Community integrated energy system optimization scheduling method considering work load characteristics

Abstract

The invention discloses a community integrated energy system optimization scheduling method considering a work load characteristic, and the method takes the minimum daily operation cost of a community integrated energy system as an optimization target, combines the energy balance constraint of a combined heat and power generation system, and researches the uncertainty of the alternative energy consumption behavior of community residents based on the consumer psychology theory. The price type demand response and the incentive policy are considered for improving the load curve, the electric hydrogen production equipment considering the variable load start-stop characteristic is introduced, the operation process of the electric hydrogen production equipment and the operation characteristic of an electric hydrogen coupling unit are analyzed, a community comprehensive energy system is established for hydrogen energy utilization and resident comprehensive demands, and optimization scheduling is conducted on the community comprehensive energy system.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy, and specifically relates to an optimization scheduling method for a community integrated energy system taking into account load characteristics. Background Art

[0002] Integrated energy systems with the characteristics of environmental protection, energy saving and cleanliness have become a hot topic in recent years. Integrated energy systems can realize multi-energy conversion and storage, improve the energy utilization rate of the system and promote the use of new energy.

[0003] As an environmentally friendly and efficient secondary energy source, hydrogen energy has begun to be applied and developed in integrated energy systems in recent years. However, the current research on hydrogen production from electricity has not taken the variable load characteristics and start-stop characteristics of the electrolyzer as a research direction, and has not considered the overload, variable load, and low load states of the electrolyzer, which have an impact on the flexible operation of the integrated energy system. Summary of the invention

[0004] Technical problem of the present invention: The research on demand response of community integrated energy system mainly targets households, and mainly considers energy conversion equipment such as air conditioners, electric heaters, and electric water heaters, but lacks consideration of the multi-energy complementary characteristics of users. When studying such optimization problems, the utilization of hydrogen energy was studied and analyzed, and the energy efficiency characteristics of electric hydrogen coupling equipment were ignored. At the same time, when studying demand response, the price elasticity matrix method is generally used to characterize its reduction, transfer and substitution behaviors under different electricity prices, without considering the actual user's alternative energy.

[0005] The purpose of the present invention is to provide an optimization scheduling method for a community integrated energy system taking into account the operating load characteristics in order to address the above-mentioned problems, and to establish a refined hydrogen energy utilization model taking into account the start-stop characteristics and variable load characteristics of the electrolyzer, and to divide the user load into transferable, reducible and replaceable loads based on consumer psychology on the user side, and to formulate incentive policies based on the replaceable loads to further optimize the load curve, so as to achieve the purpose of optimizing the operation of the integrated energy system and the consumption of new energy on the basis of considering the carbon trading mechanism.

[0006] The technical solution of the present invention is: The community integrated energy system optimization dispatching method considering load characteristics includes the following steps: S1: Establish a physical model of the community integrated energy system and consider the uncertainty of demand response; S2: Establish an electrolyzer model that takes into account the load characteristics and the corresponding hydrogen energy conversion model, and establish the model and operation mode of the cogeneration equipment; S3: Determine the user substitution effect based on consumer psychology theory, determine the alternative demand response and consider the transferable and reducible loads, and draw the electricity and heat load curves on the user side; S4: Taking the lowest daily cost of the community integrated energy system as the optimization goal, and adding constraints to meet functional requirements and its own equipment, combined with the electricity and heat load curves on the user side, establish the optimal scheduling model of the community integrated energy system; S5: Based on step S3, the electricity and heat load curves on the user side are obtained, and the optimization target of step S4 is used as the objective function to solve the optimal scheduling model of the integrated energy system. The scheduling results are obtained using CPLEX, and the impact of the adopted model and mechanism on the integrated energy system is analyzed.

[0007] Preferably, in step S1, the community comprehensive energy system established in step S1 includes fans, photovoltaics, energy storage equipment, gas turbines, bromide refrigerators, air source heat pumps, methane reactors, fuel cells, and electrolyzers; fans and photovoltaic units provide electrical energy, and the community comprehensive energy system makes up for energy loss through the main grid and the gas grid; the electric-hydrogen coupling unit composed of the electrolyzer and the fuel cell reduces the energy cascade loss of the community comprehensive energy system and improves the comprehensive utilization rate; the heat pump device converts electrical energy into thermal energy; the cogeneration system composed of the gas turbine and the bromide refrigerator is thermally decoupled to improve the electric-to-heat output ratio.

[0008] Furthermore, in step S2, the gas turbine, bromide refrigeration machine, heat pump, methane reactor, fuel cell and electrolyzer operated by the cogeneration system are respectively: (1) The calculation formula for the heating power of the gas turbine is: ; ; In the formula , They represent the power generation and heating power of the gas turbine during period t, , They represent the power generation efficiency and waste heat loss rate of the gas turbine respectively. represents the heating power of the gas turbine during period t, , They represent the waste heat recovery rate and heating efficiency of the gas turbine respectively, and GT represents gas turbine.

[0009] (2) The calculation formula for heat pump heating power is: ; Where: represents the heating power of the heat pump during period t, represents the input power of the heat pump during period t, It represents the heating coefficient, and HP represents the heat pump; (3) The fuel cell adjusts its own thermal-to-electric ratio by changing its operating state, which is expressed as: ; Where: , and for Hydrogen consumption, electricity generation and heat generation at each moment; is the energy conversion efficiency of the fuel cell, HFC means Fuel cells; (4) The methane reactor converts hydrogen into natural gas and supplies it for heat and electricity generation. The expression is: ; Where: and for Hydrogen consumption and gas production at all times; represents the hydrogen-gas conversion efficiency of the methane reactor, MT Represents a methane reactor.

[0010] (5) The calculation formula for the electric-hydrogen conversion relationship of the electrolyzer equipment is: ; In the formula, , Indicates The electrolytic cell Hydrogen production power and electricity consumption in the time period; It represents the electricity-to-hydrogen conversion efficiency of the electrolyzer; Indicates the electrical power required when the electrolyzer is in cold standby state; Indicates the penalty coefficient for the electrolyzer being in cold standby; binary variable is a control variable. When it is 1, it indicates that the electrolyzer switches from cold standby to working state. PEM represents an electrolytic cell; The preferred electrolytic cell includes three states: shutdown, cold standby, and working. , To indicate shutdown and cold standby status; the working status can be divided into three states: low load, variable load and overload, and only the working state produces hydrogen. , , To describe, the above variables are all binary variables.

[0011] Shutdown state: The electrolyzer can be shut down quickly at any time.

[0012] Cold standby state: The electrolyzer is in standby mode at low power to maintain the operation of the control and antifreeze units.

[0013] Working state: In order to ensure the safety of hydrogen production by electrolyzer, it should be in variable load state most of the time. The electrolyzer should not be overloaded or underloaded for too long.

[0014] (1) The control variable constraints are: ; In the formula, the control variable represents the state of the nth electrolytic cell at time t, . , , , represents the state of the nth electrolytic cell at time t; (2) The coordinated control expression between different states of the electrolytic cell is: ; Where: is the rated power of the electrolyzer; (3) The logical constraint expression of the state transition of the electrolytic cell is: ; ; ; ; ; In the formula: the above formula is start-stop, start interval, operation state mutual exclusion constraint, overload and underload maximum time limit constraint; and The longest operation time of the electrolytic cell in overload and underload state; (4) The shutdown and cold standby time constraints are: ; ; Where: , They are the shortest time that the electrolyzer can operate continuously in shutdown and cold standby states respectively. Furthermore, in step S2, the cogeneration system includes four parts: energy supply, energy coupling, energy storage, and energy consumption unit. Wind turbines and photovoltaic units provide electricity, while the community comprehensive energy system makes up for the energy loss through the main grid and gas grid; the electricity-hydrogen coupling unit can reduce the energy cascade loss of the community comprehensive energy system and improve the comprehensive utilization rate; the heat pump device can convert electricity into heat energy, helping the cogeneration system composed of gas turbines and bromide refrigeration machines to achieve heat-electricity decoupling and improve the system's electricity-heat output ratio; at the same time, the multi-energy storage equipment and the comprehensive demand response of residents will provide guarantees for the flexibility of the system.

[0015] Preferably, the energy storage in step S3 is an important component of the comprehensive energy system. During the charging and discharging process in the energy storage link, there is loss and the characteristic of dissipation over time. The mathematical model is: ; ; Where: represents the power of the energy storage device in period t, and Respectively represent the input and output power of energy storage, Indicates the remaining capacity of energy storage, represents the loss coefficient, and Represents the energy conversion rate.

[0016] Furthermore, in step S3, the analysis of residents' demand response is to analyze the user load based on the Logtic regression curve to describe the user's substitution behavior. The user load is divided into basic load, curtailable load, transferable load and replaceable load. The response degree of different types of loads will be different under the same price signal.

[0017] (1) The expression for load reduction and transfer is: ; Where: is the initial load that can be reduced and transferred at time t; is the load price demand elasticity matrix, represents the electricity price at time j; Indicates the actual amount of transferred load; Indicates the electricity price at the initial time (2) Substitution load, a fuzzy demand response mechanism based on the Logistic function is introduced to describe the user substitution behavior. When the stimulus level is lower than the threshold, the user will not respond, and this area is the dead zone. When the stimulus reaches the threshold, the user will respond, and as the stimulus level increases, the degree of response continues to increase. However, when the stimulus reaches a certain level, the user's response level will no longer change and enter the saturation zone. The user's load substitution behavior only considers the substitution effect of electric heat, that is, the electric load is converted into a thermal load, and its energy conversion is achieved through air conditioners, electric water heaters, electric heaters and other equipment. The user's substitution behavior is mainly affected by the relative prices of electricity and thermal energy and energy usage habits. It is assumed that the community is accustomed to using the community comprehensive energy system for heating in winter, but when the price difference between electricity and heat reaches a certain level, the heat demand will be met by converting the electric load into a thermal load. The concept of energy substitution rate is introduced to characterize the proportion of substitution load. The mathematical model is: ; Where: represents the difference in electricity prices; represents the load substitution rate function, is the maximum value of the function, is the intermediate value of the function, is the upward translation function.

[0018] In step S4, in order to improve the accuracy of the model, probability constraints are imposed through user response randomness and optimistic response membership. When the price difference between electricity and heat is in the dead zone, the user response behavior is highly random. In order to describe the load substitution rate in this area, the average value of the two prediction curves is used to determine it. With the increase of the price difference between electricity and heat, when the price difference between electricity and heat is in the response zone, the degree of user substitution is obviously a large semi-trapezoidal membership function as the probability constraint of demand response. However, the price difference between electricity and heat continues to increase and reaches the saturation zone. The two curves merge and the substitution rate is expressed by the maximum substitution rate.

[0019] ; ; Where: represents the load transfer rate; and are optimistic and pessimistic prediction results respectively; m represents the optimistic response membership. The mathematical model of the electric heating load substitution behavior is as follows: ; ; In the formula, , and , They represent the electric load and heat load in time period t before and after the electric-heat substitution respectively.

[0020] Preferably, under the premise of knowing the relevant parameters of the terminal user's electric heat substitution rate curve, the electric heat substitution rate of the replaceable load corresponding to the electric heat price difference in each section can be calculated according to the above formula. In order to enhance the degree of substitution of replaceable loads and increase incentive policies, users who participate in the replacement load demand response will be incentivized by electricity price subsidies. For these users, the increased electricity consumption by air conditioning for heating will be subject to targeted electricity price subsidies. The heat load expression after the incentive policy is adopted is: ; In the formula, , They represent the heat load in the period t before and after the response, and the heat load after the theoretical excitation response.

[0021] Furthermore, in step 4, the optimization goal is to minimize the daily cost of the community integrated energy system, and the objective function of minimizing the daily cost includes interaction, fuel, operation and maintenance, and environmental governance; the constraint condition of the community integrated energy system model is that its decision variables must meet the functional requirements at the peak of heat and load and the operation constraints of its own equipment; Taking the minimum daily cost of the community integrated energy system as the optimization goal, the objective function is: ; Where: represents the objective function of the operation cost of the community integrated energy system; , , , are the interaction, fuel, penalty, and subsidy costs in period t respectively; (1) Interaction cost ; Where: Indicates the interaction unit price; represents the interaction power; (2) Fuel costs ; Where: I represents the total number of devices; represents the output power of device i in time period t; represents the efficiency of equipment i; (3) Penalty costs ; Where: represents the unit penalty cost; represents the predicted amount of renewable energy power generation during period t, It represents the grid-connected power of renewable energy in period t; represents the penalty cost in period t.

[0022] (4) Subsidy costs ; Where: represents the subsidy price of unit load during period t; It represents the replacement amount of electric load in period t; Furthermore, in step S4, the constraints that meet the functional requirements and the device itself are: (1) Energy balance constraints ; ; ; (2) Energy storage equipment constraints ; ; ; Where: and All variables are 0 or 1 (3) Unit constraints ; ; Where: , , and The upper and lower limits of the equipment's output and climbing grade (4) Interaction power constraints ; Where: , Indicates the upper and lower limits of the interaction power.

[0023] Furthermore, in step S5, the CPLEX solver is used to solve the optimal scheduling model of the integrated energy system, including optimizing the optimal scheduling model through the CPLEX solver, simulating the optimization curve of the electric and thermal loads in various scenarios, and then modeling the community integrated energy system, introducing the carbon trading mechanism, and establishing constraints for the safe operation of the community integrated energy system. Finally, the objective function of the community integrated energy system is determined, and various types of operating costs are defined. The constraints are input into the CPLEX solver for solution, and finally the operating cost of the community integrated energy system, the equipment output, and the optimization results of the community integrated system are obtained.

[0024] Compared with the prior art, the beneficial effects of the present invention include: 1) The present invention studies the load characteristics of the community integrated energy system, analyzes the alternative energy of actual energy users, and optimizes the user load curve based on the user's demand response. On the basis of considering the environmental pollution cost, it realizes the optimal operation of the integrated energy system and increases the consumption of new energy.

[0025] 2) The present invention analyzes and studies the alternative energy sources of actual users, divides the user load into transferable, reducible and replaceable loads, further optimizes the load curve based on the replaceable load, and uses the Logtic regression curve to describe the user's replacement behavior, and analyzes the impact of incentive policies on demand response.

[0026] 3) The proposed electric-hydrogen coupling unit consisting of an electrolyzer and a fuel cell can adjust the equipment output in real time according to changes in load, effectively improving the flexibility of the system, promoting the consumption of new energy, and improving the economic efficiency of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0028] Figure 1 A structural diagram of a community integrated energy system according to an embodiment of the present invention; Figure 2 A demand response model for an embodiment of the present invention; Figure 3 This is a schematic diagram of the electrolytic cell state transformation principle of an embodiment of the present invention; Figure 4 A day-ahead forecast data diagram of a community integrated energy system according to an embodiment of the present invention; Figure 5 This is a heat load output diagram of a basic cogeneration system according to an embodiment of the present invention; Figure 6 This is an electric load output diagram of a basic cogeneration system according to an embodiment of the present invention; Figure 7 This is a heat load output diagram of an embodiment of the present invention; Figure 8 This is an electric load output diagram of an embodiment of the present invention; Fig. 9 This is a gas load output diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The community integrated energy system optimization dispatching method considering load characteristics includes the following steps: S1: Establish a physical model of the community integrated energy system and consider the uncertainty of demand response; S2: Establish an electrolyzer model that takes into account the load characteristics and the corresponding hydrogen energy conversion model, and establish the model and operation mode of the cogeneration equipment; S3: Determine the user substitution effect based on consumer psychology theory, determine the alternative demand response and consider the transferable and reducible loads, and draw the electricity and heat load curves on the user side; S4: Taking the lowest daily cost of the community integrated energy system as the optimization goal, and adding constraints to meet functional requirements and its own equipment, combined with the electricity and heat load curves on the user side, establish the optimal scheduling model of the community integrated energy system; S5: Based on step S3, the electricity and heat load curves on the user side are obtained, and the optimization target of step S4 is used as the objective function to solve the optimal scheduling model of the integrated energy system. The scheduling results are obtained using CPLEX, and the impact of the adopted model and mechanism on the integrated energy system is analyzed.

[0030] In step S1, the community comprehensive energy system established in step S1 includes a wind turbine, a photovoltaic, an energy storage device, a gas turbine, a bromide refrigerator, an air source heat pump, a methane reactor, a fuel cell, and an electrolyzer; Among them, wind turbines and photovoltaic units provide electricity, and the community comprehensive energy system makes up for the energy loss through the main grid and gas grid; the electric-hydrogen coupling unit composed of EL and HFC reduces the energy cascade loss of the community comprehensive energy system and improves the comprehensive utilization rate. The HP device converts electrical energy into thermal energy, and the cogeneration system composed of gas turbines and bromide refrigeration machines decouples heat and electricity to improve the electricity-to-heat output ratio.

[0031] In step S2, the gas turbine, bromide refrigeration machine, heat pump, methane reactor, fuel cell and electrolyzer operated by the cogeneration system are respectively: (1) The calculation formula for the heating power of the gas turbine is: ; ; In the formula , They represent the power generation and heating power of the gas turbine during period t, , They represent the power generation efficiency and waste heat loss rate of the gas turbine respectively. represents the heating power of the gas turbine during period t, , They represent the waste heat recovery rate and heating efficiency of the gas turbine respectively, and GT represents gas turbine.

[0032] (2) The calculation formula for heat pump heating power is: ; Where: represents the heating power of the heat pump during period t, represents the input power of the heat pump during period t, It represents the heating coefficient, and HP represents the heat pump; (3) The fuel cell adjusts its own thermal-to-electric ratio by changing its operating state, which is expressed as: ; Where: , and for Hydrogen consumption, electricity generation and heat generation at each moment; is the energy conversion efficiency of the fuel cell, HFC means Fuel cells; (4) The methane reactor converts hydrogen into natural gas and supplies it for heat and electricity generation. The expression is: ; Where: and for Hydrogen consumption and gas production at all times; represents the hydrogen-gas conversion efficiency of the methane reactor, MT Represents a methane reactor.

[0033] (5) The calculation formula for the electric-hydrogen conversion relationship of the electrolyzer equipment is: ; In the formula, , Indicates The electrolytic cell Hydrogen production power and electricity consumption in the time period; It represents the electricity-to-hydrogen conversion efficiency of the electrolyzer; Indicates the electrical power required when the electrolyzer is in cold standby state; Indicates the penalty coefficient for the electrolyzer being in cold standby; binary variable is a control variable. When it is 1, it indicates that the electrolyzer switches from cold standby to working state. PEM represents an electrolytic cell; The electrolytic cell has three states: shutdown, cold standby, and working. , To indicate shutdown and cold standby status; the working status can be divided into three states: low load, variable load and overload, and only the working state produces hydrogen. , , To describe, the above variables are all binary variables.

[0034] Shutdown state: The electrolyzer can be shut down quickly at any time.

[0035] Cold standby state: The electrolyzer is in standby mode at low power to maintain the operation of the control and antifreeze units.

[0036] Working state: In order to ensure the safety of hydrogen production by electrolyzer, it should be in variable load state most of the time. The electrolyzer should not be overloaded or underloaded for too long.

[0037] (1) The control variable constraints are: ; In the formula, the control variable represents the state of the nth electrolytic cell at time t, . , , , represents the state of the nth electrolytic cell at time t; (2) The coordinated control expression between different states of the electrolytic cell is: ; Where: is the rated power of the electrolyzer; (3) The logical constraint expression of the state transition of the electrolytic cell is: ; ; ; ; ; In the formula: the above formula is start-stop, start interval, operation state mutual exclusion constraint, overload and underload maximum time limit constraint; and The longest operation time of the electrolytic cell in overload and underload state; (4) The shutdown and cold standby time constraints are: ; ; Where: , They are the shortest time that the electrolyzer can operate continuously in shutdown and cold standby states respectively. In step S2, the cogeneration system includes four parts: energy supply, energy coupling, energy storage, and energy consumption unit. Wind turbines and photovoltaic units provide electricity, while the community comprehensive energy system makes up for the energy loss through the main grid and gas grid; the electricity-hydrogen coupling unit can reduce the energy cascade loss of the community comprehensive energy system and improve the comprehensive utilization rate; the heat pump device can convert electricity into heat energy, helping the cogeneration system composed of gas turbines and bromide refrigeration machines to achieve heat-electricity decoupling and improve the system's electricity-heat output ratio; at the same time, the multi-energy storage equipment and the comprehensive demand response of residents will provide guarantees for the flexibility of the system.

[0038] In step S3, energy storage is an important part of the comprehensive energy system. During the charging and discharging process in the energy storage link, there is loss and the characteristic of dissipation over time. The mathematical model is: ; ; Where: represents the power of the energy storage device in period t, and Respectively represent the input and output power of energy storage, Indicates the remaining capacity of energy storage, represents the loss coefficient, and Represents the energy conversion rate.

[0039] In step S3, the analysis of residents' demand response is to analyze the user load based on the Logtic regression curve to describe the user's substitution behavior. The user load is divided into basic load, curtailable load, transferable load and replaceable load. The response degree of different types of loads will be different under the same price signal.

[0040] (1) The expression for load reduction and transfer is: ; Where: is the initial load that can be reduced and transferred at time t; is the load price demand elasticity matrix, represents the electricity price at time j; Indicates the actual amount of transferred load; Indicates the electricity price at the initial time (2) Substitution load, a fuzzy demand response mechanism based on the Logistic function is introduced to describe the user substitution behavior. When the stimulus level is lower than the threshold, the user will not respond, and this area is the dead zone. When the stimulus reaches the threshold, the user will respond, and as the stimulus level increases, the degree of response continues to increase. However, when the stimulus reaches a certain level, the user's response level will no longer change and enter the saturation zone. The user's load substitution behavior only considers the substitution effect of electric heat, that is, the electric load is converted into a thermal load, and its energy conversion is achieved through air conditioners, electric water heaters, electric heaters and other equipment. The user's substitution behavior is mainly affected by the relative prices of electricity and thermal energy and energy usage habits. It is assumed that the community is accustomed to using the community comprehensive energy system for heating in winter, but when the price difference between electricity and heat reaches a certain level, the heat demand will be met by converting the electric load into a thermal load. The concept of energy substitution rate is introduced to characterize the proportion of substitution load. The mathematical model is: ; Where: represents the difference in electricity prices; represents the load substitution rate function, is the maximum value of the function, is the intermediate value of the function, is the upward translation function.

[0041] In step S4, in order to improve the accuracy of the model, probability constraints are imposed through user response randomness and optimistic response membership. When the price difference between electricity and heat is in the dead zone, the user response behavior is highly random. In order to describe the load substitution rate in this area, the average value of the two prediction curves is used to determine it. With the increase of the price difference between electricity and heat, when the price difference between electricity and heat is in the response zone, the degree of user substitution is obviously a large semi-trapezoidal membership function as the probability constraint of demand response. However, the price difference between electricity and heat continues to increase and reaches the saturation zone. The two curves merge and the substitution rate is expressed by the maximum substitution rate.

[0042] ; ; Where: represents the load transfer rate; and are optimistic and pessimistic prediction results respectively; m represents the optimistic response membership. The mathematical model of the electric heating load substitution behavior is as follows: ; ; In the formula, , and , They represent the electric load and heat load in time period t before and after the electric-heat substitution respectively.

[0043] Under the premise that the relevant parameters of the terminal user's electric heat substitution rate curve are known, the electric heat substitution rate of the replaceable load corresponding to the electric heat price difference in each section can be calculated according to the above formula. In order to enhance the degree of substitution of replaceable loads and increase incentive policies, users who participate in the demand response of alternative loads will be incentivized by electricity price subsidies. For these users, the increased electricity consumption by air conditioning for heating will be subject to targeted electricity price subsidies. The heat load expression after the incentive policy is adopted is: ; In the formula, , They represent the heat load in the period t before and after the response, and the heat load after the theoretical excitation response.

[0044] In step S4, the daily cost of the community integrated energy system is minimized as the optimization goal, and the objective function of the daily cost minimum includes interaction, fuel, operation and maintenance, and environmental governance; the constraint condition of the community integrated energy system model is that its decision variables must meet the functional requirements at the peak of heat and load and the operation constraints of its own equipment; Taking the minimum daily cost of the community integrated energy system as the optimization goal, the objective function is: ; Where: represents the objective function of the operation cost of the community integrated energy system; , , , are the interaction, fuel, penalty, and subsidy costs in period t respectively; (1) Interaction cost ; Where: Indicates the interaction unit price; represents the interaction power; (2) Fuel costs ; Where: I represents the total number of devices; represents the output power of device i in time period t; represents the efficiency of equipment i; (3) Penalty costs ; Where: represents the unit penalty cost; represents the predicted amount of renewable energy power generation during period t, It represents the grid-connected power of renewable energy in period t; represents the penalty cost in period t.

[0045] (4) Subsidy costs ; Where: represents the subsidy price of unit load during period t; It represents the replacement amount of electric load in period t; In step S4, the constraints that meet the functional requirements and the device itself are: (1) Energy balance constraints ; ; ; (2) Energy storage equipment constraints ; ; ; Where: and All variables are 0 or 1 (3) Unit constraints ; ; Where: , , and The upper and lower limits of the equipment's output and climbing grade (4) Interaction power constraints ; Where: , Indicates the upper and lower limits of the interaction power.

[0046] like Figure 2 As shown, in step S5, the CPLEX solver is used to solve the optimal scheduling model of the integrated energy system, specifically including: optimizing and calculating the optimal scheduling model through the CPLEX solver, simulating the optimization curve of the electric and thermal loads in various scenarios, then modeling the community integrated energy system, introducing the carbon trading mechanism, establishing the constraints under the safe operation of the community integrated energy system, and finally determining the objective function of the community integrated energy system, defining various types of operating costs, and inputting the constraints into the CPLEX solver for solving, and finally obtaining the operating cost of the community integrated energy system, the equipment output and the optimization results of the community integrated system.

[0047] Case analysis: Establish Figure 1 For the community integrated energy system shown, the upper limit of the interactive power is corrected to 200KWH. Figure 4 Provides forecast data for the community’s electricity and heat loads, as well as photovoltaic and power generation data for the community’s integrated energy system.

[0048] Experimental analysis The basic electric heating system and the equipment considering demand response and power-to-hydrogen conversion are selected for comparison. The output comparison results are shown in the figure below. Figure 5-Figure 9 shown.

[0049] Depend on Figure 6As shown in the figure, during the low electricity price period, the 1-8 and 24 systems have extremely serious wind abandonment phenomenon. For this reason, the heat pump is used first for heating, and then the heat storage tank and the cogeneration unit are used for heating. In order to improve this phenomenon, the alternative load response mechanism proposed in this paper is introduced to reduce the heat load demand during the low electricity price period. At the same time, the electric-to-hydrogen conversion considering the load variation characteristics proposed in this paper is added to convert the excess electricity into hydrogen. Part of the hydrogen is directly supplied to the fuel cell, so that part of the heat energy is supplied by the fuel cell, further reducing the heat production of the cogeneration and reducing the occurrence of wind abandonment. At the same time, due to the large hydrogen production of the electrolyzer, the excess hydrogen is converted into natural gas by the methane generator and supplied to the cogeneration unit, reducing the energy purchase cost. When the electricity price is 9-16 and 23, the electricity purchase / sale point price of the system is higher than that of the gas turbine. The gas turbine is used to generate as much as possible under the premise of heat-based electricity to facilitate the sale of electricity to the power grid. The heat load is provided by the cogeneration, fuel cell and heat storage tank. The heat pump is gradually shut down and provides part of the heat load under the premise of meeting the ramp constraint. Similarly, the amount of power wasted during this period is small, but the methane generator and fuel cell are both at high levels. In order to meet their ramp constraints, the electrolyzer gradually reduces hydrogen production to cooperate with their operation. During the peak electricity price period of 17-22, the system purchase / sale point electricity price is higher than that of the gas turbine. Figure 6 It can be seen that under the effect of price-based demand response, the system's load is reduced during peak hours and transferred to valley hours, which reduces the load demand during this period and allows the system to sell more electricity to the power grid. In order to generate more electricity during this period, a heat storage tank is used to store heat, so that the cogeneration unit can generate as much electricity as possible under the premise of meeting the heat-to-electricity requirement. At the same time, due to the limited interactive power, when the amount of electricity generated by the unit is greater than the interactive limit, waste will occur. Therefore, a heat pump is introduced at some times to achieve thermal-electric decoupling to avoid waste and reduce IES operating costs.

[0050] Based on the theory of consumer psychology, this paper studies the residential users' electric-heat substitution behavior, and uses the Logtic regression curve to describe the uncertainty of their substitution behavior. On this basis, it introduces the electric hydrogen production equipment that considers the variable load start-stop characteristics, and considers the price-based demand response and establishes an incentive policy based on the substitution behavior of community residents, and constructs a community comprehensive energy system based on user demand response. Through the analysis of the example, the main conclusions are as follows: The electric-hydrogen coupling unit composed of an electrolyzer and a fuel cell proposed in the present invention can adjust the output of the equipment in real time according to the change of load, effectively improve the flexibility of the system, promote the consumption of new energy, and improve the economic efficiency of system operation.

[0051] Users with multi-energy complementary behaviors can reduce energy purchase costs through substitution behaviors to meet their own needs. For integrated energy systems, this characteristic of users can be utilized to formulate reasonable incentive policies to influence users' substitution behaviors, thereby achieving the goal of reducing system operating costs and increasing renewable energy consumption.

Claims

1. A community integrated energy system optimization scheduling method considering load characteristics, characterized by: The following steps are involved: S1: Establish a physical model of the community integrated energy system and consider the uncertainty of demand response; S2: Establish an electrolyzer model that takes into account the load characteristics and the corresponding hydrogen energy conversion model, and establish the model and operation mode of the cogeneration equipment; S3: Determine the user substitution effect based on consumer psychology theory, determine the alternative demand response and consider the transferable and reducible loads, and draw the electricity and heat load curves on the user side; S4: Taking the lowest daily cost of the community integrated energy system as the optimization goal, and adding constraints to meet functional requirements and its own equipment, combined with the electricity and heat load curves on the user side, establish the optimal scheduling model of the community integrated energy system; S5: Based on the electricity and heat load curves on the user side obtained in step 3, the optimization target of step 4 is used as the objective function to solve the optimal scheduling model of the integrated energy system, and CPLEX is used to obtain the scheduling results, and the impact of the adopted model and mechanism on the integrated energy system is analyzed.

2. The community integrated energy system optimization scheduling method considering load characteristics according to claim 1 is characterized in that: The community comprehensive energy system established in step S1 includes wind turbines, photovoltaics, energy storage equipment, gas turbines, bromide refrigeration machines, air source heat pumps, methane reactors, fuel cells and electrolyzers; the gas turbine converts thermal energy into electrical energy by burning natural gas to supply electrical loads; the air source heat pump converts electrical energy into thermal energy as a supplement to the cogeneration system.

3. The community integrated energy system optimization scheduling method considering load characteristics according to claim 2 is characterized by: In step S2, the cogeneration system includes a gas turbine, a bromide refrigeration machine, a heat pump, a methane reactor, a fuel cell and an electrolyzer; (1) The calculation formula for the heating power of the gas turbine is: ； ； In the formula, , They represent the power generation and heating power of the bromide refrigeration machine during period t, , They represent the power generation efficiency and waste heat loss rate of the gas turbine respectively. It represents the heating power of the bromide refrigeration mechanism during period t, , They represent the waste heat recovery rate and heating efficiency of the bromide refrigeration machine respectively. h Indicates heating; (2) The operation of the heat pump optimization system reduces the operating cost. The calculation formula is: ； In the formula, represents the heating power of the heat pump during period t, represents the input power of the heat pump during period t, Indicates the heating coefficient; (3) The fuel cell adjusts its own thermal-to-electric ratio by changing its operating state. The calculation formula is: ； Where: , and Respectively Hydrogen consumption, electricity generation and heat generation at each moment; is the energy conversion efficiency of HFC; (4) The methane generator converts hydrogen into natural gas and supplies it to the system to generate heat and electricity. The calculation formula is: ； In the formula, and Respectively Hydrogen consumption and gas production at all times; represents the hydrogen-to-gas conversion efficiency of the methane generator; (5) The calculation formula for the electric-hydrogen conversion relationship of the electrolyzer equipment is: ； In the formula, , Indicates The electrolytic cell Hydrogen production power and electricity consumption in the time period; It represents the electricity-to-hydrogen conversion efficiency of the electrolyzer; Indicates the electrical power required when the electrolyzer is in cold standby state; Indicates the penalty coefficient for the electrolyzer being in cold standby; binary variable It is a control variable. When it is 1, it means that the electrolytic cell switches from cold standby to working state.

4. The community integrated energy system optimization scheduling method considering load characteristics according to claim 2 is characterized by: In step S2, the electrolytic cell equipment includes three states: shutdown, cold standby, and working, and the constraints are: (1) The control variable constraints are: ； In the formula, the control variable represents the state of the nth electrolytic cell at time t, . , , , Indicates whether the state of the nth electrolytic cell at time t is in this state; (2) The coordinated control expression between different states of the electrolytic cell is: ； Where: is the rated power of the electrolyzer; (3) The logical constraint expression of PEM state transition is: ； ； ； ； ； In the formula: the above formula is start-stop, start interval, operation state mutual exclusion constraint, overload and underload maximum time limit constraint; and The longest operation time of the electrolytic cell in overload and underload state; (4) The shutdown and cold standby time constraints are: ； ； Where: , They are the shortest time that the electrolyzer can operate continuously in shutdown and cold standby states respectively.

5. The community integrated energy system optimization scheduling method considering load characteristics according to claim 3 is characterized by: In step S2, the cogeneration system includes four links: energy supply, energy coupling, energy storage, and energy consumption unit. The energy supply link has losses and has the characteristics of dissipating over time.

6. The community integrated energy system optimization scheduling method considering load characteristics according to claim 4 is characterized by: In step S3, the user demand response is analyzed by using the Logtic regression curve to analyze the replacement behavior of the user load. The user load is divided into basic load, curtailable load, transferable load and replaceable load. The calculation formula is: (1) The expression for load reduction and transfer is: ； Where: is the initial load that can be reduced and transferred at time t, is the load price demand elasticity matrix, represents the electricity price at time j, Indicates the actual transfer load. represents the electricity price at the initial moment; (2) The alternative load expression is: ； Where: represents the electricity price difference, represents the load substitution rate function, is the maximum value of the function, is the intermediate value of the function, is the upward translation function.

7. The community integrated energy system optimization scheduling method considering load characteristics according to claim 5 is characterized in that: In step S4, the daily cost of the community integrated energy system is minimized as the optimization goal, and the objective function of the daily cost minimum includes interaction, fuel, penalty, and subsidy; the constraint condition of the community integrated energy system model is that its decision variables must meet the functional requirements at the peak of heat and load and the operation constraints of its own equipment; the objective function of the daily cost minimum of the community integrated energy system is: ； Where: represents the objective function of the operation cost of the community integrated energy system, , , , are the interaction, fuel, penalty, and incentive load subsidy costs in period t respectively; (1) Fuel costs ； Where: I represents the total number of devices, represents the output power of the ith device in time period t, represents the efficiency of the i-th device; (2) Interaction cost ； Where: Indicates the interaction unit price; represents the interaction power; (3) Penalty costs ； In the formula, represents the unit penalty cost; represents the predicted amount of renewable energy power generation in period t, Indicates the grid-connected power of renewable energy during period t; represents the penalty cost during period t; (4) Subsidy costs ； Where: represents the subsidy price per unit load during period t, Represents the replacement amount of electric load in period t.

8. The community integrated energy system optimization scheduling method considering load characteristics according to claim 6 is characterized by: In step S4, the constraints of satisfying the functional requirements and the device itself are: (1) Energy balance constraints ； ； ； (2) Energy storage equipment constraints ； ； ； Where: and All are 0 and 1 variables; (3) Unit constraints ； ； Where: , , and The upper and lower limits of the equipment's output and climbing grade; (4) Interaction power constraints ； Where: , Indicates the upper and lower limits of the interaction power.

9. The method for planning an integrated energy system considering the variable operating characteristics of equipment according to claim 7 is characterized in that: In step S5, the CPLEX solver is used to solve the optimal scheduling model of the integrated energy system, including optimizing the optimal scheduling model through the CPLEX solver, simulating the optimization curve of the electric and thermal loads under various scenarios, and then modeling the community integrated energy system, introducing the carbon trading mechanism, and establishing constraints for the safe operation of the community integrated energy system. Finally, the objective function of the community integrated energy system is determined, and various types of operating costs are defined. The constraints are input into the CPLEX solver for solution to obtain the optimization result of the community integrated energy system.

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