A distributed energy system control method based on a new energy vehicle and related equipment

Abstract

The present application belongs to the technical field of energy system optimization control, and particularly relates to a distributed energy system control method based on new energy vehicles and related equipment. In the present application, the thermal power generating unit is always operated at the optimal working condition, thereby reducing the coal consumption cost, improving the power generation efficiency, and realizing energy saving and emission reduction. On the basis of the fusion of the electric vehicle charging and swapping system, a fuel vehicle charging and swapping system is introduced to perfect the supporting charging and storage facilities of the new energy vehicles and promote the development of the new energy vehicles. The energy sharing of the new energy vehicle users is utilized to carry out peak clipping and valley filling, reduce the peak power consumption load of the national power grid, and transfer the power demand in the period with high electricity price to the period with low electricity price, so as to help reduce the overall power consumption cost without changing the power consumption behavior. On the basis of the profit of the single pure electric vehicle, the profit of the hydrogen fuel vehicle and the profit of the LNG fuel vehicle are introduced to open and expand the profit mode of the new energy vehicle user valley charging and peak discharging.

Description

Technical Field

[0001] This invention belongs to the field of energy system optimization and control technology, specifically relating to a control method and related equipment for a distributed energy system based on new energy vehicles. Background Technology

[0002] Unified management of energy supply and consumption from new energy vehicles can effectively mitigate the negative impacts of large-scale new energy vehicle use on the power grid. In particular, connecting to new energy microgrids can both locally absorb renewable energy and achieve true low-carbon development. Therefore, to meet the energy needs of new energy vehicle users, ensure the safe and stable operation of new energy microgrids, and establish a sound distributed energy system based on new energy vehicles, scientifically and orderly scheduling the charging and discharging of new energy vehicles and the operation of new energy microgrids is of great significance for balancing peak and valley loads on the power grid and achieving energy conservation and emission reduction.

[0003] Currently, scholars both domestically and internationally have conducted extensive research on the scheduling issues of electric vehicles after they are connected to new energy microgrids. They have considered the orderly charging and discharging scheduling of electric vehicles to improve the benefits of new energy microgrids. However, they have not considered the orderly charging and discharging energy scheduling of other new energy vehicles, such as hydrogen fuel cell vehicles and liquefied natural gas (LNG) vehicles, to improve the benefits of new energy microgrids. Therefore, it is necessary to include a wider range of new energy vehicles in the scheduling of new energy microgrids and build a reasonable distributed energy system that takes into account both the benefits of new energy microgrids and the benefits of new energy vehicle users. Summary of the Invention

[0004] The purpose of this invention is to provide a control method and related equipment for a distributed energy system based on new energy vehicles. This method comprehensively considers factors such as generator output, base load, and the spatiotemporal dynamic characteristics of new energy vehicles. It also incorporates the temporal variation characteristics of different types of new energy vehicles to determine the optimal energy sales and purchase prices and the optimal power allocation for the system in real time. This achieves orderly and efficient charging and discharging of new energy vehicles. It can provide decision-makers with useful information on the relationships between various indicators in the dispatching of new energy microgrids, helping to find a compromise solution that balances the interests of electric vehicle owners and the new energy microgrid. Furthermore, it provides a referable example for other distributed energy systems that consider new energy vehicles in the future.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a control method for a distributed energy system based on new energy vehicles, comprising:

[0007] Identify distributed energy systems based on new energy vehicles;

[0008] Acquire basic data, including real-time electricity price data, real-time electricity consumption information, and data on the relationship between the electricity sales and purchase prices of charging piles and the charging and discharging amounts of electric vehicle users;

[0009] Based on the distributed energy system and basic data, a first objective function and its constraints are constructed with the goal of maximizing the total electricity profit of the distributed energy system. The optimization variables of the first objective function are: real-time electricity purchase price of charging piles, real-time electricity sales price of charging piles, electricity input from thermal power units to the microgrid, electricity purchased by the microgrid from the distribution network, and electricity sold by the microgrid to the distribution network. The optimal solution for the optimization variables of the first objective function is obtained by solving the first objective function.

[0010] Based on the distributed energy system, basic data, and the electricity input from thermal power units to the microgrid, a second objective function and its constraints are constructed with the goal of maximizing the total fuel profit of the distributed energy system. The optimization variables of the second objective function are: real-time liquid hydrogen purchase price at liquid hydrogen piles, real-time liquid hydrogen sales price at liquid hydrogen piles, real-time LNG purchase price at LNG piles, real-time LNG sales price at LNG piles, electricity supplied by thermal power units to the water electrolysis hydrogen production system, and electricity supplied by thermal power units to the electric natural gas production system. Solving the second objective function yields the optimal solution for the optimization variables of the second objective function.

[0011] The optimal solution for the first objective function optimization variable and the optimal solution for the second objective function optimization variable are determined as the optimal control scheme.

[0012] Furthermore, the basic data specifically includes:

[0013] Price of electricity sold at each charging station at any given moment The amount of pure electric vehicles charged at the same time Relationship data; purchase price of electricity at each charging pile at any given time Discharge of pure electric vehicles at the same time Relationship data; liquid hydrogen sales price at each moment. Hydrogen refueling capacity of hydrogen fuel cell vehicles at the same time Relationship data; purchase price of liquid hydrogen from hydrogen piles at each moment Hydrogen emission from hydrogen fuel cell vehicles at the same time Relationship data; LNG selling price at each LNG pile LNG fuel car charging volume at the same time Relationship data; LNG purchase price at each LNG pile LNG release volume of LNG fuel vehicles at the same time Relational data;

[0014] The electricity purchase price of microgrids from the distribution network The electricity price sold from the microgrid to the distribution network The electricity price of the microgrid to users in the park Purchase prices of liquid hydrogen in other markets LNG purchase prices in other markets Wind turbine output Photovoltaic unit output and the load demand of users in the park System power consumption The amount of liquid hydrogen stored in the liquid hydrogen storage tank, M H2，store The amount of LNG stored in the LNG storage tank, M LNG ，store .

[0015] Furthermore, based on the distributed energy system and basic data, and with the goal of maximizing the total electricity profit of the distributed energy system, a first objective function and its constraints are constructed, wherein the first objective function is as follows:

[0016]

[0017]

[0018] In the formula, Felec represents the total electricity profit of the distributed energy system. This indicates that the grid of a distributed energy system is profitable. This indicates the profitability of pure electric vehicle transactions within a distributed energy system. This indicates the real-time electricity price that the microgrid sells to the distribution network. This represents the amount of electricity sold by the microgrid to the distribution network at time t; This indicates the real-time electricity purchase price of the microgrid from the distribution network. This represents the amount of electricity purchased by the microgrid from the distribution network at time t; This indicates the real-time selling price of electricity at the charging station. This represents the amount of charge generated by the pure electric vehicle at time t. This indicates the real-time purchase price of electricity at the charging station. This represents the discharge amount of the pure electric vehicle at time t; This indicates the real-time electricity price that the microgrid sells to users in the park. This represents the load demand of users in the park at time t.

[0019] Furthermore, a power conservation equation is constructed for the control center of the new energy microgrid:

[0020]

[0021] In the formula, This represents the output of the wind turbine at time t. This represents the output of the photovoltaic unit at time t. This represents the amount of electricity input from the thermal power unit to the microgrid at time t. This represents the power consumption of the system equipment at time t;

[0022] when hour:

[0023]

[0024] when hour:

[0025]

[0026] In the inequality, This indicates the output under optimal operating conditions;

[0027] when hour:

[0028]

[0029] Furthermore, based on the distributed energy system, basic data, and the electricity input from the thermal power unit to the microgrid, a second objective function and its constraints are constructed with the goal of maximizing the total fuel profit of the distributed energy system. The second objective function is as follows:

[0030]

[0031]

[0032] In the formula, Ffeed represents the total fuel profit of the distributed energy system. This indicates that hydrogen fuel is profitable in distributed energy systems. This indicates the profitability of LNG fuel in distributed energy systems; This indicates the real-time purchase price of liquid hydrogen in other markets. This represents the mass of liquid hydrogen that the system sells to other markets at time t; This indicates the real-time selling price of liquid hydrogen at the liquid hydrogen pile. This represents the amount of hydrogen carried by the hydrogen fuel cell vehicle at time t. This indicates the real-time purchase price of liquid hydrogen for liquid hydrogen piles. This represents the amount of hydrogen emitted by a hydrogen fuel cell vehicle at time t. This indicates the real-time purchase price of LNG in other markets. This represents the mass of LNG sold by the system to other markets at time t; This indicates the real-time LNG selling price at LNG piles. This represents the amount of LNG charged into the LNG-fueled vehicle at time t. This indicates the real-time LNG purchase price at LNG terminals. This represents the amount of LNG released by an LNG-fueled vehicle at time t.

[0033] Furthermore, an energy conservation equation is constructed for thermal power units:

[0034]

[0035] In the formula, This represents the amount of electricity delivered by the thermal power unit to the water electrolysis hydrogen production system at time t. This represents the amount of electricity delivered by the thermal power unit to the electric natural gas system at time t;

[0036] The fuel production calculations for the water electrolysis hydrogen production system and the electro-natural gas production system are as follows:

[0037]

[0038] In the formula, KH2 represents the amount of hydrogen produced by the water electrolysis hydrogen production system at time t, and KH2 represents the amount of electricity consumed by the water electrolysis hydrogen production system to produce 1 unit of hydrogen. K represents the amount of natural gas produced by the electrically powered natural gas system at time t. LNG This indicates the amount of electricity consumed by the electric natural gas system to produce one unit of natural gas.

[0039] when hour:

[0040]

[0041] In the equation, K1 represents the power distribution coefficient; in the inequality, M H2，store This indicates the amount of liquid hydrogen stored in the liquid hydrogen storage tank. Indicates the optimal storage capacity of liquid hydrogen in the liquid hydrogen storage tank; M LNG，store This indicates the amount of LNG stored in the LNG storage tank. This indicates the optimal storage capacity of LNG in the LNG storage tank;

[0042] when hour:

[0043]

[0044] when hour:

[0045]

[0046] when hour:

[0047]

[0048] Furthermore, the distributed energy system based on new energy vehicles specifically includes: wind turbines, photovoltaic units, thermal power units, power distribution networks, a new energy microgrid control center, power piles, liquid hydrogen piles, LNG piles, a water electrolysis hydrogen production system, an electric natural gas production system, and several pumps and storage tank equipment.

[0049] In a second aspect, the present invention provides a control device for a distributed energy system based on new energy vehicles, comprising:

[0050] The target determination module is used to determine distributed energy systems based on new energy vehicles;

[0051] The data acquisition module is used to acquire basic data, including real-time electricity price data, real-time electricity consumption information, and data on the relationship between the electricity sales and purchase prices of charging piles and the charging and discharging amounts of electric vehicle users.

[0052] The first optimization solution module is used to construct a first objective function and its constraints based on the distributed energy system and basic data, with the goal of maximizing the total electricity profit of the distributed energy system. The optimization variables of the first objective function are: real-time electricity purchase price of charging piles, real-time electricity sales price of charging piles, electricity input from thermal power units to the microgrid, electricity purchased by the microgrid from the distribution network, and electricity sold by the microgrid to the distribution network. The module solves the first objective function to obtain the optimal solution for the optimization variables of the first objective function.

[0053] The second optimization module is used to construct a second objective function and its constraints based on the distributed energy system, basic data, and the electricity input from the thermal power units to the microgrid, with the goal of maximizing the total fuel profit of the distributed energy system. The optimization variables of the second objective function are: real-time liquid hydrogen purchase price at liquid hydrogen piles, real-time liquid hydrogen sales price at liquid hydrogen piles, real-time LNG purchase price at LNG piles, real-time LNG sales price at LNG piles, electricity supplied by the thermal power units to the water electrolysis hydrogen production system, and electricity supplied by the thermal power units to the electric natural gas production system. Solving the second objective function yields the optimal solution for the optimization variables of the second objective function.

[0054] The control scheme determination module is used to determine the optimal solution of the first objective function optimization variable and the optimal solution of the second objective function optimization variable as the optimal control scheme.

[0055] In a third aspect, the present invention provides an electronic device including a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the control method for a distributed energy system based on new energy vehicles as described above.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the above-described control method for a distributed energy system based on new energy vehicles.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] Compared to existing distributed energy systems for new energy vehicles, the distributed energy system control method provided by this invention eliminates the need for thermal power units to function as peak-shaving units, ensuring they operate at optimal conditions. This reduces coal consumption costs, improves power generation efficiency, and achieves energy conservation and emission reduction. Furthermore, it innovatively introduces a fuel cell vehicle charging / swapping system based on the integration of electric vehicle charging / swapping systems, improving supporting charging and storage facilities for new energy vehicles and promoting their development. By utilizing energy sharing among new energy vehicle users, it achieves peak shaving and valley filling, participates in grid peak regulation, reduces peak-hour load on the national grid, and shifts electricity demand during periods of higher electricity prices to periods of lower prices, thereby helping to reduce overall electricity costs without altering electricity consumption behavior. In addition to the profitability of pure electric vehicles, it introduces profitability from hydrogen fuel cell vehicles and LNG fuel cell vehicles, expanding the "valley charging, peak discharging" profit model for new energy vehicle users. The distributed energy system control device, electronic equipment, and computer-readable storage medium provided by this invention also solve the problems raised in the background section. Attached Figure Description

[0059] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0060] Figure 1 This is a flowchart illustrating a distributed energy system control method according to an embodiment of the present invention;

[0061] Figure 2 This is a flowchart of the operation of a distributed energy system based on new energy vehicles provided by the present invention;

[0062] Figure 3 This is a flowchart of the control method for a distributed energy system based on new energy vehicles provided by the present invention.

[0063] Figure 4 This is a structural block diagram of a distributed energy system control device according to an embodiment of the present invention;

[0064] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0065] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0066] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0067] Example 1

[0068] like Figure 1 As shown, this solution provides a control method for a distributed energy system based on new energy vehicles, including:

[0069] S1. Determine a distributed energy system based on new energy vehicles;

[0070] S2. Obtain basic data, including real-time electricity price data, real-time electricity consumption information, and data on the relationship between the electricity sales and purchase prices of charging piles and the charging and discharging amounts of electric vehicle users;

[0071] S3. Based on the distributed energy system and basic data, with the goal of maximizing the total electricity profit of the distributed energy system, construct a first objective function and its constraints; wherein, the optimization variables of the first objective function are the real-time electricity purchase price of charging piles, the real-time electricity sales price of charging piles, the electricity input from thermal power units to the microgrid, the electricity purchased by the microgrid from the distribution network, and the electricity sold by the microgrid to the distribution network; solve the first objective function to obtain the optimal solution of the optimization variables of the first objective function;

[0072] S4. Based on the distributed energy system, basic data, and the electricity input from the thermal power unit to the microgrid, a second objective function and its constraints are constructed with the goal of maximizing the total fuel profit of the distributed energy system. The optimization variables of the second objective function are: real-time liquid hydrogen purchase price at liquid hydrogen piles, real-time liquid hydrogen sales price at liquid hydrogen piles, real-time LNG purchase price at LNG piles, real-time LNG sales price at LNG piles, electricity supplied by the thermal power unit to the water electrolysis hydrogen production system, and electricity supplied by the thermal power unit to the electric natural gas production system. The optimal solution for the optimization variables of the second objective function is obtained by solving the second objective function.

[0073] S5. Determine the optimal solution of the first objective function optimization variable and the optimal solution of the second objective function optimization variable as the optimal control scheme.

[0074] The distributed energy system control method provided by the above scheme incorporates the three most common new energy vehicle types—pure electric, hydrogen, and LNG vehicles—into the energy system. It addresses load demand optimization by rationally allocating the output of various distributed power sources through the scheduling of new energy microgrids, achieving peak shaving and valley filling for the power grid. Simultaneously, following the distributed energy system control method based on new energy vehicles, it derives the optimal energy selling price and optimal power allocation for the system.

[0075] Specifically, distributed energy systems mainly include: wind turbines, photovoltaic (PV) turbines, thermal power units, distribution networks, a new energy microgrid control center, power piles, liquid hydrogen piles, LNG piles, water electrolysis hydrogen production systems, electrolytic natural gas production systems, and various pumps and storage tanks. Among these, wind and PV turbines are power generation equipment whose operation is determined by the environment, and all the electricity generated is sent to the new energy microgrid control center for distribution. Thermal power units are also power generation equipment, but they are less affected by the environment and operate at optimal load. After receiving signals from the new energy microgrid control center, they distribute the generated electricity.

[0076] During peak periods, all the electricity generated by thermal power units is used for scheduling by the new energy microgrid control center, and the water electrolysis hydrogen production system and the electric natural gas production system stop working to meet users' electricity demand. During off-peak periods, while meeting users' electricity demand, the excess electricity generated by thermal power units is fed into the water electrolysis hydrogen production system and the electric natural gas production system in an appropriate proportion to meet the fuel demand of fuel cell vehicle users, thereby achieving the purpose of peak shaving and valley filling of the power grid.

[0077] The role of the distribution network is to compensate for or purchase the load of the energy system when its output load is insufficient or excessive. The role of the new energy microgrid control center is to collect all system information, calculate the optimal price and power allocation scheme, and control other equipment in the system. Electricity charging piles, liquid hydrogen charging piles, and LNG charging piles serve as gateways for interaction between the system and new energy vehicle users, facilitating the sale and purchase of energy. Other markets are used to absorb excess fuel products produced by the system when there is an overproduction of liquid hydrogen or LNG. Water electrolysis hydrogen production systems and electric natural gas production systems consume large amounts of electricity to produce hydrogen and natural gas respectively. Pumps are devices that convert gaseous hydrogen and natural gas into liquid hydrogen and liquefied natural gas. Liquid hydrogen storage tanks and LNG storage tanks are devices that store liquid hydrogen and liquefied natural gas respectively. Electric vehicles, hydrogen fuel cell vehicles, and LNG fuel cell vehicles are the smallest energy storage units and can be charged and released through electricity charging piles, liquid hydrogen charging piles, and LNG charging piles, respectively. The new energy microgrid control center will release real-time charging and discharging prices to new energy vehicle users through equipment such as charging piles, liquid hydrogen piles, and LNG piles. This will encourage new energy vehicle users to discharge energy during peak periods to share energy and obtain some profits, and to charge energy during off-peak periods to absorb excess grid electricity, achieving a win-win situation for both the park and users.

[0078] Figure 2 This is a flowchart illustrating the operation of a distributed energy system based on new energy vehicles, provided by this invention. It incorporates electric vehicles, hydrogen fuel cell vehicles, and LNG fuel cell vehicles as energy storage units into the distributed energy system, forming a new "shared energy" system. The operating principle is as follows:

[0079] After receiving signals from the distribution network, park users, charging piles, liquid hydrogen charging piles, LNG charging piles, liquid hydrogen storage tanks, and LNG storage tanks, the new energy microgrid control center issues reasonable energy purchase and sale prices to the charging piles, liquid hydrogen charging piles, and LNG storage tanks, offering energy rewards to encourage new energy vehicle users to contribute energy to the system during peak hours and charge the system during off-peak hours. The new energy microgrid control center also receives power generation from wind turbines and photovoltaic units, as well as power transmission from charging piles. It then issues appropriate power allocation signals to thermal power units and receives their power generation to meet the power needs of park users and electric vehicle users. Excess power is sold to the distribution network, and any missing power is purchased from the distribution network.

[0080] The thermal power unit operates under optimal conditions. Upon receiving signals from the new energy microgrid control center, it distributes the generated electricity: a portion goes to the control center, another portion to the water electrolysis hydrogen production system, and the remainder to the electric natural gas production system. The electrolysis hydrogen production system and the electric natural gas production system operate while receiving electricity, producing hydrogen and natural gas respectively. After compression, these are stored in liquid form in liquid hydrogen tanks and LNG tanks, respectively. The liquid hydrogen and LNG tanks receive production materials from the electrolysis hydrogen production system and the electric natural gas production system, respectively, as well as liquid hydrogen and liquefied natural gas supplied by new energy fuel vehicle users via liquid hydrogen charging stations and LNG charging stations, respectively. The liquid hydrogen and LNG tanks supply fuel to hydrogen fuel cell vehicles and LNG fuel cell vehicles through these charging stations, respectively. Additionally, when the storage capacity exceeds a certain limit, fuel is also sold to other markets.

[0081] Figure 3 This is a flowchart illustrating the control method for a distributed energy system based on new energy vehicles, as implemented in this invention. The entire flowchart describes the control calculation process of a distributed energy system based on new energy vehicles, covering the complete process from data acquisition, load calculation, energy purchase and sale price selection to system power allocation.

[0082] In one specific embodiment, the control method for a distributed energy system is as follows:

[0083] First, identify the distributed energy system to be optimized and control, and obtain the basic data used to establish the optimization objective function.

[0084] (1) Experimental data: the selling price of electricity generated by the charging pile at each moment The amount of pure electric vehicles charged at the same time Relationship data, purchase price of electricity at each charging pile at any given time Discharge of pure electric vehicles at the same time Relationship data, liquid hydrogen sales price at each moment Hydrogen refueling capacity of hydrogen fuel cell vehicles at the same time Relationship data, liquid hydrogen purchase price at each moment Hydrogen emission from hydrogen fuel cell vehicles at the same time Relationship data, LNG pile LNG selling price at each moment LNG fuel car charging volume at the same time Relationship data, LNG purchase price of LNG piles at each moment LNG release volume of LNG fuel vehicles at the same time Relationship data.

[0085] (2) Real-time acquisition: the electricity purchase price of the microgrid from the distribution network The electricity price sold from the microgrid to the distribution network The electricity price of the microgrid to users in the park Purchase prices of liquid hydrogen in other markets LNG purchase prices in other markets Wind turbine output Photovoltaic unit output and the load demand of users in the park System power consumption The amount of liquid hydrogen stored in the liquid hydrogen storage tank, M H2，store The amount of LNG stored in the LNG storage tank, M LNG，store .

[0086] Then, establish the revenue and simulation operation functions for the entire project planning cycle, which serve as the primary objective function.

[0087] The first objective function is shown below:

[0088]

[0089] In the formula, F elec To generate total electricity revenue for distributed energy systems, This indicates that the grid of a distributed energy system is profitable. F represents the profitability of pure electric vehicle transactions in distributed energy systems. elec , A positive value indicates a gain, while a negative value indicates a loss.

[0090] The calculation formula is as follows:

[0091]

[0092] In the formula, This indicates the real-time electricity price that the microgrid sells to the distribution network. This represents the amount of electricity sold by the microgrid to the distribution network at time t; This indicates the real-time electricity purchase price of the microgrid from the distribution network. This represents the amount of electricity purchased by the microgrid from the distribution network at time t; This indicates the real-time selling price of electricity at the charging station. This represents the amount of charge generated by the pure electric vehicle at time t. This indicates the real-time purchase price of electricity at the charging station. This represents the discharge amount of the pure electric vehicle at time t; This indicates the real-time electricity price that the microgrid sells to users in the park. This represents the load demand of users in the park at time t.

[0093] Constructing the power conservation equation for the control center of the new energy microgrid:

[0094]

[0095] In the formula, This represents the output of the wind turbine at time t. This represents the output of the photovoltaic unit at time t. This represents the amount of electricity input from the thermal power unit to the microgrid at time t. This represents the power consumption of the system equipment at time t.

[0096] when hour:

[0097]

[0098] when hour:

[0099]

[0100] In the inequality, This indicates the output under optimal operating conditions.

[0101] when hour:

[0102]

[0103] Solving the first objective function yields the optimal solution. and best and

[0104] Next, establish the revenue and simulated operation functions for the entire project planning cycle as the second objective function.

[0105] The second objective function is shown below:

[0106]

[0107] In the formula, F feed For total fuel profitability of distributed energy systems, This indicates that hydrogen fuel is profitable in distributed energy systems. This indicates that the distributed energy system is profitable using LNG fuel. feed , A positive value indicates a gain, while a negative value indicates a loss.

[0108] The calculation formula is as follows:

[0109]

[0110] In the formula, This indicates the real-time purchase price of liquid hydrogen in other markets. This represents the mass of liquid hydrogen that the system sells to other markets at time t; This indicates the real-time selling price of liquid hydrogen at the liquid hydrogen pile. This represents the amount of hydrogen carried by the hydrogen fuel cell vehicle at time t. This indicates the real-time purchase price of liquid hydrogen for liquid hydrogen piles. This represents the amount of hydrogen emitted by a hydrogen fuel cell vehicle at time t. This indicates the real-time purchase price of LNG in other markets. This represents the mass of LNG sold by the system to other markets at time t; This indicates the real-time LNG selling price at LNG piles. This represents the amount of LNG charged into the LNG-fueled vehicle at time t. This indicates the real-time LNG purchase price at LNG terminals. This represents the amount of LNG released by an LNG-fueled vehicle at time t.

[0111] Construct the energy conservation equation for thermal power units:

[0112]

[0113] In the formula, This represents the amount of electricity delivered by the thermal power unit to the water electrolysis hydrogen production system at time t. This represents the amount of electricity delivered by the thermal power unit to the electric natural gas system at time t.

[0114] The fuel production calculations for the water electrolysis hydrogen production system and the electro-natural gas production system are as follows:

[0115]

[0116] In the formula, K represents the amount of hydrogen produced by the water electrolysis hydrogen production system at time t. H2 This indicates the amount of electricity consumed by the water electrolysis hydrogen production system to produce one unit of hydrogen. K represents the amount of natural gas produced by the electrically powered natural gas system at time t. LNG This indicates the amount of electricity consumed by an electric natural gas system to produce one unit of natural gas.

[0117] when hour:

[0118]

[0119] In the equation, K1 represents the power distribution coefficient, which is given by the user. The larger the value of K1, the more power is distributed in the water electrolysis hydrogen production system.

[0120] In the inequality, M H2，store This indicates the amount of liquid hydrogen stored in the liquid hydrogen storage tank. Indicates the optimal storage capacity of liquid hydrogen in the liquid hydrogen storage tank; M LNG，store This indicates the amount of LNG stored in the LNG storage tank. This indicates the optimal amount of LNG to be stored in the LNG storage tank.

[0121] when hour:

[0122]

[0123] when hour:

[0124]

[0125] when hour:

[0126]

[0127] Solving the second objective function yields the optimal solution. and best and

[0128] Finally, the optimal solution for the first objective function's optimization variables and the optimal solution for the second objective function's optimization variables are determined as the optimal control scheme.

[0129] Compared with existing inventions, the present invention has the following advantages:

[0130] 1. Thermal power units will be used as the main power generation equipment for continuous operation, and will no longer be used for peak shaving, in order to reduce coal consumption costs and improve power generation efficiency;

[0131] 2. Introducing fuel-electric vehicle charging and swapping systems and improving the supporting charging and storage facilities for new energy vehicles will promote the development of new energy vehicles and help improve their ease of use and popularity.

[0132] 3. Utilize energy sharing among new energy vehicle users to perform peak shaving and valley filling, participate in grid peak regulation, reduce the peak electricity load of the State Grid, and help reduce overall electricity costs by using the energy stored in new energy vehicles during periods of high electricity prices and then charging them during periods of low electricity prices.

[0133] 4. In addition to the profitability of pure electric vehicles, introducing profitability of hydrogen fuel cell vehicles and LNG fuel cell vehicles, and opening up and expanding the "valley charging and peak release" profit model for new energy vehicle users will help expand the new energy vehicle market and promote diversified use of clean energy.

[0134] 5. Provide a new control method for distributed energy systems based on new energy vehicles, which comprehensively considers the output of sub-units, base load, and the spatiotemporal dynamic characteristics of new energy vehicles, so as to realize the orderly and efficient charging and discharging of new energy vehicles, provide useful information for decision-makers, and help to find a scheduling scheme that takes into account the interests of new energy vehicle owners and new energy microgrids.

[0135] In summary, this invention reduces coal consumption costs, improves power plant generation efficiency, optimizes the dispatch of new energy microgrids, utilizes new energy vehicle users to participate in energy sharing and peak shaving, creates diversified profit models for new energy vehicles, promotes the development of the new energy vehicle industry, achieves more economical, efficient, and sustainable energy utilization, and provides a reference solution for the design of new regional distributed multi-energy systems.

[0136] Example 2

[0137] like Figure 4 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a distributed energy system control device based on new energy vehicles, comprising:

[0138] The target determination module is used to determine distributed energy systems based on new energy vehicles;

[0139] The data acquisition module is used to acquire basic data, including real-time electricity price data, real-time electricity consumption information, and data on the relationship between the electricity sales and purchase prices of charging piles and the charging and discharging amounts of electric vehicle users.

[0140] The first optimization solution module is used to construct a first objective function and its constraints based on the distributed energy system and basic data, with the goal of maximizing the total electricity profit of the distributed energy system. The optimization variables of the first objective function are: real-time electricity purchase price of charging piles, real-time electricity sales price of charging piles, electricity input from thermal power units to the microgrid, electricity purchased by the microgrid from the distribution network, and electricity sold by the microgrid to the distribution network. The module solves the first objective function to obtain the optimal solution for the optimization variables of the first objective function.

[0141] The second optimization module is used to construct a second objective function and its constraints based on the distributed energy system, basic data, and the electricity input from the thermal power units to the microgrid, with the goal of maximizing the total fuel profit of the distributed energy system. The optimization variables of the second objective function are: real-time liquid hydrogen purchase price at liquid hydrogen piles, real-time liquid hydrogen sales price at liquid hydrogen piles, real-time LNG purchase price at LNG piles, real-time LNG sales price at LNG piles, electricity supplied by the thermal power units to the water electrolysis hydrogen production system, and electricity supplied by the thermal power units to the electric natural gas production system. Solving the second objective function yields the optimal solution for the optimization variables of the second objective function.

[0142] The control scheme determination module is used to determine the optimal solution of the first objective function optimization variable and the optimal solution of the second objective function optimization variable as the optimal control scheme.

[0143] Example 3

[0144] like Figure 5 As shown, the present invention also provides an electronic device 100 for implementing a distributed energy system control method based on new energy vehicles according to the above embodiments;

[0145] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0146] The memory 101 can be used to store computer program 103. The processor 102 implements the steps of the control method for a distributed energy system based on new energy vehicles in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0147] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0148] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0149] The memory 101 in the electronic device 100 stores multiple instructions to implement a distributed energy system control method based on new energy vehicles, and the processor 102 can execute multiple instructions to achieve the following:

[0150] Identify distributed energy systems based on new energy vehicles;

[0151] Acquire basic data, including real-time electricity price data, real-time electricity consumption information, and data on the relationship between the electricity sales and purchase prices of charging piles and the charging and discharging amounts of electric vehicle users;

[0152] Based on the distributed energy system and basic data, a first objective function and its constraints are constructed with the goal of maximizing the total electricity profit of the distributed energy system. The optimization variables of the first objective function are: real-time electricity purchase price of charging piles, real-time electricity sales price of charging piles, electricity input from thermal power units to the microgrid, electricity purchased by the microgrid from the distribution network, and electricity sold by the microgrid to the distribution network. The optimal solution for the optimization variables of the first objective function is obtained by solving the first objective function.

[0153] Based on the distributed energy system, basic data, and the electricity input from thermal power units to the microgrid, a second objective function and its constraints are constructed with the goal of maximizing the total fuel profit of the distributed energy system. The optimization variables of the second objective function are: real-time liquid hydrogen purchase price at liquid hydrogen piles, real-time liquid hydrogen sales price at liquid hydrogen piles, real-time LNG purchase price at LNG piles, real-time LNG sales price at LNG piles, electricity supplied by thermal power units to the water electrolysis hydrogen production system, and electricity supplied by thermal power units to the electric natural gas production system. Solving the second objective function yields the optimal solution for the optimization variables of the second objective function.

[0154] The optimal solution for the first objective function optimization variable and the optimal solution for the second objective function optimization variable are determined as the optimal control scheme.

[0155] Example 4

[0156] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0157] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0158] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0161] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A control method for a distributed energy system based on new energy vehicles, characterized in that, include: Identify distributed energy systems based on new energy vehicles; Acquire basic data, including real-time electricity price data, real-time electricity consumption information, and data on the relationship between the electricity sales and purchase prices of charging piles and the charging and discharging amounts of electric vehicle users; Based on the distributed energy system and basic data, a first objective function and its constraints are constructed with the goal of maximizing the total electricity profit of the distributed energy system. The optimization variables of the first objective function are: real-time electricity purchase price of charging piles, real-time electricity sales price of charging piles, electricity input from thermal power units to the microgrid, electricity purchased by the microgrid from the distribution network, and electricity sold by the microgrid to the distribution network. The optimal solution for the optimization variables of the first objective function is obtained by solving the first objective function. Based on the distributed energy system, basic data, and the electricity input from thermal power units to the microgrid, a second objective function and its constraints are constructed with the goal of maximizing the total fuel profit of the distributed energy system. The optimization variables of the second objective function are: real-time liquid hydrogen purchase price at liquid hydrogen piles, real-time liquid hydrogen sales price at liquid hydrogen piles, real-time LNG purchase price at LNG piles, real-time LNG sales price at LNG piles, electricity supplied by thermal power units to the water electrolysis hydrogen production system, and electricity supplied by thermal power units to the electric natural gas production system. Solving the second objective function yields the optimal solution for the optimization variables of the second objective function. The optimal solution of the first objective function optimization variable and the optimal solution of the second objective function optimization variable are determined as the optimal control scheme; The specific data includes: the electricity sales price of the charging piles at each moment. The amount of pure electric vehicles charged at the same time Relationship data; purchase price of electricity at each charging pile at any given time Discharge of pure electric vehicles at the same time Relationship data; liquid hydrogen sales price at each moment. Hydrogen refueling capacity of hydrogen fuel cell vehicles at the same time Relationship data; purchase price of liquid hydrogen from hydrogen piles at each moment Hydrogen emission from hydrogen fuel cell vehicles at the same time Relationship data; LNG selling price at each LNG pile LNG fuel car charging volume at the same time Relationship data; LNG purchase price at each LNG pile LNG release volume of LNG fuel vehicles at the same time Relationship data; the electricity purchase price from the distribution network to the microgrid The electricity price sold from the microgrid to the distribution network The electricity price sold by the microgrid to users in the park Purchase prices of liquid hydrogen in other markets LNG purchase prices in other markets Wind turbine output Photovoltaic unit output and the load demand of users in the park System power consumption The amount of liquid hydrogen stored in the liquid hydrogen storage tank The amount of LNG stored in the LNG storage tank ; The first objective function is shown below: In the formula, To generate total electricity profit for distributed energy systems, This indicates that the grid of a distributed energy system is profitable. This indicates the profitability of pure electric vehicle transactions within a distributed energy system. This indicates the real-time electricity price that the microgrid sells to the distribution network. This represents the amount of electricity sold by the microgrid to the distribution network at time t; This indicates the real-time electricity purchase price of the microgrid from the distribution network. This represents the amount of electricity purchased by the microgrid from the distribution network at time t; This indicates the real-time selling price of electricity at the charging station. This represents the amount of charge generated by the pure electric vehicle at time t. This indicates the real-time purchase price of electricity at the charging station. This represents the discharge amount of the pure electric vehicle at time t; This indicates the real-time electricity price that the microgrid sells to users in the park. This represents the load demand of users in the park at time t; Constructing a power conservation equation for the control center of a new energy microgrid: In the formula, This represents the output of the wind turbine at time t. This represents the output of the photovoltaic unit at time t. This represents the amount of electricity input from the thermal power unit to the microgrid at time t. This represents the power consumption of the system equipment at time t; when hour: when hour: In the inequality, This indicates the output under optimal operating conditions; when hour: ； The second objective function is shown below: In the formula, For total fuel profitability of distributed energy systems, This indicates that hydrogen fuel is profitable in distributed energy systems. This indicates the profitability of LNG fuel in distributed energy systems; This indicates the real-time purchase price of liquid hydrogen in other markets. This represents the mass of liquid hydrogen that the system sells to other markets at time t; This indicates the real-time selling price of liquid hydrogen at the liquid hydrogen pile. This represents the amount of hydrogen carried by the hydrogen fuel cell vehicle at time t. This indicates the real-time purchase price of liquid hydrogen for liquid hydrogen piles. This represents the amount of hydrogen emitted by a hydrogen fuel cell vehicle at time t. This indicates the real-time purchase price of LNG in other markets. This represents the quality of LNG sold by the system to other markets at time t; This indicates the real-time LNG selling price at LNG piles. This represents the amount of LNG charged into the LNG-fueled vehicle at time t. This indicates the real-time LNG purchase price at LNG terminals. This represents the amount of LNG released by an LNG-fueled vehicle at time t. Construct the energy conservation equation for thermal power units: In the formula, This represents the amount of electricity delivered by the thermal power unit to the water electrolysis hydrogen production system at time t. This represents the amount of electricity delivered by the thermal power unit to the electric natural gas system at time t; The fuel production calculations for the water electrolysis hydrogen production system and the electro-natural gas production system are as follows: In the formula, This represents the amount of hydrogen produced by the water electrolysis hydrogen production system at time t. This indicates the amount of electricity consumed by the water electrolysis hydrogen production system to produce 1 unit of hydrogen. This represents the amount of natural gas produced by the electro-gas system at time t. This indicates the amount of electricity consumed by the electric natural gas system to produce one unit of natural gas. when , hour: In the equation, Represents the power distribution coefficient; in the inequality, This indicates the amount of liquid hydrogen stored in the liquid hydrogen storage tank. This indicates the optimal amount of liquid hydrogen to be stored in the liquid hydrogen storage tank; This indicates the amount of LNG stored in the LNG storage tank. This indicates the optimal storage capacity of LNG in the LNG storage tank; when , hour: when , hour: when , hour: 。 2. The control method for a distributed energy system based on new energy vehicles according to claim 1, characterized in that, The distributed energy system based on new energy vehicles specifically includes: wind turbines, photovoltaic units, thermal power units, power distribution networks, a new energy microgrid control center, power piles, liquid hydrogen piles, LNG piles, a water electrolysis hydrogen production system, an electric natural gas production system, and several pumps and storage tanks.

3. A control device for a distributed energy system based on new energy vehicles, used to implement the control method for a distributed energy system based on new energy vehicles as described in claim 1 or 2, characterized in that, include: The target determination module is used to determine distributed energy systems based on new energy vehicles; The data acquisition module is used to acquire basic data, including real-time electricity price data, real-time electricity consumption information, and data on the relationship between the electricity sales and purchase prices of charging piles and the charging and discharging amounts of electric vehicle users. The first optimization solution module is used to construct a first objective function and its constraints based on the distributed energy system and basic data, with the goal of maximizing the total electricity profit of the distributed energy system. The optimization variables of the first objective function are: real-time electricity purchase price of charging piles, real-time electricity sales price of charging piles, electricity input from thermal power units to the microgrid, electricity purchased by the microgrid from the distribution network, and electricity sold by the microgrid to the distribution network. The module solves the first objective function to obtain the optimal solution for the optimization variables of the first objective function. The second optimization module is used to construct a second objective function and its constraints based on the distributed energy system, basic data, and the electricity input from the thermal power units to the microgrid, with the goal of maximizing the total fuel profit of the distributed energy system. The optimization variables of the second objective function are: real-time liquid hydrogen purchase price at liquid hydrogen piles, real-time liquid hydrogen sales price at liquid hydrogen piles, real-time LNG purchase price at LNG piles, real-time LNG sales price at LNG piles, electricity supplied by the thermal power units to the water electrolysis hydrogen production system, and electricity supplied by the thermal power units to the electric natural gas production system. Solving the second objective function yields the optimal solution for the optimization variables of the second objective function. The control scheme determination module is used to determine the optimal solution of the first objective function optimization variable and the optimal solution of the second objective function optimization variable as the optimal control scheme.

4. An electronic device, characterized in that, It includes a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the control method for a distributed energy system based on new energy vehicles as described in claim 1 or 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the distributed energy system control method based on new energy vehicles as described in claim 1 or 2.

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