Multi-element hybrid energy storage system with Carnot battery running under variable working conditions and scheduling optimization method

Abstract

The invention relates to the technical field of comprehensive energy systems, and provides a multi-element hybrid energy storage system with a Carnot battery running under variable working conditions and a scheduling optimization method, and the multi-element hybrid energy storage system with the Carnot battery running under variable working conditions is constructed to meet the requirements of electric and thermal loads of users; constructing a Carnot battery variable working condition operation model by adopting a segmentation processing method; the operation cost model of the multi-element hybrid energy storage system is constructed by taking the single-day operation cost as a target, and a system scheduling operation scheme is determined by adopting a mixed integer linear programming method. According to the invention, the scheduling optimization problem that the existing distributed energy system does not contain the variable working condition operation of the Carnot battery can be solved, the system operation is more economical and environment-friendly, renewable energy sources are fully utilized, the flexible conversion between electricity and heat is realized, the supporting effect of the distributed energy system on the power grid is realized, and the maximum capacity reaches 31.61% of the rated capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to a multi-element hybrid energy storage system and a scheduling optimization method for variable operating conditions of Carnot batteries. Background Art

[0002] As the global energy crisis and environmental problems become increasingly severe, the traditional energy structure dominated by fossil fuels can no longer meet the needs of sustainable development in modern society. Governments around the world have accelerated the development and utilization of renewable energy, striving to achieve the goals of energy conservation, emission reduction and green growth. Among these renewable energy sources, photovoltaic and wind power are highly favored due to their cleanliness and renewability, and have become an important part of the future energy structure. However, the volatility of power generation from these renewable energy sources has brought great challenges to the peak load regulation of the power grid, resulting in energy waste and unstable power supply.

[0003] As a key means to solve the volatility problem of renewable energy, energy storage technology has received widespread attention and application in recent years. At present, pumped storage and electrochemical energy storage are two widely used energy storage methods. Pumped storage power stations pump water to high places for storage during low-peak hours and release water to generate electricity during peak hours to achieve the regulation and storage of electricity. However, the construction of pumped storage power stations is strictly restricted by geographical conditions and has high investment costs, making it difficult to promote and apply on a large scale. In addition, the operating efficiency of pumped storage is affected by water sources and environmental conditions, which limits its application in some areas.

[0004] Electrochemical energy storage technologies, including lithium-ion batteries and lead-acid batteries, have the advantages of fast response speed and high energy density, and have been widely used in distributed energy systems and microgrids. However, electrochemical energy storage has a limited lifespan, and its performance gradually decreases with the increase in the number of cycles, leading to increased maintenance costs. In addition, the safety issues of large-scale electrochemical energy storage systems cannot be ignored, especially under extreme conditions such as high temperature or overcharging, which may cause safety hazards.

[0005] In scenarios such as industrial parks, the demand for electric and thermal loads is often mismatched, especially when the demand for heat is high. Traditional energy systems are difficult to meet the needs of efficient conversion and storage, which leads to low energy efficiency and energy waste. At the same time, as a large energy consumer, the optimization of the energy structure of industrial parks is of great significance to achieving energy conservation and emission reduction goals. Existing energy systems are often unable to flexibly respond to changes in electric and thermal loads, resulting in waste of resources and increased burden on the environment.

[0006] As the proportion of renewable energy generation continues to increase, the stability of the power grid has become increasingly prominent. The volatility of renewable energy generation such as photovoltaic and wind power makes it more difficult to regulate the peak of the power grid, which can easily lead to problems such as power grid frequency fluctuations and voltage instability. In addition, the large-scale access of renewable energy may also place higher demands on the transmission and distribution capabilities of the power grid, increasing the cost of power grid construction and operation and maintenance. When facing the access of renewable energy, the existing power grid often lacks sufficient flexibility and adaptability, making it difficult to effectively manage the flow of electricity.

[0007] In order to overcome the limitations of existing energy storage technologies and solve the problem of mismatch between electric and thermal loads in industrial parks, it is urgent to develop new energy conversion technologies, especially to achieve efficient conversion between electricity and heat. In the existing technology, although some emerging energy conversion devices have been proposed, their actual application effects and economic feasibility still need to be further verified. For example, Carnot battery, as a new type of thermoelectric conversion device, has the advantages of high efficiency, reliability, and environmental protection, but in actual applications, its operating characteristics are complex, and variable operating conditions put forward higher requirements for system scheduling.

[0008] In summary, existing energy storage technologies have limitations in solving the problems of renewable energy volatility and the mismatch of electric and thermal loads in industrial parks. It is necessary to develop new energy conversion technologies and combine multiple energy storage methods to build a multi-energy complementary distributed energy system. How to optimize the scheduling and dispatching strategies of multi-energy complementary distributed energy systems to achieve the best balance between system economy, environmental protection and reliability is still an important research topic. Summary of the invention

[0009] In view of this, the present invention aims to propose a multi-element hybrid energy storage system and a scheduling optimization method for variable operating conditions containing Carnot batteries, realize flexible conversion between electricity and heat through Carnot batteries, build a distributed energy system model with multi-energy complementarity, plan the system scheduling operation strategy with the single-day operating cost as the target, and thus reduce the system operating cost.

[0010] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0011] A multi-element hybrid energy storage system with Carnot battery and variable operating conditions, including energy supply part, energy conversion part, energy storage part and terminal energy use part,

[0012] The energy supply part includes a photovoltaic power generation system, a wind power generation system and a power grid that provides power to the system;

[0013] The energy conversion part includes a combined heat and power unit, an electrolyzer, a hydrogen fuel cell, a gas boiler and a Carnot battery;

[0014] The energy storage part includes a heat storage tank and a hydrogen storage tank;

[0015] Wherein, the cogeneration unit can generate electricity and heat energy at the same time, the electrolyzer can use electricity to decompose water to produce hydrogen, the hydrogen fuel cell can convert hydrogen energy into electricity and generate heat energy at the same time, the hydrogen storage tank can store excess hydrogen generated by electrolysis in the electrolyzer, the Carnot battery can convert electricity and heat energy into each other, the Carnot battery converts excess electricity into heat energy through the electricity-to-heat part and stores it in the form of heat energy, the heat energy can be used to meet the heat load or generate electricity through the heat-to-electricity process to meet the electricity load, the cogeneration unit can generate electricity and heat energy by burning natural gas, the gas boiler can burn natural gas to generate heat energy, and can store excess heat in the heat storage tank;

[0016] The Carnot battery is divided into multiple performance parameters according to different load intervals;

[0017] It also includes an intelligent control system, which is used to regulate the operation of the energy supply part, energy conversion part, and energy storage part according to the electric and thermal load demand and power generation situation, so as to realize the conversion and output of the three energy forms of electricity, heat and hydrogen.

[0018] Furthermore, the intelligent control system includes an equipment model, a mathematical model for scheduling optimization, an operating cost model, and a constraint model, wherein the operating cost model contains an objective function of daily operating cost, and the constraint model includes constraints including overall balance constraints, upper and lower limit constraints of equipment, and operation constraints of energy storage equipment. The intelligent control system outputs optimized scheduling results and optimized objective function results based on the objective function and the constraint model.

[0019] Furthermore, the intelligent control system sets the scheduling optimization problem as a mixed integer linear programming problem based on the equipment model, the mathematical model of scheduling optimization, constraints, initial data and equipment energy supply strategy, and uses an intelligent algorithm to solve the mathematical model of scheduling optimization, and outputs the equipment scheduling results and optimization target results.

[0020] Furthermore, the Carnot battery adopts a segmented processing method according to the round-trip efficiency, and divides its load range into three sections: 0-30% THA, 30%-75% THA, and 75%-100% THA, to establish a Carnot battery operation model.

[0021] Furthermore, the equipment model includes a wind power generation model, a photovoltaic power generation model, a cogeneration unit model, an electrolyzer model, a hydrogen fuel cell model, a gas boiler model, a Carnot battery model and an energy storage device model, wherein:

[0022] The wind power generation model is:

[0023]

[0024] vt is the instantaneous wind speed, v in , v r and v out are cut-in wind speed, rated wind speed and cut-out wind speed respectively, P r is the rated power of the fan, P DG is the output power of the fan;

[0025] The photovoltaic power generation model is:

[0026] P SP =η s SI t

[0027] P SP is the output power of the photovoltaic panel, S is the total area of ​​the photovoltaic panel, η S is the photovoltaic conversion efficiency, I is the solar irradiance;

[0028] The combined heat and power unit model is:

[0029]

[0030] Among them, P CHP,e Output electric energy for the combined heat and power unit; P CHP,h Output heat energy for the combined heat and power unit; P GT,e P is the electrical energy output by the gas turbine; GT,h P is the heat energy output by the gas turbine; ORC,e Output electrical energy to the low-temperature waste heat power generation device; is the heat conversion efficiency of the waste heat boiler; The power generation efficiency of the low-temperature waste heat power generation device; The efficiency of converting gas turbine input power into electrical output power; The efficiency of converting the input natural gas power of the gas turbine into the output heat energy; μ WHB is the ratio of waste heat generated by the gas turbine to the waste heat boiler, μ ORC is the ratio of waste heat generated by gas turbine to low-temperature waste heat power generation device, μ WHB :μ ORC =1;Q g,CHP V is the natural gas input to the cogeneration unit; g is the calorific value of natural gas, 9.88kWh / m 3 ;

[0031] Electrolyzer model:

[0032]

[0033] Output hydrogen energy for electrolyzer, P e,EL is the electrical energy input to the electrolytic cell, ηEL is the conversion efficiency of the electrolyzer;

[0034] Hydrogen fuel cell model:

[0035]

[0036] Among them, P HFC,e Hydrogen fuel cells output electrical energy; P HFC,h Output heat energy for hydrogen fuel cells; To input hydrogen energy into hydrogen fuel cells; The efficiency of hydrogen fuel cells in converting hydrogen energy into electricity; The efficiency of hydrogen fuel cells in converting hydrogen energy into heat energy;

[0037] Gas boiler model:

[0038] P GB,h =η GB Q g,GB V g

[0039] Among them, P GB,h Output heat energy for gas boiler; Q g,GB is the natural gas input to the gas boiler; GB is the conversion efficiency of the gas boiler; V g is the calorific value of natural gas, 9.88kWh / m 3 ;

[0040] Carnot battery model:

[0041] Based on the variable working condition operation of Carnot battery, the round trip efficiency of Carnot battery is divided into three sections by segment processing method:

[0042]

[0043]

[0044] Among them, S CB It is the equivalent storage capacity of Carnot battery; is the charging efficiency of the Carnot battery; is the discharge efficiency of the Carnot cell; is the heating efficiency of the Carnot battery; P e,CB The charge of the Carnot battery; P CB,e is the discharge capacity of the Carnot battery; P CB,h Remove heat from the Carnot battery;

[0045] Energy storage device, including heat storage device and hydrogen storage device, its model is:

[0046]

[0047] Among them, S i is the capacity of the energy storage device, is the charging power of the energy storage device; is the discharge power of the energy storage device; is the charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device.

[0048] Furthermore, the objective function is:

[0049]

[0050] Among them, F buy is the gas purchase cost, F OM It is the operation and maintenance cost. Carbon trading costs, F A Grid interaction cost; F is the total system operation cost;

[0051] Gas purchase cost F buy The model is:

[0052]

[0053] P g,buy (t) is the natural gas purchase amount in time period t; β t is the natural gas price in time period t;

[0054] Operation and maintenance cost OM The model is:

[0055]

[0056] C i is the operation and maintenance cost coefficient of the i-th equipment in the system; P i (t) is the operating power of the i-th device in the system during the cycle;

[0057] Carbon trading costs Adopt a tiered carbon trading model, which includes a carbon emission quota model, an actual carbon emission model and a tiered carbon emission trading model;

[0058] Carbon emission quota model:

[0059]

[0060] Actual carbon emission model:

[0061]

[0062] Tiered carbon emissions trading model:

[0063] E s =E a -E

[0064] E s is the total amount of carbon emission rights trading; E a is the actual carbon emissions; E is the carbon emission quota;

[0065]

[0066] The grid interaction cost model is:

[0067]

[0068] Among them, P e,buy (t) is the power purchase amount of the system in time period t; P e,sale (t) is the power sales of the system in time period t; α t is the electricity purchase price; t For the on-grid electricity price.

[0069] Furthermore, the constraint model includes the following constraints:

[0070] (1) Overall electrical balance, thermal balance, gas balance, and hydrogen balance constraints of equipment in the intelligent control system;

[0071] (2) Upper and lower limit constraints of equipment in the intelligent control system;

[0072] (3) Operation constraints of energy storage devices in intelligent control systems.

[0073] Furthermore, the device energy supply strategy includes:

[0074] Step A: Give priority to the use of photovoltaic power generation systems and wind power generation systems to maximize the proportion of renewable energy in the energy supply of the multi-hybrid energy storage system;

[0075] Step B: Using Carnot batteries to dynamically allocate energy supply capacity according to the electric and thermal load requirements of the multi-hybrid energy storage system to ensure stable operation of the multi-hybrid energy storage system;

[0076] Step C: When the energy supply of renewable energy is insufficient, the internal cogeneration unit and the Carnot battery are used to supplement the energy supply of the electric load; when the energy supply of renewable energy is excessive, the excess electricity is stored in the Carnot battery for subsequent use;

[0077] Step D: If the multi-hybrid energy storage system still cannot meet the load demand after the above steps, it will purchase electricity from the external large power grid through connection with the external large power grid to meet the remaining load demand.

[0078] Furthermore, the mathematical model of scheduling optimization includes:

[0079] A data input module for receiving real-time information on electricity and gas prices, user load demand data, real-time weather conditions for wind power and photovoltaic power supply, preset values ​​of economic parameters, and equipment efficiency data;

[0080] The optimization processing module includes an initialization step, wherein the initialization step sets an objective function with the goal of minimizing the total daily operating cost of the system, designs corresponding constraints according to the actual operating conditions of the system, and sets adjustable operating variables. In the optimization processing process, a mixed integer linear programming problem is constructed and solved by a solver to obtain an optimized scheduling result of the energy supply of each device;

[0081] Result output module, used to output the optimization scheduling results and the optimal solution of the objective function;

[0082] The data processing module is used to calculate and output the daily operating cost of the output optimization scheduling result plan.

[0083] Compared with the prior art, the multi-element hybrid energy storage system with Carnot batteries operating under variable operating conditions described in the present invention has the following advantages:

[0084] (1) The multi-energy hybrid energy storage system containing Carnot batteries and operating under variable operating conditions described in the present invention constructs a multi-energy complementary model, which enables efficient conversion and complementarity between electricity, heat and hydrogen, significantly improving the economy and flexibility of the multi-energy hybrid energy storage system, especially in dealing with the volatility of renewable energy and changes in user loads. Through the segmented processing strategy of Carnot batteries, the system can accurately match the charging and discharging efficiency of different load intervals, thereby reducing the loss in the energy conversion process, improving energy utilization, and enhancing the adaptability and stability of the system.

[0085] (2) The multi-element hybrid energy storage system with variable operating conditions containing Carnot batteries described in the present invention realizes variable operating conditions of Carnot batteries by introducing Carnot batteries and adopting a segmented processing method to build a Carnot battery operation model, which significantly enhances the adaptability and flexibility of the system under different load conditions, enabling it to dynamically adjust the energy supply strategy according to actual needs, thereby improving the utilization rate of renewable energy and the overall operating efficiency. The introduction of an intelligent control system is based on an operating cost model and a constraint condition model that include a daily operating cost objective function, which can respond to changes in the external environment in real time, optimize the operating strategy, and ensure that when the renewable energy supply is insufficient, the power grid is supplemented with electricity to meet the user's electric and thermal load needs.

[0086] Another object of the present invention is to propose a scheduling optimization method for a multi-element hybrid energy storage system with Carnot batteries in variable operating conditions, which is applied to the multi-element hybrid energy storage system with Carnot batteries in variable operating conditions as described above, comprising:

[0087] S1: Establish equipment models for energy conversion and storage of electricity, heat and hydrogen in variable operating conditions including Carnot batteries;

[0088] S2: Setting the objective function including daily operation cost parameters and constructing the operation cost model of the multivariate hybrid energy storage system including the variable operating condition operation of the Carnot battery;

[0089] S3: According to the actual operating conditions of the intelligent control system, set constraints and build a constraint model;

[0090] S4: constructing a mathematical model for scheduling optimization based on the equipment model, operation cost model and constraint condition model established in steps S1 to S3;

[0091] S5: Adjust the equipment energy supply strategy of the intelligent control system;

[0092] S6: The optimization processing module of the mathematical model of scheduling optimization transforms the energy scheduling optimization problem into a mixed integer linear programming problem, and uses the solver to solve it to obtain the optimal scheduling of each equipment link;

[0093] S7: The mathematical model of scheduling optimization outputs the optimized scheduling results and the optimal solution of the objective function, and outputs the daily operating cost of the multi-hybrid energy storage system.

[0094] The scheduling optimization method of the multi-element hybrid energy storage system with Carnot batteries operating under variable operating conditions has the same advantages as the multi-element hybrid energy storage system with Carnot batteries operating under variable operating conditions compared with the prior art, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0096] Figure 1 It is a structural block diagram of a multi-element hybrid energy storage system with Carnot batteries operating under variable working conditions according to an embodiment of the present invention;

[0097] Figure 2 It is a flow chart of a method for optimizing the scheduling of a multi-element hybrid energy storage system with Carnot batteries operating under variable operating conditions according to an embodiment of the present invention;

[0098] Figure 3 A schematic diagram of wind power and photovoltaic power supply of the multi-element hybrid energy storage system according to an embodiment of the present invention on a typical day;

[0099] Figure 4 This is an optimized operation diagram of each device of the multi-element hybrid energy storage system according to an embodiment of the present invention on a typical day;

[0100] Figure 5An optimized operation diagram of each device when providing grid support for the multi-element hybrid energy storage system disclosed in the present invention;

[0101] Figure 6 It is a logic block diagram of a method for optimizing the scheduling of a multi-element hybrid energy storage system with Carnot batteries operating under variable operating conditions as described in an embodiment of the present invention. DETAILED DESCRIPTION

[0102] In order to make the technical means, objectives and effects of the present invention easy to understand, the embodiments of the present invention are described in detail below with reference to specific drawings.

[0103] It should be noted that all the terms used in the present invention for directional and positional indications, such as "up", "down", "left", "right", "front", "back", "vertical", "horizontal", "inside", "outside", "top", "low", "lateral", "longitudinal", "center", etc., are only used to explain the relative positional relationship and connection status between the components in a certain state (as shown in the accompanying drawings), and are only for the convenience of describing the present invention, rather than requiring the present invention to be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes, and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features.

[0104] In the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0105] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0106] like Figures 1 to 6 As shown, the present application discloses a multi-element hybrid energy storage system with Carnot battery variable operating conditions, including an energy supply part, an energy conversion part, an energy storage part and a terminal energy use part:

[0107] The energy supply part includes a photovoltaic power generation system, a wind power generation system and a power grid that provides power to the system, which is used to provide power to the system;

[0108] The energy conversion part includes a combined heat and power unit, an electrolyzer, a hydrogen fuel cell, a gas boiler and a Carnot battery;

[0109] The energy storage part includes a heat storage tank and a hydrogen storage tank, which are used to store the converted heat energy and hydrogen energy;

[0110] The terminal energy consumption part includes electrical load and thermal load, which are used to consume the electrical energy and thermal energy provided by the system;

[0111] Wherein, the cogeneration unit can generate electricity and heat energy at the same time, the electrolyzer can use electricity to decompose water to produce hydrogen, the hydrogen fuel cell can convert hydrogen energy into electricity and generate heat energy at the same time, the hydrogen storage tank can store excess hydrogen generated by electrolysis in the electrolyzer, the Carnot battery can convert electricity and heat energy into each other, the Carnot battery converts excess electricity into heat energy through the electric-to-heat part and stores it in the form of heat energy, the heat energy can be used to meet the heat load or generate electricity through the heat-to-electricity process to meet the electric load, the cogeneration unit can generate electricity and heat energy by burning natural gas, the gas boiler can burn natural gas to generate heat energy, and can store excess heat in the heat storage tank;

[0112] The Carnot battery is divided into multiple performance parameters according to different load intervals;

[0113] It also includes an intelligent control system, which is used to regulate the operation of the energy supply part, energy conversion part, and energy storage part according to the electric and thermal load demand and power generation situation, so as to realize the conversion and output of the three energy forms of electricity, heat and hydrogen.

[0114] The present application discloses a multi-element hybrid energy storage system with Carnot batteries for variable operating conditions. By integrating a variety of energy supply and conversion equipment such as photovoltaic power generation systems, wind power generation systems, power grids, cogeneration units, electrolyzers, hydrogen fuel cells, gas boilers and Carnot batteries, under the action of an intelligent control system, flexible complementarity and conversion of electricity, heat and hydrogen are achieved. Among them, photovoltaic power generation and wind power generation are the main sources of renewable energy and are connected to the power grid at the same time to ensure the stability of power supply. The energy conversion part combines cogeneration units, electrolyzers, hydrogen fuel cells, gas boilers and Carnot batteries, and can efficiently convert electrical energy into thermal energy or hydrogen energy under different operating conditions, and vice versa. Specifically, the cogeneration unit generates heat energy while generating electricity, the electrolyzer uses electricity to decompose water to produce hydrogen, and the hydrogen fuel cell converts the chemical energy of hydrogen into electricity and produces water. The energy supply strategy of each part is flexibly adjusted according to the load demand and energy supply. In particular, the Carnot battery divides its load range into multiple segments through segmented processing to adapt to different charging and discharging efficiency requirements, thereby ensuring that when the load demand changes and the renewable energy supply fluctuates, the system can flexibly adjust the output strategy of each part, and store the converted heat and hydrogen energy through heat storage tanks and hydrogen storage tanks to cope with unstable energy supply and changing load demand.

[0115] The multi-element hybrid energy storage system with Carnot batteries operating under variable operating conditions disclosed in the present application constructs a multi-energy complementary model, which enables efficient conversion and complementarity between electricity, heat and hydrogen, significantly improving the economy and flexibility of the multi-element hybrid energy storage system, especially in dealing with the volatility of renewable energy and changes in user loads. Through the segmented processing strategy of the Carnot battery, the system can accurately match the charging and discharging efficiency of different load ranges, thereby reducing losses in the energy conversion process, improving energy utilization, and enhancing the adaptability and stability of the system.

[0116] As a preferred example of the present application, the Carnot battery adopts a segmented processing method according to the simulation results to divide its load interval into three sections: 0-30% THA, 30%-75% THA, and 75%-100% THA, and establishes a Carnot battery operation model. In the example of the present application, the Carnot battery operation model is:

[0117]

[0118]

[0119] Among them, S CB It is the equivalent storage capacity of Carnot battery; are the charging efficiency, discharging efficiency and heating efficiency of the Carnot battery respectively; P e,CB , P CB,e , P CB,hThey are the charge capacity, discharge capacity and heat release of the Carnot battery respectively.

[0120] This setting further optimizes the operation model of the Carnot battery. According to the performance of the Carnot battery in different load ranges (i.e., state of charge SOC), the working state of the battery is finely divided into three stages: 0-30% THA (or SOC), 30%-75% THA (or SOC) and 75%-100% THA (or SOC). For each stage, the model sets specific efficiency parameters, including charging efficiency, discharging efficiency and heating efficiency, which work together on the battery's charging and discharging process.

[0121] The multi-element hybrid energy storage system with variable operating conditions containing Carnot batteries described in this application builds a Carnot battery operation model through a segmented processing method to realize variable operating conditions of the Carnot battery. The Carnot battery can flexibly adjust its working state according to the real-time load demand and energy conversion situation to ensure that the best energy utilization efficiency can be achieved under different operating conditions, which not only improves the operating efficiency of the battery in different load ranges, but also greatly reduces the loss in the energy conversion process, thereby improving the economy and stability of the overall system. In addition, because the model can more accurately characterize the operating characteristics of the battery under different charge states, the calculation of battery efficiency becomes more accurate, thereby enhancing the system's adaptability to power grid fluctuations, effectively reducing energy waste and improving the utilization rate of renewable energy, making the conversion between the three energy forms of electricity, heat, and hydrogen more flexible.

[0122] As a preferred example of the present application, the intelligent control system includes an equipment model, a mathematical model for scheduling optimization, an operating cost model, and a constraint model, wherein the operating cost model includes an objective function of daily operating cost, and the constraint model includes constraints including overall balance constraints and / or upper and lower limits of equipment and / or energy storage equipment operation constraints. The intelligent control system outputs the optimization scheduling result and the optimization objective function result according to the objective function and the constraint model, and performs electricity and heat conversion. The intelligent control system described in the present application is used to realize efficient scheduling and management of the power system by integrating the equipment model, the mathematical model for scheduling optimization, the operating cost model and the constraint model. The equipment model is used to simulate the actual operating state of the power system to provide basic data for scheduling optimization; the operating cost model contains daily operating cost parameters, quantitatively evaluates the economic and environmental benefits of different scheduling schemes, and helps decision makers choose the best scheme among multiple schemes; at the same time, the constraint model comprehensively considers the overall balance requirements of the power system, the upper and lower limits of the equipment operation, and the constraints of the operating characteristics of the energy storage equipment, ensuring that the scheduling scheme is efficient and feasible and safe; the scheduling optimization mathematical model solves the optimal scheduling strategy through an algorithm, thereby realizing efficient conversion of electricity and heat energy. Through iterative calculation of the algorithm, the system outputs the optimal scheduling result and the corresponding optimized objective function value based on the objective function and constraint model, and finally realizes the mutual conversion and efficient utilization of electricity and thermal energy, thereby achieving the dual goals of cost savings and environmental protection while ensuring the stable operation of the system.

[0123] The intelligent control system described in this application can automatically screen out scheduling plans that are both economical and environmentally friendly, thereby effectively reducing the overall cost of electricity production and conversion, reducing the emission of environmental pollutants, and promoting green and low-carbon energy transformation.

[0124] The intelligent control system sets the scheduling optimization problem as a mixed integer linear programming problem based on the equipment model, the mathematical model of scheduling optimization, the constraints, the initial data and the equipment energy supply strategy, and uses an intelligent algorithm to solve the mathematical model of scheduling optimization, and outputs the equipment scheduling results and the optimization target results. As a preferred example of this application, the CPLEX solver and the YALMIP toolbox are used in Matlab to solve and obtain the optimal economic scheduling result.

[0125] The intelligent control system described in the present application can formulate a more accurate and efficient equipment scheduling plan by comprehensively considering the equipment model, the mathematical model of scheduling optimization and various constraints, effectively avoiding the energy waste and inefficiency problems that may exist in traditional scheduling methods. The mathematical model of scheduling optimization sets the scheduling optimization problem as a mixed integer linear programming problem, and uses an intelligent algorithm to solve the mathematical model of scheduling optimization, which not only improves the speed and accuracy of the solution, but also can adapt to the complex needs in different scenarios and realize flexible and changeable scheduling strategies.

[0126] As a preferred example of the present application, the equipment model includes a wind power generation model, a photovoltaic power generation model, a cogeneration unit model, an electrolyzer model, a hydrogen fuel cell model, a gas boiler model, a Carnot battery model and an energy storage device model, wherein:

[0127] The wind power generation model is:

[0128]

[0129] v t is the instantaneous wind speed, v in , v r and v out are cut-in wind speed, rated wind speed and cut-out wind speed respectively, P r is the rated power of the fan, P DG is the output power of the fan;

[0130] The photovoltaic power generation model is:

[0131] P SP =η s SI t ;

[0132] P SP is the output power of the photovoltaic panel, S is the total area of ​​the photovoltaic panel, η S is the photovoltaic conversion efficiency, I is the solar irradiance;

[0133] The combined heat and power unit model is:

[0134]

[0135] Among them, P CHP,e Output electric energy for the combined heat and power unit; P CHP,h Output heat energy for the combined heat and power unit; P GT,e P is the electrical energy output by the gas turbine; GT,h P is the heat energy output by the gas turbine; ORC,e Output electrical energy to the low-temperature waste heat power generation device; is the heat conversion efficiency of the waste heat boiler; The power generation efficiency of the low-temperature waste heat power generation device; The efficiency of converting gas turbine input power into electrical output power; The efficiency of converting the input natural gas power of the gas turbine into the output heat energy; μ WHB is the ratio of waste heat generated by the gas turbine to the waste heat boiler, μ ORC is the ratio of waste heat generated by gas turbine to low-temperature waste heat power generation device, μ WHB :μ ORC =1;Q g,CHP V is the natural gas input to the cogeneration unit; g is the calorific value of natural gas, 9.88kWh / m 3 ;

[0136] Electrolyzer model:

[0137]

[0138] Output hydrogen energy for electrolyzer, P e,EL is the electrical energy input to the electrolytic cell, η EL is the conversion efficiency of the electrolyzer;

[0139] Hydrogen fuel cell model:

[0140]

[0141] Among them, P HFC,e Hydrogen fuel cells output electrical energy; P HFC,h Output heat energy for hydrogen fuel cells; To input hydrogen energy into hydrogen fuel cells; The efficiency of hydrogen fuel cells in converting hydrogen energy into electricity; The efficiency of hydrogen fuel cells in converting hydrogen energy into heat energy;

[0142] Gas boiler model:

[0143] P GB,h =η GB Q g,GB V g ;

[0144] Among them, P GB,h Output heat energy for gas boiler; Q g,GB is the natural gas input to the gas boiler; GB is the conversion efficiency of the gas boiler; V g is the calorific value of natural gas, 9.88kWh / m 3 ;

[0145] Carnot battery model:

[0146] Based on the variable working condition operation of Carnot battery, the round trip efficiency of Carnot battery is divided into three sections by segment processing method:

[0147]

[0148]

[0149] Among them, S CB It is the equivalent storage capacity of Carnot battery; is the charging efficiency of the Carnot battery; is the discharge efficiency of the Carnot cell; is the heating efficiency of the Carnot battery; P e,CB The charge of the Carnot battery; P CB,e is the discharge capacity of the Carnot battery; P CB,h Remove heat from the Carnot battery;

[0150] Energy storage device, including heat storage device and hydrogen storage device, its model is:

[0151]

[0152] Among them, S i is the capacity of the energy storage device, is the charging power of the energy storage device; is the discharge power of the energy storage device; is the charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device.

[0153] The scheme integrates a variety of energy conversion devices, including wind power generation, photovoltaic power generation, cogeneration, electrolyzer, hydrogen fuel cell, gas boiler and Carnot battery, to build an efficient multi-energy complementary and utilization system equipment model. Wind power generation and photovoltaic power generation are the main sources of renewable energy. Electric energy is input into the electrolyzer through the corresponding conversion device for hydrogen production and storage, so that efficient storage of green energy can be achieved; cogeneration generates electricity and heat energy by burning natural gas, improving the overall efficiency of energy utilization; hydrogen fuel cell can convert hydrogen energy into electricity and heat energy, and can provide heat energy through gas boiler; Carnot battery adjusts its operating performance efficiency according to different working conditions, thereby improving the overall energy utilization efficiency. The various energy conversion devices in the equipment model described in this application are coordinated by optimizing the operation strategy to form a multi-level energy conversion and utilization system, ensuring that the overall system can operate efficiently under different load demands and environmental conditions, while realizing intelligent scheduling and management of energy, significantly improving the comprehensive utilization efficiency of the energy system, reducing the degree of dependence on a single energy source, and enhancing the stability and reliability of the system.

[0154] As a preferred example of this application, the objective function is:

[0155]

[0156] Among them, F buy is the gas purchase cost, F OM It is the operation and maintenance cost. Carbon trading costs, F A Grid interaction cost; F is the total system operation cost;

[0157] Gas purchase cost F buy The model is:

[0158]

[0159] Pg,buy(t) is the natural gas purchase volume in time period t; β t is the natural gas price in time period t;

[0160] Operation and maintenance cost OM The model is:

[0161]

[0162] C i is the operation and maintenance cost coefficient of the i-th equipment in the system; P i (t) is the operating power of the i-th device in the system during the cycle;

[0163] Carbon trading costs Adopting a tiered carbon trading model, which consists of three parts: carbon emission quota model, actual carbon emission model and tiered carbon emission trading model;

[0164] Carbon emission quota model:

[0165]

[0166] Actual carbon emission model:

[0167]

[0168] Tiered carbon emissions trading model:

[0169] E s =E a -E;

[0170] E s is the total amount of carbon emission rights trading; E a is the actual carbon emissions; E is the carbon emission quota;

[0171]

[0172] The grid interaction cost model is:

[0173]

[0174] Among them, P e,buy (t) is the power purchase amount of the system in time period t; P e,sale (t) is the power sales of the system in time period t; α t is the electricity purchase price; t For the on-grid electricity price.

[0175] The scheme comprehensively analyzes and optimizes cost factors such as natural gas purchase, equipment operation and maintenance, carbon trading, and grid interaction. By constructing an objective function to minimize the total operating cost, it achieves comprehensive optimization of the daily operating cost of the multi-hybrid energy storage system, especially in terms of the economic and environmental benefits of the natural gas system. The scheme adopts a stepped carbon trading model, which flexibly combines carbon emission quotas with actual carbon emissions to determine the cost of carbon trading. This method can more accurately reflect the impact of market mechanisms on carbon trading, which not only effectively reduces carbon trading costs, but also enhances the system's ability to adapt to market changes, making carbon emissions management more flexible and efficient. The setting of the above objective function achieves economic optimization of natural gas system operation through a systematic and multi-dimensional cost control strategy, which not only improves overall operational efficiency, but also provides strong support for the realization of environmental protection goals.

[0176] As a preferred example of the present application, the constraint model includes the following constraints:

[0177] (1) Overall electrical balance, thermal balance, gas balance, and hydrogen balance constraints of equipment in the intelligent control system;

[0178] The power balance constraint is:

[0179]

[0180] Thermal energy balance constraints:

[0181]

[0182] Natural gas balance constraints:

[0183] Q g,buy (t) = Q g,CHP (t)+Q g,GB (t);

[0184] Hydrogen energy balance constraints:

[0185]

[0186] (2) Upper and lower limit constraints of equipment in the intelligent control system;

[0187] P K,min ≤P K ≤P K,max ;

[0188] P K is the operating power of the equipment in the system, P K,min The minimum power required for the device to operate, P K,max the maximum power required for the operation of the equipment;

[0189] (3) Operation constraints of energy storage devices in intelligent control systems;

[0190]

[0191] It is the charging and discharging power of the energy storage device; is the charging and discharging state parameter of the energy storage device, Indicates that the energy storage device is charged, and vice versa for energy release; S i (T) is the capacity of energy storage equipment, They are respectively the minimum and maximum capacities required for the operation of energy storage equipment.

[0192] The intelligent control system disclosed in this application ensures the efficient, stable and reliable operation of the system through a series of precise constraints. Specifically, in the example of this application, the constraint model first ensures that the entire system meets the basic constraints of overall electrical balance, thermal balance, gas balance and hydrogen balance. These balances are the basis for the stable operation of the system and can effectively prevent resource waste and energy shortages; secondly, the intelligent control system also takes into account the upper and lower limit constraints of each device to ensure that the output power of the device is within the specified range to avoid overload or underload, thereby protecting the safety of the equipment and extending its service life; in addition, in order to ensure the efficient operation of the energy storage device, the system strictly manages the power of the energy storage device to ensure that it fluctuates between the preset minimum and maximum values, and keeps the initial and final power consistent to maintain the continuous and stable operation of the system. This series of constraints work together to ensure the dynamic adjustment capability of the intelligent control system, meet the overall load requirements, and achieve efficient use of energy, providing strong support for the practical application of multi-hybrid energy storage systems.

[0193] As a preferred example of this application, the device energy supply strategy includes:

[0194] Step A: Give priority to the use of photovoltaic power generation systems and wind power generation systems to maximize the proportion of renewable energy in the energy supply of the multi-hybrid energy storage system;

[0195] Step B: Using Carnot batteries to dynamically allocate energy supply capacity according to the electric and thermal load requirements of the multi-hybrid energy storage system to ensure stable operation of the multi-hybrid energy storage system;

[0196] Step C: When the energy supply of renewable energy is insufficient, the internal cogeneration unit and the Carnot battery are used to supplement the energy supply of the electric load; when the energy supply of renewable energy is excessive, the excess electricity is stored in the Carnot battery for subsequent use;

[0197] Step D: If the multi-hybrid energy storage system still cannot meet the load demand after the above steps, it will purchase electricity from the external large power grid through connection with the external large power grid to meet the remaining load demand.

[0198] Through the above-mentioned equipment energy supply strategy, the multi-hybrid energy storage system of this application realizes the maximum utilization of renewable energy and the effective management of load. The multi-hybrid energy storage system gives priority to the use of renewable energy such as photovoltaic and wind power generation, which not only effectively improves the utilization ratio of clean energy, but also reduces the dependence on fossil energy, and promotes environmental protection and sustainable development; secondly, the dynamic adjustment capability of the Carnot battery ensures that the system can respond in time when the load changes, and improves the stability and reliability of energy supply; in the case of insufficient energy supply from renewable energy, the flexible use of Carnot batteries can effectively make up for the energy supply gap and avoid the risk of system downtime caused by insufficient energy supply; in addition, the connection with the external large power grid provides additional protection for the system, ensuring continuous and stable power supply under any circumstances. The equipment energy supply strategy described in this application, through intelligent adjustment and multi-energy complementarity, not only improves the overall efficiency of the energy system, but also provides strong support for the realization of a green, low-carbon, safe and efficient energy supply system, and promotes the widespread application of renewable energy and future sustainable development.

[0199] As a preferred example of the present application, the mathematical model of scheduling optimization includes:

[0200] A data input module for receiving real-time information on electricity and gas prices, user load demand data, real-time weather conditions for wind power and photovoltaic power supply, preset values ​​of economic parameters, and equipment efficiency data;

[0201] The optimization processing module includes an initialization step, wherein the initialization step sets an objective function with the goal of minimizing the total daily operating cost of the system, designs corresponding constraints according to the actual operating conditions of the system, and sets adjustable operating variables. In the optimization processing process, a mixed integer linear programming problem is constructed and solved by a solver to obtain an optimized scheduling result of the energy supply of each device;

[0202] Result output module, used to output the optimization scheduling results and the optimal solution of the objective function;

[0203] The data processing module is used to calculate and output the daily operating cost of the output optimization scheduling result plan.

[0204] As a preferred example of the present application, during the optimization process of the optimization processing module, the intelligent control system sets the scheduling optimization problem as a mixed integer linear programming problem through the mathematical model of scheduling optimization, and uses the CPLEX solver and YALMIP toolbox in Matlab to solve it, and the output data includes the optimization scheduling results and the optimal result of the objective function.

[0205] The scheduling optimization function of the intelligent control system described in this application relies on a finely constructed mathematical model, which is composed of four modules: data input, optimization processing, result output and data processing. The data input module first collects real-time electricity price and gas price information, user load demand, weather condition data of renewable energy (wind power and photovoltaic), preset values ​​of economic parameters and efficiency data of various equipment; then, the optimization processing module is started, and the function with the goal of minimizing the total cost of system operation is established in the initialization step, and the necessary constraints are set according to the actual operation scenario, and the adjustable operating variables are set at the same time; on this basis, the module converts the scheduling optimization problem into a mixed integer linear programming problem, and uses the CPLEX solver and YALMIP toolbox on the Matlab platform for efficient solution to determine the optimal scheduling of each energy supply equipment. After the solution is completed, the result output module displays the optimal scheduling results and the optimal value of the objective function; finally, the data processing module conducts an in-depth analysis of these results, calculates and outputs the specific value of the daily operating cost.

[0206] Through the above process, the multi-hybrid energy storage system can not only respond to fluctuations in electricity and gas prices in real time and flexibly adjust energy supply strategies to reduce costs, but also fully consider changes in weather conditions and maximize the use of renewable energy; at the same time, the optimization processing module accurately calculates the optimal scheduling configuration of each device through the solution of mixed integer linear programming problems, ensuring the flexible conversion and efficient use of various energy forms such as electricity, heat, and hydrogen, thereby fully meeting the diversified energy needs of the park without sacrificing environmental benefits, and achieving a dual improvement in the economy and sustainability of energy utilization.

[0207] The present application also discloses a scheduling optimization method for a multi-element hybrid energy storage system with Carnot batteries in variable operating conditions, which is applied to the multi-element hybrid energy storage system with Carnot batteries in variable operating conditions as described above, comprising:

[0208] S1: Establish equipment models for energy conversion and storage of electricity, heat and hydrogen in variable operating conditions including Carnot batteries;

[0209] S2: Setting the objective function including daily operation cost parameters and constructing the operation cost model of the multivariate hybrid energy storage system including the variable operating condition operation of the Carnot battery;

[0210] S3: According to the actual operating conditions of the intelligent control system, set constraints and build a constraint model;

[0211] S4: constructing a mathematical model for scheduling optimization based on the equipment model, operation cost model and constraint condition model established in steps S1 to S3;

[0212] S5: Adjust the equipment energy supply strategy of the intelligent control system;

[0213] S6: The optimization processing module of the mathematical model of scheduling optimization transforms the energy scheduling optimization problem into a mixed integer linear programming problem, and uses the solver to solve it to obtain the optimal scheduling of each equipment link;

[0214] S7: The mathematical model of scheduling optimization outputs the optimized scheduling results and the optimal solution of the objective function, and outputs the daily operating cost of the multi-hybrid energy storage system.

[0215] The scheduling optimization method of the multi-element hybrid energy storage system with variable operating conditions containing Carnot batteries described in the present application is to build a distributed energy system with variable operating conditions containing Carnot batteries to meet the user's electricity and heat loads; use a segmented processing method to build a Carnot battery variable operating model; take the single-day operating cost as the target, construct an operating cost model of the multi-element hybrid energy storage system, and use a mixed integer linear programming method to determine the system scheduling operation plan. This application can solve the scheduling optimization problem of existing distributed energy systems that do not contain Carnot batteries with variable operating conditions, can make the system operation more economical and environmentally friendly, make full use of renewable energy, realize flexible conversion between electricity and heat, and realize the supporting role of the distributed energy system to the power grid, up to 31.61% of the rated capacity.

[0216] The present application discloses a method for optimizing the scheduling of a multi-element hybrid energy storage system with variable operating conditions including Carnot batteries. Step S1 provides a theoretical basis for the optimal scheduling of the system by establishing a device model for energy conversion and storage of electricity, heat and hydrogen in the variable operating conditions including Carnot batteries. Step S2 constructs an operating cost model including the variable operating conditions of Carnot batteries by setting daily operating cost parameters, aiming to minimize the operating cost and improve the economic benefits of the system. Step S3 sets constraints that match the actual operating conditions of the intelligent control system to ensure that the system meets the requirements of stability and safety in actual operation. Based on these basic models, step S4 establishes a mathematical model for scheduling optimization, providing a framework for the optimal scheduling of the system. In step S5, the energy supply strategy of the equipment of the intelligent control system is adjusted to achieve load demand balance and maximum utilization of renewable energy. Step S6 converts the optimization problem into a mixed integer linear programming problem, solves it with the help of a solver, and obtains the optimal scheduling scheme for each equipment link. Finally, step S7 outputs the optimized scheduling result and the optimal solution of the objective function, and provides the daily operating cost of the multi-element hybrid energy storage system, thereby providing an optimization scheme for the system operation. Through the above series of steps, efficient scheduling of distributed energy systems is achieved. The entire method can realize fine regulation of distributed energy systems to meet users' electricity and heat load requirements, while achieving efficient energy utilization and optimization of system operation.

[0217] The scheduling optimization method of the multi-element hybrid energy storage system with variable operating conditions including Carnot batteries disclosed in the present application solves the scheduling optimization problem of the existing distributed energy system that does not include the variable operating conditions of Carnot batteries. By introducing the variable operating condition operation model of Carnot batteries, the adaptability and flexibility of the system under different load conditions are enhanced, so that the system can optimize operation in a larger range. The system is precisely scheduled by combining the mixed integer linear programming method, and the economic efficiency of the system is improved by minimizing the daily operating cost, while ensuring the efficient use of renewable energy. The scheduling optimization method of the multi-element hybrid energy storage system with variable operating conditions including Carnot batteries disclosed in the present application not only realizes the flexible conversion between electricity and heat, optimizes the absorption capacity of renewable energy, but also can effectively support the power grid, improve the reliability and stability of the distributed energy system, and the maximum is 31.61% of the rated capacity, which greatly enhances the system's support for the external power grid; in addition, the multi-element hybrid energy storage system can dynamically adjust the energy supply strategy according to the actual operating conditions and load demand, so that the energy storage equipment, power generation equipment and load management are more coordinated, and the operation efficiency and economic benefits of the overall system are improved.

[0218] The electric-heat-hydrogen multi-element hybrid energy storage system with Carnot battery variable operating conditions provided by the present invention constructs a Carnot battery operation model through a segmented processing method, so that it can better adapt to the actual operating conditions and realize flexible conversion between electricity and heat to meet the user's electric and thermal load requirements; the multi-element hybrid energy storage system takes the single-day operating cost including environmental costs as the optimization target, combines the output strategies of each part in the system, and establishes a scheduling optimization method, thereby realizing an economical and low-carbon operating mode and improving the multi-energy complementarity and conversion capabilities. This design not only improves the operating efficiency of the system, but also ensures the effective use of resources and promotes the consumption of renewable energy.

[0219] The scheduling optimization method for a multi-element hybrid energy storage system with variable operating conditions containing Carnot batteries proposed in the present invention discloses a scheduling optimization scheme for a flexible distributed energy system, introduces Carnot batteries into traditional distributed energy systems, and constructs a more efficient way of converting electricity into heat. This flexibility enables the system to dynamically adjust according to changes in the user's electric and thermal loads, ensuring stable energy supply under different load conditions. The research results show that the distributed energy system containing Carnot batteries excels in flexibility and stability, and can effectively support the power grid, ensuring that the system can maintain stable power supply during peak loads or fluctuations in renewable energy. This innovative solution not only improves the overall efficiency of the energy system, but also provides strong support for achieving green and low-carbon future energy supply.

[0220] Embodiment 1:

[0221] like Figure 1 As shown, this embodiment discloses a multi-element hybrid energy storage system with Carnot battery variable operating conditions, which consists of four main parts: energy supply part, energy conversion part, energy storage part and terminal energy use part. The energy supply part includes a photovoltaic power generation system, a wind power generation system and a power grid that provides power to the system; the energy conversion part consists of a cogeneration unit, an electrolyzer, a hydrogen fuel cell, a gas boiler and a Carnot battery.

[0222] In order to meet the demand for electricity and heat, the multi-hybrid energy storage system includes a cogeneration unit, a gas boiler and a Carnot battery, which can supply electricity and heat. The Carnot battery takes into account its variable operating conditions. Based on the simulation results of the Carnot battery, the segmented processing method is used to divide its load range into three sections: 0-30% THA, 30%-75% THA, and 75%-100% THA, and the Carnot battery operation model is established.

[0223] In order to realize the utilization and conversion of hydrogen, electrolyzers and hydrogen fuel cells are introduced into the system.

[0224] Energy storage system, including heat storage tanks and hydrogen storage tanks.

[0225] Embodiment 2:

[0226] like Figure 2 As shown, this embodiment discloses a scheduling optimization method for an electric-thermal-hydrogen multi-component hybrid energy storage system operating under variable operating conditions with a Carnot battery, and shows a flow chart of the system operation strategy. It includes input data, an optimization process, and output data. The input data includes electricity prices and gas prices, user loads and wind power and photovoltaic outputs, economic parameters, and equipment efficiency. The optimization process includes initialization, which includes setting the objective function, design constraints, and setting operating variables. Based on the system model, objective function, constraints, initial data, and operation strategy, a mixed integer linear programming problem and a mathematical model for optimal scheduling of a distributed energy system are proposed, which are solved in Matlab using the CPLEX solver and the YALMIP toolbox. The output data includes the optimal scheduling results and the optimal result of the objective function.

[0227] The low-carbon economic dispatch model with the minimum total operating cost of the electric-thermal-hydrogen multi-compound energy storage system with Carnot battery variable operating conditions is established:

[0228] The operating cost model of the energy storage system is:

[0229]

[0230] Among them, F buy is the gas purchase cost, F OM It is the operation and maintenance cost. Carbon trading costs, F A Grid interaction cost; F is the total system operating cost.

[0231] Gas purchase cost F buy The model is:

[0232]

[0233] Among them, P g,buy (t) is the natural gas purchase amount in time period t; β t is the natural gas price in time period t.

[0234] Operation and maintenance cost OM The model is:

[0235]

[0236] Among them, C i is the operation and maintenance cost coefficient of the i-th equipment in the system; P i (t) is the operating power of the i-th device in the system during the cycle.

[0237] Carbon trading costs A stepped carbon trading model is adopted, which consists of three parts: carbon emission quota model, actual carbon emission model and stepped carbon emission trading model.

[0238] Carbon emission quota model:

[0239]

[0240] Actual carbon emission model:

[0241]

[0242] Tiered carbon emissions trading model:

[0243] E s =E a -E;

[0244] Among them, E s is the total amount of carbon emission rights trading; E a is the actual carbon emissions; E is the carbon emission quota.

[0245]

[0246] The grid interaction cost model is:

[0247]

[0248] Among them, P e,buy (t), P e,sale (t) are the power purchase and sales of the system in time period t; α t , δ t They are the electricity purchase price and the on-grid electricity price respectively.

[0249] The proposed equipment energy supply strategy for the electric-thermal-hydrogen multi-element hybrid energy storage system with Carnot battery variable operating conditions is as follows:

[0250] (1) First, photovoltaic and wind power generation should be used to absorb as much renewable energy as possible and increase the proportion of renewable energy output;

[0251] (2) Secondly, the output of each link is distributed by focusing on meeting the heat load through the Carnot battery;

[0252] (3) If the power system output is still less than the load demand, the power load will be met by the cogeneration units and Carnot batteries in the system; if it is greater than the load demand, the surplus power will be stored in the Carnot batteries;

[0253] (4) If the demand is still not met, it is necessary to purchase electricity from the external power grid.

[0254] Embodiment 3:

[0255] The system is located in an industrial park in northwest China. On a typical day, the wind power and photovoltaic power generation output is as follows: Figure 3 The region adopts the peak-valley electricity price model, and its time-of-use electricity price is shown in Table 1. The peak electricity price of the system purchased from the external power grid is set at 1.2 yuan / kWh, the normal electricity price is 0.68 yuan / kWh, and the valley electricity price is 0.38 yuan / kWh.

[0256] Table 1 Local time-of-use electricity prices

[0257] Time Time (h) Electricity price (yuan / kWh) Peak 12:00-14:00,19:00-22:00 1.2 Flat section 8:00-11:00,15:00-18:00 0.68 Valley 0:00-7:00,23:00-24:00 0.38

[0258] Embodiment 4:

[0259] According to the objective function, constraints and energy supply strategy, a mixed integer linear programming problem and scheduling optimization mathematical model of the electric-thermal-hydrogen multi-element hybrid energy storage system containing Carnot batteries are established, and the CPLEX solver and YALMIP toolbox are used in Matlab to solve and obtain the optimal scheduling scheme for each device. The typical day operation cost results are shown in Table 2. The typical day scheduling optimization method is as follows Figure 4 When the system provides grid support, each device operates optimally as shown in Figure 5 As shown;

[0260] Table 2 Results of the final optimization cost index of the system

[0261]

[0262] The present invention provides an electric-heat-hydrogen multi-element hybrid energy storage system with variable operating conditions containing a Carnot battery. A Carnot battery operation model is constructed by a segmented processing method to realize the variable operating condition operation of the Carnot battery, which is more in line with the actual operating conditions. The Carnot battery can realize the flexible conversion of electricity and heat, and flexibly meet the user's electric and thermal load. Taking the system's daily operating cost including environmental costs as the goal, according to the energy supply strategy of each part in the system, a scheduling optimization method for the electric-heat-hydrogen multi-element hybrid energy storage system with variable operating conditions containing a Carnot battery is established to achieve economical and low-carbon operation of the system and improve the system's multi-energy complementarity and conversion capabilities. Compared with traditional distributed energy systems, the introduction of Carnot batteries into distributed energy systems constructs a more flexible and efficient way of electric-heat conversion, which flexibly meets the user's electric-heat load. The research results show that the distributed energy system containing Carnot batteries is flexible and stable, and can provide stable support for the power grid.

[0263] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-element hybrid energy storage system with Carnot batteries operating under variable conditions, characterized in that: It includes energy supply, energy conversion, energy storage and terminal energy use. The energy supply part includes a photovoltaic power generation system, a wind power generation system and a power grid that provides power to the system; The energy conversion part includes a combined heat and power unit, an electrolyzer, a hydrogen fuel cell, a gas boiler and a Carnot battery; The energy storage part includes a heat storage tank and a hydrogen storage tank; Wherein, the cogeneration unit can generate electricity and heat energy at the same time, the electrolyzer can use electricity to decompose water to produce hydrogen, the hydrogen fuel cell can convert hydrogen energy into electricity and generate heat energy at the same time, the hydrogen storage tank can store excess hydrogen generated by electrolysis in the electrolyzer, the Carnot battery can convert electricity and heat energy into each other, the Carnot battery converts excess electricity into heat energy through the electricity-to-heat part and stores it in the form of heat energy, the heat energy can be used to meet the heat load or generate electricity through the heat-to-electricity process to meet the electricity load, the cogeneration unit can generate electricity and heat energy by burning natural gas, the gas boiler can burn natural gas to generate heat energy, and can store excess heat in the heat storage tank; The Carnot battery is divided into multiple performance parameters according to different load intervals; It also includes an intelligent control system, which is used to regulate the operation of the energy supply part, energy conversion part, and energy storage part according to the electric and thermal load demand and power generation situation, so as to realize the conversion and output of the three energy forms of electricity, heat and hydrogen.

2. The multi-element hybrid energy storage system with Carnot battery variable operating conditions according to claim 1 is characterized in that: The intelligent control system includes an equipment model, a mathematical model for scheduling optimization, an operation cost model, and a constraint model, wherein the operation cost model contains an objective function of daily operation cost, and the constraint model includes constraints including overall balance constraints, upper and lower limit constraints of equipment, and operation constraints of energy storage equipment. The intelligent control system outputs optimized scheduling results and optimized objective function results according to the objective function and the constraint model.

3. The multi-element hybrid energy storage system with Carnot battery variable operating conditions according to claim 2 is characterized in that: The intelligent control system sets the scheduling optimization problem as a mixed integer linear programming problem based on the equipment model, the mathematical model of scheduling optimization, constraints, initial data and equipment energy supply strategy, and uses an intelligent algorithm to solve the mathematical model of scheduling optimization, and outputs the equipment scheduling results and optimization target results.

4. The multi-element hybrid energy storage system with Carnot battery variable operating conditions according to claim 2 is characterized in that: The Carnot battery adopts a segmented processing method according to the round-trip efficiency, and divides its load interval into three sections: 0-30% THA, 30%-75% THA, and 75%-100% THA, to establish a Carnot battery operation model.

5. The multi-element hybrid energy storage system with Carnot batteries and variable operating conditions according to claim 4 is characterized in that: The equipment models include a wind power generation model, a photovoltaic power generation model, a cogeneration unit model, an electrolyzer model, a hydrogen fuel cell model, a gas boiler model, a Carnot battery model and an energy storage device model, wherein: The wind power generation model is: v t is the instantaneous wind speed, v in , v r and v out are cut-in wind speed, rated wind speed and cut-out wind speed respectively, P r is the rated power of the fan, P DG is the output power of the fan; The photovoltaic power generation model is: P SP =η s YES t ; P SP is the output power of the photovoltaic panel, S is the total area of ​​the photovoltaic panel, η S is the photovoltaic conversion efficiency, I is the solar irradiance; The combined heat and power unit model is: Among them, P CHP,e Output electric energy for the combined heat and power unit; P CHP,h Output heat energy for the combined heat and power unit; P GT,e P is the electrical energy output by the gas turbine; GT,h P is the heat energy output by the gas turbine; ORC,e Output electrical energy to the low-temperature waste heat power generation device; is the heat conversion efficiency of the waste heat boiler; The power generation efficiency of the low-temperature waste heat power generation device; The efficiency of converting gas turbine input power into electrical output power; The efficiency of converting the input natural gas power of the gas turbine into the output heat energy; μ WHB is the ratio of waste heat generated by the gas turbine to the waste heat boiler, μ ORC is the ratio of waste heat generated by gas turbine to low-temperature waste heat power generation device, μ WHB :μ ORC =1;Q g,CHP V is the natural gas input to the cogeneration unit; g is the calorific value of natural gas, 9.88kWh / m 3 ; Electrolyzer model: Output hydrogen energy for electrolyzer, P e,EL is the electrical energy input to the electrolytic cell, η EL is the conversion efficiency of the electrolyzer; Hydrogen fuel cell model: Among them, P HFC,e Hydrogen fuel cells output electrical energy; P HFC,h Output heat energy for hydrogen fuel cells; To input hydrogen energy into hydrogen fuel cells; The efficiency of hydrogen fuel cells in converting hydrogen energy into electricity; The efficiency of hydrogen fuel cells in converting hydrogen energy into heat energy; Gas boiler model: P GB,h =η GB Q g,GB 5 g ; Among them, P GB,h Output heat energy for gas boiler; Q g,GB is the natural gas input to the gas boiler; GB is the conversion efficiency of the gas boiler; V g is the calorific value of natural gas, 9.88kWh / m 3 ; Carnot battery model: Based on the variable working condition operation of Carnot battery, the round trip efficiency of Carnot battery is divided into three sections by segment processing method: Among them, S CB It is the equivalent storage capacity of Carnot battery; is the charging efficiency of the Carnot battery; is the discharge efficiency of the Carnot cell; is the heating efficiency of the Carnot battery; P e,CB The charge of the Carnot battery; P CB,e is the discharge capacity of the Carnot battery; P CB,h Remove heat from the Carnot battery; Energy storage device, including heat storage device and hydrogen storage device, its model is: Among them, S i is the capacity of the energy storage device, is the charging power of the energy storage device; is the discharge power of the energy storage device; is the charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device.

6. The multi-element hybrid energy storage system with Carnot batteries and variable operating conditions according to claim 2 is characterized in that: The objective function is: Among them, F buy is the gas purchase cost, F OM It is the operation and maintenance cost. Carbon trading costs, F A Grid interaction cost; F is the total system operation cost; Gas purchase cost F buy The model is: Pg,buy(t) is the natural gas purchase volume in time period t; β t is the natural gas price in time period t; Operation and maintenance cost OM The model is: C i is the operation and maintenance cost coefficient of the i-th equipment in the system; P i (t) is the operating power of the i-th device in the system during the cycle; Carbon trading costs Adopt a tiered carbon trading model, which includes a carbon emission quota model, an actual carbon emission model and a tiered carbon emission trading model; Carbon emission quota model: Actual carbon emission model: Tiered carbon emissions trading model: AND s =And a -AND; E s is the total amount of carbon emission rights trading; E a is the actual carbon emissions; E is the carbon emission quota; The grid interaction cost model is: Among them, P e,buy (t) is the power purchase amount of the system in time period t; P e,sale (t) is the power sales of the system in time period t; α t is the electricity purchase price; t For the on-grid electricity price.

7. The multi-element hybrid energy storage system with Carnot batteries and variable operating conditions according to claim 2 is characterized in that: The constraint model includes the following constraints: (1) Overall electrical balance, thermal balance, gas balance, and hydrogen balance constraints of equipment in the intelligent control system; The power balance constraint is: P e,buy (t)=P e,sale (t)+P e,load (t)+P e,EL (t)+P e,CB (t)-P DG (t)- P SP (t)-P CHP,e (t)-P HFC,e (t)-P CB,e (t); Thermal energy balance constraints: Natural gas balance constraints: Q g,buy (t)=Q g,CHP (t)+Q g,GB (t); Hydrogen energy balance constraints: (2) Upper and lower limit constraints of equipment in the intelligent control system; P K,min ≤P K ≤P K,max ; (3) Operation constraints of energy storage devices in intelligent control systems; 8. The multi-element hybrid energy storage system with Carnot batteries and variable operating conditions according to claim 2 is characterized in that: The device energy supply strategy in the multi-element hybrid energy storage system includes: Step A: Give priority to the use of photovoltaic power generation systems and wind power generation systems to maximize the proportion of renewable energy in the energy supply of the multi-hybrid energy storage system; Step B: Using Carnot batteries to dynamically allocate energy supply capacity according to the electric and thermal load requirements of the multi-hybrid energy storage system to ensure stable operation of the multi-hybrid energy storage system; Step C: When the energy supply of renewable energy is insufficient, the internal cogeneration unit and the Carnot battery are used to supplement the energy supply of the electric load; when the energy supply of renewable energy is excessive, the excess electricity is stored in the Carnot battery for subsequent use; Step D: If the multi-hybrid energy storage system still cannot meet the load demand after the above steps, it will purchase electricity from the external large power grid through connection with the external large power grid to meet the remaining load demand.

9. The multi-element hybrid energy storage system with Carnot batteries and variable operating conditions according to claim 2, characterized in that: The mathematical model of scheduling optimization includes: A data input module for receiving real-time information on electricity and gas prices, user load demand data, real-time weather conditions for wind power and photovoltaic power supply, preset values ​​of economic parameters, and equipment efficiency data; The optimization processing module includes an initialization step, wherein the initialization step sets an objective function with the goal of minimizing the total daily operating cost of the system, designs corresponding constraints according to the actual operating conditions of the system, and sets adjustable operating variables. In the optimization processing process, a mixed integer linear programming problem is constructed and solved by a solver to obtain an optimized scheduling result of the energy supply of each device; Result output module, used to output the optimization scheduling results and the optimal solution of the objective function; The data processing module is used to calculate and output the daily operating cost of the output optimization scheduling result plan.

10. A scheduling optimization method for a multi-element hybrid energy storage system with Carnot batteries operating under variable conditions, characterized in that: A multi-element hybrid energy storage system for variable operating conditions containing Carnot batteries as claimed in any one of claims 1 to 9, comprising: S1: Establish equipment models for energy conversion and storage of electricity, heat and hydrogen in variable operating conditions including Carnot batteries; S2: Setting the objective function including daily operation cost parameters and constructing the operation cost model of the multivariate hybrid energy storage system including the variable operating condition operation of the Carnot battery; S3: According to the actual operating conditions of the intelligent control system, set constraints and build a constraint model; S4: constructing a mathematical model for scheduling optimization based on the equipment model, operation cost model and constraint condition model established in steps S1 to S3; S5: Adjust the equipment energy supply strategy of the intelligent control system; S6: The optimization processing module of the mathematical model of scheduling optimization transforms the energy scheduling optimization problem into a mixed integer linear programming problem, and uses the solver to solve it to obtain the optimal scheduling of each equipment link; S7: The mathematical model of scheduling optimization outputs the optimized scheduling results and the optimal solution of the objective function, and outputs the daily operating cost of the multi-hybrid energy storage system.

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