Pricing method and device for hydrogen-containing comprehensive energy system based on master-slave game

Abstract

The invention provides a hydrogen-containing integrated energy system pricing method and device based on a master-slave game, and belongs to the field of integrated energy system optimization. The method comprises the following steps: establishing a leader model at a comprehensive energy operator side by taking an energy purchase price as a decision variable according to an energy purchase strategy determined by a load aggregator side; according to the energy purchase price determined by the comprehensive energy operator side, establishing a follower model at the load aggregator side by taking an energy purchase strategy as a decision variable; determining a double-layer game model according to the leader model and the follower model; and solving the double-layer game model to obtain an optimal energy purchase price. The reasonable energy price can be formulated, and long-term stable operation of the hydrogen-containing comprehensive energy system is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimization, and in particular to a pricing method and device for a hydrogen-containing integrated energy system based on a master-slave game. Background Art

[0002] In recent years, due to the shortage of traditional fossil energy, energy security, environmental protection and other issues, the integrated energy system with the main features of "source, network, load, storage" integration and multi-energy complementary synergy has attracted much attention. The integrated energy system can couple various energy networks, flexibly convert electrical energy, thermal energy, cold energy, etc., effectively supply energy, and absorb distributed renewable energy, improve energy utilization efficiency, and promote energy sustainable development. It has important research value.

[0003] The hydrogen-containing integrated energy system is a complex energy system that uses hydrogen as an important energy carrier and organically integrates it with other energy forms (such as electricity, heat, etc.). Compared with the disadvantage that electric energy storage can only balance the supply and demand of electricity in a short period of time, the biggest advantage of hydrogen energy storage is that it has a longer battery life, a larger system capacity, and a lower investment cost per unit capacity. Combined with its characteristics of low self-attenuation rate and high energy density, hydrogen energy storage has obvious comparative advantages in multi-day continuous peak regulation and quarterly peak regulation.

[0004] In a hydrogen-containing integrated energy system, integrated energy operators and load aggregators are usually introduced to coordinate energy transactions between the two and establish an energy transaction framework. The integrated energy operator is located between the upper power grid and the load aggregator, responsible for managing the energy production equipment of the hydrogen-containing integrated energy system, supplying energy to the load aggregator and setting the purchase price of energy. The load aggregator accepts its price, integrates the demand response resources of electricity, heat and cooling loads, and introduces energy market transactions. When the integrated energy operator has a surplus after providing energy to the load aggregator, it can sell electricity to the upper power grid; when the equipment managed by the integrated energy operator is not enough to support the energy consumption of the load aggregator, it can purchase electricity from the upper power grid. Specifically, the energy price set by the integrated energy operator determines the total cost and energy purchasing strategy of the load aggregator, and the energy purchasing strategy of the load aggregator determines the operating cost and energy price setting of the integrated energy operator, and the maximization of the interests of the two affects each other. Therefore, it is crucial to formulate reasonable energy prices and ensure the interests of all parties for the long-term and stable operation of the hydrogen-containing integrated energy system. Summary of the invention

[0005] The embodiments of the present invention provide a master-slave game pricing method and device for a hydrogen-containing integrated energy system to formulate reasonable energy prices and ensure the long-term stable operation of the hydrogen-containing integrated energy system.

[0006] In a first aspect, an embodiment of the present invention provides a pricing method for a hydrogen-containing integrated energy system based on a master-slave game, comprising: According to the energy purchasing strategy determined by the load aggregator, a leader model is established on the integrated energy operator side with the energy purchasing price as the decision variable; According to the energy purchase price determined by the integrated energy operator, a follower model is established on the load aggregator side with the energy purchase strategy as the decision variable; Determine a two-layer game model according to the leader model and the follower model; Solve the two-layer game model to obtain the optimal energy purchase price.

[0007] In combination with the first aspect, in a possible implementation of the first aspect, establishing a leader model includes: The first objective function is established with the goal of minimizing the operating cost, and the second objective function is established with the goal of minimizing carbon emissions, thereby obtaining a multi-objective optimization function; Constraints of the multi-objective optimization function are established to obtain the leader model.

[0008] In combination with the first aspect, in a possible implementation manner of the first aspect, establishing a follower model includes: A third objective function is established with the goal of minimizing the comprehensive energy cost, and constraints of the third objective function are established to obtain the follower model.

[0009] In combination with the first aspect, in a possible implementation of the first aspect, solving the two-layer game model to obtain an optimal energy purchase price includes: Step 1: Determine the initial energy purchase price; Step 2: determining the optimal solution of the follower model according to the energy purchase price, wherein the optimal solution includes an energy purchase strategy; Step 3: determining the optimal solution of the leader model according to the energy purchase price and the energy purchase strategy, and obtaining the optimal function value of the first objective function; Step 4: updating the energy purchase price through cross-mutation, and recalculating the optimal function value of the first objective function; Step 5, return to step 2, and continuously iterate to determine the energy purchase price that minimizes the optimal function value of the first objective function as the optimal energy purchase price.

[0010] In combination with the first aspect, in a possible implementation of the first aspect, determining the optimal solution of the leader model according to the energy purchase price and the energy purchase strategy includes: Solve the Pareto set of the leader model by using the compromise programming method; According to the Pareto set, the optimal solution of the leader model is determined by the maximum and minimum fuzzy method.

[0011] In combination with the first aspect, in a possible implementation manner of the first aspect, the multi-objective optimization function includes: ; ; in, F 1 is the first objective function, The cost of purchasing natural gas is To cover the cost of buying and selling electricity to the grid, Equipment operation and maintenance costs, Cost of curtailing wind and solar power. For the sale of energy revenue, is the second objective function, The carbon emission coefficient of electricity purchased by integrated energy operators, Carbon emission coefficient of natural gas purchased by integrated energy operators, For the purchased power, For selling electricity, T The period to be optimized. t The time within the period to be optimized.

[0012] In combination with the first aspect, in a possible implementation of the first aspect, the constraints of the multi-objective optimization function include: equipment operation constraints, hydrogen energy storage constraints, energy price constraints, robust constraints and integrated energy operator power balance constraints.

[0013] In combination with the first aspect, in a possible implementation manner of the first aspect, the third objective function includes: ; in, , They are energy purchase expenditure and demand response cost. , , are the price compensation coefficients for reducing electricity, heating and cooling loads in the system, , , They are the electricity, heating and cooling loads that can be reduced. For the sale of energy revenue, T The period to be optimized. t The time within the period to be optimized.

[0014] In combination with the first aspect, in a possible implementation manner of the first aspect, the constraint conditions of the third objective function include: a robust constraint, a demand response constraint, and a power balance constraint.

[0015] In a second aspect, an embodiment of the present invention provides a pricing device for a hydrogen-containing integrated energy system based on a master-slave game, comprising: Establishing a module for establishing a leader model on the integrated energy operator side with the energy purchase price as a decision variable according to the energy purchase strategy determined by the load aggregator side; establishing a follower model on the load aggregator side with the energy purchase strategy as a decision variable according to the energy purchase price determined by the integrated energy operator side; determining a two-layer game model according to the leader model and the follower model; The solution module is used to solve the two-layer game model to obtain the optimal energy purchase price.

[0016] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The embodiment of the present invention introduces integrated energy operators and load aggregators in the hydrogen-containing integrated energy system, establishes an energy trading framework, with energy operators as leaders and load aggregators as followers; according to the energy purchase strategy determined by the load aggregator side, a leader model is established on the integrated energy operator side with the energy purchase price as the decision variable; according to the energy purchase price determined by the integrated energy operator side, a follower model is established on the load aggregator side with the energy purchase strategy as the decision variable; according to the leader model and the follower model, a two-layer game model is determined and solved to obtain the optimal energy purchase price. Through this method, the interests between the integrated energy operator and the load aggregator can be balanced, the system's ability to cope with uncertain risks can be improved, and the long-term stable operation of the hydrogen-containing integrated energy system can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is an operation framework diagram of a hydrogen-containing integrated energy system provided by an embodiment of the present invention; Figure 2 It is a diagram of the energy trading framework provided by an embodiment of the present invention; Figure 3 It is a master-slave game framework diagram provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of the implementation flow of the master-slave game pricing method for a hydrogen-containing integrated energy system provided by an embodiment of the present invention; Figure 5 is a flowchart of solving a double-layer model provided by an embodiment of the present invention; Figure 6 is a flow chart of an adaptive differential evolution algorithm provided by an embodiment of the present invention; Figure 7 is a flow chart of a compromise planning solution provided by an embodiment of the present invention; Figure 8 It is a schematic diagram of new energy output and load prediction provided by an embodiment of the present invention; Fig. 9 It is a schematic diagram of the solution result of the upper-layer multi-objective model provided by an embodiment of the present invention; Fig.10 This is a schematic diagram of electricity prices set by a comprehensive energy operator provided by an embodiment of the present invention; Fig.11 is a schematic diagram of heat prices set by a comprehensive energy operator provided by an embodiment of the present invention; Fig.12 It is a schematic diagram of cooling prices set by a comprehensive energy operator provided by an embodiment of the present invention; Fig.13 is a schematic diagram of the power optimization output result provided by an embodiment of the present invention; Fig.14 is a schematic diagram of thermal energy optimization output results provided by an embodiment of the present invention; Fig.15 is a schematic diagram of cooling energy optimization output results provided by an embodiment of the present invention; Fig.16 is a schematic diagram of hydrogen energy optimization output results provided by an embodiment of the present invention; Fig.17 is a schematic diagram of an electric load optimization result provided by an embodiment of the present invention; Fig.18 is a schematic diagram of heat load optimization results provided by an embodiment of the present invention; Fig.19 is a schematic diagram of cooling load optimization results provided by an embodiment of the present invention; Fig. 20 It is a structural schematic diagram of a pricing device for a hydrogen-containing integrated energy system based on a master-slave game provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0021] In this embodiment, the operation framework of the hydrogen-containing integrated energy system and the energy trading framework are firstly established.

[0022] The hydrogen-containing integrated energy system is a key component of the new power system. It integrates multiple energy forms and is equipped with various energy conversion equipment. Its operating framework is as follows: Figure 1 shown.

[0023] The system includes a hydrogen energy coupling system consisting of an electrolyzer and a hydrogen fuel generator, an electric heat and cooling supply system consisting of a cogeneration unit, a gas boiler, a heat pump, an electric refrigerator and an absorption refrigerator, a multi-source energy storage system consisting of electric energy storage, heat energy storage, cold energy storage and hydrogen energy storage, and two renewable energy units: wind turbines and photovoltaic generators.

[0024] (1) Electrolyzer model The electrolyzer is a key equipment in the process of electric hydrogen production. This paper selects alkaline electrolyzer as the hydrogen production equipment. The relationship between its input power and hydrogen production is as follows: ; In the formula, is the hydrogen output mass flow rate at time t, is the overall efficiency of hydrogen production from electricity, is the molar mass of hydrogen, is the AC power used in the electrolytic cell, U is the electrolytic cell voltage, z is the number of charges transferred by the electrolytic reaction, and F is the Faraday constant.

[0025] (2) Hydrogen fuel generator model The hydrogen fuel generator can realize the coupling between hydrogen energy and heat and electricity, and is an important coupling device in the system. The expression of the relationship between its output power and input hydrogen volume is as follows: ; In the formula, for t The output power of the hydrogen fuel generator at any moment, is the energy conversion efficiency of the fuel generator, yes t The mass of hydrogen input at any moment, is the calorific value of hydrogen, is the density of hydrogen under standard conditions.

[0026] (3) Cogeneration model The cogeneration system adopted by this method consists of a gas turbine and a waste heat recovery device. The gas turbine generates electricity and produces waste heat by burning natural gas. The waste heat recovery device recovers the waste heat and transmits it to the heat load end to meet the heat load demand, thereby avoiding energy waste and promoting the efficient operation of the integrated energy system. Its working expression is as follows: ; ; In the formula, It's time t The electricity output of the combined heat and power generation It's time t The thermal energy output of the combined heat and power generation is the power generation efficiency of CHP, is the heat production efficiency of the combined heat and power generation, for t Natural gas consumption for CHP at any given moment, is the calorific value of natural gas.

[0027] (4) Gas boiler model Gas boilers use natural gas as fuel, and the output power is related to the boiler air intake and conversion efficiency: ; In the formula, for t The heating power of the gas boiler at all times, is the heat generation efficiency of the gas boiler, for t The amount of natural gas consumed by the gas boiler at any given moment.

[0028] (5) Heat pump model A heat pump is a device that consumes electrical energy and generates thermal energy. Its output power expression is: ; In the formula, for t The heat output of the heat pump at any given moment, is the electrical and thermal conversion efficiency of the heat pump, for t The electrical power input from the system to the heat pump at any given moment.

[0029] (6) Electric refrigerator model An electric refrigerator is a device that converts electrical energy into cold energy. Its output power expression is: ; In the formula, for t The cooling power output of the electric refrigerator at that moment, is the electricity-to-cooling conversion efficiency of the electric refrigerator, For the moment t The electrical power input from the system to the electric refrigerator.

[0030] (7) Absorption Refrigeration Machine Model Absorption refrigerator is a device that uses a special working fluid to convert heat energy into cold energy. Its output power expression is: ; In the formula, for t The cooling power output of the absorption chiller at any moment, is the heat and cold conversion efficiency of the absorption refrigerator AC, For the moment t The thermal power input of the system to the absorption chiller.

[0031] Since users cannot trade directly with the upper-level energy system, integrated energy operators and load aggregators are introduced to coordinate energy transactions between the two and establish an energy trading framework. Figure 2 As shown. The integrated energy operator is located between the upper power grid and the load aggregator, responsible for managing the energy production equipment of the hydrogen-containing integrated energy system, supplying energy to the load aggregator and setting the purchase price of energy. The load aggregator accepts its price, integrates the demand response resources of electricity, heat and cooling loads, and introduces energy market transactions. When the integrated energy operator has a surplus after providing energy to the load aggregator, it can sell electricity to the upper power grid; when the equipment managed by the integrated energy operator is not enough to support the energy consumption of the load aggregator, it can purchase electricity from the upper power grid.

[0032] In this embodiment, the integrated energy operator, as a leader, aims to minimize operating costs and carbon emissions and formulates an output plan for the system energy production equipment. The lower-level follower load aggregator aims to minimize the comprehensive energy cost, accepts the energy purchase price set by the integrated energy operator, and formulates an energy purchase strategy based on demand response constraints and load-side uncertainty constraints. The energy price set by the integrated energy operator determines the total cost and energy purchase strategy of the load aggregator, and the energy purchase strategy of the load aggregator determines the operating cost of the integrated energy operator. The maximization of the interests of the two affects each other and constitutes a game. The master-slave game framework is as follows: Figure 3 shown.

[0033] The following combination Figure 4 , the pricing method of the master-slave game of hydrogen-containing integrated energy system provided by the embodiment of the present invention is described in detail: Step S401, based on the energy purchasing strategy determined by the load aggregator side, a leader model is established on the integrated energy operator side with the energy purchasing price as a decision variable.

[0034] Here, the leader establishes the first objective function with the lowest operating cost as the goal, and establishes the second objective function with the lowest carbon emission as the goal, thus obtaining a multi-objective optimization function; the constraints of the multi-objective optimization function are established to obtain the leader model.

[0035] Optionally, the first objective function is: ; In the formula, The cost of purchasing natural gas is To cover the cost of buying and selling electricity to the grid, Equipment operation and maintenance costs, Cost of curtailing wind and solar power. For sales revenue.

[0036] ; In the formula, , , They are t Energy prices for electricity, heating and cooling at all times, , , They are t Electricity, heating and cooling energy trading volume at all times. is the natural gas price, for t The amount of natural gas consumed by the cogeneration units and gas boilers at each moment. , are the purchase and sale prices of electricity, , for The system purchases and sells electricity power at the moment. For the Unit operation and maintenance cost coefficient of this type of equipment, For the Class Equipment The power of the moment. , are the penalty coefficients for abandoning wind and solar power, , They are t Always abandon wind and solar power.

[0037] Optionally, the second objective function is: ; In the formula, The carbon emission coefficient of electricity purchased by integrated energy operators, Carbon emission coefficient of natural gas purchased by integrated energy operators, For the purchased power, For selling electricity, T The period to be optimized. t The time within the period to be optimized.

[0038] Optionally, the constraints of the multi-objective optimization function include: equipment operation constraints, hydrogen energy storage constraints, energy price constraints, robustness constraints and integrated energy operator power balance constraints.

[0039] Equipment operation constraints: ; In the formula, , It is the upper and lower limits of the power generation capacity of the cogeneration unit; , It is the upper and lower limits of the heating power of the cogeneration unit; , It is the upper and lower limits of the heating power of the gas boiler. , The upper and lower limits of the heat energy output of the heat pump. , The upper and lower limits of the cooling energy output by the electric refrigerator. , The upper and lower limits of the cooling energy output of the absorption chiller; , The upper and lower limits of the hydrogen production power of the electrolyzer. , The upper and lower limits of hydrogen fuel power generation.

[0040] Energy storage equipment constraints: ; In the formula, k Indicates three types of energy storage devices; for t SOC value of the device at the moment; is the energy loss coefficient of the equipment; , The charging and discharging efficiency of the equipment; , is the minimum and maximum SOC value of the device; , for t The charging and discharging power of the equipment at all times; is the capacity of the equipment; is the maximum power of the energy storage device; , It is an auxiliary binary variable for the charging and discharging of the equipment; It is the ratio coefficient of the equipment energy storage capacity to power.

[0041] Hydrogen storage constraints: ; In the formula, , The upper and lower limits of the hydrogen storage tank capacity; , It is the upper limit of hydrogen charging and decharging; , The amount of hydrogen to be filled and discharged from the hydrogen storage tank; is the hydrogen storage capacity at the initial moment, is the minimum hydrogen demand at the initial moment.

[0042] Energy price constraints: ; In the formula, , , caps on electricity, heating and cooling prices for integrated energy operators; , , The lower limits of electricity, heating and cooling prices are set respectively. , , The average value of the established electricity, heating and cooling prices.

[0043] Robust constraints (taking wind power output and electric load demand as an example): ; In the formula, , To consider the wind power output and electric load demand after robust optimization; The actual power supply of wind power; and It is the upper and lower limits of wind power output; and are the upper and lower limits of the electrical load; , , , , , is the introduced robust auxiliary coefficient.

[0044] Power balance constraints for integrated energy operators ; In the formula, , , They are respectively the electricity, heat and cooling power purchased by load aggregators from integrated energy operators.

[0045] Step S402: According to the energy purchase price determined by the integrated energy operator, a follower model is established on the load aggregator side with the energy purchase strategy as a decision variable.

[0046] Here, the follower aims to minimize the comprehensive energy cost, which mainly includes energy purchase cost and demand response cost: ; in, , They are energy purchase expenditure and demand response cost. , , are the price compensation coefficients for reducing electricity, heating and cooling loads in the system, , , They are the electricity, heating and cooling loads that can be reduced. For the sale of energy revenue, T The period to be optimized. t The time within the period to be optimized.

[0047] Optionally, the constraints include: robust constraints, demand response constraints and power balance constraints.

[0048] Robust constraints: ; In the formula, , , , , , , , , These are all introduced robust auxiliary coefficients.

[0049] Demand response constraints (taking electric load as an example): ; In the formula, The electricity purchased by the load aggregator from the energy operator for demand response. For the system t The original power demand during the period, is the proportional coefficient of the electrical load that can be reduced.

[0050] Power balance constraints ; In the formula , They are the heating power and cooling power purchased by the demand response load aggregator from the energy operator respectively; , They are the heat power and cooling power that can be reduced.

[0051] Step S403, determining a two-layer game model according to the leader model and the follower model.

[0052] Step S404, solving the double-layer game model to obtain the optimal energy purchase price.

[0053] The embodiment of the present invention introduces integrated energy operators and load aggregators in the hydrogen-containing integrated energy system, establishes an energy trading framework, with energy operators as leaders and load aggregators as followers; according to the energy purchase strategy determined by the load aggregator side, a leader model is established on the integrated energy operator side with the energy purchase price as the decision variable; according to the energy purchase price determined by the integrated energy operator side, a follower model is established on the load aggregator side with the energy purchase strategy as the decision variable; according to the leader model and the follower model, a two-layer game model is determined and solved to obtain the optimal energy purchase price. Through this method, the interests between the integrated energy operator and the load aggregator can be balanced, the system's ability to cope with uncertain risks can be improved, and the long-term stable operation of the hydrogen-containing integrated energy system can be ensured.

[0054] In some embodiments, the established optimization model is stored as a two-layer structure, and the upper optimization model is a multi-objective optimization problem and cannot be solved directly. Therefore, the upper model can be solved by compromise programming to obtain the Pareto set, and then the maximum and minimum fuzzy method is used to solve the optimal solution. The adaptive differential evolution algorithm is used to solve the two-layer model to obtain the formulated energy price.

[0055] See also Figure 5 and Figure 6 The detailed solution process is as follows: Step 1: Determine the initial energy purchase price.

[0056] Step 2: Determine the optimal solution of the follower model based on the energy purchase price. The optimal solution includes the energy purchase strategy.

[0057] Step three, according to the energy purchase price and energy purchase strategy, determine the optimal solution of the leader model and obtain the optimal function value of the first objective function.

[0058] Here, calculating the optimal function value of the first objective function is actually solving the multi-objective function. For the solution, see Figure 7 .

[0059] When solving, additional constraints need to be considered as follows: ; In the formula, is the Chebyshev distance; and are the values ​​calculated at each iteration of objective functions 1 and 2, i.e., the criterion values; and is the weight coefficient; and are the minimum values ​​of objective functions 1 and 2 respectively.

[0060] After obtaining the Pareto frontier solution, the two objective function values ​​need to be normalized and then the optimal solution is found: ; In the formula, The objective function The unit vector of the secondary solution; For the The objective function Second solution; and Respectively The minimum and maximum values ​​of the Pareto frontier solutions of the objective function.

[0061] The optimal solution is obtained by comparing the following equations: ; In the formula, It is the minimum value of all solutions after unifying the numerical dimension of the objective function; for The maximum value among the minimum values ​​of all solutions in is the optimal compromise solution.

[0062] Step 4: Update the energy purchase price through cross-mutation, and recalculate the optimal function value of the first objective function and the optimal equipment output plan.

[0063] Step 5, return to step 2, and iterate continuously to determine the energy purchase price that minimizes the optimal function value of the first objective function of the leader model as the optimal energy purchase price.

[0064] The adaptive differential evolution algorithm ensures population diversity in the early stage by using an adaptive mutation operator that changes with the number of iterations, and gradually reduces it in the later stage to ensure good convergence and strong optimization ability. On the other hand, the upper layer is a multi-objective function that cannot be solved directly, and compromise programming can solve it quickly and accurately. The core of the algorithm is the mutation operation: ; The adaptive mutation operator is as follows: ; In the formula, is the initial mutation operator, is the maximum number of iterations, G is the current iteration number.

[0065] In one embodiment, in order to prove the effectiveness of the method, a simulation verification was carried out by taking a large-scale wind-solar complementary coupling hydrogen production system application demonstration project as an example.

[0066] The system load demand and new energy output forecast data are as follows Figure 8The load aggregator is set to reduce the amount of electricity, heat, and cooling loads that account for 10% of the total demand electricity, heat, and cooling loads. The maximum number of iterations is set to 150, the population size is 80, F0 is 0.3, and the crossover probability cr is 0.9. The upper and lower limits of the electricity price are set to 1.2 yuan / Wh and 0.35 yuan Wh, respectively, the upper and lower limits of the heat price are set to 0.6 yuan / Wh and 0.15 yuan / kWh, respectively, and the upper and lower limits of the cooling price are set to 0.5 yuan Wh and 0.1 yuan / kWh, respectively.

[0067] from Figure 8 It can be seen that wind power resources are sufficient at night and photovoltaic resources are abundant during the day. The complementary power generation of the two can better supply energy to the load, and the time-space coupling characteristics of multi-energy flow can achieve multi-energy complementarity. Table 1-Table 3 show the main technical parameters.

[0068] Table 1 Energy price information

[0069] Table 2 Economic parameters of each equipment in the system

[0070] Table 3 Main equipment operating efficiency parameters

[0071] (1) Multi-objective solution under compromise planning The global optimal solution under the compromise planning is -20730.64 yuan and 5323.79 kg. The operating cost is a negative value, which can be understood as the income of the integrated energy operator. Fig. 9 From the position of the global optimal solution in the game, we can see that during the game between the integrated energy operator and the load aggregator, the integrated energy operator has carried out multi-objective optimization of the output of equipment in the system, made a reasonable trade-off between the operating costs and carbon emissions of the integrated energy operator, and balanced the economy and environmental protection of energy production.

[0072] (2) Analysis of pricing results After the game, the energy price set by the integrated energy operator is as follows: Figures 10 to 12 As shown. Fig.10 It can be seen that the electricity price is always between the purchase and sale prices of the upper power grid, providing users with a more affordable price, and the price fluctuation trend is similar to the fluctuation trend of the user's actual power load, which can stimulate users to purchase more electricity and thus increase the profits of integrated energy operators. Fig.12It can be seen that the heating and cooling prices set by the integrated energy operators are roughly the same as the trends of the heating and cooling loads on the user side, proving that the pricing in this article can provide users with lower prices during periods of low heating and cooling loads, encourage users to purchase more energy, and promote integrated energy operators to increase their profits; increase energy prices during periods of high heating and cooling loads, inhibit user energy transactions, and reduce the total cost of load aggregators.

[0073] (3) Analysis of equipment output results After the game is balanced, the output plan of each device in the system is as follows: Figure 13-16 shown.

[0074] Fig.13 At night, wind power output is higher and load demand is lower. After the electricity load is met, the integrated energy operator will store the surplus electricity in hydrogen through the electrolyzer; during the day when electricity prices are higher, hydrogen fuel generators provide auxiliary supply for the electricity load. In addition, because the upper-level integrated energy operator has carried out multi-objective optimization of the output of equipment in the system, in order to take into account both economy and environmental protection, wind power, photovoltaic and hydrogen fuel generators account for a large proportion of power generation, which not only reduces the cost of purchasing electricity during high electricity prices, but also reduces carbon emissions.

[0075] Fig.14 In the process, the gas turbine mainly bears the heat load and absorption chiller consumption during the day. At night, the power price of the power grid is at the valley price. The appropriate addition of heat pumps for auxiliary heating reduces the heating cost and carbon emissions. The excess heat at 18:00 and 22:00 is absorbed by the thermal energy storage, avoiding energy waste.

[0076] Fig.15 , when electricity prices are low at night, the output of the electric refrigeration machine is increased to reduce operating costs; during the day, the absorption refrigeration machine is basically in full operation, using waste heat to provide cooling energy, thereby maximizing energy utilization.

[0077] Fig.16 During the night, renewable resources are abundant and electricity is converted into hydrogen for storage in large quantities. During the day, electricity prices are higher and hydrogen energy is used to generate electricity through hydrogen fuel generators, reducing system operating costs and carbon emissions.

[0078] (4) Demand response analysis To analyze the impact of demand response, the load optimization before and after demand response is as follows: Figure 17-19 shown.

[0079] Combination Fig.17 and Fig.10It can be seen that after participating in the game, under the incentive of electricity prices, load aggregators are more inclined to reduce electricity purchases during the peak electricity price periods of 8:00-15:00 and 18:00-21:00; and do not reduce electricity purchases during the period from 22:00 to 07:00 the next morning when electricity prices are lower. After the reduction, the user's electricity cost is reduced, indicating that this pricing strategy can enable load aggregators to participate in peak shaving and valley filling to a greater extent. Fig.18 It can be seen that the heat load demand response basically did not change from 24:00 to 05:00 in the morning, while other periods were reduced to varying degrees. This is because the heat price set by the integrated energy operator is lower during the 24:00-05:00 period, while the heat price in other periods is higher. Fig.10 The results of heat price setting are the same. Fig.19 and Fig.12 It can be seen that the cooling load was reduced when the cooling price set by the integrated energy operator was higher.

[0080] The present invention proposes a multi-objective robust game model with integrated energy operators as leaders and load aggregators as followers. Among them, the upper integrated energy operator sets energy prices for load aggregators and performs multi-objective optimization on the output of system equipment with the goal of minimum operating cost and minimum carbon emission; the lower load aggregators adjust the energy purchase amount according to the energy price released by the upper layer with the goal of minimum total cost and respond to the upper leader. In addition, the uncertainties on both the source and load sides of the model are handled by robust optimization methods. Finally, the model is solved by an adaptive differential evolution algorithm combined with compromise planning. The compromise planning solves the upper multi-objective model, and the adaptive differential evolution algorithm solves the double-layer model. The results show that the proposed multi-objective robust operation optimization strategy for hydrogen-containing integrated energy system based on master-slave game promotes energy network security, achieves a balance of interests between integrated energy operators and load aggregators, further reduces the operating cost and carbon emissions of the system, and improves the robustness of the system.

[0081] This advantage mainly comes from two aspects: First, the complex interest relationship between the energy production side and the user side was taken into consideration, and the interest relationship between the energy production side and the user side in the hydrogen-containing integrated energy system under the market environment was deeply analyzed.

[0082] Second, since the purchase of electricity and natural gas is the main source of carbon emissions, reducing carbon emissions requires continuously reducing the purchase of electricity and natural gas. Minimizing costs requires appropriately increasing the purchase amount according to the optimal economic principle. These two goals cannot be achieved at the same time. In order to achieve a win-win situation for both goals, compromise programming is used to solve the multi-objective model and obtain the Pareto frontier solution. Then, the optimal solution can be solved by the maximum-minimum fuzzy method. The adaptive differential evolution algorithm is used to solve the two-layer model and obtain the formulated energy price.

[0083] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0084] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0085] Fig. 20 The schematic diagram of the structure of the master-slave game pricing device for the hydrogen-containing integrated energy system provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows: like Fig. 20 As shown, the master-slave game pricing device 200 for a hydrogen-containing integrated energy system includes: Establishing module 201, for establishing a leader model on the integrated energy operator side with the energy purchase price as a decision variable according to the energy purchase strategy determined by the load aggregator side; establishing a follower model on the load aggregator side with the energy purchase strategy as a decision variable according to the energy purchase price determined by the integrated energy operator side; determining a two-layer game model according to the leader model and the follower model; The solution module 202 is used to solve the double-layer game model to obtain the optimal energy purchase price.

[0086] As a possible implementation, the establishment module 201 is specifically used for: The first objective function is established with the goal of minimizing the operating cost, and the second objective function is established with the goal of minimizing carbon emissions, thereby obtaining a multi-objective optimization function; The constraints of the multi-objective optimization function are established to obtain the leader model.

[0087] As a possible implementation, the establishment module 201 is specifically used for: The third objective function is established with the goal of minimizing the comprehensive energy cost, and the constraints of the third objective function are established to obtain the follower model.

[0088] As a possible implementation, the solution module 202 is specifically used for: Step 1: Determine the initial energy purchase price; Step 2: Determine the optimal solution of the follower model based on the energy purchase price, and the optimal solution includes the energy purchase strategy; Step 3: According to the energy purchase price and energy purchase strategy, determine the optimal solution of the leader model and obtain the optimal function value of the first objective function; Step 4: Update the energy purchase price through cross-mutation, and recalculate the optimal function value of the first objective function; Step 5, return to step 2, and iterate continuously to determine the energy purchase price that minimizes the optimal function value of the first objective function of the leader model as the optimal energy purchase price.

[0089] As a possible implementation, the solution module 202 is specifically used for: Solve the Pareto set of the first objective function by using the compromise programming method; According to the Pareto set, the optimal solution of the leader model is determined by the maximum and minimum fuzzy method.

[0090] As a possible implementation, the multi-objective optimization function includes: ; ; in, F 1 is the first objective function, The cost of purchasing natural gas is To cover the cost of buying and selling electricity to the grid, Equipment operation and maintenance costs, Cost of curtailing wind and solar power. For the sale of energy revenue, is the second objective function, The carbon emission coefficient of electricity purchased by integrated energy operators, Carbon emission coefficient of natural gas purchased by integrated energy operators, For the purchased power, For selling electricity, T The period to be optimized. t The time within the period to be optimized.

[0091] As a possible implementation method, the constraints of the multi-objective optimization function include: equipment operation constraints, hydrogen energy storage constraints, energy price constraints, robustness constraints and integrated energy operator power balance constraints.

[0092] In conjunction with the second aspect, in a possible implementation manner of the second aspect, the third objective function includes: ; in, , They are energy purchase expenditure and demand response cost. , , are the price compensation coefficients for reducing electricity, heating and cooling loads in the system, , , They are the electricity, heating and cooling loads that can be reduced. For the sale of energy revenue, T The period to be optimized.t The time within the period to be optimized.

[0093] As a possible implementation manner, the constraints of the third objective function include: robustness constraints, demand response constraints and power balance constraints.

[0094] The embodiment of the present invention introduces integrated energy operators and load aggregators in the hydrogen-containing integrated energy system, establishes an energy trading framework, with energy operators as leaders and load aggregators as followers; according to the energy purchase strategy determined by the load aggregator side, a leader model is established on the integrated energy operator side with the energy purchase price as the decision variable; according to the energy purchase price determined by the integrated energy operator side, a follower model is established on the load aggregator side with the energy purchase strategy as the decision variable; according to the leader model and the follower model, a two-layer game model is determined and solved to obtain the optimal energy purchase price. Through this method, the interests between the integrated energy operator and the load aggregator can be balanced, the system's ability to cope with uncertain risks can be improved, and the long-term stable operation of the hydrogen-containing integrated energy system can be ensured.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0097] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned master-slave game hydrogen-containing integrated energy system pricing method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0098] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A pricing method for a hydrogen-containing integrated energy system based on a master-slave game, characterized in that: include: According to the energy purchasing strategy determined by the load aggregator, a leader model is established on the integrated energy operator side with the energy purchasing price as the decision variable; According to the energy purchase price determined by the integrated energy operator, a follower model is established on the load aggregator side with the energy purchase strategy as the decision variable; Determine a two-layer game model according to the leader model and the follower model; Solve the two-layer game model to obtain the optimal energy purchase price.

2. The pricing method for a hydrogen-containing integrated energy system according to claim 1, characterized in that: The establishment of the leadership model includes: The first objective function is established with the goal of minimizing the operating cost, and the second objective function is established with the goal of minimizing carbon emissions, thereby obtaining a multi-objective optimization function; Constraints of the multi-objective optimization function are established to obtain the leader model.

3. The pricing method for a hydrogen-containing integrated energy system according to claim 1, characterized in that: The establishing of the follower model comprises: A third objective function is established with the goal of minimizing the comprehensive energy cost, and constraints of the third objective function are established to obtain the follower model.

4. The pricing method for a hydrogen-containing integrated energy system according to claim 1, characterized in that: Solving the two-layer game model to obtain the optimal energy purchase price includes: Step 1: Determine the initial energy purchase price; Step 2: determining the optimal solution of the follower model according to the energy purchase price, wherein the optimal solution includes an energy purchase strategy; Step 3: determining the optimal solution of the leader model according to the energy purchase price and the energy purchase strategy, and obtaining the optimal function value of the first objective function; Step 4: updating the energy purchase price through cross-mutation, and recalculating the optimal function value of the first objective function; Step 5, return to step 2, and continuously iterate to determine the energy purchase price that minimizes the optimal function value of the first objective function as the optimal energy purchase price.

5. The pricing method for a hydrogen-containing integrated energy system according to claim 4, characterized in that: Determining the optimal solution of the leader model according to the energy purchase price and the energy purchase strategy includes: Solve the Pareto set of the leader model by using the compromise programming method; According to the Pareto set, the optimal solution of the leader model is determined by the maximum and minimum fuzzy method.

6. The pricing method for a hydrogen-containing integrated energy system according to claim 2, characterized in that: The multi-objective optimization function includes: ； ； in, F 1 is the first objective function, The cost of purchasing natural gas is To cover the cost of buying and selling electricity to the grid, Equipment operation and maintenance costs, Cost of curtailing wind and solar power. For the sale of energy revenue, is the second objective function, The carbon emission coefficient of electricity purchased by integrated energy operators, Carbon emission coefficient of natural gas purchased by integrated energy operators, For the purchased power, For selling electricity, T The period to be optimized. t The time within the period to be optimized.

7. The pricing method for a hydrogen-containing integrated energy system according to claim 6, characterized in that: The constraints of the multi-objective optimization function include: equipment operation constraints, hydrogen energy storage constraints, energy price constraints, robustness constraints and integrated energy operator power balance constraints.

8. The pricing method for a hydrogen-containing integrated energy system according to claim 3, characterized in that: The third objective function includes: ； in, , They are energy purchase expenditure and demand response cost. , , are the price compensation coefficients for reducing electricity, heating and cooling loads in the system, , , They are the electricity, heating and cooling loads that can be reduced. For the sale of energy revenue, T The period to be optimized. t The time within the period to be optimized.

9. The pricing method for a hydrogen-containing integrated energy system according to claim 8, characterized in that: The constraints of the third objective function include: robustness constraints, demand response constraints and power balance constraints.

10. A master-slave game pricing device for a hydrogen-containing integrated energy system, characterized in that: include: Establishing a module for establishing a leader model on the integrated energy operator side with the energy purchase price as a decision variable according to the energy purchase strategy determined by the load aggregator side; establishing a follower model on the load aggregator side with the energy purchase strategy as a decision variable according to the energy purchase price determined by the integrated energy operator side; determining a two-layer game model according to the leader model and the follower model; The solution module is used to solve the two-layer game model to obtain the optimal energy purchase price.

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