud platform. Solar irradiance refers to the solar radiation energy per unit area, and its value can be obtained through a solar radiation measurement station or a weather station, or estimated through weather data analysis and model calculations; by measuring and calculating the solar irradiance, the power generation capacity and efficiency of solar photovoltaic units can be determined; in addition, the prediction and analysis of solar irradiance can also help photovoltaic power plant operators formulate reasonable operation strategies, reduce operation costs and increase revenues.

[0088] Specifically, then, based on the collected irradiance data, use the photovoltaic power generation estimation model to calculate the solar irradiance actually received by the photovoltaic power plant. The expression of the photovoltaic power generation estimation model is:

[0089] H T =H0×R+[R+(1-1 / H b )×(1+cosβ) / 2]

[0090] In the formula, H T is the solar irradiance of the photovoltaic power plant; H0 is the horizontal plane photovoltaic power plant solar irradiance area; R is the ratio of direct and oblique irradiance on the horizontal plane; H b is the horizontal plane diffuse irradiance; β is the module tilt angle.

[0091] Specifically, considering the influence of temperature on the power generation efficiency of photovoltaic modules, calculate the temperature loss correction coefficient. The expression of the temperature loss correction coefficient is:

[0092] S=(T-η)×λ

[0093] In the formula, S is the actual power generation correction coefficient considering the temperature loss of the photovoltaic module; T is the actual working temperature of the photovoltaic module; η is the standard working temperature of the photovoltaic module (25°C is taken in this embodiment); λ is the maximum power temperature coefficient of the photovoltaic module.

[0094] Specifically, finally, according to the standard power of the photovoltaic module, combined with the actually received solar irradiance and the temperature correction coefficient, calculate the actual output power of the solar photovoltaic unit, that is, its output characteristics. The expression of the output characteristics of the solar photovoltaic unit is:

[0095] P PG实际 =P PG标准 ×(H T / 1000)×(1+S)

[0096] In the formula, P PG实际is the actual output power of the solar photovoltaic unit; P PG标准 is the rated power of the photovoltaic module under standard test conditions; H T is the actually received solar irradiance; S is the temperature loss correction coefficient.

[0097] Specifically, taking a 100-kilowatt photovoltaic system in a highway service area as an example, install a TBQ-2 total radiation meter on the roof of the service area at 35 degrees north latitude and 118 degrees east longitude, and collect data at 14:00 on July 15, 2023. The horizontal irradiance is measured to be 1000 watts per square meter, the ratio of direct to oblique radiation is 0.85, the diffuse irradiance is 0.75, the temperature of the photovoltaic module is 48 degrees Celsius, and the installation inclination of the module is 30 degrees. First, substitute the measured data into the irradiance calculation formula:

[0098] H T = 1000×0.85 + [0.85 + (1 - 1 / 0.75)×(1 + cos30°) / 2]

[0099] The actually received solar irradiance is calculated to be 1158.6 watts per square meter; secondly, calculate the temperature loss correction coefficient:

[0100] S = (48 - 25)×(-0.4%)

[0101] The temperature correction coefficient is obtained as -9.2%; finally, substitute the above calculation results into the actual output power calculation formula:

[0102] P PG实际 = 100kW×(1158.6 / 1000)×(1 - 9.2%)

[0103] The output characteristic of the solar photovoltaic unit at this moment is obtained as 105.2 kilowatts.

[0104] S12. According to the wind speed data collected by the meteorological station, use the wind power generation estimation model and the wind speed correction coefficient to analyze the output characteristics of the wind power generation unit;

[0105] Specifically, first install a wind speed measuring device in the highway service area, and collect wind speed data at different heights through an anemometer; then, based on the collected wind speed data, calculate the theoretical output power of the wind turbine through the wind power generation estimation model. The expression of the wind power generation estimation model is:

[0106] P W = 1 / 2×ρ×A×C p ×V 3 ×μ

[0107] In the formula, P W is the theoretical output power of the fan; ρ is the air density; A is the swept area of the wind wheel; Cp is the wind energy utilization coefficient; V is the wind speed; μ is the mechanical transmission efficiency.

[0108] Specifically, then, considering the actual operating characteristics of the wind turbine, the wind speed correction coefficient is calculated. The expression of the wind speed correction coefficient is:

[0109] K = 0 when V < V ci or V > V co

[0110] K = 1 when V r ≤ V ≤ V co

[0111] K = (V 3 - V ci 3 ) / (V r 3 - V ci 3 ) when V ci ≤ V < V r

[0112] In the formula, K is the wind speed correction coefficient; V is the measured wind speed; V ci is the cut-in wind speed; V co is the cut-out wind speed; V r is the rated wind speed.

[0113] Specifically, finally, according to the theoretical output power of the wind turbine generator set and the wind speed correction coefficient, the actual output power of the wind power generation unit is calculated, that is, its output characteristic. The expression of the output characteristic of the wind power generation unit is:

[0114] P WG实际 = P W × K

[0115] In the formula, P WG实际 is the actual output power of the wind power generation unit; P W is the theoretical output power of the wind turbine; K is the wind speed correction coefficient.

[0116] Specifically, taking a 50-kilowatt wind power generation system in a certain highway service area as an example, a ZF-1 type anemometer is installed in the service area at 35 degrees north latitude and 118 degrees east longitude. Data is collected at 14:00 on July 15, 2023, and the environmental parameters are measured: the air density is 1.225 kg / m³, the wind speed is 8 m / s, and the environmental temperature is 28 °C. The parameters of the wind turbine generator set are: the wind wheel diameter is 20 m, the wind energy utilization coefficient is 0.4, the mechanical transmission efficiency is 0.95, the cut-in wind speed is 3 m / s, the cut-out wind speed is 25 m / s, and the rated wind speed is 12 m / s. First, calculate the wind wheel swept area and substitute it into the theoretical power calculation formula:

[0117] A = π×(20 / 2) 2 = 314.16 square meters

[0118] P W = 1 / 2×1.225×314.16×0.4×8 3 ×0.95 = 30.8 kilowatts

[0119] Secondly, since the measured wind speed of 8 m / s is between the cut-in wind speed and the rated wind speed, calculate the wind speed correction coefficient:

[0120] K = (8 3 - 3 3 ) / (12 3 - 3 3 ) = 0.94

[0121] Finally, substitute the above calculation results into the actual output power calculation formula:

[0122] P WG实际 = 30.8×0.94 = 28.9 kilowatts

[0123] The output characteristic of the wind power generation unit at this moment is 28.9 kilowatts.

[0124] S13. Based on the capacity parameters of the solar photovoltaic unit, the wind power generation unit and the energy storage unit, establish an energy complementary constraint set, and combine the output characteristics of the solar photovoltaic unit and the wind power generation unit to construct an energy complementary model, where the energy complementary constraint set includes annual power balance constraint, monthly balance constraint and power supply reliability constraint.

[0125] Specifically, according to the output characteristics of the solar photovoltaic unit and the wind power generation unit, calculate the monthly power generation by time integration, and the calculation expression of the monthly power generation is:

[0126] E PGi = ∫P PG实际 dt

[0127] E WGi = ∫P WG实际 dt

[0128] In the formula, E WGi is the power generation of the wind power generation unit in the i-th month, E PGi is the power generation of the solar photovoltaic unit in the i-th month, and t is the operation time of the cloud platform.

[0129] Specifically, ① according to the output characteristics of the solar photovoltaic unit and the wind power generation unit, establish an annual power balance constraint, and the expression is:

[0130] E WG + EPG = E L

[0131] Wherein, E WG is the annual power generation of the wind power generation unit, and E PG is the annual power generation of the solar photovoltaic power generation unit, and E L is the annual power consumption of the load.

[0132] Specifically, ② when there is power generation surplus in the wind power generation unit and the solar photovoltaic power generation unit in the i-th month, it is necessary to ensure that the energy storage unit can be charged to full capacity, and the monthly balance constraint is obtained:

[0133] E WGi + E PGi - E Li ≥ E N

[0134] Wherein, E WGi is the power generation of the wind power generation unit in the i-th month, E PGi is the power generation of the solar photovoltaic power generation unit in the i-th month, E Li is the power consumption of the load in the i-th month, and E N is the rated capacity of the energy storage unit.

[0135] Specifically, ③ when the power generation of the wind power generation unit and the solar photovoltaic power generation unit is insufficient and in adverse conditions (no wind, no sunlight) in the k-th month, a reliable power supply constraint for ensuring the load is established:

[0136] E WGk + E PGk + E N ≥ E Lk

[0137] Wherein, E WGk is the power generation of the wind power generation unit in the k-th month, E PGk is the power generation of the solar photovoltaic power generation unit in the k-th month, E Lk is the power consumption of the load in the k-th month, and E N is the rated capacity of the energy storage unit.

[0138] Specifically, taking an energy complementary control cloud platform of a highway service area as an example, the cloud platform includes a 100-kilowatt photovoltaic power generation unit, a 50-kilowatt wind power generation unit, and a 200-kilowatt-hour energy storage unit. Based on the actual photovoltaic output power P PG实际 obtained from S11 and the actual wind output power P WG实际 obtained from S12, statistical analysis is carried out for July 2023. First, calculate the monthly power generation:

[0139] E PGi = ∫P PG实际dt = 15,500 kWh

[0140] E WGi = ∫P WG实际 dt = 7,200 kWh

[0141] Among them, the photovoltaic power generation is mainly concentrated in the daytime period from 9:00 to 16:00, with an average daily power generation of 500 kWh; the wind power generation is distributed throughout the day, but is more in the period from 22:00 at night to 6:00 the next day, with an average daily power generation of 232 kWh. Then, the load power consumption in July is recorded as 21,900 kWh, and the rated capacity of the energy storage system is 200 kWh. Next, verify the monthly balance constraint:

[0142] E WGi + E PGi – E Li = 15,500 + 7,200 - 21,900 = 800 kWh > 200 kWh (E N )

[0143] Meet the monthly balance constraint. Finally, verify the power supply reliability constraint:

[0144] E WGk + E PGk + E N = 15,500 + 7,200 + 200 = 22,900 kWh > 21,900 kWh (E Lk )

[0145] Meet the power supply reliability constraint. The constraint verification shows that the energy complementary model can guarantee the power consumption demand, where the solar photovoltaic power generation unit undertakes 70.8% of the power supply task, the wind power generation unit undertakes 28.3% of the power supply task, and the energy storage unit provides 0.9% of the regulation capacity.

[0146] In one embodiment, based on the load data of the service area, a two - layer optimization model of seasonal time - of - use electricity price is established, and the electricity price schemes for peak and valley periods in each season are determined, including:

[0147] S21. According to the annual load data of the service area, the output characteristics of the solar photovoltaic unit, and the output characteristics of the wind power generation unit, through classified statistical analysis, the typical load characteristic parameters in each season are obtained. Among them, the typical load characteristic parameters in each season include the peak - valley difference rate and the renewable energy coverage rate;

[0148] Specifically, in this embodiment, taking the operation data of a certain highway service area in 2023 as an example, first, analyze the load characteristics of the highway service area:

[0149] In the peak season (from November to February of the next year), the daily maximum load is 180 kW (at 14:00), and the minimum load is 60 kW (at 4:00);

[0150] During the shoulder seasons (March, April, August, and October), the maximum daily load is 150 kW (at 15:00), and the minimum daily load is 50 kW (at 3:00).

[0151] During the off-peak seasons (the remaining months), the maximum daily load is 120 kW (at 13:00), and the minimum daily load is 40 kW (at 5:00).

[0152] Specifically, then, the renewable energy output is statistically calculated:

[0153] The daily average power generation of the solar photovoltaic unit is 450 kWh during the peak season, 600 kWh during the shoulder season, and 680 kWh during the off-peak season;

[0154] The daily average power generation of the wind power generation unit is 280 kWh during the peak season, 220 kWh during the shoulder season, and 200 kWh during the off-peak season.

[0155] Specifically, finally, the characteristic parameters are obtained:

[0156] The peak-valley difference rate is 66.7% during the peak season, 66.7% during the shoulder season, and 66.7% during the off-peak season;

[0157] The renewable energy coverage rate is 25.3% during the peak season, 34.2% during the shoulder season, and 45.8% during the off-peak season.

[0158] S22. Based on the upper-layer constraint conditions, the upper-layer optimization model is iteratively solved to obtain the initial time-of-use electricity price plan;

[0159] Specifically, the two-layer optimization model of seasonal time-of-use electricity price includes the upper-layer optimization model and the lower-layer optimization model; First, the upper-layer optimization model is constructed, and its objective function is to minimize the total cost of the cloud platform:

[0160]

[0161] In the formula, C s is the total cost of the cloud platform after implementing the time-of-use electricity price; is the annual power generation cost of the generator set; is the investment cost of the planned power source; is the line investment cost.

[0162] Specifically, the upper-layer constraint conditions of the upper-layer optimization model include:

[0163] ① Time-of-use electricity price constraint:

[0164] P ps ≥P fs ≥P vs

[0165] 0.3 ≤ P vs / P ps ≤ 0.5

[0166] In the formula, Pps , P fs , P vs are the electricity prices during peak hours, normal hours, and valley hours respectively;

[0167] ② Revenue balance constraint:

[0168] 0.9R0 ≤ R ≤ 1.1R0

[0169] where R is the power grid revenue after implementing time-of-use electricity prices, and R0 is the benchmark revenue.

[0170] Specifically, through the above iterative calculation, the initial electricity price plan for peak seasons is obtained:

[0171] During peak hours (10:00 - 15:00, 18:00 - 21:00), it is 1.40 yuan / kWh;

[0172] During normal hours (07:00 - 10:00, 15:00 - 18:00, 21:00 - 23:00), it is 0.90 yuan / kWh;

[0173] During valley hours (23:00 - 07:00 the next day), it is 0.45 yuan / kWh.

[0174] S23. According to the initial time-of-use electricity price plan and load response characteristics, construct a lower-level optimization model to obtain the final time-of-use electricity price plan.

[0175] Specifically, construct a lower-level optimization model, and its objective function is to minimize the user's electricity cost:

[0176]

[0177] where C u is the total user electricity cost; P T is the electricity price at time T, L T is the load demand at time T, and T is the time series (in this embodiment, T = 1, 2,..., 24).

[0178] Specifically, the lower-level constraint conditions of the lower-level optimization model include:

[0179] ① Load response constraint:

[0180] L T = L0 × (P T / P0)^(-0.35)

[0181] where L0 is the benchmark load, P0 is the benchmark electricity price, and -0.35 is the electricity price elasticity coefficient;

[0182] ② Generation capacity constraint:

[0183] P gT = LT +P LT

[0184] P g-min ≤P gT ≤P g-max

[0185] Wherein, P gT is the output of the generating unit at time T, P LT is the network loss at time T, P g-min and P g-max are the minimum output and the maximum output of the unit, respectively.

[0186] Specifically, through the above iterative optimization process, the final electricity price plan for the peak season is obtained:

[0187] The peak period is 1.50 yuan / kWh; the normal period is 1.00 yuan / kWh; the valley period is 0.50 yuan / kWh.

[0188] In one embodiment, the heating and cooling capacity parameters of geothermal energy are used to optimize the energy complementary model, and combined with the electricity price plans for peak and valley periods in each season, a coordinated operation strategy for wind-solar-geothermal energy storage is established. The cloud platform energy complementary scheduling includes:

[0189] S31. Based on the thermal parameters of the geothermal energy utilization unit, through the geothermal energy utilization estimation model, obtain the heating capacity parameters of geothermal energy;

[0190] Specifically, first determine the geothermal resource parameters of the service area through geothermal exploration. In this embodiment, geothermal resource exploration is carried out in the service area at 35° north latitude and 118° east longitude. The TEM-WDJC temperature sensor is used to measure at different depths, and the following thermal parameters are obtained:

[0191] The formation temperature at a depth of 50 meters is 15°C, the soil thermal conductivity is 2.0 W / (m·K), the groundwater flow velocity is 2.5×10 -7 m / s, the soil bulk density is 1850 kg / m 3 , and the specific heat capacity is 1.28 kJ / (kg·K).

[0192] Specifically, then establish a geothermal energy utilization estimation model to calculate the heating capacity of the geothermal energy utilization unit. The expression of the geothermal energy utilization estimation model is:

[0193] Q H =COP×W

[0194] COP=T c / (T c -T e )

[0195] Wherein, Q HThe heating capacity of the geothermal energy utilization unit; COP is the performance coefficient of the ground source heat pump; W is the input power of the ground source heat pump; T c is the condensation temperature of the ground source heat pump; T e is the evaporation temperature of the ground source heat pump.

[0196] The calculation expression for the heat exchange capacity of the buried pipe is:

[0197] Q L =U×A m ×ΔT m

[0198] In the formula, Q L is the heat exchange capacity between the buried pipe and the soil (kW), U is the heat transfer coefficient [W / (m 2 ·K)], A m is the heat exchange area (m 2 ), ΔT m is the logarithmic mean temperature difference between the buried pipe and the soil (K).

[0199] Specifically, taking a 200kW ground source heat pump system in a highway service area as an example, a single U-shaped buried pipe is used, with a pipe diameter of 32mm, a burial depth of 50m, a spacing of 6m, and a total of 30 groups. On January 15, 2023, a heating condition test was carried out, with an ambient temperature of -5°C, and the following operating parameters were obtained: condensation temperature 45°C, evaporation temperature 5°C, system input power 200kW, buried pipe inlet water temperature 2°C, buried pipe outlet water temperature 7°C;

[0200] Substitute the above parameters into the performance coefficient calculation formula:

[0201] COP=(273.15 + 45) / [(273.15 + 45)-(273.15 + 5)] = 4.5

[0202] Then calculate the heating capacity:

[0203] Q H =4.5×200 = 900kW

[0204] Similarly, on July 15, 2023, a cooling condition test was carried out, with an ambient temperature of 35°C, and the following operating parameters were obtained: condensation temperature 35°C, evaporation temperature 7°C, system input power 200kW, buried pipe inlet water temperature 30°C, buried pipe outlet water temperature 25°C.

[0205] Calculate the performance coefficient:

[0206] COP=(273.15 + 35) / [(273.15 + 35)-(273.15 + 7)] = 5.2

[0207] Calculate the cooling capacity:

[0208] Q H = 5.2×200 = 1040 kW

[0209] Through the above calculations, the heating and cooling capacity parameters of the ground source heat pump system are obtained: the heating COP in winter is 4.5, the heating capacity is 900 kW, the cooling COP in summer is 5.2, the cooling capacity is 1040 kW, the hot water supply temperature is 45 °C, the cold water supply temperature is 7 °C, the heat exchange efficiency of the buried pipe is 85%, and the system stability is ±2%.

[0210] S32. According to the energy complementary model and the electricity price schemes for peak and valley periods in each season, combined with the time-of-use energy allocation rules, construct a collaborative operation strategy for wind-solar-ground energy storage;

[0211] Specifically, first establish the time-of-use energy allocation rules. In this embodiment, based on the seasonal time-of-use electricity price scheme obtained in S2, the 24 hours of a day are divided into three time periods:

[0212] Peak period (10:00 - 15:00, 18:00 - 21:00), the electricity price is 1.50 yuan / kWh;

[0213] Normal period (07:00 - 10:00, 15:00 - 18:00, 21:00 - 23:00), the electricity price is 1.00 yuan / kWh;

[0214] Valley period (23:00 - 07:00 the next day), the electricity price is 0.50 yuan / kWh.

[0215] Specifically, then according to the characteristics of each time period, formulate corresponding energy allocation rules:

[0216] ① Peak period rules:

[0217] When P WG实际 + P PG实际 ≥ P L At this time, preferentially use renewable energy (solar photovoltaic unit, wind power generation unit) for power supply, the excess electricity enters the energy storage unit, and the geothermal energy utilization unit stops operating;

[0218] When P WG实际 + P PG实际 < P L At this time, all renewable energy is used for power supply, the energy storage unit discharges to supplement, and the geothermal energy utilization unit is started if necessary;

[0219] In the formula, P PG实际 is the actual output power of the solar photovoltaic unit, P WG实际 is the actual output power of the wind power generation unit, and P L is the load demand.

[0220] ② Normal period rules:

[0221] When P WG实际 +P PG实际 ≥0.7P L , the main power supply is from renewable energy, and the geothermal energy utilization unit operates at a low load (≤30% of the rated power);

[0222] When P WG实际 +P PG实际 <0.7P L , the renewable energy and energy storage jointly supply power, and the geothermal energy utilization unit operates normally (≤70% of the rated power).

[0223] ③ Valley period rule:

[0224] Give priority to using wind power for power supply, charging the energy storage unit, and the geothermal energy utilization unit operates for energy storage.

[0225] S33. Based on the coordinated operation strategy of wind, light, geothermal and energy storage, the output power and operating status of the solar photovoltaic unit, wind power generation unit, geothermal energy utilization unit and energy storage unit are adjusted in real time.

[0226] To facilitate the understanding of the above technical solutions of the present invention, the following takes the service area of a highway equipped with the energy complementary control cloud platform proposed by the present invention as an example for specific description as follows:

[0227] In the service area of this highway, the solar photovoltaic unit 1 is used as the main power supply device, adopting a 300kW photovoltaic panel array to convert sunlight into electric energy. It is connected to the energy storage system to realize the storage of excess electric energy. According to the measured data, the daily average power generation is between 450 - 680 kWh, and the power generation efficiency is about 17%.

[0228] The wind power generation unit 2 adopts 3 sets of 50kW wind turbines to convert wind energy into electric energy. It is connected to the energy storage system to realize the storage of excess electric energy. The daily average power generation is between 200 - 280 kWh, and the annual utilization hours are about 2200 hours.

[0229] The geothermal energy utilization unit 3 adopts a 200kW ground source heat pump system to utilize geothermal resources at a depth of 50 meters for heating and cooling. It is connected to the heat energy storage system to realize the storage of excess heat energy. The heating COP is 4.5, and the cooling COP is 5.2.

[0230] The energy storage unit 4 includes a 500kWh battery energy storage system and a 200kWh heat energy storage system, which are used to store excess electric energy and heat energy. It is connected to the intelligent energy management system and releases electric energy and heat energy according to the dispatching instructions.

[0231] The intelligent energy management unit 5 realizes the optimal allocation and dispatching of energy by monitoring and managing the production, storage and consumption of energy, including:

[0232] (1) Dynamic electricity price mechanism: Promote the consumption of distributed renewable energy by adjusting electricity prices. Connected to the intelligent energy management system, the electricity price is dynamically adjusted according to the real-time supply and demand situation.

[0233] (2) Load response scheduling: When receiving a response signal, determine the scheduling priority according to the user comprehensive evaluation value (including response speed, response volume, and response duration). For m users with the same evaluation value, the response volume is allocated according to the following formula:

[0234]

[0235] Where: ΔP i and P i are the actually allocated response volume and the maximum response volume of the i-th user respectively, ΔP′(t) is the total scheduling demand of m users, and t1 and t2 are the start time and end time of the scheduling demand in the current period respectively.

[0236] Through the above cloud platform configuration and operation strategies, the service area has achieved efficient utilization of renewable energy, with the annual renewable energy utilization rate reaching over 85%, the overall system operation efficiency increased by 30%, and significant economic benefits.

[0237] In summary, by means of the above technical solutions of the present invention, through integrating various renewable energy technologies such as solar energy, wind energy, and geothermal energy, and combining the real-time monitoring and scheduling of the intelligent energy management unit, the present invention can make full use of the rich natural resources such as solar energy and wind energy along the highway, and achieve efficient conversion and utilization of energy through technologies such as photovoltaic conversion and wind energy conversion, significantly reducing the dependence on fossil energy and realizing the self-sufficiency of energy supply in the service area. By introducing a dynamic electricity price mechanism composed of the benchmark electricity price, the distributed renewable energy electricity price control quantity, and the diesel generator electricity price control quantity, the present invention can autonomously adjust the electricity price according to the changes in the renewable energy access quantity and the generator output, effectively promoting the consumption and utilization of distributed renewable energy, reducing the output share of traditional energy such as diesel generators, thereby reducing carbon emissions and greenhouse gas emissions, and providing strong support for the green and low-carbon development of highway service areas. Based on the photovoltaic power generation estimation model, the wind power generation estimation model, and the geothermal energy utilization estimation model, and combining the energy complementary constraint set and the seasonal time-of-use electricity price double-layer optimization model, the present invention realizes the intelligent optimized allocation and coordinated scheduling of energy, which can not only reduce the electricity purchase cost of users, but also obtain additional benefits by selling the excess electricity to the grid, while ensuring the power supply reliability and improving the economic benefits and operation efficiency of the cloud platform. By establishing the energy allocation rules for different time periods and formulating differentiated operation strategies according to the characteristics of peak and valley periods, the present invention has strong flexibility and adaptability, can be customized and dynamically adjusted according to different geographical locations, climatic conditions, and specific energy consumption requirements of the service area, effectively improving the regional adaptability and practical value of the cloud platform, and providing a feasible technical solution for the construction of a zero-carbon energy system in highway service areas.

[0238] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A zero-carbon highway energy complementary control cloud platform, characterized in that The zero-carbon highway energy complementary control cloud platform includes: A solar photovoltaic unit, which is used to convert sunlight into electrical energy based on solar photovoltaic panels through photovoltaic conversion technology; A wind power generation unit, which is used to convert wind energy into electrical energy by using wind turbines through wind energy conversion technology; A geothermal energy utilization unit, which is used to obtain heat energy through a ground-source heat pump according to shallow and medium-deep geothermal resources; A energy storage unit, which is used to store surplus electrical energy and heat energy through a battery energy storage module and a heat energy storage module; An intelligent energy management unit, which is used to establish an energy complementary model based on the output characteristics of photovoltaic power generation and wind power generation, and construct a collaborative operation strategy for wind, light, ground and storage to achieve optimal allocation and scheduling of energy.

2. The zero-carbon highway energy complementary control cloud platform according to claim 1, characterized in that When the intelligent energy management unit establishes an energy complementary model based on the output characteristics of photovoltaic power generation and wind power generation, and constructs a collaborative operation strategy for wind, light, ground and storage to achieve optimal allocation and scheduling of energy, it includes: According to the solar irradiance data and wind speed data, through a photovoltaic power generation estimation model and a wind power generation estimation model, obtain the output characteristics of the solar photovoltaic unit and the wind power generation unit, and combine with the energy complementary constraint set to establish an energy complementary model; Based on the service area load data, establish a two-layer optimization model for seasonal time-of-use electricity prices, and determine the electricity price schemes for peak and valley periods in each season. Among them, the two-layer optimization model for seasonal time-of-use electricity prices includes an upper-layer optimization model and a lower-layer optimization model; Optimize the energy complementary model by using the heating and cooling capacity parameters of geothermal energy, and combine with the electricity price schemes for peak and valley periods in each season to establish a collaborative operation strategy for wind, light, ground and storage, and conduct energy complementary scheduling of the cloud platform.

3. A zero-carbon highway energy complementary control cloud platform according to claim 2, characterized in that, The step of obtaining the output characteristics of the solar photovoltaic unit and the wind power generation unit through a photovoltaic power generation estimation model and a wind power generation estimation model according to the solar irradiance data and wind speed data, and combining with the energy complementary constraint set to establish an energy complementary model includes: Based on the solar irradiance data collected by the meteorological station, through a photovoltaic power generation estimation model and a temperature loss correction coefficient, obtain the output characteristics of the solar photovoltaic unit; According to the wind speed data collected by the meteorological station, use a wind power generation estimation model and a wind speed correction coefficient to analyze the output characteristics of the wind power generation unit; Based on the capacity parameters of the solar photovoltaic unit, the wind power generation unit and the energy storage unit, establish an energy complementary constraint set, and combine with the output characteristics of the solar photovoltaic unit and the wind power generation unit to construct an energy complementary model. Among them, the energy complementary constraint set includes annual electricity balance constraints, monthly balance constraints and power supply reliability constraints.

4. A zero-carbon highway energy complementary control cloud platform according to claim 3, characterized in that, The expression of the photovoltaic power generation estimation model is: H T =H0×R+[R+(1-1 / H b )×(1+cosβ) / 2] Where, H T is the solar irradiance of the PV power station; H0 is the solar irradiance area of the horizontal PV power station; R is the ratio of direct and oblique irradiance on the horizontal plane; H b is the diffuse irradiance on the horizontal plane; β is the inclination angle of the module; The expression of the temperature loss correction coefficient is: S=(T-η)×λ In the formula, S is the actual power generation correction coefficient considering the temperature loss of the photovoltaic module; T is the actual working temperature of the photovoltaic module; η is the standard working temperature of the photovoltaic module; λ is the maximum power temperature coefficient of the photovoltaic module.

5. A zero-carbon highway energy complementary control cloud platform according to claim 3, characterized in that, The expression of the wind power generation estimation model is: P W = 1 / 2 × ρ × A × C p × V 3 × μ Where, P W is the theoretical output power of the fan; ρ is the air density; A is the swept area of the wind turbine rotor; C p is the wind energy utilization coefficient; V is the wind speed; μ is the mechanical transmission efficiency; The expression of the wind speed correction coefficient is: K = 0 when V < V ci or V > V co K = 1 when V r ≤ V ≤ V co K=(V 3 -V ci 3 ) / (V r 3 -V ci 3 ) When V ci ≤V<V r Where K is the wind speed correction coefficient; V is the measured wind speed; V ci is the cut-in wind speed; V co is the cut-out wind speed; V r is the rated wind speed.

6. The zero-carbon highway energy complementary control cloud platform according to claim 3, characterized in that The expression of the annual electricity balance constraint is: E WG +E PG =E L wherein, E WG is the annual power generation of the wind power generation unit, E PG is the annual power generation of the solar photovoltaic power generation unit, E L is the annual power consumption of the load; The expression of the monthly balance constraint is: E WGi +E PGi -E Li ≥E N where, E WGi is the power generation of the wind power generation unit in the i-th month; E PGi is the power generation of the solar photovoltaic power generation unit in the i-th month; E Li is the power consumption of the load in the i-th month; E N is the rated capacity of the energy storage unit; The expression of the power supply reliability constraint is: E WGk +E PGk +E N ≥E Lk where, E WGk is the power generation of the wind power generation unit in the k-th month; E PGk is the power generation of the solar photovoltaic power generation unit in the k-th month; E Lk is the power consumption of the load in the k-th month; E N is the rated capacity of the energy storage unit.

7. A zero-carbon highway energy complementary control cloud platform according to claim 2, characterized in that, Based on the service area load data, establish a two - layer optimization model for seasonal time - of - use electricity price, and determining the electricity price schemes for peak - valley periods in each season includes: According to the annual load data of the service area, the output characteristics of solar photovoltaic units and the output characteristics of wind power generation units, through classified statistical analysis, obtain the typical load characteristic parameters in each season. Among them, the typical load characteristic parameters in each season include the peak - valley difference rate and the renewable energy coverage rate; Based on the upper - layer constraint conditions, iteratively solve the upper - layer optimization model to obtain the initial time - of - use electricity price scheme; According to the initial time - of - use electricity price scheme and the load response characteristics, construct a lower - layer optimization model to obtain the final time - of - use electricity price scheme.

8. A zero-carbon highway energy complementary control cloud platform according to claim 7, characterized in that The objective function expression of the two - layer optimization model for seasonal time - of - use electricity price is: ； ； Where C s is the total cost of the cloud platform after implementing time-of-use electricity price; is the annual power generation cost of the generator set; is the investment cost of the planned power source; is the line investment cost; C u is the total electricity consumption cost of users; P T is the electricity price at time T, L T is the load demand at time T, and T is the time series.

9. The zero-carbon highway energy complementary control cloud platform according to claim 2, characterized in that Optimize the energy complementary model using the heating and cooling capacity parameters of geothermal energy, and combined with the electricity price schemes for peak - valley periods in each season, establish a coordinated operation strategy for wind - solar - geothermal - energy storage, and conduct energy complementary scheduling on the cloud platform, including: Based on the thermal parameters of the geothermal energy utilization unit, through the geothermal energy utilization estimation model, obtain the heating capacity parameters of geothermal energy; According to the energy complementary model and the electricity price schemes for peak - valley periods in each season, combined with the time - segmented energy allocation rules, construct a coordinated operation strategy for wind - solar - geothermal - energy storage; Based on the coordinated operation strategy for wind - solar - geothermal - energy storage, adjust the output power and operating status of solar photovoltaic units, wind power generation units, geothermal energy utilization units, and energy storage units in real - time.

10. A zero-carbon highway energy complementary control cloud platform according to claim 9, characterized in that, The expression of the geothermal energy utilization estimation model is: Q H =COP × W; COP = T c / (T c -T e ); Wherein, Q H is the heating capacity of the geothermal energy utilization unit; COP is the performance coefficient of the ground source heat pump; W is the input power of the ground source heat pump; T c is the condensation temperature of the ground source heat pump; T e is the evaporation temperature of the ground source heat pump.

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