Electricity-hydrogen collaborative micro-grid multi-objective optimization scheduling method and system

Abstract

The invention discloses a multi-objective optimization scheduling method and system for an electricity-hydrogen collaborative micro-grid, and the equipment unit in the electricity-hydrogen collaborative micro-grid comprises a photovoltaic output unit, a fan output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit, and a flexible load demand side management unit. The method comprises the following steps: constructing a multi-target optimization scheduling model of the power-hydrogen collaborative micro-grid based on an operation model of an equipment unit in the power-hydrogen collaborative micro-grid; and solving the established multi-target optimization scheduling model of the electricity-hydrogen collaborative micro-grid to obtain an optimization scheduling scheme. According to the method, an emerging hydrogen energy technology is incorporated into a micro-grid system, a hydrogen energy storage unit in the micro-grid is incorporated into an optimal scheduling scheme, a hydrogen power generation and electric hydrogen production system is integrated with traditional battery energy storage and renewable energy sources, and the diversity and flexibility of micro-grid scheduling are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid dispatching, and in particular relates to a multi-objective optimization dispatching method and system for an electric-hydrogen coordinated microgrid. Background Art

[0002] At present, the research on microgrids is mainly focused on operation and scheduling to solve the uncertainty problem of renewable energy (RES). Among them, demand response and energy storage systems (such as battery energy storage systems) are considered to be one of the main tools to deal with RES uncertainty. Some scholars have proposed a stochastic method for microgrid operation and scheduling in grid-connected mode considering the uncertainty of wind and solar energy, and the uncertainty of renewable energy is controlled by adding a hybrid energy storage system to the microgrid. Some studies have developed a two-stage two-level method for the operation and scheduling of multi-carrier automatic scheduling systems in day-ahead and real-time modes, modeled demand response, and provided greater flexibility for the proposed operation mechanism. For microgrid planning considering the uncertainty of renewable energy output and load, some literature has proposed a two-stage framework to determine the operation and management of the automatic scheduling system by considering the uncertainty of RES through a robust framework; some studies have also adopted dynamic programming methods to adjust the system operation strategy to cope with the uncertainty of renewable energy by real-time monitoring of the performance of solar energy and battery energy storage systems. However, as the problem size of dynamic programming increases, the dimensionality of the state space may lead to a significant increase in storage and computing costs, while the robust optimization method may lead to a relatively conservative solution, thereby sacrificing a certain degree of optimization efficiency and accuracy. Compared with the above methods, scenario generation and reduction technology can better adapt to different problem requirements and application scenarios, and verify the robustness and adaptability of system design by generating and screening different test scenarios. However, existing research focuses on scenario generation that considers single or partial uncertainty factors (such as wind energy or solar energy), and there are few methods that consider multiple uncertainties of wind energy, solar energy, load and electricity price at the same time. Therefore, it is of great research significance to study how to comprehensively consider these multiple uncertainties in scenario generation and reduction, and then optimize the microgrid operation strategy.

[0003] In addition, current research often ignores the role of hydrogen energy systems in improving system flexibility. When hydrogen energy systems are integrated into microgrids, they can not only be used as energy resources, but also as energy storage systems, providing stable power supply through fuel cells or hydrogen turbines during peak loads, and converting excess electricity into hydrogen through electric hydrogen production systems for storage and subsequent use.

[0004] Moreover, most current research focuses on single-objective scheduling of economic operation and technical optimization, with less attention paid to emission reduction targets and operational reliability targets. However, single-objective scheduling may lead to sacrificing other aspects of performance (such as environmental impact or operational reliability) in pursuit of a specific goal (such as economy). Through multi-objective scheduling, the best balance can be found between economy, environmental protection and system stability, achieving comprehensive optimization in many aspects.

[0005] Therefore, how to propose a multi-objective optimization scheduling method and system for an electric-hydrogen coordinated microgrid to incorporate emerging hydrogen energy technologies into the microgrid and fully utilize the multiple uncertainties between wind and solar power output, load and electricity price is an urgent problem to be solved by technicians in this field. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the present invention proposes a multi-objective optimization scheduling method and system for an electric-hydrogen coordinated microgrid.

[0007] The technical solution of the present invention is as follows:

[0008] A multi-objective optimization scheduling method for an electric-hydrogen coordinated microgrid, wherein the equipment units in the electric-hydrogen coordinated microgrid include a photovoltaic output unit, a wind turbine output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit and a flexible load demand side management unit, including:

[0009] Based on the operation model of the equipment unit in the electric-hydrogen cooperative microgrid, a multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid is constructed. The objective functions of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include minimizing the operation cost, minimizing the carbon emission and minimizing the peak load. The constraints of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering the wind and solar output, multiple uncertainties between load and electricity price, energy trading constraints with the main grid and demand response constraints; the decision variables of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include the operation plans of the battery energy storage unit, the diesel generator unit, the hydrogen energy storage unit and the flexible load demand side management unit and the energy trading plan with the main grid;

[0010] The established multi-objective optimization scheduling model of the electric-hydrogen collaborative microgrid is solved to obtain the optimal scheduling plan.

[0011] Furthermore, the operation model of the diesel generator unit in the operation model of the equipment unit in the electric-hydrogen cooperative microgrid includes power generation constraints and cost functions, and the expressions are as follows: i,t P i min ≤P i,t ≤μ i,t P i max ,

[0012] Where P i,t and P i,t-1 are the output values ​​of diesel generator i at time t and time t-1 respectively; P i min and P i max are the minimum power generation boundary and the maximum power generation boundary of diesel generator i respectively; and are the maximum increase and decrease of power generation rate of diesel generator i respectively; cost DG is the cost function in the operation model of the diesel generator unit; α i is the fixed cost coefficient of diesel generator i; β i is the variable cost coefficient of diesel generator i; i is the emission of diesel generator i; γ i is the compensation cost of diesel generator i;

[0013] The operation model of the battery energy storage unit includes: SOC b,t1 =SOC b,t4

[0014] In the formula, SOC b,t+1 and SOC b,t is the charge state of the energy storage battery b at time t+1 and time t; and are the charging efficiency and discharging efficiency of energy storage battery b; and is the charging power and discharging power of the energy storage battery b at time t; and are the minimum and maximum boundaries of the state of charge of the energy storage battery b at time t respectively; and are the charging mode and discharging mode of the energy storage battery t at time t, and All are 0-1 variables; and are the maximum charging power and minimum charging power of energy storage battery b respectively; SOC b,t1 and SOC b,t4 are the storage energies of energy storage battery b at the initial and final moments respectively;

[0015] The operation model of the hydrogen energy storage unit includes:

[0016]

[0017] σ h,t +χ h,t ≤1

[0018] In the formula, is the power consumption of electrolytic cell h at time t; h,t is the transformation efficiency; is the hydrogen gas input into hydrogen storage tank h at time t; HL h,t is the amount of hydrogen stored in the hydrogen storage tank h at time t; is the initial value in the hydrogen storage tank h at time t=1; HL h,t-1 is the amount of hydrogen stored in the hydrogen storage tank h at time t-1; φ h,t is the charge and discharge efficiency of the hydrogen storage tank h at time t; is the hydrogen gas input into the hydrogen storage tank h at time t; is the hydrogen output from the hydrogen storage tank h at time t; is the amount of hydrogen consumed by the hydrogen fuel cell h for power generation at time t; is the amount of hydrogen sold to industry at time t; ψ h The efficiency of hydrogen-to-electricity conversion; is the power generation of the hydrogen fuel cell h at time t; and are the minimum / maximum hydrogen storage capacity of the hydrogen storage tank h respectively; is the amount of hydrogen consumed by the hydrogen fuel cell h for power generation at time t; The maximum hydrogen value of the hydrogen storage tank used for hydrogen fuel cell power generation; is the amount of hydrogen sold to industry at time t; is the maximum hydrogen value of the hydrogen storage tank for sale to industry; h,t and χ h,t is a binary variable, indicating that at the same time t, the produced hydrogen can only be sold to industry or consumed by hydrogen fuel cells;

[0019] The expression of the operation model of the photovoltaic output unit is:

[0020]

[0021] In the formula, is the output of photovoltaic unit i under scenario s at time t; η PV is the efficiency of PV unit i; is the number of photovoltaic panels of photovoltaic unit i; is the photovoltaic panel area of ​​photovoltaic unit i; I t,s is the solar radiation under scene s at time t; T t out is the ambient temperature at time t;

[0022] The expression of the operation model of the fan output unit is:

[0023] In the formula, and v i,r are the rated power and rated speed of fan i respectively; v ci and v co is the fan cut-in speed and cut-out speed; v t,s is the wind speed in scene s at time t; is the output of wind turbine i under scenario s at time t;

[0024] The operation model of the flexible load demand side management unit includes: DR min ≤DR l,t ≤DR max ,

[0025]

[0026] In the formula, DR min and DR max are the minimum and maximum demand response levels, respectively; DR l,t is the demand response level of load l at time t; ldr l,t is the transferable load at time t; is the load level before demand response at time t; T is the dispatch period; is the load level after demand response at time t;

[0027] Furthermore, the method for obtaining the scenario probability constraint considering multiple uncertainties among wind and solar power output, load and electricity price is as follows: respectively generate the probability distribution of the operation scenarios of wind turbine output, photovoltaic output, load and electricity price, wherein the operation scenario of photovoltaic output is an operation scenario related to solar radiation, and its probability distribution is generated in the form of an equation using the Beta probability distribution function as follows:

[0028]

[0029] Where PDF(x) is the Beta PDF function, which is used to describe the probability distribution of solar radiation x; parameters α and β are both shape parameters, parameter α determines the right shape of the solar radiation probability distribution, and parameter β determines the left shape of the solar radiation probability distribution; μ PV is the average value of photovoltaic output; δ PV is the standard deviation of photovoltaic output; Γ is the Gamma function;

[0030] The operation scenario of the wind turbine output is an operation scenario related to the wind speed, and its probability distribution is generated by the WeibullPDF method through the equation as follows:

[0031]

[0032] Where PDF(v) is the probability density function of the Weibull distribution, which is used to describe the probability distribution of wind speed v; parameters c and k are the shape indexes of the Weibull PDF. Parameter k affects the sharpness of the wind speed distribution, and parameter c determines the distribution range of the wind speed; μ WT is the average wind speed; δ WT is the standard deviation of wind speed;

[0033] The probability distribution of the load and electricity price operation scenarios is generated using the normal distribution PDF as follows:

[0034]

[0035] Where PDF(y) is the normal distribution PDF, which is used to describe the probability distribution of electricity price y; μ EP is the average value of electricity price; EP is the standard deviation of electricity price; PDF(z) is the normal distribution PDF; μ LD is the average value of the load; δ LD is the standard deviation of the load;

[0036] The reduction of operation scenarios is based on mixed integer linear programming.

[0037] Furthermore, the specific steps of reducing the operation scenarios based on mixed integer linear programming include:

[0038] First, the probability distribution of wind turbine output, photovoltaic output, load and electricity price operation scenarios is segmented into N 1 、N 2 、N 3 and N 4 Each segment corresponds to a discrete scenario, and then the probability and center value of each segment are calculated, thereby converting the probability distribution of wind turbine output, photovoltaic output, load and electricity price operation scenarios from continuous probability distribution to discrete scenarios;

[0039] The calculation formulas of the probability and center value of the segment are as follows:

[0040]

[0041] In the formula, is the uncertainty factor xn x The probability of each segment; PDF(x) is the distribution probability curve of the uncertainty factor x; and Respectively represent the nth x The starting and ending points of each segment; Represents the nthx The center value of each segment;

[0042] Then the mixed integer linearization is performed as follows:

[0043]

[0044] In the formula, is a binary variable, representing the scenario combination of the n1th wind speed scenario, the n2th radiation scenario, the n3th electricity price scenario, and the n4th load scenario; minf is a variable for selecting the number of scenarios The weighted sum of represents the selection of a set of representative and minimal scenes; ρ s (n 1 ,n 2 ,n 3 ,n 4 ) is a probability variable, which indicates the probability of a combination of multiple uncertain factors occurring in a specific scenario. 1 ,n 2 ,n 3 ,n 4 An index representing different categories of uncertainty scenarios; n is the wind speed in different scenarios 1 Probability of occurrence; n is the solar radiation in different scenarios 2 Probability of occurrence; is the electricity price in different scenarios 3 Probability of occurrence; For different load scenarios n 4 Probability of occurrence.

[0045] Furthermore, the operating cost minimization objective function of the electric-hydrogen coordinated microgrid multi-objective optimization scheduling model is:

[0046]

[0047] Where P t Grid is the electricity purchased from the main grid at time t; is the electricity price at time t; P t sell is the electricity sold to the main grid at time t; α i is the fixed cost coefficient of diesel generator i; β i is the variable cost coefficient of diesel generator i; i is the emission of diesel generator i; γ i is the compensation cost of diesel generator i; is the price of hydrogen at time t; is the amount of hydrogen sold to industry at time t.

[0048] The minimum objective function of carbon emissions is:

[0049]

[0050] Where P t Grid is the electricity purchased from the main grid at time t; is the carbon emission coefficient of the main power grid; P i,t is the output value of diesel generator i at time t; is the carbon emission coefficient of the diesel generator; is the power generation of the hydrogen fuel cell h at time t; is the carbon emission coefficient of hydrogen fuel cells.

[0051] The peak load minimization objective function is: min Max

[0052] In the formula, min Max is an optimization strategy to find the minimum value among all possible maximum values; is the load level after demand response at time t.

[0053] Furthermore, the energy transaction constraints of the electric-hydrogen coordinated microgrid multi-objective optimization scheduling model with the main grid are:

[0054] P g,min ≤P t Grid ≤P g,max , P sell,min ≤P t sell ≤P sell,max

[0055] Where P t Grid is the electricity purchased from the main grid at time t; P g,min and P g,max is the minimum and maximum value of electricity purchased from the main grid; P t sell is the electricity sold to the main grid at time t; P sell,min and P sell,max Minimum and maximum values ​​for selling electricity to the main grid;

[0056] The load supply and demand balance constraint is:

[0057]

[0058] Where P t Grid P is the electricity purchased from the main grid at time t; i,t is the output value of diesel generator i at time t; ρs is the probability of scenario s occurring, which is used to represent the uncertainty of photovoltaic and wind power output under different scenarios; is the output of wind turbine i under scenario s at time t; is the output of PV unit i under scenario s at time t; and is the charging power and discharging power of the energy storage battery b at time t; is the power generation of hydrogen fuel cell h at time t; P t sell is the electricity sold to the main grid at time t; is the power consumption of electrolytic cell h at time t; is the active power demand of load l at time t; is the reactive power absorbed from the main grid at time t; Q i,t is the reactive power generated by diesel generator i at time t; is the reactive power generated by PV unit i at time t; is the reactive power generated by wind turbine unit i at time t; Q b,t is the reactive power generated by the energy storage battery b at time t; Q c,t is the reactive power generated by capacitor c at time t; Q l,t is the reactive power demand of load l at time t.

[0059] Furthermore, the specific steps of solving the established electric-hydrogen coordinated microgrid multi-objective optimization scheduling model and obtaining the optimization scheduling solution include:

[0060] The three objective functions are transformed into a single objective function using the hybrid normalized minimum-maximum method, which is expressed as follows:

[0061]

[0062] In the formula, ω 1 ,ω 2 ,ω 3 are the weights of the objective function of minimum operating cost, minimum carbon emission and minimum peak load, respectively, indicating the importance of each objective function, and ω 1 +ω 2 +ω 3 =1; cost and Emission are operating cost and carbon emission respectively; cost * and Emission * are the optimal values ​​of operating cost and carbon emissions, respectively;

[0063] For the single objective function Make a substitution, the replacement formula is; Where:l,t is a free variable;

[0064] The multi-objective optimization dispatch model of the replaced electric-hydrogen coordinated microgrid is as follows. The constraints also include the above-mentioned unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering multiple uncertainties between wind and solar output, load and electricity price, energy trading constraints with the main grid, and demand response constraints:

[0065]

[0066] a. Initialize the main problem: set the initial values ​​and boundary conditions, that is, initialize the parameters and decision variables in the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid;

[0067] b. Solve the embedded mixed integer linear optimization model: In each iteration step, for the given variable values ​​of the main problem, solve the mixed integer linear optimization model to obtain the optimal scenario selection and the corresponding probability distribution;

[0068] c. Update the variables of the main problem: Use the solution obtained from the mixed integer linear optimization model to update the variable values ​​in the main problem, that is, according to the scenario probabilities and weights solved in the mixed integer linear optimization model, bring the specific conditions of each scenario into the objective function and constraints of the main problem, calculate its contribution to the objective function of the main problem and update the decision variables in the main problem, including the operation plans of the battery energy storage unit, diesel generator unit, hydrogen energy storage unit and flexible load demand side management unit, and the energy trading plan with the main grid;

[0069] d. Iterative solution: Repeat the above steps until the objective function of the main problem converges, that is, the global optimal solution is found.

[0070] A multi-objective optimization dispatching system for an electric-hydrogen coordinated microgrid, wherein the equipment units in the electric-hydrogen coordinated microgrid include a photovoltaic output unit, a wind turbine output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit, and a flexible load demand side management unit, including: a model building module and a model solving module;

[0071] The model building module is used to build an electric-hydrogen collaborative microgrid multi-objective optimization scheduling model based on the operation model of the equipment unit in the electric-hydrogen collaborative microgrid. The objective function of the electric-hydrogen collaborative microgrid multi-objective optimization scheduling model includes minimizing the operating cost, minimizing the carbon emission and minimizing the peak load. The constraints of the electric-hydrogen collaborative microgrid multi-objective optimization scheduling model include unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering wind and solar output, multiple uncertainties between load and electricity price, energy trading constraints with the main grid and demand response constraints; the decision variables of the electric-hydrogen collaborative microgrid multi-objective optimization scheduling model include the operation plans of the battery energy storage unit, the diesel generator unit, the hydrogen energy storage unit and the flexible load demand side management unit, and the energy trading plan with the main grid;

[0072] The model solving module is used to solve the established electric-hydrogen collaborative microgrid multi-objective optimization scheduling model to obtain an optimized scheduling solution.

[0073] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0074] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] The present invention proposes a new energy consumption capacity assessment method and system that integrates time-series production simulation and random production simulation. The method incorporates emerging hydrogen energy technology into the microgrid system, incorporates the hydrogen energy storage unit in the microgrid into the optimization scheduling plan, and integrates the hydrogen power generation and hydrogen production system with traditional battery energy storage and renewable energy, thereby improving the diversity and flexibility of microgrid scheduling.

[0077] The evaluation method of the present invention takes into account the multiple uncertainties in the microgrid when constructing a multi-objective optimization scheduling model for an electric-hydrogen collaborative microgrid. Through scenario generation and reduction, the model is made to more accurately reflect the actual operating conditions, thereby forming a scenario probability constraint that takes into account the multiple uncertainties between wind and solar power output, load and electricity price.

[0078] The objective functions of the electric-hydrogen collaborative microgrid multi-objective optimization scheduling model of the evaluation method of the present invention include minimizing operating costs, minimizing carbon emissions, and minimizing peak loads, thereby achieving comprehensive optimization of the environment, economy, and reliability, improving the operating efficiency and sustainability of the microgrid system, and providing an accurate and complete operating mechanism for daily scheduling of microgrids that takes into account the environment, economy, and reliability.

[0079] The evaluation method of the present invention effectively reduces the computational complexity through mixed integer linear optimization (MILP), making it convenient for technicians to quickly obtain scheduling solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 Flow chart of the multi-objective optimization scheduling method of the electric-hydrogen coordinated microgrid in the embodiment.

[0081] Figure 2 Schematic diagram of the solar radiation scene Beta PDF when α and β take different values.

[0082] Figure 3 Schematic diagram of the wind speed scenario Weibull PDF when c and k take different values.

[0083] Figure 4 It is a schematic diagram of normal PDF.

[0084] Figure 5 Schematic diagram of solar radiation values ​​in 7 different scenes after scene reduction.

[0085] Figure 6 Schematic diagram of wind speed values ​​in 7 different scenarios after scene reduction.

[0086] Figure 7 This is a schematic diagram of the load distribution diagram.

[0087] Figure 8 This is a schematic diagram of the electrolytic cell output. DETAILED DESCRIPTION

[0088] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0089] Embodiment 1:

[0090] The present invention provides a multi-objective optimization scheduling method for an electric-hydrogen coordinated microgrid, wherein the equipment units in the electric-hydrogen coordinated microgrid include a photovoltaic output unit, a wind turbine output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit, and a flexible load demand side management unit, such as Figure 1 As shown, including:

[0091] Based on the operation model of the equipment units in the electric-hydrogen cooperative microgrid, a multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid is constructed. The objective functions of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include minimizing the operating cost, minimizing the carbon emission and minimizing the peak load. The constraints of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering wind and solar output, multiple uncertainties between load and electricity price, energy trading constraints with the main grid and demand response constraints. The decision variables of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include the operation plans of the battery energy storage unit, the diesel generator unit, the hydrogen energy storage unit and the flexible load demand side management unit, as well as the energy trading plan with the main grid. Among them, the operation plan of the battery energy storage unit is the battery energy storage output, the operation plan of the diesel generator unit is the diesel generator output, the operation plan of the hydrogen energy storage unit is the hydrogen energy storage output, the operation plan of the flexible load demand side management unit is the flexible load transfer amount, and the energy trading plan with the main grid is the energy trading amount with the main grid.

[0092] The established multi-objective optimization scheduling model of the electric-hydrogen collaborative microgrid is solved to obtain the optimal scheduling plan.

[0093] Embodiment 2:

[0094] This embodiment is further designed on the basis of the first embodiment in that the operation model of the diesel generator unit in the operation model of the equipment unit in the electric-hydrogen coordinated microgrid in this embodiment includes power generation constraints and cost functions, and the expressions are as follows:

[0095] μ i,t P i min ≤P i,t ≤μ i,t P i max (1)

[0096]

[0097] Where P i,t and P i,t-1 are the output values ​​of diesel generator i at time t and time t-1 respectively; P i min and P i max are the minimum power generation boundary and the maximum power generation boundary of diesel generator i respectively; and are the maximum increase and decrease of power generation rate of diesel generator i respectively; cost DG is the cost function in the operation model of the diesel generator unit, describes the cost of electricity generation from a diesel generator; α iis the fixed cost coefficient of diesel generator i; β i is the variable cost coefficient of diesel generator i; i is the emission of diesel generator i; γ i is the compensation cost of diesel generator i, which depends on whether diesel generator i is applicable, that is, the binary variable μ i,t Decide;

[0098] The present invention introduces the state of charge (SOC) to reflect the remaining power of the battery energy storage. The operation model of the battery energy storage unit includes:

[0099]

[0100]

[0101] SOC b,t1 =SOC b,t4 (10)

[0102] In the formula, SOC b,t+1 and SOC b,t is the charge state of the energy storage battery b at time t+1 and time t; and are the charging efficiency and discharging efficiency of energy storage battery b; and is the charging power and discharging power of the energy storage battery b at time t; and are the minimum and maximum boundaries of the state of charge of the energy storage battery b at time t respectively; and are the charging mode and discharging mode of the energy storage battery t at time t, and All are 0-1 variables;

[0103] and are the maximum charging power and minimum charging power of energy storage battery b respectively; SOC b,t1 and SOC b,t4 are the storage energy of energy storage battery b at the initial moment and the final moment, SOC b,t1 =SOC b,t4 Ensure that the initial and final levels of stored energy are the same;

[0104] The present invention integrates hydrogen energy storage into the microgrid system to control the uncertain behavior of wind energy and photovoltaic energy, thereby increasing the flexibility of electricity and energy. Hydrogen energy storage includes an electrolyzer, a hydrogen storage tank and a hydrogen fuel cell. The operating model of the hydrogen energy storage unit includes:

[0105]

[0106]

[0107] σ h,t +χ h,t ≤1 (18)

[0108] In the formula, is the power consumption of electrolytic cell h at time t; h,t is the transformation efficiency; is the hydrogen gas input into the hydrogen storage tank h at time t; formula Indicates the electricity consumption of the electrolyzer converted into hydrogen; HL h,t is the amount of hydrogen stored in the hydrogen storage tank h at time t; is the initial value in the hydrogen storage tank h at time t=1; HL h,t-1 is the amount of hydrogen stored in the hydrogen storage tank h at time t-1; φ h,t is the charge and discharge efficiency of the hydrogen storage tank h at time t; is the hydrogen gas input into the hydrogen storage tank h at time t; is the hydrogen output from the hydrogen storage tank h at time t; formula Indicates the level of hydrogen storage in the tank, and the hydrogen storage level HL at time t-1 h,t-1 , storage efficiency at time t Filling and deflation volume and related; is the amount of hydrogen consumed by the hydrogen fuel cell h for power generation at time t; is the amount of hydrogen sold to industry at time t; It means that the hydrogen output from the hydrogen storage tank can be sold to industry or consumed by hydrogen fuel cells for power generation; h The efficiency of hydrogen-to-electricity conversion; is the power generation of the hydrogen fuel cell h at time t; formula Indicates calculation of power generation of hydrogen fuel cell; and are the minimum / maximum hydrogen storage capacity of the hydrogen storage tank h; formula It indicates determining the minimum and maximum boundaries of the hydrogen storage tank; is the amount of hydrogen consumed by the hydrogen fuel cell h for power generation at time t; The maximum hydrogen value of the hydrogen storage tank used for hydrogen fuel cell power generation; is the amount of hydrogen sold to industry at time t; is the maximum hydrogen value of the hydrogen storage tank for sale to industry; h,t and χ h,t is a binary variable, indicating that at the same time t, the generated hydrogen can only be sold to industry or consumed by hydrogen fuel cells; Formula and formula The maximum value of hydrogen delivered to the hydrogen fuel cell and sold to the industry is represented by the formula σ h,t +χ h,t ≤1 means that at the same time t, hydrogen energy storage can only sell the generated hydrogen or consume it by fuel cells.

[0109] The expression of the operation model of the photovoltaic output unit is:

[0110]

[0111] In the formula, is the output of photovoltaic unit i under scenario s at time t; η PV is the efficiency of PV unit i; is the number of photovoltaic panels of photovoltaic unit i; is the photovoltaic panel area of ​​photovoltaic unit i; I t,s is the solar radiation under scene s at time t; T t out is the ambient temperature at time t; this operation model represents the output of photovoltaic Mainly determined by the efficiency η of the photovoltaic system PV 、The number of photovoltaic panels area Solar Radiation I t,s and ambient temperature T t out Decide;

[0112] The expression of the operation model of the fan output unit is:

[0113]

[0114] In the formula, and v i,r are the rated power and rated speed of fan i respectively; v ci and v co is the fan cut-in speed and cut-out speed; v t,s is the wind speed in scene s at time t; is the output of wind turbine i under scenario s at time t; this operation model represents the output of wind turbine With wind speed v t,s V ci and v co are the cut-in and cut-out speeds of the fan respectively;

[0115] The load of the microgrid in the present invention considers the transferable load and can provide demand response service. The operation model of the flexible load demand side management unit includes:

[0116] DR min ≤DR l,t ≤DR max(twenty one)

[0117]

[0118] In the formula, DR min and DR max are the minimum and maximum demand response levels, respectively; DR l,t is the demand response level of load l at time t; ldr l,t is the transferable load at time t; is the load level before demand response at time t; T is the dispatch period; is the load level after demand response at time t; formula The total load before and after the transfer remains unchanged, the formula Indicates the load distribution after considering load transfer.

[0119] Embodiment three:

[0120] This embodiment is further designed on the basis of the second embodiment in that the method for obtaining the scenario probability constraints considering multiple uncertainties between wind and solar power output, load and electricity price in this embodiment is as follows: the uncertainty in the electric-hydrogen cooperative microgrid includes load, wind turbine output and photovoltaic output, and the uncertainty of electricity price is further considered. The probability distribution of the operation scenarios of wind turbine output, photovoltaic output, load and electricity price is generated respectively, among which the wind turbine output and photovoltaic output depend on weather conditions, which are specifically reflected in solar radiation and wind speed, so the uncertainty modeling of radiation and wind speed changes is performed.

[0121] The operating scenario of PV output is the operating scenario related to solar radiation, and its probability distribution is generated in the form of an equation using the Beta probability distribution function (PDF) as follows:

[0122]

[0123] Where PDF(x) is the BetaPDF function, which is used to describe the probability distribution of solar radiation x; parameters α and β are both shape parameters, parameter α determines the right shape of the solar radiation probability distribution, and parameter β determines the left shape of the solar radiation probability distribution; μ PV is the average value of photovoltaic output, usually the average level of solar radiation; δ PV is the standard deviation of photovoltaic output, indicating the volatility or uncertainty of output; Γ is the Gamma function; Figure 2 The values ​​of BetaPDF for different α and β are shown.

[0124] The operating scenario of the wind turbine output is the operating scenario related to the wind speed, and its probability distribution is generated by the Weibull PDF method through the equation as follows:

[0125]

[0126] Where PDF(v) is the probability density function of the Weibull distribution, which is used to describe the probability distribution of wind speed v; parameters c and k are the shape indexes of the Weibull PDF. Parameter k affects the sharpness of the wind speed distribution, and parameter c determines the distribution range of the wind speed; μ WT is the average wind speed, indicating the average wind speed per unit time; δ WT is the standard deviation of wind speed, indicating the volatility or uncertainty of wind speed; it should be noted that the average values ​​of solar radiation and wind speed μ PV , μ WT and standard deviation δ PV , δ WT It is the statistical characteristics of the actual wind speed data. The parameters α and β, c and k calculated from these characteristics enable Beta and Weibull distributions to effectively simulate and predict the output of photovoltaic and wind turbines. Figure 3 The values ​​of the Weibull PDF for different c and k are shown. The standard deviation δ is assumed to be 10% for both Beta and Weibull PDF.

[0127] The probability distribution of the operating scenarios of load and electricity price is generated using the normal distribution PDF as follows:

[0128]

[0129] Where PDF(y) is the normal distribution PDF, which is used to describe the probability distribution of electricity price y; μ EP is the average value of electricity price; EP is the standard deviation of the electricity price, reflecting the volatility of the electricity price; PDF(z) is the normal distribution PDF, which is used to describe the probability distribution of load z; μ LD is the average value of the load; δ LD is the standard deviation of the load, reflecting the fluctuation degree of the load data;

[0130] The reduction of operation scenarios is based on mixed integer linear programming.

[0131] Embodiment 4:

[0132] This embodiment is further designed on the basis of the third embodiment in that the specific steps of reducing the operation scenarios based on mixed integer linear programming in this embodiment include:

[0133] First, the probability distribution of wind turbine output, photovoltaic output, load and electricity price operation scenarios is segmented into N 1 、N 2 、N 3 and N 4Each segment corresponds to a discrete scenario, and then the probability and center value of each segment are calculated, thereby converting the probability distribution of wind turbine output, photovoltaic output, load and electricity price operation scenarios from continuous probability distribution to discrete scenarios;

[0134] In the specific implementation, Figure 4 In order to improve the calculation accuracy, the number of wind conditions, radiation conditions, price conditions and load conditions can be limited to N according to the probability distribution diagram. 1 、N 2 、N 3 and N 4 indivual, Figure 4 Medium 1 =N 2 =N 3 =N 4 =7, the probability of each scene is equal to the area of ​​the part, and the central value of the scene is the representative of the scene, that is, ..., μ-3δ, μ-2δ, μ-δ, μ, μ+δ, μ+2δ, μ+3δ, ..., where μ = μ PV ,μ WT ,μ EP ,μ LD , δ=δ PV ,δ WT ,δ EP ,δ LD .

[0135] The calculation formulas for the probability of the segment and the center value are as follows:

[0136]

[0137] In the formula, is the nth value of the uncertain factor x (wind speed, radiation, electricity price, load) x The probability of each segment; PDF(x) is the distribution probability curve of the uncertainty factor x (wind speed, radiation, electricity price, load); and Respectively represent the nth x The starting and ending points of each segment; Represents the nth x The center value of each segment;

[0138] Then, mixed integer linearization is performed as follows to reduce the number of scenarios while improving the calculation accuracy:

[0139]

[0140] In the formula, is a binary variable, representing the scenario combination of the n1th wind speed scenario, the n2th radiation scenario, the n3th electricity price scenario, and the n4th load scenario; minf is a variable for selecting the number of scenarios The weighted sum of represents the selection of a set of representative and minimal scenes; ρ s (n 1 ,n 2 ,n 3 ,n 4 ) is a probability variable, which indicates the probability of a combination of multiple uncertain factors occurring in a specific scenario. 1 ,n 2 ,n 3 ,n 4 An index representing different categories of uncertainty scenarios; n is the wind speed in different scenarios 1 Probability of occurrence; n is the solar radiation in different scenarios 2 Probability of occurrence; is the electricity price in different scenarios 3 Probability of occurrence; For different load scenarios n 4 Specifically, formula (32) is the objective function, which is to minimize the number of representative scenarios; formulas (33)-(36) ensure that the sum of the probabilities of each scenario type (such as wind speed, radiation, electricity price, load) is consistent with the probability when the scenario is considered alone; formula (37) ensures that the total probability of all scenarios is 1; formula (38) ensures that the probability of a scenario is greater than zero only when a scenario is selected; formula (39) ensures that the scenario probability value is non-negative; formula (40) ensures that the scenario selection variable It can only be 0 or 1, indicating whether the scene is selected.

[0141] Embodiment five:

[0142] This embodiment is further designed on the basis of the fourth embodiment in that the operating cost minimum objective function of the multi-objective optimization scheduling model of the electric-hydrogen coordinated microgrid in this embodiment is:

[0143]

[0144] Where P t Grid is the electricity purchased from the main grid at time t; is the electricity price at time t; P t sell is the electricity sold to the main grid at time t; α i is the fixed cost coefficient of diesel generator i; β i is the variable cost coefficient of diesel generator i; iis the emission of diesel generator i; γ i is the compensation cost of diesel generator i, which depends on whether diesel generator i is used, represented by the binary variable μ i,t Decide; is the price of hydrogen at time t; is the amount of hydrogen sold to industry at time t.

[0145] The total cost of the electric-hydrogen synergistic microgrid consists of different items. The first item refers to the purchase of energy from the main grid. represents the electricity price at time t; the second term is the profit from energy trading with the main grid; the third term represents the cost of electricity generation from diesel generators; and the last term is the profit from selling hydrogen to industry. represents the price of hydrogen at time t.

[0146] The minimum objective function for carbon emissions is:

[0147]

[0148] Where P t Grid is the electricity purchased from the main grid at time t; is the carbon emission coefficient of the main power grid; P i,t is the output value of diesel generator i at time t; is the carbon emission coefficient of the diesel generator; is the power generation of the hydrogen fuel cell h at time t; is the carbon emission coefficient of hydrogen fuel cells. The carbon emissions of the electric-hydrogen synergistic microgrid include indirect carbon emissions, i.e., carbon emissions from purchasing electricity from the main power grid, and direct carbon emissions, i.e., carbon emissions from diesel generators and hydrogen fuel cells.

[0149] The peak load minimization objective function is:

[0150]

[0151] In the formula, min Max is an optimization strategy to find the minimum value among all possible maximum values; is the load level after demand response at time t. Reducing the peak load by the min-max method, that is, minimizing the maximum load, helps balance the grid load.

[0152] Embodiment six:

[0153] This embodiment is further designed on the basis of the fifth embodiment in that the energy transaction constraints with the main grid in the multi-objective optimization scheduling model of the electric-hydrogen coordinated microgrid in this embodiment are:

[0154] P g,min ≤P t Grid ≤Pg,max (44)

[0155] P sell,min ≤P t sell ≤P sell,max (45)

[0156] Where P t Grid P is the electricity purchased from the main grid at time t; g,min and P g,max is the minimum and maximum value of electricity purchased from the main grid; P t sell is the electricity sold to the main grid at time t; P sell,min and P sell,max Minimum and maximum values ​​for selling electricity to the main grid;

[0157] The load supply and demand balance constraint is:

[0158]

[0159] Where P t Grid is the electricity purchased from the main grid at time t; P i,t is the output value of diesel generator i at time t;

[0160] ρ s is the probability of scenario s occurring, which is used to represent the uncertainty of photovoltaic and wind power output under different scenarios; is the output of wind turbine i under scenario s at time t; is the output of PV unit i under scenario s at time t; and is the charging power and discharging power of the energy storage battery b at time t; is the power generation of hydrogen fuel cell h at time t; P t sell is the electricity sold to the main grid at time t; is the power consumption of electrolytic cell h at time t; is the active power demand of load l at time t; is the reactive power absorbed from the main grid at time t; Q i,t is the reactive power generated by diesel generator i at time t; is the reactive power generated by PV unit i at time t; is the reactive power generated by wind turbine unit i at time t; Q b,t is the reactive power generated by the energy storage battery b at time t; Q c,t is the reactive power generated by capacitor c at time t; Q l,tis the reactive power demand of load l at time t.

[0161] Embodiment seven:

[0162] This embodiment is further designed on the basis of the sixth embodiment in that the established electric-hydrogen cooperative microgrid multi-objective optimization scheduling model is solved in this embodiment, and the specific steps of obtaining the optimization scheduling scheme include:

[0163] The three objective functions are transformed into a single objective function using the hybrid normalized minimum-maximum method, which is expressed as follows:

[0164]

[0165] In the formula, ω 1 ,ω 2 ,ω 3 are the weights of the objective function of minimum operating cost, minimum carbon emission and minimum peak load, respectively, indicating the importance of each objective function, and ω 1 +ω 2 +ω 3 =1; cost and Emission are operating cost and carbon emission respectively; cost * and Emission * are the optimal values ​​of operating cost and carbon emissions, respectively;

[0166] For a single objective function Make a substitution, the replacement formula is;

[0167]

[0168] Where: l,t is a free variable;

[0169] The multi-objective optimization dispatch model of the replaced electric-hydrogen coordinated microgrid is as follows. The constraints also include the above-mentioned unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering multiple uncertainties between wind and solar output, load and electricity price, energy trading constraints with the main grid, and demand response constraints:

[0170]

[0171] a. Initialize the main problem: set the initial values ​​and boundary conditions, that is, initialize the parameters and decision variables in the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid;

[0172] b. Solve the embedded mixed integer linear optimization model: In each iteration step, for the given variable values ​​of the main problem, solve the mixed integer linear optimization model to obtain the optimal scenario selection and the corresponding probability distribution;

[0173] c. Update the variables of the main problem: Use the solution obtained from the mixed integer linear optimization model to update the variable values ​​in the main problem, that is, according to the scenario probabilities and weights solved in the mixed integer linear optimization model, bring the specific conditions of each scenario into the objective function and constraints of the main problem, calculate its contribution to the objective function of the main problem and update the decision variables in the main problem, including the operation plans of the battery energy storage unit, diesel generator unit, hydrogen energy storage unit and flexible load demand side management unit, and the energy trading plan with the main grid;

[0174] d. Iterative solution: Repeat the above steps until the objective function of the main problem converges, that is, the global optimal solution is found.

[0175] Through the above method, the mixed integer linear optimization model is embedded into the main problem, and all constraints are comprehensively considered, which can effectively deal with the uncertainty of renewable energy generation and achieve the global optimal solution.

[0176] Embodiment eight:

[0177] A multi-objective optimization dispatching system for an electric-hydrogen coordinated microgrid, wherein the equipment units in the electric-hydrogen coordinated microgrid include a photovoltaic output unit, a wind turbine output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit, and a flexible load demand side management unit, characterized in that it includes: a model building module and a model solving module;

[0178] A model building module is used to build a multi-objective optimization scheduling model for the electric-hydrogen coordinated microgrid based on the operation model of the equipment unit in the electric-hydrogen coordinated microgrid. The objective functions of the multi-objective optimization scheduling model for the electric-hydrogen coordinated microgrid include minimizing the operating cost, minimizing the carbon emission and minimizing the peak load. The constraints of the multi-objective optimization scheduling model for the electric-hydrogen coordinated microgrid include unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering the wind and solar output, multiple uncertainties between load and electricity price, energy trading constraints with the main grid and demand response constraints; the decision variables of the multi-objective optimization scheduling model for the electric-hydrogen coordinated microgrid include the operation plans of the battery energy storage unit, the diesel generator unit, the hydrogen energy storage unit and the flexible load demand side management unit, as well as the energy trading plan with the main grid;

[0179] The model solving module is used to solve the established multi-objective optimization scheduling model of the electric-hydrogen collaborative microgrid and obtain the optimized scheduling plan.

[0180] Embodiment nine:

[0181] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0182] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0183] Application examples:

[0184] This example simulates the implementation of the optimization scheduling method of the present invention on a certain electric-hydrogen cooperative microgrid. The microgrid is equipped with wind turbines with rated powers of 225kW, 100kW and 90kW, and 3 battery energy storages. The parameters are shown in Table 1. The maximum contribution rate of the power load in the demand response scheme is 20%, and the maximum power transaction with the upstream power grid is 1000kW. There are two types of hydrogen energy storage systems in the microgrid: the maximum capacity of the hydrogen storage system is 100m 3 and 150m 3 , the minimum capacity is 10m 3 and 15m 3 The number of photovoltaic panels in the solar power plant is 100, 200 and 150 respectively. The characteristics of the dispatchable resources, namely the diesel generators, are shown in Table 2. Figure 5 and Figure 6 The solar radiation and wind speed under different scenarios are shown respectively.

[0185] Table 1 Battery energy storage parameters

[0186] Maximum charge and discharge capacity (kW) Charge and discharge efficiency (%) SOC minimum value (kwh) SOC maximum value (kwh) Maximum energy storage capacity (kwh) 100 95 21.5 193.5 200

[0187] Table 2 Scheduling resource parameters

[0188]

[0189] To evaluate the performance of the proposed multi-objective model, the following two cases are studied:

[0190] Case 1: The optimization goal of the electric-hydrogen coordinated microgrid is to provide power load at the lowest cost. Therefore, it only considers the cost function as the objective function, while ignoring carbon emission reduction and peak load reduction.

[0191] Case 2: The optimization goal of the electric-hydrogen synergistic microgrid considers three objective functions at the same time, including cost function, carbon emission reduction and peak load minimization, that is, the electric-hydrogen synergistic microgrid multi-objective optimization scheduling model proposed in this invention is used for optimization. Table 3 compares the scheduling results of the two cases.

[0192] It can be seen from Table 3 that the carbon emissions of the electric-hydrogen synergistic microgrid in Case 1 are 7422.82 kg, while in the multi-objective model, the carbon emissions are reduced to 7394.77 kg. In other words, the proposed model reduces carbon emissions by 28.05 kg due to the consideration of carbon emissions as one of the objective functions. In addition, the load factor in Case 1 is 77.22%, while the proposed model increases it to 83.48% through the hybrid normalized minimum-maximum method. Since the electric-hydrogen synergistic microgrid takes carbon reduction and load profile as objective functions in the proposed model, the case reduces carbon emissions and increases the load factor. However, the hybrid normalized minimum-maximum method only increases the operating cost of the electric-hydrogen synergistic microgrid by 1.96%.

[0193] Table 3 Case study comparison

[0194] Case Optimizing the model Total cost (yuan) Carbon emissions (kg) Load Factor 1 Single Target 32866.38 7422.82 77.22 2 Multiple Targets 33513.3 7394.77 83.48

[0195] Figure 7 The changes in the transferable load in the example are shown in detail, and the changes in the load curves before and after the demand response are given, reflecting the effectiveness of the demand response management. It can be seen that the load distribution of the electric-hydrogen cooperative microgrid under the model is uniform and reaches an acceptable level. In addition, it can be seen that the electric-hydrogen cooperative microgrid shifts its consumption from peak time to off-peak time. There are two reasons for providing this load transfer. First, since the electricity price during off-peak hours is lower than the electricity price during peak hours, the electric-hydrogen cooperative microgrid can reduce its cost function through load transfer. There, the electric-hydrogen cooperative microgrid shifts its consumption to off-peak hours to reduce its operating costs. Second, in this model, one of the objective functions of the electric-hydrogen cooperative microgrid is to reduce the peak load. The simulation results show that compared with Case 1, the proposed peak model reduces the peak load by 7.26% and increases the valley load by 7.18%.

[0196] The electric-hydrogen synergistic microgrid uses two electrolyzer units that use excess electricity to split water into hydrogen and oxygen. The hydrogen produced can be stored in hydrogen storage tanks. Figure 8 The output of the two electrolyzers in the example is shown in detail, reflecting the flexibility of the electrolyzers with electricity prices and renewable energy output. During off-peak hours, the HHPS uses the remaining renewable energy to generate electricity and produce hydrogen through the electrolyzer device. In addition, during peak hours, the HHPS operates dispatchable resources at maximum capacity to convert electricity into hydrogen. The HHPS sells the generated hydrogen to industry to increase revenue.

[0197] Table 4 lists the different revenues and costs of the electric-hydrogen synergistic microgrid. The electric-hydrogen synergistic microgrid has been able to earn 16,109.58 yuan by selling electricity to the upstream grid. In addition, in the proposed model (Case 2), the revenue from selling hydrogen to the industry is 38,613.6 yuan. The cost of power generation from the three dispatchable resources is 38,059.128 yuan, while the cost of importing electricity from the upstream grid in the proposed model is 50,177.34 yuan.

[0198] Table 4 Revenue and cost of electric-hydrogen synergistic microgrid

[0199]

[0200] The present invention proposes a multi-objective optimization method for an electric-hydrogen coordinated microgrid taking into account multiple uncertainties. The proposed method improves the diversity and flexibility of the microgrid system by integrating hydrogen power generation and electric hydrogen production systems with traditional battery energy storage and renewable energy. By considering the multiple uncertainties of wind, solar, load and electricity prices through scenario generation and reduction, the model can more accurately reflect the actual operation situation, and further effectively reduce the computational complexity through mixed integer linear optimization (MILP). A multi-objective optimization model including operating cost, carbon emission and peak load minimization is constructed, which realizes the comprehensive optimization of environment, economy and reliability, and improves the operating efficiency and sustainability of the microgrid system.

[0201] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A multi-objective optimization scheduling method for an electric-hydrogen coordinated microgrid, wherein the equipment units in the electric-hydrogen coordinated microgrid include a photovoltaic output unit, a wind turbine output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit and a flexible load demand side management unit, characterized in that: include: Based on the operation model of the equipment unit in the electric-hydrogen cooperative microgrid, a multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid is constructed. The objective functions of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include minimizing the operation cost, minimizing the carbon emission and minimizing the peak load. The constraints of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering the wind and solar output, multiple uncertainties between load and electricity price, energy trading constraints with the main grid and demand response constraints; the decision variables of the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid include the operation plans of the battery energy storage unit, the diesel generator unit, the hydrogen energy storage unit and the flexible load demand side management unit and the energy trading plan with the main grid; The established multi-objective optimization scheduling model of the electric-hydrogen collaborative microgrid is solved to obtain the optimal scheduling plan.

2. The multi-objective optimization scheduling method of the electric-hydrogen coordinated microgrid according to claim 1 is characterized in that: The operation model of the diesel generator unit in the operation model of the equipment unit in the electric-hydrogen coordinated microgrid includes power generation constraints and cost functions, and the expressions are as follows: μ i,t P i min ≤P i,t ≤μ i,t P i max Where P i,t and P i,t-1 are the output values ​​of diesel generator i at time t and time t-1 respectively; P i min and P i max are the minimum power generation boundary and the maximum power generation boundary of diesel generator i respectively; and are the maximum increase and decrease of power generation rate of diesel generator i respectively; cost DG is the cost function in the operation model of the diesel generator unit; α i is the fixed cost coefficient of diesel generator i; β i is the variable cost coefficient of diesel generator i; i is the emission of diesel generator i; γ i is the compensation cost of diesel generator i; The operation model of the battery energy storage unit includes: SOCIETY b,t1 =SOC b,t4 In the formula, SOC b,t+1 and SOC b,t is the charge state of the energy storage battery b at time t+1 and time t; and are the charging efficiency and discharging efficiency of energy storage battery b; and is the charging power and discharging power of the energy storage battery b at time t; and are the minimum and maximum boundaries of the state of charge of the energy storage battery b at time t respectively; and are the charging mode and discharging mode of the energy storage battery t at time t, and All are 0-1 variables; and are the maximum charging power and minimum charging power of energy storage battery b respectively; SOC b,t1 and SOC b,t4 are the storage energies of energy storage battery b at the initial and final moments respectively; The operation model of the hydrogen energy storage unit includes: s h,t +x h,t ≤1 In the formula, is the power consumption of electrolytic cell h at time t; h,t is the transformation efficiency; is the hydrogen gas input into hydrogen storage tank h at time t; HL h,t is the amount of hydrogen stored in the hydrogen storage tank h at time t; is the initial value in the hydrogen storage tank h at time t=1; HL h,t-1 is the amount of hydrogen stored in the hydrogen storage tank h at time t-1; φ h,t is the charge and discharge efficiency of the hydrogen storage tank h at time t; is the hydrogen gas input into the hydrogen storage tank h at time t; is the hydrogen output from the hydrogen storage tank h at time t; is the amount of hydrogen consumed by the hydrogen fuel cell h for power generation at time t; is the amount of hydrogen sold to industry at time t; ψ h The efficiency of hydrogen-to-electricity conversion; is the power generation of the hydrogen fuel cell h at time t; and are the minimum / maximum hydrogen storage capacity of the hydrogen storage tank h respectively; is the amount of hydrogen consumed by the hydrogen fuel cell h for power generation at time t; The maximum hydrogen value of the hydrogen storage tank used for hydrogen fuel cell power generation; is the amount of hydrogen sold to industry at time t; is the maximum hydrogen value of the hydrogen storage tank for sale to industry; h,t and χ h,t is a binary variable, indicating that at the same time t, the produced hydrogen can only be sold to industry or consumed by hydrogen fuel cells; The expression of the operation model of the photovoltaic output unit is: In the formula, is the output of photovoltaic unit i under scenario s at time t; η PV is the efficiency of PV unit i; is the number of photovoltaic panels of photovoltaic unit i; is the photovoltaic panel area of ​​photovoltaic unit i; I t,s is the solar radiation under scene s at time t; T t out is the ambient temperature at time t; The expression of the operation model of the fan output unit is: In the formula, and v i,r are the rated power and rated speed of fan i respectively; v ci and v co is the fan cut-in speed and cut-out speed; v t,s is the wind speed in scene s at time t; is the output of wind turbine i under scenario s at time t; The operation model of the flexible load demand side management unit includes: DR min ≤DR l,t ≤DR max In the formula, DR min and DR max are the minimum and maximum demand response levels, respectively; DR l,t is the demand response level of load l at time t; ldr l,t is the transferable load at time t; is the load level before demand response at time t; T is the dispatch period; is the load level after demand response at time t.

3. The multi-objective optimization scheduling method of the electric-hydrogen coordinated microgrid according to claim 2 is characterized in that: The method for obtaining the scenario probability constraints considering multiple uncertainties among wind and solar power output, load and electricity price is as follows: Generate the probability distribution of wind turbine output, photovoltaic output, load and electricity price operation scenarios respectively, where: The operation scenario of the photovoltaic output is an operation scenario related to solar radiation, and its probability distribution is generated in the form of an equation using the Beta probability distribution function as follows: Where PDF(x) is the BetaPDF function, which is used to describe the probability distribution of solar radiation x; parameters α and β are both shape parameters, parameter α determines the right shape of the solar radiation probability distribution, and parameter β determines the left shape of the solar radiation probability distribution; μ PV is the average value of photovoltaic output; δ PV is the standard deviation of photovoltaic output; Γ is the Gamma function; The operation scenario of the wind turbine output is an operation scenario related to the wind speed, and its probability distribution is generated by the Weibull PDF method through the equation as follows: Where PDF(v) is the probability density function of the Weibull distribution, which is used to describe the probability distribution of wind speed v; parameters c and k are the shape indexes of the Weibull PDF. Parameter k affects the sharpness of the wind speed distribution, and parameter c determines the distribution range of the wind speed; μ WT is the average wind speed; δ WT is the standard deviation of wind speed; The probability distribution of the load and electricity price operation scenarios is generated using the normal distribution PDF as follows: Where PDF(y) is the normal distribution PDF, which is used to describe the probability distribution of electricity price y; μ EP is the average value of electricity price; EP is the standard deviation of electricity price; PDF(z) is the normal distribution PDF; μ LD is the average value of the load; δ LD is the standard deviation of the load; The reduction of operation scenarios is based on mixed integer linear programming.

4. The multi-objective optimization scheduling method for the electric-hydrogen coordinated microgrid according to claim 3 is characterized in that: The specific steps of reducing the operation scenarios based on mixed integer linear programming include: First, the probability distribution of wind turbine output, photovoltaic output, and load and electricity price operation scenarios is segmented into N1, N2, N3 and N4 segments, each segment corresponds to a discrete scenario, and then the probability and center value of each segment are calculated, thereby converting the probability distribution of wind turbine output, photovoltaic output, load and electricity price operation scenarios from continuous probability distribution to discrete scenarios; The calculation formulas of the probability and center value of the segment are as follows: In the formula, is the uncertainty factor xn x The probability of each segment; PDF(x) is the distribution probability curve of the uncertainty factor x; and Respectively represent the nth x The starting and ending points of each segment; Represents the nth x The center value of each segment; Then the mixed integer linearization is performed as follows: In the formula, is a binary variable, representing the scenario combination of the n1th wind speed scenario, the n2th radiation scenario, the n3th electricity price scenario, and the n4th load scenario; minf is a variable for selecting the number of scenarios The weighted sum of represents the selection of a set of representative and minimal scenes; ρ s (n1, n2, n3, n4) is a probability variable, which indicates the probability of a combination of multiple uncertain factors occurring in a specific scenario. n1, n2, n3, n4 represent different categories of uncertainty scenario indexes. is the probability of n1 occurring in different wind speed scenarios; is the probability of n2 occurring in different scenarios of solar radiation; is the probability of n3 occurring in different scenarios of electricity prices; is the probability of n4 occurring in different load scenarios.

5. The multi-objective optimization scheduling method of the electric-hydrogen coordinated microgrid according to claim 4 is characterized in that: The operating cost minimization objective function of the multi-objective optimization scheduling model of the electric-hydrogen coordinated microgrid is: Where P t Grid is the electricity purchased from the main grid at time t; is the electricity price at time t; P t sell is the electricity sold to the main grid at time t; α i is the fixed cost coefficient of diesel generator i; β i is the variable cost coefficient of diesel generator i; i is the emission of diesel generator i; γ i is the compensation cost of diesel generator i; is the price of hydrogen at time t; is the amount of hydrogen sold to industry at time t. The minimum objective function of carbon emissions is: Where P t Grid is the electricity purchased from the main grid at time t; is the carbon emission coefficient of the main power grid; P i,t is the output value of diesel generator i at time t; is the carbon emission coefficient of the diesel generator; is the power generation of the hydrogen fuel cell h at time t; is the carbon emission coefficient of hydrogen fuel cells. The peak load minimization objective function is: In the formula, min Max is an optimization strategy to find the minimum value among all possible maximum values; is the load level after demand response at time t.

6. The multi-objective optimization scheduling method for the electric-hydrogen coordinated microgrid according to claim 5 is characterized in that: The energy transaction constraints of the electric-hydrogen coordinated microgrid multi-objective optimization scheduling model with the main grid are: P g,min ≤P t Grid ≤P g,max P sell,min ≤P t sell ≤P sell,max Where P t Grid is the electricity purchased from the main grid at time t; P g,min and P g,max is the minimum and maximum value of electricity purchased from the main grid; P t sell is the electricity sold to the main grid at time t; P sell,min and P sell,max Minimum and maximum values ​​for selling electricity to the main grid; The load supply and demand balance constraint is: Where P t Grid is the electricity purchased from the main grid at time t; P i,t is the output value of diesel generator i at time t; ρ s is the probability of scenario s occurring, which is used to represent the uncertainty of photovoltaic and wind power output under different scenarios; is the output of wind turbine i under scenario s at time t; is the output of PV unit i under scenario s at time t; and is the charging power and discharging power of the energy storage battery b at time t; is the power generation of hydrogen fuel cell h at time t; P t sell is the electricity sold to the main grid at time t; is the power consumption of electrolytic cell h at time t; is the active power demand of load l at time t; Q t Grid is the reactive power absorbed from the main grid at time t; Q i,t is the reactive power generated by diesel generator i at time t; is the reactive power generated by PV unit i at time t; is the reactive power generated by wind turbine unit i at time t; Q b,t is the reactive power generated by the energy storage battery b at time t; Q c,t is the reactive power generated by capacitor c at time t; Q l,t is the reactive power demand of load l at time t.

7. The multi-objective optimization scheduling method for the electric-hydrogen coordinated microgrid according to claim 6 is characterized in that: The specific steps of solving the established electric-hydrogen coordinated microgrid multi-objective optimization scheduling model and obtaining the optimization scheduling solution include: The three objective functions are transformed into a single objective function using the hybrid normalized minimum-maximum method, which is expressed as follows: In the formula, ω1, ω2, and ω3 are the weights of the objective function of minimum operating cost, minimum carbon emission, and minimum peak load, respectively, indicating the importance of each objective function, and ω1+ω2+ω3=1; cost and Emission are the operating cost and carbon emission, respectively; cost * and Emission * are the optimal values ​​of operating cost and carbon emissions, respectively; For the single objective function Make a substitution, the replacement formula is; minλ l,t Where: l,t is a free variable; The multi-objective optimization dispatch model of the replaced electric-hydrogen coordinated microgrid is as follows. The constraints also include the above-mentioned unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering multiple uncertainties between wind and solar output, load and electricity price, energy trading constraints with the main grid, and demand response constraints: a. Initialize the main problem: set the initial values ​​and boundary conditions, that is, initialize the parameters and decision variables in the multi-objective optimization scheduling model of the electric-hydrogen cooperative microgrid; b. Solve the embedded mixed integer linear optimization model: In each iteration step, for the given variable values ​​of the main problem, solve the mixed integer linear optimization model to obtain the optimal scenario selection and the corresponding probability distribution; c. Update the variables of the main problem: Use the solution obtained from the mixed integer linear optimization model to update the variable values ​​in the main problem, that is, according to the scenario probabilities and weights solved in the mixed integer linear optimization model, bring the specific conditions of each scenario into the objective function and constraints of the main problem, calculate its contribution to the objective function of the main problem and update the decision variables in the main problem, including the operation plans of the battery energy storage unit, diesel generator unit, hydrogen energy storage unit and flexible load demand side management unit, and the energy trading plan with the main grid; d. Iterative solution: Repeat the above steps until the objective function of the main problem converges, that is, the global optimal solution is found.

8. A multi-objective optimization dispatching system for an electric-hydrogen coordinated microgrid, wherein the equipment units in the electric-hydrogen coordinated microgrid include a photovoltaic output unit, a wind turbine output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit and a flexible load demand side management unit, characterized in that: include: Model building module and model solving module; The model building module is used to build an electric-hydrogen collaborative microgrid multi-objective optimization scheduling model based on the operation model of the equipment unit in the electric-hydrogen collaborative microgrid. The objective function of the electric-hydrogen collaborative microgrid multi-objective optimization scheduling model includes minimizing the operating cost, minimizing the carbon emission and minimizing the peak load. The constraints of the electric-hydrogen collaborative microgrid multi-objective optimization scheduling model include unit operation constraints, load supply and demand balance constraints, scenario probability constraints considering wind and solar output, multiple uncertainties between load and electricity price, energy trading constraints with the main grid and demand response constraints; the decision variables of the electric-hydrogen collaborative microgrid multi-objective optimization scheduling model include the operation plans of the battery energy storage unit, the diesel generator unit, the hydrogen energy storage unit and the flexible load demand side management unit, and the energy trading plan with the main grid; The model solving module is used to solve the established electric-hydrogen collaborative microgrid multi-objective optimization scheduling model to obtain an optimized scheduling solution.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

Images