Multi-target distribution robust planning method for multi-energy coupling system

Abstract

The invention relates to a multi-target distribution robust planning method for a multi-energy coupling system. The method comprises the following steps: acquiring historical data in a park power grid dispatching period; constructing an equipment operation model of the wind, light, hydrogen and ammonia storage multi-energy coupling system of the park power grid; obtaining a constraint condition set based on an equipment operation model of the wind-light-hydrogen-storage ammonia multi-energy coupling system, and constructing a first-stage multi-target optimization model with the minimum carbon emission of the multi-energy coupling system, the minimum user energy consumption plan adjustment amount and the minimum wind and light abandoning amount; based on the first-stage multi-objective optimization model, considering uncertainty wind power and photovoltaic output prediction deviation, and constructing a second-stage multi-objective optimization model; fusing the first-stage multi-objective optimization model and the second-stage multi-objective optimization model into a two-stage multi-objective distribution robust optimization model; and based on historical data in a park power grid dispatching period, solving the two-stage multi-target distribution robust optimization model to obtain a planning scheme of the park power grid wind-solar-hydrogen storage ammonia multi-energy coupling system.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a multi-objective distributed robust planning method for a multi-energy coupling system. Background Art

[0002] As a clean and efficient secondary energy carrier, hydrogen energy has become a key support for energy transformation due to its advantages of zero carbon emissions, high energy density and applicability in multiple scenarios. By coordinating with distributed power sources such as wind power and photovoltaics, hydrogen energy can effectively improve the capacity to absorb new energy and enhance the flexibility of the power system. Especially in the chemical industry, wind-solar-hydrogen coupled ammonia production technology realizes the cross-temporal and spatial transfer of renewable energy through the "green electricity → green hydrogen → green ammonia" conversion chain, providing an innovative path for the coordinated decarbonization of the power-chemical industry. As an integrated carrier of the above-mentioned technical route, the wind-solar-hydrogen-ammonia multi-energy coupling system can smooth the volatility of renewable energy, improve overall energy efficiency, and significantly reduce dependence on fossil energy through the coordinated optimization of multiple energy flows and hydrogen energy storage buffering. However, current research focuses more on the hydrogen production / storage links, and there is still a clear gap in the systematic optimization of high-value-added utilization scenarios such as downstream hydrogen synthesis ammonia.

[0003] Existing planning methods for multi-energy coupled systems often prioritize improving system performance and static efficiency, failing to consider the significant source-side uncertainty challenges faced by their actual operation, as well as the significant volatility and intermittency of wind and solar output due to meteorological factors. Existing uncertainty management methods primarily rely on stochastic optimization and robust optimization, which have inherent limitations. Stochastic optimization requires a precise probability distribution to generate expected solutions for multiple scenarios, and its "averaging" scheduling strategy is poorly adaptable to actual fluctuations. Robust optimization designs solutions only for extremely harsh scenarios. While ensuring system safety, it forces equipment to operate at low load for extended periods, failing to fully utilize the power system's capabilities. Summary of the Invention

[0004] In view of the above analysis, an embodiment of the present invention aims to provide a multi-objective distributed robust planning method for a multi-energy coupling system, so as to solve the technical problem that the planning scheme of the park power grid under the influence of wind and solar uncertainty in the existing method is too ideal or too conservative.

[0005] The purpose of the present invention is mainly achieved through the following technical solutions:

[0006] The present invention provides a multi-objective distributed robust planning method for a multi-energy coupling system, comprising the following steps:

[0007] Obtain historical data within the park power grid dispatch cycle;

[0008] Construct an equipment operation model for the wind-solar-hydrogen-ammonia multi-energy coupling system of the park power grid;

[0009] Based on the equipment operation model of the wind-solar-hydrogen-ammonia multi-energy coupling system, a set of constraints is obtained, and a first-stage multi-objective optimization model is constructed to minimize the carbon emissions of the multi-energy coupling system, minimize the adjustment of user energy consumption plans, and minimize the amount of wind and solar power curtailment;

[0010] Based on the first-stage multi-objective optimization model, the second-stage multi-objective optimization model is constructed by considering the uncertain wind power and photovoltaic output forecast deviations; the first-stage multi-objective optimization model and the second-stage multi-objective optimization model are integrated into a two-stage multi-objective distributed robust optimization model;

[0011] Based on the historical data within the park power grid dispatching period, the two-stage multi-objective distributed robust optimization model is solved to obtain a planning scheme for the park power grid wind, solar, hydrogen and ammonia multi-energy coupling system.

[0012] Furthermore, the equipment operation model of the wind-solar-hydrogen-ammonia multi-energy coupling system of the park power grid includes equipment operation models of the electric energy subsystem, the hydrogen energy subsystem and the ammonia energy subsystem;

[0013] The equipment operation model of the electric energy subsystem includes equipment operation models of wind turbines, photovoltaic units, biomass units, electrochemical energy storage, carbon capture and storage devices, and transferable loads;

[0014] The equipment operation model of the hydrogen energy subsystem includes equipment operation models of the electrolyzer, the hydrogen storage tank and the transferable hydrogen load;

[0015] The equipment operation model of the ammonia energy subsystem includes equipment operation models of a nitrogen production device and a synthetic ammonia device.

[0016] Furthermore, the multi-objective function is expressed as follows:

[0017]

[0018] Among them, f′ en 、f′ st 、f′ gr are the normalized minimum carbon emission sub-goal f en , the minimum sub-goal f of user energy consumption plan adjustment st , the minimum sub-target of wind and solar curtailment amount f gr ;ω en 、ω st 、ω gr f′ en 、f′ st 、f′ gr The weight of .

[0019] Furthermore, the constraint condition set also includes power balance constraints and energy transmission constraints of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system.

[0020] Furthermore, the minimum carbon emission sub-goal is as follows:

[0021]

[0022] Among them, f en is the carbon emissions of the multi-energy coupling system; T is the scheduling period; is the carbon emission of the biomass unit at time t; is the carbon capture amount at time t;

[0023] The minimum sub-goal of the user energy consumption plan adjustment amount is as follows:

[0024]

[0025] Among them, f st Adjustment of energy consumption plan for users of multi-energy coupling system; is the original value of the park's electrical load at time t; is the original value of the park's hydrogen load at time t; are the transferable electric load transfer-in and transfer-out amounts at time t respectively;

[0026] The minimum sub-target of wind and solar curtailment is as follows:

[0027]

[0028] Among them, f gr is the amount of wind and solar power curtailment in the multi-energy coupling system; are the amount of wind and solar power curtailment at time t respectively.

[0029] Furthermore, the uncertainty of wind power and photovoltaic output forecast deviation ε of the park power grid at time t is calculated t ,as follows:

[0030]

[0031] in, are the actual output value and predicted output power value of the wind turbine at time t respectively; are the actual output value and predicted output power value of the photovoltaic unit at time t respectively;

[0032] Wind power and photovoltaic output forecast deviation ε considering uncertainty t , a second-stage multi-objective optimization model is constructed that considers the distributional robust optimization of wind power and photovoltaic output forecast deviations under the worst distribution, including the second-stage objective function, as follows:

[0033]

[0034] Where Γ and Ω are the fuzzy sets of actual distribution and prediction deviation distribution considering prediction deviation respectively; x is the decision variable of each unit in the first stage without considering the influence of uncertainty; According to x and ε t The value of the multi-objective function F after scheduling adjustment; sup(·) and E(·) are used to solve the supremum and expected value respectively.

[0035] Furthermore, the decision variables x of each unit that do not consider the influence of uncertainty in the first stage include photovoltaic output power Wind power output Biomass unit output power P BU,t , Charging power of chemical energy storage and discharge power Carbon capture operation power consumption P CCS,t , Transferable electric load transfer amount and transfer-out amount Electrolyzer input power P ET,t and output power Hydrogen storage tank filling power and hydrogen desorption power Transferable hydrogen load and transfer-out amount Nitrogen production equipment operating power consumption P PSA,t And the operating power consumption of synthetic ammonia equipment P P2A,t .

[0036] Furthermore, the second-stage multi-objective optimization model is based on the first-stage multi-objective optimization model and is based on the wind power and photovoltaic output forecast deviation ε t , using affine strategy to characterize the actual power of adjustable resources in multi-energy coupling systems;

[0037] The adjustable resources include biomass units, electrochemical energy storage, transferable electrical loads, electrolyzers and hydrogen storage tanks.

[0038] Furthermore, the two-stage multi-objective distributed robust optimization model is as follows:

[0039]

[0040]

[0041] Where x is the decision variable matrix with A rows and B columns, A is the number of decision variables of each unit without considering the influence of uncertainty in the first stage, and B is the number of sampling points in the scheduling cycle; a T is the coefficient matrix of the decision variable matrix; It is an abstract representation of the objective function F of the first-stage multi-objective optimization model; is the objective function of the second stage; a, P, and q are the coefficient vector of x, the coefficient matrix of constraints, and the parameter vector, respectively; y is the decision variable of each unit considering the influence of uncertainty on the basis of x; b, G, and h are the coefficient vector of y, the coefficient matrix of constraints, and the parameter vector, respectively.

[0042] Furthermore, the planning scheme of the park power grid wind, solar, hydrogen and ammonia multi-energy coupling system is the planning value of the decision variable y of each unit considering the influence of uncertainty, as well as the corresponding minimum carbon emissions, user energy consumption plan adjustment and wind and solar power abandonment.

[0043] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0044] 1. Breaking through the traditional single efficiency optimization model, this approach achieves the coordinated optimization of three technical indicators: carbon emissions, energy plan adjustments, and wind and solar curtailment. Dynamically adjusting weight coefficients satisfies the planning requirements of different park power systems and improves the stability of the park power system.

[0045] 2. By minimizing the amount of wind and solar power curtailment, the system's ability to absorb renewable energy such as wind power and photovoltaics is improved, reducing energy waste; by minimizing carbon emissions, the environmental performance of the multi-energy coupling system is optimized, which helps reduce greenhouse gas emissions and is in line with sustainable development goals.

[0046] 3. By synergistically optimizing the electricity, hydrogen, and ammonia energy of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system, the volatility of renewable energy can be mitigated and the overall energy utilization rate can be improved;

[0047] 4. Use affine strategies to accurately and efficiently control the power adjustment range of adjustable resources such as biomass units, electrochemical energy storage, transferable electrical loads, electrolyzers, and hydrogen storage tanks. This can flexibly respond to forecast deviations in wind power and photovoltaic output, optimize energy distribution, and improve the operating efficiency and stability of the park's power system.

[0048] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0050] Figure 1This is a flow chart of a multi-objective distributed robust planning method for a multi-energy coupling system according to an embodiment of the present invention;

[0051] Figure 2 Schematic diagram of the wind-solar-hydrogen-ammonia multi-energy coupling system structure and energy flow relationship in an embodiment of the present invention;

[0052] Figure 3 Schematic diagram of the electrolytic cell operating state conversion in an embodiment of the present invention;

[0053] Figure 4 Schematic diagram of the two-stage optimization model solution process in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0055] The present invention proposes a multi-objective distributed robust planning method for a multi-energy coupling system, aiming to improve the ability of the multi-energy coupling system to cope with the uncertain influence of the source side on carbon emissions, user energy consumption plan adjustment, and wind and solar power curtailment.

[0056] A specific embodiment of the present invention discloses a multi-objective distributed robust planning method for a multi-energy coupling system, such as Figure 1 As shown, the following steps are included:

[0057] Step S1: Obtain historical data within the park power grid dispatch cycle;

[0058] Step S2: constructing an equipment operation model of a wind-solar-hydrogen-ammonia multi-energy coupling system of the park power grid;

[0059] Step S3: Based on the wind-solar-hydrogen-ammonia multi-energy coupling system equipment operation model, a constraint condition set is obtained, and a first-stage multi-objective optimization model is constructed to minimize the carbon emissions of the multi-energy coupling system, minimize the adjustment of user energy consumption plans, and minimize the amount of wind and solar power curtailment;

[0060] Step S4: Based on the first-stage multi-objective optimization model, a second-stage multi-objective optimization model is constructed by considering the uncertain wind power and photovoltaic output forecast deviations; the first-stage multi-objective optimization model and the second-stage multi-objective optimization model are integrated into a two-stage multi-objective distributed robust optimization model;

[0061] Step S5: Based on the historical data within the park power grid dispatching period, the two-stage multi-objective distributed robust optimization model is solved to obtain a planning scheme for the park power grid wind, solar, hydrogen and ammonia storage multi-energy coupling system.

[0062] Step S1 includes steps S11-S12.

[0063] Step S11: Acquire historical data within the park power grid dispatch period. For example, the preset dispatch period T is one year.

[0064] (1) For wind turbines: actual wind turbine output (power output corresponding to real-time wind speed), predicted wind turbine output power, rated power, real-time wind speed, rated wind speed, cut-in wind speed, and cut-out wind speed;

[0065] (2) For photovoltaic units: predicted output power of the photovoltaic unit, maximum output power of the photovoltaic unit under standard test conditions, ambient temperature and light intensity under standard test conditions, actual ambient temperature, actual light intensity, and power-temperature coefficient;

[0066] (3) For biomass units: biogas consumption, output power, carbon emissions, output power and carbon emissions per unit volume of biogas consumed, start / stop status, maximum output power, ramp limit, and maximum biogas consumption;

[0067] (4) Electrochemical energy storage: charging power, discharging power, energy value, charging efficiency, discharging efficiency, energy self-consumption rate, maximum charging power, maximum discharging power, maximum energy storage capacity, minimum energy storage capacity, charging state, discharging state, energy value at the initial moment, and energy value at the end of the scheduling period;

[0068] (5) For carbon capture and packaging devices: operating power consumption, carbon capture amount, fixed power consumption of carbon capture, energy consumption corresponding to unit volume of carbon dioxide captured, start and stop status, maximum operating energy consumption, and upper limit of ramp rate;

[0069] (6) For transferable electric loads: transfer-in quantity, transfer-out quantity, maximum transfer-in quantity, maximum transfer-out quantity, transfer-in status, transfer-out status;

[0070] (7) For the electrolyzer: electrolyzer input power, output power, standby power, rated power, output power corresponding to unit power consumption, hydrogen production loss coefficient when switching from standby state to hydrogen production state, standby state, low-load state, variable load state, overload state, state transition from standby state to hydrogen production state, shutdown state, state transition from shutdown state to hydrogen production state, minimum continuous operation time of the electrolyzer in shutdown state, minimum continuous operation time of the electrolyzer in standby state, maximum continuous operation time of the electrolyzer in low-load state, and maximum continuous operation time of the electrolyzer in overload state;

[0071] (8) For hydrogen storage tanks: hydrogen storage tank charging power, hydrogen discharge power, hydrogen storage capacity; hydrogen charging efficiency, hydrogen discharge efficiency, maximum hydrogen charging power, maximum hydrogen discharge power, maximum hydrogen storage capacity, minimum hydrogen storage capacity, hydrogen charging status, hydrogen discharge status, hydrogen storage capacity at the beginning of the hydrogen storage tank scheduling cycle, and hydrogen storage capacity at the end of the scheduling cycle;

[0072] (9) For transferable hydrogen load: hydrogen load transfer-in amount, transfer-out amount, maximum transfer-in amount, minimum transfer-out amount, transfer-in status, transfer-out status;

[0073] (10) For nitrogen production equipment: operating power consumption, nitrogen production volume, energy consumption per unit volume of nitrogen, start and stop status, maximum operating energy consumption, and upper limit of ramp rate;

[0074] (11) For synthetic ammonia equipment: operating power consumption, amount of synthetic ammonia, hydrogen consumption, energy consumption per unit volume of ammonia, start and stop status, maximum operating energy consumption, upper limit of ramp rate, and nitrogen consumption.

[0075] Step S12: pre-processing the acquired historical data of the park power grid.

[0076] After acquiring historical data from the park's power grid dispatch cycle, data preprocessing is performed, including data cleaning, missing value processing (for example, using linear interpolation or compensating for data from adjacent days), outlier detection, and timestamp alignment to ensure the quality and reliability of the historical data. This historical data will serve as the foundation for subsequent model construction and the two-stage multi-objective optimization process.

[0077] The purpose of step S1 is to obtain historical data within the park power grid dispatch cycle, including the operating data of wind power, photovoltaic power, biomass units, electrochemical energy storage, carbon capture and storage devices, transferable electrical loads, electrolyzers, hydrogen storage tanks, nitrogen production equipment and synthetic ammonia equipment, etc., to provide basic data for constructing the operation model and optimization model of the multi-energy coupling system.

[0078] Step S2 includes steps S21-S23.

[0079] The equipment operation model of the wind-solar-hydrogen-ammonia multi-energy coupling system of the park power grid includes equipment operation models of the electric energy subsystem, the hydrogen energy subsystem and the ammonia energy subsystem;

[0080] The equipment operation model of the electric energy subsystem includes equipment operation models of wind turbines, photovoltaic units, biomass units, electrochemical energy storage, carbon capture and storage devices, and transferable loads;

[0081] The equipment operation model of the hydrogen energy subsystem includes equipment operation models of the electrolyzer, the hydrogen storage tank and the transferable hydrogen load;

[0082] The equipment operation model of the ammonia energy subsystem includes equipment operation models of a nitrogen production device and a synthetic ammonia device.

[0083] like Figure 2 As shown, the wind-solar-hydrogen-ammonia multi-energy coupling system constructed by the present invention adopts a three-level architecture of electricity-hydrogen-ammonia:

[0084] The electric energy subsystem integrates wind power / photovoltaic (fluctuating power source) and biomass units (controllable power source), and uses the biomass units to smooth out new energy fluctuations; electrochemical energy storage and transferable electric loads are configured on the load side to achieve energy time shifting, and a carbon capture and storage device (CCS) is used to capture carbon emissions from the biomass units, achieving near-zero emission operation.

[0085] The hydrogen energy subsystem includes an electrolyzer (which consumes electricity from the electrical energy subsystem), a hydrogen storage tank, and a transferable hydrogen load. After meeting internal demand, the surplus hydrogen is transported to the ammonia energy subsystem.

[0086] The ammonia energy subsystem receives electricity from the electric energy subsystem and hydrogen from the hydrogen energy subsystem, and synthesizes green ammonia through the Haber process, completing the upgrade of electric energy to high-value chemical energy, while realizing the cross-seasonal storage carrier conversion of renewable energy.

[0087] Step S21: constructing an equipment operation model of the power subsystem, including the following:

[0088] (1) Wind turbine equipment operation model. The output power of a wind turbine is closely related to the rated power generation and wind speed, as shown below:

[0089]

[0090] in, Predict the output power of the wind turbine at time t; is the rated power of the wind turbine; v t is the real-time wind speed of the wind turbine at time t; v sp 、v ci 、v co It is the rated, cut-in and cut-out wind speed of the wind turbine.

[0091] (a) Zero power interval: When the real-time wind speed v t Below cut-in wind speed v ci Or higher than the cut-out wind speed v co The predicted power of the wind turbine is 0, which prevents the wind turbine from operating in an invalid wind speed range and protects the equipment safety.

[0092] (b) Power ramp-up range: When the real-time wind speed v t Between cut-in wind speed v ci and rated wind speed v spWhen the wind speed is between 0 and 1, the predicted power of the generator set is proportional to the cube of the wind speed;

[0093] (c) Rated power range: When the real-time wind speed v t Between rated wind speed v sp and cut-out wind speed v co During this time, the rated power of the wind turbine is locked through pitch control to ensure the life of the wind turbine equipment and maintain the stability of the grid frequency.

[0094] (2) Photovoltaic unit equipment operation model. The output power of a photovoltaic unit is closely related to light intensity and ambient temperature, as shown below:

[0095]

[0096] in, Predict the output power of the photovoltaic unit at time t; is the maximum output power of the photovoltaic unit under standard test conditions; T sp , G sp are the ambient temperature and light intensity under standard test conditions; T t , G t are the actual ambient temperature and actual light intensity at time t respectively; k is the power-temperature coefficient, which indicates the degree of influence of temperature change on photovoltaic output power.

[0097] For example, the value of k is -0.47% / °C, which means that the photovoltaic output power will decrease by 0.47% for every 1°C increase in temperature.

[0098] (3) Biomass unit equipment operation model. The biomass unit generates electricity by burning biogas, but it will produce carbon emissions, and its operation must meet constraints such as power and ramp rate. The expression is as follows:

[0099]

[0100] in, P BU,t 、 are the biogas consumption, output power and carbon emissions of the biomass unit at time t; k BU 、 are the output power and carbon emissions corresponding to the unit volume of biogas consumed by the biomass unit; u BU,t The start / stop state variable of the biomass unit is a 0-1 variable, 0 is the stop state, and 1 is the start state; They are the maximum output power and climbing limit of the biomass unit respectively; is the maximum amount of biogas consumed within the biomass unit scheduling period T; P BU,t-1 is the output power of the biomass unit at time t-1.

[0101] (4) Electrochemical energy storage device operation model. The operation of electrochemical energy storage must simultaneously meet the power, power and operating state constraints, as shown below:

[0102]

[0103] in, E EES,t are the charge and discharge power and capacity value of electrochemical energy storage at time t respectively; σ EES are the electrochemical energy storage charging and discharging efficiency and the self-consumption rate of electricity; are the maximum charging and discharging powers of electrochemical energy storage, respectively; The maximum and minimum values ​​of electrochemical energy storage capacity; are the charge and discharge state variables of electrochemical energy storage; E EES,t-1 E is the charge value of electrochemical energy storage at time t-1; EES,0 E is the charge value at the initial moment of electrochemical energy storage; EES,T is the electricity value at the end of the electrochemical energy storage scheduling period; T is the scheduling period.

[0104] (5) Operation model of carbon capture and storage device. The carbon capture and storage device captures and stores the carbon dioxide generated by the biomass unit. Its operation energy consumption includes two parts: fixed energy consumption and carbon capture energy consumption. Its operation must meet the energy consumption and ramp constraints, as shown below:

[0105]

[0106] Among them, P CCS,t 、 are the operating power consumption and carbon capture amount of carbon capture at time t respectively; is the fixed power consumption of carbon capture; k CCS u is the energy consumption corresponding to capturing unit volume of carbon dioxide at time t; CCS,t is the start / stop state variable of carbon capture at time t, which is a 0-1 variable; are the maximum energy consumption and climbing limit of carbon capture at time t; P CCS,t-1 is the carbon capture amount at time t-1.

[0107] (6) Transferable load equipment operation model. Transferable load refers to those electricity demands that can be flexibly transferred or adjusted within different time periods. These loads do not include basic needs that must use electricity at a specific time (such as lighting, refrigerators, etc.), but rather those electricity demands that can be postponed or advanced. By transferring these loads, the supply and demand of the power grid can be balanced, energy utilization efficiency can be improved, and electricity consumption during peak hours can be reduced, thereby reducing electricity costs and environmental impact.

[0108] The transferable electric load must meet the transfer power, transfer state and other constraints, as shown below:

[0109]

[0110] in, are the transferable electric load transfer-in and transfer-out amounts at time t respectively; are the maximum transferable inflow and outflow of transferable electric load at time t respectively; are the transferable electric load transfer-in and transfer-out state variables at time t respectively.

[0111] Characteristics of transferable electric loads:

[0112] (a) Flexibility: Shiftable loads can be adjusted over different time periods based on grid demand;

[0113] (b) Controllability: These loads can be transferred by automated systems or manual user control;

[0114] (c) Environmentally friendly: Reducing electricity consumption during peak hours helps reduce the use of fossil fuel power generation and lower carbon emissions.

[0115] For example:

[0116] (a) Electric vehicle charging: Electric vehicle charging times can be arranged flexibly. Users can choose to charge at night or during off-peak hours when electricity prices are lower, rather than during peak hours during the day;

[0117] (b) Industrial production: Some industrial production processes (such as heating, cooling, etc.) can be carried out at different times of the day, and these processes can be adjusted according to the needs of the power grid;

[0118] (c) Washing machines and dryers: These appliances can be operated during off-peak hours instead of during peak hours.

[0119] Shiftable electric loads can increase electricity consumption when grid demand is low and reduce electricity consumption when grid demand is high, thereby achieving balance and optimization of the grid power system.

[0120] Step S22: constructing an equipment operation model of the hydrogen energy subsystem, including the following:

[0121] (1) Electrolyzer equipment operation model. The electrolyzer can realize the rapid conversion of electrical energy generated by the power subsystem into hydrogen energy. According to the operating characteristics, its operating conditions can be divided into three states: shutdown, standby, and hydrogen production;

[0122] According to the input power during hydrogen production, the hydrogen production status is divided into three categories: low load, variable load and overload.

[0123] Low load: input power is 10% to 30% of the rated power, with short-term flexible adjustment;

[0124] Variable load: input power is 30% to 100% of the rated power, the main range of safe operation;

[0125] Overload: Input power is 100% to 150% of the rated power, temporarily increasing output.

[0126] In the shutdown state, the electrolyzer has zero power and usually takes 1 hour to complete a cold start; in the standby state, the electrolyzer maintains operation at a lower power and only takes 1 / 6 hour to complete a hot start.

[0127] To ensure the safety of hydrogen production, the electrolyzer operates in a variable load state most of the time; at the same time, it can also serve as a flexibility resource and operate in a low load or overload state for a short period of time.

[0128] like Figure 3 As shown in Figure 2, the schematic diagram of the electrolytic cell operating transformation.

[0129] Considering that the time it takes for the electrolyzer to switch from standby mode to hydrogen production mode is less than 1 hour, this process generates hydrogen production losses. Therefore, the output power of the electrolyzer can be expressed as follows:

[0130]

[0131] The electrolyzer input power range constraints are as follows:

[0132]

[0133] Among them, P ET,t 、 are the input power and output power of the electrolytic cell at time t respectively; are the standby power and rated power of the electrolyzer respectively; k ET The output power corresponding to the unit power consumption of the electrolytic cell; is the hydrogen production loss coefficient when the electrolyzer is converted from standby state to hydrogen production state; They are the electrolyzer standby, low load, variable load, and overload state variables; It is the state conversion variable when the electrolyzer changes from the standby state to the hydrogen production state at time t.

[0134] At the same time, the electrolytic cell operating condition conversion and the operating time constraints under each operating condition are as follows:

[0135]

[0136] in, is the shutdown state variable of the electrolytic cell at time t; is the state transition variable when the electrolyzer changes from the shutdown state to the hydrogen production state at time t; The minimum continuous operation time for the electrolyzer to remain in shutdown and standby states respectively; are the maximum continuous operating time of the electrolyzer in low-load and overload states respectively; are the shutdown state variables of the electrolyzer at time t-1 and t+1 respectively; is the standby state variable of the electrolytic cell at time t-1.

[0137] (2) Hydrogen storage tank equipment operation model. Similar to electrochemical energy storage, the operation of hydrogen storage tanks must also meet the constraints of power, hydrogen storage capacity and operating status; however, hydrogen storage tanks are more suitable for long-term energy storage, and their energy self-consumption can be ignored. The expression is as follows:

[0138]

[0139] in, E HST,t are the hydrogen charging and discharging power and hydrogen storage capacity of the hydrogen storage tank at time t respectively; They are the hydrogen charging and discharging efficiency of the hydrogen storage tank; They are the maximum power of hydrogen storage tank charging and discharging respectively; They are the maximum and minimum hydrogen storage capacity of the hydrogen storage tank respectively; are the state variables of hydrogen storage tank charging and discharging; E HST,0 、E HST,T are the hydrogen storage capacity at the beginning and end of the hydrogen storage tank scheduling cycle; E HST,t-1 is the hydrogen storage capacity of the hydrogen storage tank at time t-1.

[0140] (3) Operation model of transferable hydrogen load equipment. The transferable hydrogen load must also meet the constraints of transfer power, transfer state variables, etc., and the expression is as follows:

[0141]

[0142] in, are the transferable hydrogen load transfer-in and transfer-out amounts at time t, respectively; are the maximum transferable in and out amounts of transferable hydrogen load at time t, respectively; are the transferable hydrogen load transfer-in and transfer-out state variables at time t, respectively.

[0143] The constraints ensure that at any moment, the transfer-in and transfer-out power of the transferable hydrogen load cannot exceed its maximum value, and the transfer-in and transfer-out cannot occur simultaneously.

[0144] The sum of the input power at all times is equal to the sum of the output power at all times, ensuring the balance of hydrogen load.

[0145] The state variable constraints ensure that at any moment, the transferable hydrogen load cannot be transferred in and out simultaneously.

[0146] Step S23: constructing an equipment operation model of the ammonia energy subsystem, including the following:

[0147] (1) Equipment operation model of nitrogen production equipment. The nitrogen production equipment adopts pressure swing adsorption technology, which has the advantages of simple process flow and can be operated at room temperature. The operation must meet the energy consumption and ramp constraints, as shown below:

[0148]

[0149] Among them, P PSA,t 、 are the operating power consumption and nitrogen production of the nitrogen generator at time t; k PSA The energy consumption of nitrogen production equipment for preparing unit volume of nitrogen; u PSA,t The start / stop status variable of the nitrogen generator is 0-1. are the maximum energy consumption and climbing limit of nitrogen production equipment respectively; P PSA,t-1 is the energy consumption of the nitrogen production equipment at time t-1.

[0150] The first formula is the power consumption calculation constraint. The power consumption of nitrogen production equipment is determined by the nitrogen production capacity.

[0151] The second formula is the power range constraint. The power consumption of the nitrogen generator must be between 0 and the maximum value and is controlled by the start-stop state variable.

[0152] The third formula is the ramp constraint. The power change of the nitrogen production equipment cannot exceed the ramp upper limit to ensure the smooth operation of the equipment.

[0153] (2) Equipment operation model of synthetic ammonia equipment. The synthetic ammonia quantity is determined by the method of "determining ammonia by hydrogen". Due to the low flexibility of the synthetic ammonia equipment, its operation is generally only allowed to adjust energy consumption once every 4 hours in addition to meeting the energy consumption and ramp constraints, as shown below:

[0154]

[0155] in, are the operating power consumption, amount of synthetic ammonia and hydrogen consumption of the synthetic ammonia equipment at time t; k P2A u is the energy consumption of ammonia synthesis equipment per unit volume; P2A,t It is the start and stop state variable of the synthetic ammonia equipment; They are the maximum energy consumption and climbing limit of the synthetic ammonia equipment respectively; The amount of synthetic ammonia produced by the synthetic ammonia plant at time t-1; is the nitrogen consumption of the synthetic ammonia equipment at time t.

[0156] The first and second formulas are power consumption constraints. The power consumption of the ammonia synthesis equipment is determined by the amount of synthesized ammonia and nitrogen consumption.

[0157] The third formula is the power range constraint. The power consumption of the ammonia synthesis equipment must be between 0 and the maximum operating energy consumption and is controlled by the start-stop state variable.

[0158] The fourth formula is the ramp constraint. The power change of the synthetic ammonia equipment does not exceed the maximum ramp limit to ensure the smooth operation of the equipment.

[0159] The fifth formula is the power adjustment constraint. The power adjustment of the synthetic ammonia plant is allowed only once every 4 hours, and the adjustment step size is 4m+2 or 4(m+1). m is an integer variable used to control the step size of the power adjustment of the synthetic ammonia plant.

[0160] The function of step S2 is to construct the wind, solar, hydrogen, storage and ammonia multi-energy coupling system operation model of the park power grid, including the equipment operation models of the electricity, hydrogen and ammonia subsystems. Step S3 uses these equipment operation models as constraints to construct the first-stage multi-objective optimization model to achieve multi-objective collaborative optimization with minimum carbon emissions, minimum adjustment of user energy consumption plans and minimum wind and solar power curtailment.

[0161] Step S3: Construct the first-stage multi-objective optimization model, as follows.

[0162] The distributed robust multi-objective optimization model constructed by the present invention includes two stages. The first stage is a multi-objective operation optimization model under certain conditions, and the second stage is a multi-objective operation optimization model taking into account the influence of uncertainty.

[0163] The first-stage multi-objective optimization model includes a multi-objective coordinated minimization function F with the minimization of carbon emissions of the multi-energy coupling system, the minimization of user energy consumption plan adjustment, and the minimization of wind and solar power curtailment, as well as a set of constraint conditions.

[0164] The multi-objective function is expressed as follows:

[0165]

[0166] Among them, f′ en 、f′ st 、f′ gr are the normalized minimum carbon emission sub-goal f en , the minimum sub-goal f of user energy consumption plan adjustment st , the minimum sub-target of wind and solar curtailment amount f gr ;ω en 、ω st 、ω gr f′ en 、f′st 、f′ gr The weight of .

[0167] For multi-objective optimization problems, the multi-objective optimization problem is transformed into a single-objective optimization problem by normalizing the sub-objective function and increasing the weight coefficient.

[0168] The present invention establishes a multi-objective operation optimization model for a wind-solar-hydrogen-storage-ammonia multi-energy coupling system with three optimization objectives: carbon emissions, user energy consumption plan adjustment, and wind and solar power curtailment, in order to reduce the impact on the atmospheric environment and the energy stability of the park, and to improve the absorption rate of wind and solar renewable energy by the park power system.

[0169] The minimum carbon emission sub-targets are as follows:

[0170]

[0171] Among them, f en is the carbon emission of the multi-energy coupling system; T is the scheduling period; is the carbon emission of the biomass unit at time t; is the carbon capture amount at time t;

[0172] The minimum sub-goal of the user energy consumption plan adjustment amount is as follows:

[0173]

[0174] Among them, f st Adjustment of energy consumption plan for users of multi-energy coupling system; is the original value of the park's electrical load at time t; is the original value of the park's hydrogen load at time t; are the transferable electric load transfer-in and transfer-out amounts at time t respectively;

[0175] The minimum sub-target of wind and solar curtailment is as follows:

[0176]

[0177] Among them, f gr is the amount of wind and solar power curtailment in the multi-energy coupling system; are the amount of wind and solar power curtailment at time t respectively.

[0178] In multi-objective optimization problems, the values ​​of weight coefficients depend on the relative importance of each objective. These weight coefficients need to be determined based on actual conditions.

[0179] For example, the three weight coefficients are Each objective has equal weight in the overall objective function, meaning that each objective contributes equally to the final optimization result. In practice, these weights may need to be adjusted based on specific circumstances, and in some cases, experiments or further analysis may be needed to determine the optimal weight distribution.

[0180] The constraint condition set of the first-stage multi-objective optimization model includes the equipment operation model of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system as a constraint condition; in addition to the operation models of each equipment, the operation of the multi-energy coupling system must meet the power balance and energy transmission constraints.

[0181] The constraint condition set also includes power balance constraints and energy transmission constraints of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system.

[0182] (1) Power balance constraint.

[0183] The multi-energy coupling system must meet the power balance constraints of electricity and hydrogen at time t, which can be expressed as follows:

[0184]

[0185] in, The actual electricity and hydrogen consumption of electricity and hydrogen load users in the entire park at time t; The actual output of wind power and photovoltaic units at time t.

[0186] (2) Energy transfer constraints.

[0187] In order to ensure the safety of energy transmission, the amount of synthetic ammonia in the multi-energy coupling system at time t is The following constraints must be met:

[0188]

[0189] Among them, P A,max It is the upper limit of ammonia pipeline transmission.

[0190] The first-stage multi-objective optimization model is based on distributed robust optimization (DRO) theory and is constructed under certain conditions. Under known conditions, the first-stage multi-objective optimization model optimizes the carbon emissions of the park's power grid, the amount of adjustments to user energy plans, and the amount of wind and solar power curtailment.

[0191] Through mathematical modeling, a multi-objective function F and a set of constraints are defined, and the optimal solution is found within these constraints. The output is the determined decision variable x, which includes the planned power output and energy consumption values ​​for various devices. Uncertainty in the forecast deviations for wind and photovoltaic power output is not considered at this stage.

[0192] In the first stage, the decision variables x of each unit without considering the influence of uncertainty include photovoltaic output power Wind power output Biomass unit output power P BU,t , Charging power of chemical energy storage and discharge power Carbon capture operation power consumption P CCS,t , Transferable electric load transfer amount and transfer-out amount Electrolyzer input power P ET,t and output power Hydrogen storage tank filling power and hydrogen desorption power Transferable hydrogen load and transfer-out amount Nitrogen production equipment operating power consumption P PSA,t And the operating power consumption of synthetic ammonia equipment P P2A,t .

[0193] The purpose of step S3 is to construct a multi-objective optimization model for the first stage. By defining the multi-objective functions of minimizing carbon emissions of the multi-energy coupling system, minimizing the adjustment of user energy consumption plans, and minimizing the amount of wind and solar power curtailment, as well as a set of constraints, the planning values ​​of the decision variables in the first stage without considering the influence of uncertainty are obtained.

[0194] Step S4 includes steps S41-S42, specifically.

[0195] Based on the multi-objective optimization model under the conditions determined in the first stage, the second stage is the power adjustment problem considering the forecast deviation of wind power and photovoltaic output.

[0196] Step S41: Construct a second-stage multi-objective optimization model.

[0197] The impact of wind power and photovoltaic output forecast deviations on the operation of the multi-energy coupling system is essentially the same. Therefore, the relationship between the wind power and photovoltaic forecast values ​​and the actual wind power and photovoltaic output values ​​can be combined to obtain the wind power and photovoltaic forecast deviation ε t .

[0198] Calculate the uncertainty of wind power and photovoltaic output forecast deviation ε of the park power grid at time t t ,as follows:

[0199]

[0200] in, are the actual output value and predicted output power value of the wind turbine at time t respectively; are the actual output value and predicted output power value of the photovoltaic unit at time t respectively;

[0201] Wind power and photovoltaic output forecast deviation ε considering uncertainty t , a second-stage multi-objective optimization model is constructed that considers the distributional robust optimization of wind power and photovoltaic output forecast deviations under the worst distribution, including the second-stage objective function, as follows:

[0202]

[0203] Where Γ and Ω are the fuzzy sets of actual distribution and prediction deviation distribution considering prediction deviation respectively; x is the decision variable of each unit in the first stage without considering the influence of uncertainty; According to x and ε t The value of the multi-objective function F after scheduling adjustment; sup(·) and E(·) are used to solve the supremum and expected value respectively.

[0204] The physical meaning of formula (21) is to find the minimum value of the multi-objective function in the second stage by considering the decision variable x determined in the first stage under the worst distribution of the prediction deviation.

[0205] Γ and Ω are the actual distribution of prediction deviations and the fuzzy set (all possible sets) of the prediction deviation distribution, respectively. The distance between the two is called the Wasserstein distance. By adjusting the Wasserstein distance, the robustness of the model can be controlled. The larger the distance, the stronger the robustness, but the more conservative the solution may be.

[0206] Before determining the Wasserstein distance, the prediction error data needs to be standardized and scaled to a uniform range to ensure the comparability of uncertainty parameters in different dimensions, avoid some dimensions from excessively dominating the distance calculation due to their large original numerical range, and make the defined fuzzy set more reasonable and the robustness more balanced.

[0207] In order to determine the Wasserstein distance, a binary search method can be used. This method continuously divides the range into half within the initially set parameter range, and each time takes the midpoint parameter value to solve the optimization problem. Based on the result, it is judged whether the target value meets the required conditions (such as probability), and then decides to discard the left half or the right half of the current range, gradually narrowing the search range, and finally finding the parameter value that meets the requirements.

[0208] is the prediction deviation ε of wind power and photovoltaic output based on the first stage decision variable x (i.e., all power P obtained without considering the influence of uncertainty) t , the value of the multi-objective function F is obtained after the scheduling plan is adjusted; sup(·) and E(·) are the supremum (i.e., the worst case) and expected value of the function (·).

[0209] E Γ is the expected value of the actual distribution Γ of the forecast deviation.

[0210] The physical meaning of formula (21) is to minimize the expected value of the multi-objective function F under the worst distribution of wind power and photovoltaic output forecast deviations.

[0211] The second-stage multi-objective optimization model is based on the first-stage multi-objective optimization model and is based on the wind power and photovoltaic output forecast deviation ε t , using affine strategy to characterize the actual power of adjustable resources in multi-energy coupling systems;

[0212] The adjustable resources include biomass units, electrochemical energy storage, transferable electrical loads, electrolyzers and hydrogen storage tanks.

[0213] In combination with the actual characteristics of the park, the adjustable resources in the second stage of the present invention are biomass units, electrochemical energy storage, transferable electrical loads, electrolyzers and hydrogen storage tanks.

[0214] To facilitate the solution, an affine strategy is used to characterize the actual power of the adjustable resources in the second stage.

[0215] The actual power of the second stage biomass unit As shown below:

[0216]

[0217] Among them, δ BU,t Affine coefficients for adjusting biomass unit dispatch plans.

[0218] In turn,

[0219] Actual charge and discharge power of electrochemical energy storage in the second stage as follows:

[0220]

[0221] Among them, δ ESS,t Affine coefficients adjusted for electrochemical energy storage dispatch planning.

[0222] The actual amount of charge transferred in and out in the second stage as follows:

[0223]

[0224] Among them, δ TEL,t Affine coefficients adjusted for transferable charge scheduling schemes.

[0225] Actual input and output power of the second stage electrolyzer as follows:

[0226]

[0227] Among them, δ ET,t are the affine coefficients of the input and output power of the electrolytic cell.

[0228] Actual charging and discharging power of the second stage hydrogen storage tank as follows:

[0229]

[0230] Among them, δ HST,t is the affine coefficient of the hydrogen storage tank's charging and discharging power.

[0231] Step S42: The first-stage multi-objective optimization model and the second-stage multi-objective optimization model are integrated into a two-stage multi-objective distributed robust optimization model.

[0232] The two-stage multi-objective distributed robust optimization model is as follows:

[0233]

[0234] Where x is the decision variable matrix with A rows and B columns, A is the number of decision variables of each unit without considering the influence of uncertainty in the first stage, and B is the number of sampling points in the scheduling cycle; a T is the coefficient matrix of the decision variable matrix; It is an abstract representation of the objective function F of the first-stage multi-objective optimization model; is the objective function of the second stage; a, P, and q are the coefficient vector of x, the coefficient matrix of constraints, and the parameter vector, respectively; y is the decision variable of each unit considering the influence of uncertainty on the basis of x; b, G, and h are the coefficient vector of y, the coefficient matrix of constraints, and the parameter vector, respectively.

[0235] In order to facilitate representation and solution, the two-stage multi-objective optimization model is abstractly combined into a two-stage multi-objective distributed robust optimization model.

[0236] P T x≤q is the constraint condition of the multi-objective optimization model in the first stage without considering the influence of uncertainty, which corresponds to the equipment operation model of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system, the power balance constraint and energy transmission constraint of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system;

[0237] The objective function of the multi-objective optimization model under uncertainty in the second stage;

[0238] G T y≤h(x,ε t ) is the constraint condition of the multi-objective optimization model under uncertainty in the second stage. Based on the constraint condition set in the first stage, the prediction deviation ε is introduced t , and uses the affine strategy to transform the actual power of the adjustable resources; inf(·) is the infimum of the function (·).

[0239] The function of step S4 is to integrate the multi-objective optimization model of the first stage with the multi-objective optimization model of the second stage considering the influence of uncertainty, and construct a two-stage multi-objective distributed robust optimization model to optimize the carbon emissions of the park power grid system, the user energy consumption plan adjustment and the amount of wind and solar power curtailment.

[0240] Step S5, specifically.

[0241] For example, Figure 4 As shown in the figure, in Matlab software, the Yalmip platform is used to call the CPLEX solver to solve the two-stage multi-objective distributed robust optimization model, and the planning scheme of the power grid wind-solar-hydrogen-ammonia storage multi-energy coupling system is obtained.

[0242] The planning scheme of the park power grid wind, solar, hydrogen and ammonia multi-energy coupling system is the planning value of the decision variable y of each unit considering the influence of uncertainty, as well as the corresponding minimum carbon emissions, user energy consumption plan adjustment and wind and solar power curtailment.

[0243] The purpose of step S5 is to solve the two-stage multi-objective distributed robust optimization model, so as to obtain the optimal planning scheme of the power grid wind-solar-hydrogen-ammonia multi-energy coupling system considering the influence of uncertainty.

[0244] In summary, the multi-objective distributed robust planning method for a multi-energy coupling system according to an embodiment of the present invention has the following beneficial effects:

[0245] 1. Breaking through the traditional single efficiency optimization model, this approach achieves the coordinated optimization of three technical indicators: carbon emissions, energy plan adjustments, and wind and solar curtailment. Dynamically adjusting weight coefficients satisfies the planning requirements of different park power systems and improves the stability of the park power system.

[0246] 2. By minimizing the amount of wind and solar power curtailment, the system's ability to absorb renewable energy such as wind power and photovoltaics is improved, reducing energy waste; by minimizing carbon emissions, the environmental performance of the multi-energy coupling system is optimized, which helps reduce greenhouse gas emissions and is in line with sustainable development goals.

[0247] 3. By synergistically optimizing the electricity, hydrogen, and ammonia energy of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system, the volatility of renewable energy can be mitigated and the overall energy utilization rate can be improved;

[0248] 4. Use affine strategies to accurately and efficiently control the power adjustment range of adjustable resources such as biomass units, electrochemical energy storage, transferable electrical loads, electrolyzers, and hydrogen storage tanks. This can flexibly respond to forecast deviations in wind power and photovoltaic output, optimize energy distribution, and improve the operating efficiency and stability of the park's power system.

[0249] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0250] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A multi-objective distributed robust programming method for a multi-energy coupling system, characterized by: include: Obtain historical data within the park power grid dispatch cycle; Construct an equipment operation model for the wind-solar-hydrogen-ammonia multi-energy coupling system of the park power grid; Based on the equipment operation model of the wind-solar-hydrogen-ammonia multi-energy coupling system, a set of constraints is obtained, and a first-stage multi-objective optimization model is constructed to minimize the carbon emissions of the multi-energy coupling system, minimize the adjustment of user energy consumption plans, and minimize the amount of wind and solar power curtailment; Based on the first-stage multi-objective optimization model, the second-stage multi-objective optimization model is constructed by considering the uncertain wind power and photovoltaic output forecast deviations; the first-stage multi-objective optimization model and the second-stage multi-objective optimization model are integrated into a two-stage multi-objective distributed robust optimization model; Based on the historical data within the park power grid dispatching period, the two-stage multi-objective distributed robust optimization model is solved to obtain a planning scheme for the park power grid wind, solar, hydrogen and ammonia multi-energy coupling system.

2. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 1, characterized in that: The equipment operation model of the wind-solar-hydrogen-ammonia multi-energy coupling system of the park power grid includes equipment operation models of the electric energy subsystem, the hydrogen energy subsystem and the ammonia energy subsystem; The equipment operation model of the electric energy subsystem includes equipment operation models of wind turbines, photovoltaic units, biomass units, electrochemical energy storage, carbon capture and storage devices, and transferable loads; The equipment operation model of the hydrogen energy subsystem includes equipment operation models of the electrolyzer, the hydrogen storage tank and the transferable hydrogen load; The equipment operation model of the ammonia energy subsystem includes equipment operation models of a nitrogen production device and a synthetic ammonia device.

3. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 2, characterized in that: The multi-objective function is expressed as follows: Among them, f′ en 、f′ st 、f′ gr are the normalized minimum carbon emission sub-goal f en , the minimum sub-goal f of user energy consumption plan adjustment st , the minimum sub-target of wind and solar curtailment amount f gr ;ω en 、ω st 、ω gr f′ en 、f′ st 、f′ gr The weight of .

4. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 3, characterized in that: The constraint condition set also includes power balance constraints and energy transmission constraints of the wind-solar-hydrogen-storage-ammonia multi-energy coupling system.

5. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 4, characterized in that: The minimum carbon emission sub-targets are as follows: Among them, f en is the carbon emissions of the multi-energy coupling system; T is the scheduling period; is the carbon emission of the biomass unit at time t; is the carbon capture amount at time t; The minimum sub-goal of the user energy consumption plan adjustment amount is as follows: Among them, f st Adjustment of energy consumption plan for users of multi-energy coupling system; is the original value of the park's electrical load at time t; is the original value of the park's hydrogen load at time t; are the transferable electric load transfer-in and transfer-out amounts at time t respectively; The minimum sub-target of wind and solar curtailment is as follows: Among them, f gr is the amount of wind and solar power curtailment in the multi-energy coupling system; are the amount of wind and solar power curtailment at time t respectively.

6. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 5, characterized in that: Calculate the uncertainty of wind power and photovoltaic output forecast deviation ε of the park power grid at time t t ,as follows: in, are the actual output value and predicted output power value of the wind turbine at time t respectively; are the actual output value and predicted output power value of the photovoltaic unit at time t respectively; Wind power and photovoltaic output forecast deviation ε considering uncertainty t , a second-stage multi-objective optimization model is constructed that considers the distributional robust optimization of wind power and photovoltaic output forecast deviations under the worst distribution, including the second-stage objective function, as follows: Where Γ and Ω are the fuzzy sets of actual distribution and prediction deviation distribution considering prediction deviation respectively; x is the decision variable of each unit in the first stage without considering the influence of uncertainty; According to x and ε t The value of the multi-objective function F after scheduling adjustment; sup(·) and E(·) are used to solve the supremum and expected value respectively.

7. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 6, characterized in that: In the first stage, the decision variables x of each unit without considering the influence of uncertainty include photovoltaic output power Wind power output Biomass unit output power P BU,t , Charging power of chemical energy storage and discharge power Carbon capture operation power consumption P CCS,t , Transferable electric load transfer amount and transfer-out amount Electrolyzer input power P ET,t and output power Hydrogen storage tank filling power and hydrogen desorption power Transferable hydrogen load and transfer-out amount Nitrogen production equipment operating power consumption P PSA,t And the operating power consumption of synthetic ammonia equipment P P2A,t .

8. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 6, characterized in that: The second-stage multi-objective optimization model is based on the first-stage multi-objective optimization model and is based on the wind power and photovoltaic output forecast deviation ε t , using affine strategy to characterize the actual power of adjustable resources in multi-energy coupling systems; The adjustable resources include biomass units, electrochemical energy storage, transferable electrical loads, electrolyzers and hydrogen storage tanks.

9. The multi-objective distributed robust programming method for a multi-energy coupling system according to claim 8, characterized in that: The two-stage multi-objective distributed robust optimization model is as follows: Where x is the decision variable matrix with A rows and B columns, A is the number of decision variables of each unit without considering the influence of uncertainty in the first stage, and B is the number of sampling points in the scheduling cycle; a T is the coefficient matrix of the decision variable matrix; It is an abstract representation of the objective function F of the first-stage multi-objective optimization model; is the objective function of the second stage; a, P, and q are the coefficient vector of x, the coefficient matrix of constraints, and the parameter vector, respectively; y is the decision variable of each unit considering the influence of uncertainty on the basis of x; b, G, and h are the coefficient vector of y, the coefficient matrix of constraints, and the parameter vector, respectively.

10. The multi-objective distributed robust programming method for a multi-energy coupling system according to any one of claims 1 to 9, characterized in that: The planning scheme of the park power grid wind, solar, hydrogen and ammonia multi-energy coupling system is the planning value of the decision variable y of each unit considering the influence of uncertainty, as well as the corresponding minimum carbon emissions, user energy consumption plan adjustment and wind and solar power curtailment.

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