Micro-grid low-carbon economic optimization method considering hydrogen energy storage and alternating current and direct current hybrid characteristics

Abstract

The invention discloses a micro-grid low-carbon economic optimization method considering hydrogen energy storage and alternating-current and direct-current hybrid characteristics. The method comprises the following steps: establishing an AC / DC hybrid microgrid architecture comprising a hydrogen energy storage power station, wherein the architecture comprises photovoltaic power generation equipment and the hydrogen energy storage power station which are connected with a DC bus, wind power generation equipment and a power grid which are connected with an AC bus, and a VSC converter station for connecting the AC bus and the DC bus; hydrogen energy storage capacity configuration and operation scheduling are optimized by adopting a bilevel programming model, an upper-layer model takes the minimum comprehensive annual operation cost as a target, a lower-layer model takes the minimum microgrid annual operation cost as a target, and a multi-energy cooperative operation strategy is determined; and based on the model, converting the optimality condition of the lower-layer model into the constraint condition of the upper-layer model, and forming a solvable single-layer optimization problem. The method improves the new energy consumption capability and reduces the network loss.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and dispatching, and in particular to a low-carbon economic optimization method for a microgrid taking into account hydrogen energy storage and AC / DC hybrid characteristics. Background Art

[0002] In recent years, with the increasing problems of energy shortages and environmental pollution, the pace of the global energy revolution has significantly accelerated. Against this backdrop, the construction and promotion of microgrids (MGs) to improve energy efficiency, optimize energy mix, and reduce environmental pollution has become a widespread consensus and urgent need in the international community. However, in practice, traditional MGs still face challenges such as high volatility in renewable energy and low efficiency caused by insufficient system regulation. Therefore, research is urgently needed to explore collaborative optimization methods for energy storage systems and multiple MGs to improve system economics and reliability.

[0003] Currently, in the field of economically optimized dispatch of micro-energy grids, the microgrid structure studied is primarily AC microgrids. If DC loads or power sources are present in the system, VSC converter stations can be used to convert AC-DC power to meet demand. Previous studies have constructed integrated cooling, heating, and electricity energy system models. By building an AC microgrid and integrating multiple energy sources, optimized dispatch is used to meet cooling, heating, and electricity demand, achieving low carbon emissions and improving user comfort. For hydrogen-blended micro-energy grids, a low-carbon economic dispatch model based on the AC microgrid environment has also been established. This effectively mitigates the uncertainty of wind and solar power output, reducing system operating costs and carbon emissions. Furthermore, other studies have constructed a hierarchical coordinated optimization model, integrating the AC microgrid into a combined cooling, heating, and electricity (CHP) system. By constructing uncertainty scenarios and introducing joint distribution models to accurately characterize the relationships between random variables, this model effectively improves the efficiency of renewable energy consumption in regional micro-energy grids and ensures their economic operation. Research has also explored the interaction between AC microgrids and devices and energy sources, combining carbon trading with demand response mechanisms to construct a two-tiered optimization configuration model. This allows microgrids to achieve both economic efficiency and carbon reduction goals. To address source-load uncertainty in microgrids, optimal planning models for microgrids based on interval planning methods have been developed. These models, primarily AC-based multi-energy microgrids, have achieved stable operation and cost optimization under uncertain scenarios. Other research focuses on power coordination control strategies for hydrogen-storage-wind DC microgrids in scenarios with fluctuating wind power and load power. Using a DC microgrid as the core architecture, these models integrate hydrogen, energy storage, and wind power resources to construct a microgrid power coordination control model based on optimized operating intervals. This model effectively ensures stable DC bus voltage operation and maintains efficient and cost-effective hydrogen production. However, these single microgrids still face common challenges, including insufficient capacity for deep integration and efficient coordinated scheduling of multiple energy sources, high AC / DC interface conversion losses, and low stability and economic efficiency when a high proportion of renewable energy is integrated.

[0004] Research has focused on various energy storage technologies for optimizing economic dispatch in microgrids incorporating energy storage systems. Common energy storage technologies include battery storage, pumped hydro, flywheel storage, and supercapacitor storage. Each technology has its own unique characteristics in terms of response speed, energy density, cycle life, and application scenarios. Battery storage boasts high energy density and fast response, making it suitable for short-term power support and energy time shifting. It has been used in grid-connected renewable energy generation systems to achieve peak load shifting and meet power variation limits. It has also been applied to provide continuous power to critical loads in isolated microgrids. Pumped hydro offers large capacity and long life, but limited site selection and slow response. It is suitable for large-scale grid peak shaving and renewable energy consumption, and has demonstrated the advantages of long-term energy storage and multi-energy synergy in regions rich in photovoltaic and hydropower. Flywheel storage offers fast response, long cycle life, and high safety, but has a low energy density. It has been used in microgrid systems for instantaneous power regulation and improved system reliability. Supercapacitor storage boasts high power density and high charge and discharge efficiency, but low energy density. It has been used in urban rail power supply systems for efficient energy recovery and system collaborative optimization. In summary, although the above methods have shown their respective advantages in power regulation and energy time shifting in specific scenarios, they still have common problems such as limited energy storage duration, insufficient response speed or low energy density, which makes it difficult to meet the comprehensive needs of microgrids for long-term energy storage, rapid response and multi-energy coordination. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is: how to provide a microgrid low-carbon economic optimization method that can improve the new energy absorption capacity and reduce network losses.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A low-carbon economic optimization method for a microgrid considering hydrogen energy storage and AC / DC hybrid characteristics includes the following steps:

[0008] S1. Establishing an AC / DC hybrid microgrid architecture including a hydrogen energy storage power station, wherein the microgrid architecture includes photovoltaic power generation equipment and a hydrogen energy storage power station connected to a DC bus, wind power generation equipment and a power grid connected to an AC bus, and a VSC converter station for connecting the AC bus and the DC bus;

[0009] S2. Optimizing the hydrogen energy storage capacity configuration and operation scheduling of the microgrid using a two-level planning model; the upper-level planning model optimizes the capacity configuration and maximum charge and discharge power of the energy storage power station with the goal of minimizing the comprehensive annual operating cost; the lower-level planning model determines the multi-energy coordinated operation strategy of the microgrid system with the goal of minimizing the annual operating cost of the microgrid;

[0010] S3. Based on the two-level programming model, the optimality conditions of the lower-level programming model are converted into constraint conditions of the upper-level programming model, and the two-level programming model is converted into a solvable single-level optimization problem for solution.

[0011] Furthermore, in step S2, in the upper-level planning model, the comprehensive annual operating cost includes the investment and construction cost of the hydrogen energy storage power station, the microgrid electricity purchase cost, and the fuel cost, and its objective function is:

[0012] minC=T k (C Inv +C Grid +C Flue )

[0013]

[0014]

[0015] Where: T k is the number of days corresponding to the typical day; C Inv is the annual value of the investment cost of the hydrogen energy storage power station; C Grid is the annual cost of electricity purchased by the microgrid from the grid; C Flue The annual fuel cost of the microgrid; C HESS,OM is the annual operation and maintenance cost of the hydrogen energy storage power station; β is the discount rate; α m and S m is the configuration capacity of the equipment and the corresponding unit configuration cost coefficient; γ is the theoretical operating life of the hydrogen energy storage power station; C HESS,cp is the unit hydrogen compression cost; is the electricity price matrix of the busbar purchasing electricity from the grid during period t; J and H correspond to the number of microgrids and the dispatch period respectively; The power purchased by the bus from the grid during period t; is the unit volume price matrix of gas, the unit is yuan / m 3 ; is the output power of the gas turbine during period t; is the power generation efficiency of the micro gas turbine; is the calorific value of gas; is the output thermal power of the gas boiler during period t; For the efficiency of gas boilers;

[0016] The objective function of the lower-level planning model is:

[0017] minC=T k (C Grid +C HESS,buy +C Flue +C Serve -C HESS,sale )

[0018]

[0019] Where: C HESS,sale The annual income from electricity sales to hydrogen energy storage system; C HESS,buy The annual cost of electricity purchased from the hydrogen energy storage power station for the system; C serve Pay the annual cost of service fees to the hydrogen energy storage power station for the system; The unit electricity price matrix for selling electricity to the energy storage power station during the dispatch period; The power sold to the energy storage power station during each dispatch period; The unit electricity price matrix for purchasing electricity from the hydrogen energy storage power station during the dispatch period; The power purchased from the energy storage power station during each dispatch period; The unit price of the service fee paid by the microgrid to the energy storage power station during period t.

[0020] Furthermore, the hydrogen energy storage power station includes an electrolyzer for absorbing electrical energy and producing hydrogen by electrolyzing water, a hydrogen fuel cell for converting hydrogen into electrical energy, and a hydrogen storage tank for high-pressure storage of hydrogen; the mathematical model of the equivalent electric power of hydrogen production of the electrolyzer is:

[0021]

[0022] Where: and are the hydrogen production power and power consumption of the electrolyzer at time t respectively; is the electricity-to-hydrogen conversion efficiency of the electrolyzer;

[0023] The output power mathematical model of the hydrogen fuel cell is:

[0024]

[0025] Where: and is the hydrogen consumption power and power generation power of the fuel cell at time t; is the hydrogen-to-electricity conversion efficiency of the fuel cell;

[0026] The net hydrogen storage capacity equivalent state of charge of the hydrogen storage tank is:

[0027]

[0028] Where, and are the equivalent amount of hydrogen remaining in the hydrogen storage tank at time t and time t-1 respectively; ω Ch and ω Disch They are the charging and discharging efficiency of the hydrogen storage tank respectively.

[0029] Furthermore, the power loss of the VSC converter station is calculated using the following model:

[0030]

[0031] Where: Represents the power loss generated by the VSC during operation; is the current flowing through the AC side of the VSC converter; and is the active power and reactive power exchanged by the VSC on the AC side; is the AC side voltage of VSC; A, B, and C are the measured no-load loss value, linear loss coefficient, and nonlinear loss coefficient of VSC in the VSC-HVDC system, respectively.

[0032] Furthermore, the operating constraints of the lower-level planning model include power balance constraints, cooling and heating balance and power station charge and discharge balance constraints, microgrid system wind and solar power consumption constraints, microgrid equipment output upper and lower limit constraints, and power purchase and sales upper and lower limit constraints of the power grid and energy storage power station;

[0033] The power balance constraint is:

[0034]

[0035] Where, are the electric power on the AC bus and DC bus in each dispatching period respectively; The power of photovoltaic and wind power generation in each scheduling period respectively; The electric power consumed by the electric refrigerator in each scheduling period; is the electric load power in each dispatching period;

[0036] The constraints on cooling and heating balance and power station charge and discharge balance are:

[0037]

[0038] Where: are the heating power of the heat exchanger and the cooling power of the absorption chiller in each scheduling period; They are the heating load and cooling power matrices for each scheduling period; They are heat exchanger efficiency, absorption chiller energy efficiency ratio, waste heat boiler efficiency and chiller energy efficiency ratio; is the gas turbine heat-to-power ratio;

[0039] The wind and solar power consumption constraints of the microgrid system are:

[0040]

[0041] Where: are the power matrices of the maximum photovoltaic and wind power outputs in each dispatching period; ω is the wind and solar power absorption rate of the microgrid system;

[0042] The upper and lower limits of the microgrid equipment output are:

[0043]

[0044] Where: are the minimum and maximum power of the gas turbine, respectively; are the minimum and maximum powers of the absorption chiller, respectively; are the minimum and maximum powers of the electric refrigerator, respectively; are the minimum and maximum power of the gas turbine, respectively; are the minimum and maximum powers of the heat exchanger, respectively;

[0045] The upper and lower limits of power purchase and sales by the power grid and energy storage power station are:

[0046]

[0047] Where: The maximum power that the microgrid can purchase from the grid; is the maximum interactive power between the microgrid system and the energy storage power station; They are respectively the status of the system purchasing and selling electricity to the energy storage power station.

[0048] Furthermore, the operational constraints of the upper-level planning model are:

[0049]

[0050] Where: and are the maximum powers of the electrolyzer and fuel cell, respectively; and are the maximum charging and discharging power of the hydrogen energy storage power station respectively; and They are the charging and discharging status of the hydrogen energy storage station. When the hydrogen energy storage station is charging Reverse when discharging; is the electricity consumption of the hydrogen energy storage power station in period t; and They are the charging and discharging efficiency of the hydrogen energy storage power station; The maximum amount of electricity that a hydrogen energy storage power station can store.

[0051] Furthermore, in step S3, for the lower-level planning model, a Lagrangian function is constructed based on the objective function and constraints of the lower-level planning model; according to the KKT condition, the necessary conditions of the lower-level planning model at the extreme point are determined, including the original feasibility condition, the optimality condition and the complementary relaxation condition;

[0052] The original feasibility condition is the operation constraint of the lower-level planning model;

[0053] The optimality condition is formed by performing partial derivative operations on each variable in the lower-level programming model according to the Lagrangian function and setting the partial derivatives to zero;

[0054] The complementary relaxation condition is formed by the product of the inequality constraint of the lower-level programming model and the corresponding Lagrange multiplier being equal to zero;

[0055] The KKT conditions are added as constraints to the upper-level planning model to obtain an equivalent single-level optimization model.

[0056] Furthermore, for the nonlinear terms in the complementary relaxation conditions, the Big-M method is used to introduce 0-1 variables for linearization.

[0057] In summary, the present invention utilizes the advantages of long-term storage and no capacity decay of hydrogen energy storage, and uses AC / DC hybrid structure operation to improve the new energy absorption capacity and reduce network losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is the structure diagram of the AC / DC hybrid hydrogen energy storage microgrid combined heating, cooling and power supply.

[0059] Figure 2 This is a structural diagram of a hydrogen energy storage power station.

[0060] Figure 3 Graph of the solution process for a two-level optimization run.

[0061] Figure 4 It is set for time-of-use electricity price.

[0062] Figure 5 The power situation of microgrid 1 in different scenarios.

[0063] Figure 6 The power situation of microgrid 2 in different scenarios.

[0064] Figure 7 The power situation of microgrid 3 in different scenarios.

[0065] Figure 8 This is the charge state and charging and discharging power diagram of the hydrogen energy storage power station in scenario 2.

[0066] Figure 9 This is the charge state and charging and discharging power diagram of the hydrogen energy storage power station in scenario 3. DETAILED DESCRIPTION

[0067] The present invention will be further described in detail below with reference to the embodiments.

[0068] To address the shortcomings of battery energy storage stations, such as short cycle life and rapid capacity decay, and to improve the insufficient renewable energy absorption and poor operational economics of microgrids, this embodiment proposes a low-carbon economic optimization method for microgrids that considers the characteristics of hydrogen energy storage and AC / DC hybridization. Hydrogen energy storage systems replace traditional electrical energy storage modules and construct an AC / DC hybrid microgrid architecture. This approach leverages the advantages of hydrogen energy storage's long storage cycle and lack of capacity decay, and utilizes the AC / DC hybrid structure to improve renewable energy absorption capacity and reduce network losses. Furthermore, to accurately and comprehensively describe the multi-objective trade-offs required for the coordinated optimization of hydrogen energy storage capacity configuration and microgrid operation and scheduling, achieving dual optimization of investment and operating costs, the system optimization problem is formulated as a two-level planning model: the upper-level model solves the hydrogen energy storage station configuration problem, while the lower-level model solves the microgrid's optimal operation problem. To address the model's two-level and nonlinear nature, the KKT condition and the large-M method are used to achieve a transformation solution. This embodiment's method effectively reduces the hydrogen energy storage station's investment payback period and the overall operating costs of the microgrid, while significantly improving renewable energy absorption capacity and system economics.

[0069] Specifically, this embodiment establishes an AC / DC hybrid microgrid architecture containing a hydrogen energy storage power station. It breaks through the limitations of traditional technologies through the long-term energy storage, high energy density, fast response, and no capacity decay characteristics of hydrogen energy storage, and uses a two-layer planning model to achieve a multi-objective trade-off between economy and reliability, thereby achieving optimal scheduling of the microgrid and reducing the overall operating cost of the system. First, by integrating the hydrogen energy storage system with the AC / DC hybrid network, the advantages of the low loss of the AC / DC hybrid structure are utilized to enhance the microgrid's ability to absorb renewable energy and reduce the overall loss of the system. Second, in order to accurately describe the multi-objective collaborative optimization relationship between hydrogen energy storage capacity configuration and microgrid operation scheduling, while reducing investment and operating costs, the system optimization problem is constructed as a two-layer planning model. The upper-level model solves the capacity and power problem of the hydrogen energy storage power station, with the goal of minimizing the comprehensive annual operating cost. Based on the upper-level model, the lower-level model uses the hydrogen energy storage power station configuration plan to solve the microgrid's optimal operation problem, with the goal of minimizing the annual operating cost of the microgrid. By introducing the KKT conditional large-M method, the optimality conditions of the inner-layer multi-microgrid energy interaction model are transformed into constraints of the outer-layer capacity configuration model, thereby transforming the two-layer problem into a solvable single-layer optimization problem. Finally, a simulation case study was conducted to verify the proposed method. The results show that the proposed method can effectively reduce the investment payback period of hydrogen energy storage power stations and the overall operating costs of microgrids, demonstrating its adaptability and effectiveness in multi-energy coupling scenarios.

[0070] The AC / DC hybrid microgrid with hydrogen energy storage proposed in this embodiment establishes a DC bus and an AC bus. The photovoltaic power generation equipment and hydrogen energy storage station are connected to the DC bus, and the wind turbine equipment and the power grid are connected to the AC bus. The AC and DC buses are then connected via a VSC converter station. The hydrogen energy storage station, through its energy storage facilities, provides charging and discharging services to users and charges a service fee based on actual electricity consumption. The system incorporates multiple power flows to meet energy needs for cooling, heating, and electricity. Internal equipment includes wind turbines, photovoltaic equipment, gas turbines, boilers, heat exchangers, and chillers. The energy flow in the system is configured such that when the AC bus is short of power, the VSC converter station prioritizes the use of excess power from the DC bus. If this is still insufficient, during peak hours, when the hydrogen energy storage station's purchase price is lower than the grid's, electricity is purchased from the hydrogen energy storage station. During off-peak hours, when the grid's purchase price is even lower, electricity is purchased from the grid. The grid power can only be input into the microgrid users in one direction, and the excess power can only be stored or abandoned through hydrogen energy storage. Figure 1 shown.

[0071] In this system, the hydrogen energy storage station is connected to the DC bus. The hydrogen generated by water electrolysis is stored in a hydrogen storage tank to achieve the conversion of electrical energy into hydrogen energy. When the system power is insufficient, the hydrogen energy is converted into electrical energy for users through the fuel cell. The structural diagram of the hydrogen energy storage station is as follows: Figure 2 shown.

[0072] During the same time period, for all users connected to the hydrogen energy storage station, the hydrogen energy storage station will determine the charging and discharging behavior based on the total charging and discharging demand. Throughout the cycle, the capacity and maximum charging and discharging power of the energy storage device are dynamically configured based on the charging and discharging power requirements of the users in each time period. The charging and discharging process of the hydrogen energy storage station comprehensively considers the output of regional wind power and photovoltaic power as well as the various load requirements of users. On the basis of minimizing the overall operating costs of the microgrid and hydrogen energy storage station, the optimal energy storage capacity and power are first determined. Furthermore, for the energy exchange problem between the DC bus, AC bus and the energy storage station in the multi-microgrid system under hydrogen energy storage service, the operating strategy that optimizes the annual operating cost of the system is solved.

[0073] The system consists of two levels of decision makers, each with its own independent objective function, constraints, and decision variables. Their decisions are interrelated, influencing, and constraining each other. During the solution process, the upper-level decision makers make decisions first, and the lower-level decision makers optimize their own objective functions based on these decisions and provide feedback. The upper-level decision makers adjust their decisions based on this feedback, and the global optimal solution is sought through multiple rounds of iteration.

[0074] Determine the objective function of the upper-level planning model: There are two levels of decision makers in the system, each with independent objective functions, constraints and decision variables, and the decisions are interrelated, influential and restricted.

[0075] During the solution process, upper-level decision makers make decisions first, and lower-level decision makers optimize their own objective functions based on these decisions and provide feedback. The upper level adjusts its decisions based on the feedback, and the global optimal solution is sought through multiple rounds of iteration.

[0076] The upper-level model solves the problem of optimizing the combined annual operating costs of the hydrogen energy storage power station and microgrid. The decision variables include the capacity configuration of the energy storage power station and the maximum charge and discharge power. In this embodiment, the total cost consists of three parts: the investment and construction cost of the hydrogen energy storage power station, the cost of the microgrid purchasing electricity from the grid, and the cost of the microgrid purchasing fuel. The optimization objective function can be expressed as:

[0077] minC=T k (C Inv +C Grid +C Flue ) (1)

[0078] Where: T k is the number of days corresponding to the typical day; C Inv is the annual value of the investment cost of the hydrogen energy storage power station; C Grid is the annual cost of electricity purchased by the microgrid from the grid; C Flue The annual fuel purchase cost for the microgrid.

[0079] The investment and operation and maintenance costs of a hydrogen energy storage power station can be expressed as:

[0080]

[0081] Where: C HESS,OM is the annual operation and maintenance cost of the hydrogen energy storage power station; β is the discount rate; α m and S m is the configuration capacity of the equipment and the corresponding unit configuration cost coefficient; γ is the theoretical operating life of the hydrogen energy storage power station; C HESS,cp is the unit hydrogen compression cost.

[0082] The cost of purchasing electricity from the grid for the microgrid:

[0083]

[0084] Where: is the electricity price matrix of the busbar purchasing electricity from the grid during period t; J and H correspond to the number of microgrids and the dispatch period respectively; is the power purchased by the bus from the grid during period t.

[0085] The fuel purchase cost of the microgrid can be expressed as:

[0086]

[0087] Where: is the unit volume price matrix of gas, the unit is yuan / m3 ; is the output power of the gas turbine during period t; is the power generation efficiency of the micro gas turbine; is the calorific value of gas; is the output thermal power of the gas boiler during period t; For the efficiency of gas boilers.

[0088] Establish a mathematical model of a hydrogen energy storage power station: The hydrogen energy storage system is mainly composed of three parts: an electrolyzer, a hydrogen storage tank, and a fuel cell connected in a certain series-parallel structure. During the energy conversion process of the hydrogen energy storage system, hydrogen energy is represented by equivalent electrical power.

[0089] 1) The electrolyzer absorbs excess electricity to electrolyze water to produce hydrogen, which is stored in a hydrogen storage tank, realizing the conversion of electrical energy into hydrogen energy. The mathematical model of the equivalent electric power of hydrogen production by the electrolyzer is:

[0090]

[0091] Where: and are the hydrogen production power and power consumption of the electrolyzer at time t respectively; is the electrolytic cell's electricity-to-hydrogen conversion efficiency. Assuming the electrolytic cell's electricity-to-hydrogen conversion efficiency remains unchanged, the mathematical model that uses power consumption to represent its input-output characteristics is:

[0092]

[0093] Where: is the volume of hydrogen produced by the electrolyzer at time t; is the power consumption of the electrolytic cell.

[0094] 2) Hydrogen fuel cells are an effective way to directly convert chemical energy generated by the reaction of oxygen and hydrogen into electrical energy. Proton exchange membrane fuel cells have the characteristics of flexible power regulation and low operating temperature. The mathematical model of their output power is:

[0095]

[0096] Where: and is the hydrogen consumption power and power generation power of the fuel cell at time t; is the hydrogen-to-electricity conversion efficiency of the fuel cell, which is 60% in this embodiment.

[0097] 3) Common hydrogen storage technologies include compressed hydrogen storage, liquid hydrogen storage, and underground hydrogen storage. From a technical and economic perspective, hydrogen is compressed to a high pressure state and stored in a high-pressure hydrogen storage tank. The net hydrogen storage capacity and equivalent state of charge are shown below:

[0098]

[0099] Where, and are the equivalent amount of hydrogen remaining in the hydrogen storage tank at time t and time t-1 respectively; ω Ch and ω Disch They are the charging and discharging efficiency of the hydrogen storage tank respectively.

[0100] Determine the operating constraints of the hydrogen energy storage power station: During the entire operating cycle, the electrolyzer, hydrogen fuel cell and hydrogen storage tank, as the main equipment of the hydrogen energy storage power station, should have an operating power less than the original installed capacity at any time period. At the same time, to ensure the sustainability of hydrogen energy in the conversion process, the hydrogen energy capacity in the hydrogen storage tank needs to remain consistent with the power consumption.

[0101]

[0102] Where: and are the maximum powers of the electrolyzer and fuel cell, respectively.

[0103]

[0104] Where: and are the maximum charging and discharging power of the hydrogen energy storage power station respectively; and They are the charging and discharging status bits of the hydrogen energy storage station, which can be regarded as Boolean variables. The opposite happens during discharge.

[0105]

[0106] Where: is the electricity consumption of the hydrogen energy storage power station in period t; and are respectively the charging and discharging efficiency of the hydrogen energy storage power station, both of which are 98% in this embodiment; The maximum amount of electricity that a hydrogen energy storage power station can store.

[0107] Determine the objective function of the lower-level planning model: The lower-level model is responsible for solving the optimal operation of the multi-microgrid system for combined cooling, heating and power. The decision variables are the gas turbine power generation, the absorption chiller output cooling power, the electric chiller power consumption, the gas boiler output heating power, the heat exchanger output heating power, the power purchased from the grid, the power purchased from the energy storage power station and the power purchase status, and the power sold to the hydrogen energy storage power station and the power sales status. The lower-level objective function is to minimize the annual operating cost of the multi-microgrid system, that is,

[0108] minC=Tk (C Grid +C HESS,buy +C Flue +C Serve -C HESS,sale ) (12)

[0109] Where: C HESS,sale The annual income from electricity sales to hydrogen energy storage system; C HESS,buy The annual cost of electricity purchased from the hydrogen energy storage power station for the system; C serve The system pays an annual service fee to the hydrogen energy storage power station.

[0110] 1) The system's revenue from selling electricity to hydrogen energy storage power stations is:

[0111]

[0112] Where: The unit electricity price matrix for selling electricity to the energy storage power station during the dispatch period; The power sold to the energy storage power station during each dispatch period.

[0113] 2) The cost of purchasing electricity from the energy storage power station is:

[0114]

[0115] Where: The unit electricity price matrix for purchasing electricity from the hydrogen energy storage power station during the dispatch period; It is the power purchased from the energy storage power station during each dispatching period.

[0116] 3) The system pays service fees to the energy storage power station

[0117] A two-way metering method is used to calculate service fees for microgrids and energy storage power stations. This means that regardless of whether the energy storage station is charging or discharging, the service fee is determined by multiplying its power output by the power matrix. The sum of these two products is the total service fee.

[0118]

[0119] Where, The unit price of service fee paid by the microgrid to the energy storage power station during period t, in yuan / (kW·h).

[0120] Establish a mathematical model of the VSC converter station: Reduce the commutation loss by establishing an AC / DC hybrid microgrid. loss,vsc The calculation involves the product of two continuous variables, which requires the use of the McCormick envelope method for linearization.

[0121]

[0122] Where: Represents the power loss generated by the VSC during operation; is the current flowing through the AC side of the VSC converter; and is the active power and reactive power exchanged by the VSC on the AC side; is the AC side voltage of VSC; A, B, and C are the measured no-load loss value, linear loss coefficient, and nonlinear loss coefficient of VSC in the VSC-HVDC system, respectively.

[0123] Determine the constraints of the lower-level model: including power balance constraints, cold and heat balance and power station charge and discharge balance constraints, microgrid system wind and solar power absorption constraints, microgrid equipment output upper and lower limit constraints, and power grid and energy storage power station purchase and sales upper and lower limit constraints.

[0124] 1) The power balance constraint is:

[0125]

[0126] Where, are the electric power on the AC bus and DC bus in each dispatching period respectively; The power of photovoltaic and wind power generation in each scheduling period respectively; The electric power consumed by the electric refrigerator in each scheduling period; is the electric load power in each dispatching period.

[0127] 2) The constraints on cooling and heating balance and power station charge and discharge balance are:

[0128]

[0129] Where: are the heating power of the heat exchanger and the cooling power of the absorption chiller in each scheduling period; They are the heating load and cooling power matrices for each scheduling period; They are heat exchanger efficiency, absorption chiller energy efficiency ratio, waste heat boiler efficiency and chiller energy efficiency ratio; is the heat-to-electricity ratio of the gas turbine.

[0130] 3) The wind and solar power consumption constraints of the microgrid system are:

[0131]

[0132] Where: are the power matrices of the maximum photovoltaic and wind power outputs in each dispatching period; ω is the wind and solar power absorption rate of the microgrid system.

[0133] 4) The upper and lower limits of microgrid equipment output are:

[0134]

[0135] Where: are the minimum and maximum power of the gas turbine, respectively; are the minimum and maximum powers of the absorption chiller, respectively; are the minimum and maximum powers of the electric refrigerator, respectively; are the minimum and maximum power of the gas turbine, respectively; are the minimum and maximum powers of the heat exchanger respectively.

[0136] 5) The upper and lower limits for power purchase and sales by power grids and energy storage power stations are:

[0137]

[0138] Where: The maximum power that the microgrid can purchase from the grid; is the maximum interactive power between the microgrid system and the energy storage power station; These represent the system's power purchase and sales status for the energy storage power station. Energy storage power stations have only three states: charging, discharging, and idle. By establishing state constraints, the energy storage station cannot be charging or discharging simultaneously, ensuring normal operation.

[0139] Solution algorithm: The two-layer model involves complex non-convex constraints, and there is a close coupling relationship between the upper and lower models, which makes it difficult to solve directly. This embodiment requires the establishment of an upper-layer model and the use of McCormick's envelope method (McCormick) to linearize the nonlinear constraints therein, and then by constructing the Lagrangian function of the lower-layer model, the KKT conditions of the lower-layer model are converted into the constraints of the upper-layer model, and the two-layer model is simplified into a single-layer mixed integer linear programming model. The Big-M method is then used to convert the model into a single-layer mixed integer linear programming problem that is easier to solve. The solution process of the two-layer programming model is as follows: Figure 3 In Matlab 2019b, this example solves the problem using the commercial solver CPLEX and the YALMIP toolbox.

[0140] Single-layer model: For the lower-layer model, write the KKT conditions, which include the original feasibility conditions, optimality conditions and complementary relaxation conditions.

[0141] 1) Original feasible conditions: The original feasible conditions are the equality and inequality constraints related to the original lower-level model, that is, equations (17) to (21) still hold.

[0142] 2) Optimality Condition: From the KKT condition, we know that the derivative of the lower model is 0 at the extreme point. Therefore, we construct a Lagrangian function based on the objective function and constraints of the lower model, and then take the derivative of each variable involved in the lower model.

[0143] 3) Complementary slack conditions: Complementary slack constraints are constructed based on the inequality constraints of the underlying model and their corresponding Lagrange multipliers.

[0144] The steps for determining the optimality conditions and complementary relaxation conditions are as follows: first construct the Lagrangian function of the lower model.

[0145]

[0146] Taking the derivative of each variable, we get the optimality conditions:

[0147]

[0148]

[0149] The complementary relaxation conditions constructed are:

[0150]

[0151]

[0152] Here, for the formula “0≤a⊥b≥0”, it can be expressed as a≥0, b≥0 and ab=0.

[0153] After obtaining the KKT conditions of the lower-layer model, they are added to the upper-layer optimization problem as additional constraints, thereby converting the two-layer model into a single-layer model.

[0154] Linearization of single-layer problems: When using KKT conditions for single-layer transformation, the complementary relaxation conditions introduced in the constraints, i.e., Equations (15F) to (35F), contain nonlinear terms. The Big-M method can be used to linearize them by introducing several 0-1 variables.

[0155] Taking Equation (15F) as an example, there is a bilinear term in the form of multiplication of variables and Lagrange multipliers. The linearization process transforms it into:

[0156]

[0157] Where M is a sufficiently large constant and a 0-1 variable. Constraints (16F) to (35F) are linearized similarly to constraint (15F). This process effectively linearizes the nonlinear terms in the lower-level model after KKT transformation, transforming the two-level optimization model into a single-level mixed-integer linear program. This can be effectively solved using commercial solvers such as CPLEX.

[0158] Case Analysis: Based on a simulation example, the configuration of a hydrogen energy storage power station based on an AC / DC hybrid microgrid energy service model is analyzed. The example sets up three microgrid systems and divides a typical day into 24 scheduling periods. The hydrogen energy storage station is connected to the DC bus and can transmit power to each other. The AC bus is connected to the grid and can realize power transmission, but cannot sell electricity to the grid. The natural gas price is taken from the industrial and commercial gas price of 3.85 yuan / m in Sichuan Province. 3 When the peak-valley electricity price mechanism is adopted from the power grid, the electricity price setting between the microgrid and the hydrogen energy storage power station is as follows: Figure 4 As shown in Figure 2. Under this mechanism, electricity prices are adjusted according to the peak and valley changes in the grid load, guiding users to rationally adjust their electricity consumption periods and promoting the efficient allocation of power resources. The microgrid equipment includes gas boilers, waste heat boilers, gas turbines, heat exchangers, electric chillers, suction chillers, etc. The equipment parameters are set according to the example in reference

[23] . In order to analyze the interaction between the AC / DC hybrid microgrid and hydrogen energy storage configuration under the consideration of VSC losses, the following three scenarios are set for comparative analysis.

[0159] Scenario 1: The microgrid takes VSC losses into consideration and does not configure a hydrogen energy storage power station.

[0160] Scenario 2: The microgrid considers VSC losses and configures a hydrogen energy storage power station.

[0161] Scenario 3: Establish an AC / DC hybrid microgrid, taking into account VSC losses and configuring a hydrogen energy storage power station.

[0162] In the calculation example, the microgrid equipment parameters are known. The model solution for scenario 1 uses the annual operating cost of the microgrid as the objective function, without hydrogen energy storage and taking into account the VSC converter station constraints. Scenario 2 and Scenario 3 can use the two-level planning method described in Section 3 to solve the energy storage power station configuration problem and the microgrid optimization operation problem.

[0163] Analysis of the impact of hydrogen energy storage on microgrids: Scenario 1: The combined cooling, heating and power multi-microgrid system considers the losses between AC and DC but does not configure energy storage. The annual operating cost of the multi-microgrid system is 24.5264 million yuan. On a typical day, the output of each microgrid in scenario 2 (equipped with a hydrogen energy storage power station) and scenario 1 (microgrid system operates independently without a hydrogen energy storage power station) is as follows: Figures 5-7 shown.

[0164] Analysis of power balance revealed that in Scenario 1, Microgrids 2 and 3 exhibited medium-to-high levels of renewable energy consumption. Both microgrids maintained high renewable energy output for most of the time, particularly between hours 7 and 14, when output approached its maximum limit. In contrast, when renewable energy output was insufficient, Microgrid 1 relied on purchasing electricity from the grid or starting up its gas turbines to supplement it. Between hours 14 and 19, Microgrid 1 experienced significant power curtailment. Furthermore, the temporal distribution of load and power sources exhibited an imbalance. Even if Microgrids 2 and 3 were able to achieve a high proportion of renewable energy consumption, they would still need to purchase significant amounts of electricity from the grid or rely on gas turbines to generate power to meet demand.

[0165] Taking VSC losses into account, a hydrogen energy storage configuration was introduced in Scenario 2. After optimization, the specific configuration of the energy storage station was obtained: its maximum charge and discharge power is 4000 kW, its capacity is 31,175.3 kW·h, and its cost recovery period is 6.72 years.

[0166] The introduction of the hydrogen energy storage station reduced the microgrid's total annual operating costs by 6.55%. Cost analysis shows that while the initial construction investment for the energy storage station is high, accounting for approximately 24% of the total annual operating costs, this investment is economically viable when assessed through the entire lifecycle cost perspective. The hydrogen energy storage system not only effectively reduces the system's long-term operating costs but also improves power supply reliability and operational stability, creating additional economic benefits for the microgrid.

[0167] Comparing the microgrid's power profile before and after the deployment of energy storage reveals a significant improvement in the microgrid's ability to absorb renewable energy. When the microgrid's own load is low and renewable energy output is high, excess power is sold to the hydrogen energy storage power station. Conversely, when renewable energy output is insufficient and grid electricity prices or gas costs are high, the microgrid chooses to purchase power from the energy storage power station. This energy scheduling method effectively shifts energy across time and space, alleviating the challenges associated with the uncertainty and uncontrollability of renewable energy output.

[0168] Analysis of the Impact of AC / DC Hybrid Microgrids on Energy Storage Configuration: To further analyze the impact of AC / DC losses on a multi-microgrid system, a calculation analysis was performed for Scenario 3. An AC / DC hybrid microgrid was established based on Scenario 2. The annual operating cost of energy storage in this Scenario 3 hybrid microgrid was 20.0364 million yuan, a 12.59% reduction compared to the multi-microgrid system cost in Scenario 2, while still achieving full energy absorption.

[0169] The combined cooling, heating and power multi-microgrid system uses energy storage services and takes advantage of the differences and complementarities in electricity consumption behavior of different microgrids at the same time and the same microgrid at different times. The surplus electricity of multiple microgrids at the same time is supplied to the power-deficient microgrid through a hydrogen energy storage power station, and the electricity of the microgrid at multiple times is stored in the form of hydrogen in gas tanks. This can reduce the cost of purchasing electricity or gas from the power grid for each microgrid, and reduce the annual operating cost of the combined cooling, heating and power multi-microgrid system.

[0170] For a more intuitive comparison, this embodiment lists the microgrid costs and annual revenue of energy storage power stations in three scenarios. See Table 1 for details.

[0171] Table 1 System economy and configuration under three scenarios

[0172]

[0173] In Scenario 3, the annual revenue of the hydrogen energy storage station is 7.3 million yuan, and the total configuration cost of the energy storage station is 27.74 million yuan, reducing the payback period of the energy storage station to 5.26 years. This demonstrates that energy storage station operators have considerable profit margins and that investing in hydrogen energy storage stations has the potential to be profitable. Establishing hydrogen energy storage stations within an AC / DC hybrid microgrid is theoretically feasible. The energy storage configuration results for Scenario 2 and Scenario 3 are shown in Table 1. The hydrogen energy storage configuration power of the multi-microgrid system is 4000 kW. The total configuration capacity of the multi-microgrid system in Scenario 2 is 31,175.32 kW·h, while that in Scenario 3 is 17,211.33 kW·h, approximately 45% less than that in Scenario 2. The AC / DC hybrid microgrid established in Scenario 3 reduces power conversion losses, requiring a lower capacity configuration for the energy storage station, and significantly reducing the investment cost of the hydrogen energy storage station. Figure 8 、 Figure 9 The charge status and power balance diagrams of the hydrogen energy storage power station in scenarios 2 and 3 respectively.

[0174] As can be seen, the AC / DC hybrid microgrid system established in Scenario 3 offers greater independence and flexibility than a single microgrid. Its separation into two components, AC and DC, reduces the system's conversion losses and reliance on hydrogen energy storage power plants. The annual operating costs of the system in Scenario 3 are also significantly reduced.

[0175] The above comparison demonstrates that optimizing the energy storage configuration within an AC / DC hybrid microgrid, altering the system structure, and optimizing the charging and discharging strategies of the energy storage power station can reduce system operating costs and enable the energy storage power station to recover its initial investment earlier. By replacing the traditional microgrid with an AC / DC hybrid microgrid system, VSC converter stations are deployed only on the DC and AC busbars, reducing the number of VSC converter stations. This not only lowers system construction and O&M costs, but also reduces energy losses incurred by converter stations, thereby improving overall energy efficiency. After establishing the AC / DC hybrid microgrid, the total annual operating cost of the microgrid and the construction cost of the hydrogen energy storage power station were significantly reduced, fully demonstrating the significant advantages of the AC / DC hybrid hydrogen energy storage microgrid.

[0176] In summary, this embodiment constructs an economic dispatch model for an AC / DC hybrid hydrogen storage microgrid, and draws the following conclusions based on simulation case verification.

[0177] (1) Configuring a hydrogen energy storage power station for the microgrid system can enable the microgrid to obtain energy storage services at a lower cost, achieve full absorption of the microgrid, and improve the stability and safety of the system.

[0178] (2) Compared with a single microgrid, establishing an AC / DC hybrid microgrid can reduce the annual comprehensive operating cost, significantly improve the economic benefits of the energy storage power station, reduce the system's carbon emissions, and improve the system's economy and environmental protection.

[0179] The above description is only a preferred embodiment of the present invention and does not limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A low-carbon economic optimization method for microgrids considering hydrogen energy storage and AC / DC hybrid characteristics, characterized in that: The steps include: S1. Establishing an AC / DC hybrid microgrid architecture including a hydrogen energy storage power station, wherein the microgrid architecture includes photovoltaic power generation equipment and a hydrogen energy storage power station connected to a DC bus, wind power generation equipment and a power grid connected to an AC bus, and a VSC converter station for connecting the AC bus and the DC bus; S2. Optimizing the hydrogen energy storage capacity configuration and operation scheduling of the microgrid using a two-level planning model; the upper-level planning model optimizes the capacity configuration and maximum charge and discharge power of the energy storage power station with the goal of minimizing the comprehensive annual operating cost; The lower-level planning model aims to minimize the annual operating cost of the microgrid and determine the multi-energy coordinated operation strategy of the microgrid system; S3. Based on the two-level programming model, the optimality conditions of the lower-level programming model are converted into constraint conditions of the upper-level programming model, and the two-level programming model is converted into a solvable single-level optimization problem for solution.

2. The microgrid low-carbon economic optimization method considering hydrogen energy storage and AC / DC hybrid characteristics as claimed in claim 1, characterized in that: In step S2, in the upper-level planning model, the comprehensive annual operating cost includes the investment and construction cost of the hydrogen energy storage power station, the microgrid electricity purchase cost, and the fuel cost, and its objective function is: min C=T k (C Inv +C Grid +C Flue ) Where: T k is the number of days corresponding to the typical day; C Inv is the annual value of the investment cost of the hydrogen energy storage power station; C Grid is the annual cost of electricity purchased by the microgrid from the grid; C Flue The annual fuel cost of the microgrid; C HESS,OM is the annual operation and maintenance cost of the hydrogen energy storage power station; β is the discount rate; α m and S m The configuration capacity of the equipment and the corresponding unit configuration cost coefficient; γ is the theoretical operating life of the hydrogen energy storage power station; C HESS,cp is the unit hydrogen compression cost; is the electricity price matrix of the busbar purchasing electricity from the grid during period t; J and H correspond to the number of microgrids and the dispatch period respectively; The power purchased by the bus from the grid during period t; is the unit volume price matrix of gas, the unit is yuan / m 3 ; is the output power of the gas turbine during period t; is the power generation efficiency of the micro gas turbine; is the calorific value of gas; is the output thermal power of the gas boiler during period t; For the efficiency of gas boilers; The objective function of the lower-level planning model is: minC=T k (C Grid +C HESS,buy +C Flue +C Serve -C HESS,sale ) Where: C HESS,sale The annual income from electricity sales to hydrogen energy storage system; C HESS,buy The annual cost of electricity purchased from the hydrogen energy storage power station for the system; C serve Pay the annual cost of service fees to the hydrogen energy storage power station for the system; The unit electricity price matrix for selling electricity to the energy storage power station during the dispatch period; The power sold to the energy storage power station during each dispatch period; The unit electricity price matrix for purchasing electricity from the hydrogen energy storage power station during the dispatch period; The power purchased from the energy storage power station during each dispatch period; The unit price of the service fee paid by the microgrid to the energy storage power station during period t.

3. The microgrid low-carbon economic optimization method considering hydrogen energy storage and AC / DC hybrid characteristics as claimed in claim 1, characterized in that: The hydrogen energy storage power station includes an electrolyzer for absorbing electrical energy and producing hydrogen by electrolyzing water, a hydrogen fuel cell for converting hydrogen into electrical energy, and a hydrogen storage tank for high-pressure storage of hydrogen. The mathematical model of the equivalent electric power of hydrogen production by the electrolyzer is: Where: and are the hydrogen production power and power consumption of the electrolyzer at time t respectively; is the electricity-to-hydrogen conversion efficiency of the electrolyzer; The output power mathematical model of the hydrogen fuel cell is: Where: and is the hydrogen consumption power and power generation power of the fuel cell at time t; is the hydrogen-to-electricity conversion efficiency of the fuel cell; The net hydrogen storage capacity equivalent state of charge of the hydrogen storage tank is: Where, and are the equivalent amount of hydrogen remaining in the hydrogen storage tank at time t and time t-1 respectively; ω Ch and ω Disch They are the charging and discharging efficiency of the hydrogen storage tank respectively.

4. The microgrid low-carbon economic optimization method considering hydrogen energy storage and AC / DC hybrid characteristics as claimed in claim 1, characterized in that: The VSC converter station power loss is calculated using the following model: Where: Represents the power loss generated by the VSC during operation; is the current flowing through the AC side of the VSC converter; and is the active power and reactive power exchanged by the VSC on the AC side; is the AC side voltage of VSC; A, B, and C are the measured no-load loss value, linear loss coefficient, and nonlinear loss coefficient of VSC in the VSC-HVDC system, respectively.

5. The microgrid low-carbon economic optimization method considering hydrogen energy storage and AC / DC hybrid characteristics as claimed in claim 1, characterized in that: The operating constraints of the lower-level planning model include power balance constraints, cooling and heating balance and power station charge and discharge balance constraints, microgrid system wind and solar power consumption constraints, microgrid equipment output upper and lower limit constraints, and power purchase and sales upper and lower limit constraints for the power grid and energy storage power station; The power balance constraint is: Where, are the electric power on the AC bus and DC bus in each dispatching period respectively; The power of photovoltaic and wind power generation in each scheduling period respectively; The electric power consumed by the electric refrigerator in each scheduling period; is the electric load power in each dispatching period; The constraints on cooling and heating balance and power station charge and discharge balance are: Where: are the heating power of the heat exchanger and the cooling power of the absorption chiller in each scheduling period; They are the heating load and cooling power matrices for each scheduling period; They are heat exchanger efficiency, absorption chiller energy efficiency ratio, waste heat boiler efficiency and chiller energy efficiency ratio; is the gas turbine heat-to-power ratio; The wind and solar power consumption constraints of the microgrid system are: Where: are the power matrices of the maximum photovoltaic and wind power outputs in each dispatching period; ω is the wind and solar power absorption rate of the microgrid system; The upper and lower limits of the microgrid equipment output are: Where: are the minimum and maximum power of the gas turbine, respectively; are the minimum and maximum powers of the absorption chiller, respectively; are the minimum and maximum powers of the electric refrigerator, respectively; are the minimum and maximum power of the gas turbine, respectively; are the minimum and maximum powers of the heat exchanger, respectively; The upper and lower limits of power purchase and sales by the power grid and energy storage power station are: Where: The maximum power that the microgrid can purchase from the grid; is the maximum interactive power between the microgrid system and the energy storage power station; They are respectively the status of the system purchasing and selling electricity to the energy storage power station.

6. The microgrid low-carbon economic optimization method considering hydrogen energy storage and AC / DC hybrid characteristics as claimed in claim 1, characterized in that: The operational constraints of the upper-level planning model are: Where: and are the maximum powers of the electrolyzer and fuel cell, respectively; and are the maximum charging and discharging power of the hydrogen energy storage power station respectively; and They are the charging and discharging status of the hydrogen energy storage station. When the hydrogen energy storage station is charging Reverse when discharging; is the electricity consumption of the hydrogen energy storage power station in period t; and They are the charging and discharging efficiency of the hydrogen energy storage power station; The maximum amount of electricity that a hydrogen energy storage power station can store.

7. The microgrid low-carbon economic optimization method considering hydrogen energy storage and AC / DC hybrid characteristics as claimed in claim 5, characterized in that: In step S3, for the lower-level planning model, a Lagrangian function is constructed based on the objective function and constraint conditions of the lower-level planning model; according to the KKT condition, the necessary conditions of the lower-level planning model at the extreme point are determined, including the original feasibility condition, the optimality condition and the complementary relaxation condition; The original feasibility condition is the operation constraint of the lower-level planning model; The optimality condition is formed by performing partial derivative operations on each variable in the lower-level programming model according to the Lagrangian function and setting the partial derivatives to zero; The complementary relaxation condition is formed by the product of the inequality constraint of the lower-level programming model and the corresponding Lagrange multiplier being equal to zero; The KKT conditions are added as constraints to the upper-level planning model to obtain an equivalent single-level optimization model.

8. The microgrid low-carbon economic optimization method considering hydrogen energy storage and AC / DC hybrid characteristics as claimed in claim 7, characterized in that: For the nonlinear terms in the complementary relaxation conditions, the Big-M method is used to introduce 0-1 variables for linearization.

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