阅读说明：本技术 一种含水源热泵的区域综合能源系统低碳优化运行方法 (Low-carbon optimized operation method for regional comprehensive energy system containing water source heat pump ) 是由 周博文 徐恒志 杨东升 李广地 金硕巍 罗艳红 王迎春 闫士杰 于 2021-08-30 设计创作，主要内容包括：本发明的一种含水源热泵的区域综合能源系统低碳优化运行方法,包括：构建包含水源热泵、风机、燃气机组以及多元储能装置的区域综合能源系统总体架构；计算系统运行经济成本和碳排放成本,构建两阶段碳排放模型,提出两个优化目标：购能成本最小和碳排放成本最小；建立各设备机组运性约束以及系统中冷、热、电、气能量平衡约束,形成多目标低碳优化模型；采用NSGA-Ⅲ算法对低碳优化模型进行求解,得到系统最优运行策略。本发明引入水源热泵机组以多元储能装置,以解决“以热定电”的运行约束、缓解供需两侧热电比差异；建立各设备碳排放模型,进而实现系统的低碳优化运行。(The invention discloses a low-carbon optimized operation method of a regional comprehensive energy system of a water source heat pump, which comprises the following steps: constructing a regional comprehensive energy system overall framework comprising a water source heat pump, a fan, a gas unit and a multi-element energy storage device; calculating the economic cost and the carbon emission cost of system operation, constructing a two-stage carbon emission model, and proposing two optimization targets: the energy purchase cost and the carbon emission cost are minimum; establishing the operability constraint of each equipment unit and the cold, heat, electricity and gas energy balance constraint in the system to form a multi-objective low-carbon optimization model; and solving the low-carbon optimization model by adopting an NSGA-III algorithm to obtain an optimal operation strategy of the system. The invention introduces a water source heat pump unit and a multi-element energy storage device to solve the operation restriction of 'fixing power by heat' and relieve the difference of thermoelectric ratios at the two sides of supply and demand; and establishing a carbon emission model of each device, and further realizing low-carbon optimized operation of the system.)

1. A low-carbon optimized operation method of a regional comprehensive energy system containing a water source heat pump is characterized by comprising the following steps:

step 1: constructing a regional comprehensive energy system overall framework comprising a water source heat pump, a fan, a gas unit and a multi-element energy storage device;

step 2: calculating the economic cost and the carbon emission cost of system operation, constructing a two-stage carbon emission model, and proposing two optimization targets: the energy purchase cost and the carbon emission cost are minimum;

and step 3: establishing the operability constraint of each equipment unit and the cold, heat, electricity and gas energy balance constraint in the system to form a multi-objective low-carbon optimization model;

and 4, step 4: and in a python environment, solving the constructed low-carbon optimization model by adopting an NSGA-III algorithm to obtain an optimal operation strategy of the system.

2. The regional integrated energy system low-carbon optimal operation method of the water source heat pump according to claim 1, wherein the step 1 comprises the following steps:

step 101: determining the energy conversion relation of various equipment units, and establishing operation mathematical models of a water source heat pump, a fan, a gas unit and a multi-element energy storage device;

1) water source heat pump mathematical model:

in the formula:respectively supplying heat and cold to the water source heat pump in a time period t; p_t ^WHPThe input electric power of the water source heat pump is t time period; eta_H,t、η_C,tRespectively the heat supply and refrigeration scheduling coefficients of the water source heat pump in the time period t; COP_HHeat supply coefficient for water source heat pump; COP_CThe water source heat pump refrigeration performance coefficient;

2) the mathematical model of the fan is as follows:

in the formula:for prediction of fan in t periodForce is exerted; v is the real-time wind speed; v. of_iTo cut into the wind speed; v. of_oCutting out the wind speed; v. of_eRated wind speed; p_e ^WTRated power for the fan;

3) the mathematical model of the gas turbine unit is as follows:

in the formula:the natural gas amount consumed by the gas unit in the time period t;the heat production quantity of the gas turbine set in the time period t;the heat lost by the gas turbine set in the time period t; p_t ^GUGenerating power of the gas turbine set in a time period t;

in the formula: a. b is the conversion coefficient of the gas turbine generator set, which can be measured in the fuel consumption curve of the gas turbine generator set;the power generation efficiency of the gas turbine set;the value is 0, the shutdown is carried out, the value is 1, and the operation state is used for representing the operation state of the gas turbine unit;

in the formula:the heating efficiency in the gas unit;

4) mathematical model of absorption refrigerator:

in the formula:absorbing the heating capacity of the refrigerator for a period t;absorbing the heat absorption power of the refrigerator for a period t;is the energy conversion coefficient of the absorption refrigerator;

5) mathematical model of electric refrigerator:

in the formula:the heating capacity of the electric refrigerator is t time period;input electric power of the electric refrigerator for a period t;is the energy conversion coefficient of the electric refrigerator;

6) mathematical model of thermal energy storage device

In the formula:the heat storage amounts of the thermal energy storage device in the t +1 time period and the t time period are respectively;is the natural heat loss coefficient of the thermal energy storage device;the heat storage power and the heat release power of the heat energy storage device in the time period t are respectively;the heat storage efficiency and the heat release efficiency of the heat energy storage device are achieved.

7) Mathematical model of electric energy storage device

In the formula:the electric energy storage quantity of the electric energy storage device is respectively in the t +1 time period and the t time period;is the natural electrical loss coefficient of the electrical energy storage device;charging and discharging power of the electric energy storage device in a time period t;charging and discharging efficiency of the electric energy storage device;

102, constructing a regional comprehensive energy system overall architecture based on the concept of an energy hub: the system is totally input from an external power grid, an external air grid and a fan for power generation, and is correspondingly connected with four buses of electricity, heat, cold and air in the system with four loads of electricity, heat, cold and air;

step 103: and (4) correspondingly connecting the input end and the output end of each equipment unit to four buses of electricity, heat, cold and gas in the system according to the model established in the step (101), and coupling to form the overall architecture of the regional comprehensive energy system.

3. The regional integrated energy system low-carbon optimal operation method of the water source heat pump according to claim 1, wherein the step 2 comprises the following steps:

step 201, constructing a carbon emission model of power consumption equipment and electric load:

in the formula:the total carbon emission of power consumption equipment and electric load;the electric load power required by a user in the system in a period t;the conversion coefficient from electric energy to standard coal; omega_tThe carbon emission coefficient of the standard coal;

step 202, constructing a two-stage carbon emission model of the gas equipment and the gas load:

1) the first stage is as follows:

in the formula:the total carbon emission of the first stage gas equipment and gas load;the total amount of natural gas required by users in the system in the period t;the conversion coefficient from natural gas to standard coal;

2) and a second stage:

in the formula:the total carbon emission of the second stage gas equipment;the carbon emission is the carbon emission generated by unit output in the operation process of the gas unit;

step 203, building multiple optimization objectives, including minimum energy purchase cost and minimum carbon emission cost:

target 1:

in the formula: c_epTotal cost of energy purchased for the system;the electricity purchasing cost for purchasing electricity from the power grid for the system in the time period t;a gas purchase cost for gas purchase from the gas network for the rees at time t;

in the formula:purchasing power for purchasing power from the power grid for the system in the period t;selling electricity price for the power grid in the period t;total gas purchasing amount for purchasing gas from the gas network in the time period t;the first, second and third step gas selling prices of the gas network are respectively;respectively the first, second and third step air consumption;

target 2:

in the formula: c_emThe total cost of carbon emission for the system;in order to ensure that the carbon emission cost of the system in two stages is increased when the system is operated in the period t, as the cost coefficient of unit carbon emission is increased along with the increase of energy consumption,can be expressed by segmented linearization as:

in the formula: s_{c_i}I is 1, 2 and 3) is a cost coefficient of unit carbon emission of each section after piecewise linearization; l is_{c_i}I is 1, 2 and 3) is the carbon emission of each section after the piecewise linearization; i is_act、I_strActual carbon emission intensity and specified carbon emission intensity respectively; s_pfIs the carbon emission cost coefficient of the fuel gas.

4. The regional integrated energy system low-carbon optimal operation method of the water source heat pump according to claim 2, wherein the step 3 comprises the following steps:

step 301: establishing the motion constraint of each equipment unit:

1) the water source heat pump unit is restricted in the transportation:

in the formula:respectively the minimum input power and the maximum input power of each time period of the water source heat pump;

2) and (3) restricting the operation of the fan:

in the formula: p_t ^WTThe actual output of the fan in the t time period;

3) gas turbine unit operation constraints

In the formula:minimum and maximum output values specified for the gas turbine set respectively;the climbing rate of the gas turbine set.

4) Energy storage device transport restriction

In the formula:the maximum output power of the electrical/thermal energy storage device;maximum input power for the electrical/thermal energy storage device;the maximum installed capacities of the electric energy storage device and the thermal energy storage device are respectively set; beta is a_min、α_minThe minimum residual state coefficients of the electric energy storage device and the thermal energy storage device are respectively; beta is a_max、α_maxRespectively electric energy storage and heat energy storage deviceSetting a minimum residual state coefficient;

step 302, establishing the energy balance constraints of electricity, heat, cold and gas in the system:

1) electric energy balance constraint:

2) and (3) heat energy balance constraint:

in the formula:the thermal load power required by users in the system in the period t;

3) and (3) gas quantity balance constraint:

4) cold energy balance constraint:

in the formula:the total amount of cooling load required by the user in the system during the period t.

5. The regional integrated energy system low-carbon optimal operation method of the water source heat pump according to claim 2, wherein the step 4 comprises the following steps:

step 401, writing the built low-carbon optimization model of the regional comprehensive energy system in a python3.8 environment;

and step 402, solving the model by adopting an NSGA-III algorithm to obtain an optimal operation strategy of the system.

6. The method for low-carbon optimized operation of the regional integrated energy system with the water source heat pump according to claim 5, wherein the step 401 comprises:

step 501, setting initial parameters of the NSGA-III algorithm, including iteration times, population size, number of objective functions, cross parameters, variation parameters, cross probability and variation probability;

step 502, setting the number of consistent reference points according to the size of the population, and generating all the reference points to ensure the diversity of the solution;

step 503, generating an initial population, calculating an initial fitness value of each individual according to a set optimization target, and obtaining an initial ideal point;

step 504, ensuring the high efficiency of the genetic operator by simulating binary intersection and polynomial variation, then generating offspring individuals, calculating the fitness value of the offspring individuals and updating the ideal points;

step 505, giving the number of new generation population, dividing the population composed of parents and offspring according to a non-dominant layer, and preferentially entering the next generation by individuals with high dominant level;

step 506, judging whether the number of the population exceeds the set population number after all the individuals with a certain allocation grade enter the next generation, if not, all the individuals with the current allocation grade enter the next generation; if so, selecting proper individuals from the individuals at the domination level one by one according to the number of the reference point associated individuals and the distance from the reference point to enter the next generation until the number of the new generation of population meets the previous setting, thereby forming a new generation of population;

step 507, judging whether the current iteration times reach the set iteration times, if not, returning to the step 504; if yes, go to step 508;

step 508, obtaining a Pareto front of the optimal solution of the secondary multi-objective optimization model according to the finally obtained population individuals and the fitness value;

step 509, setting weight coefficients of the target 1 and the target 2, calculating and comparing weighted comprehensive costs of solutions in Pareto frontier, and obtaining an optimal operation strategy, a day-ahead supply and demand scheduling plan of various types of energy:

C_total＝α₁C_ep+α₂C_em

in the formula: c_totalWeighting the integrated cost; alpha is alpha₁、α₂The economic cost and the carbon emission cost are weighted coefficients, respectively.

Technical Field

The invention belongs to the field of optimized operation of a comprehensive energy system, and relates to a low-carbon optimized operation method of a regional comprehensive energy system containing a water source heat pump.

Background

With the transformation and upgrade of energy consumption structures, a comprehensive energy system formed by coupling and interconnecting multiple types of energy has received wide attention, research and application with its unique advantages. In regions with abundant river water systems, a water source heat pump unit is introduced to form a novel cogeneration mode, so that complementary conversion among various energy sources such as cold/heat/electricity/gas and the like can be realized, and the clean energy consumption rate of the system can be further improved. At present, most of Combined Cooling Heating and Power (CCHP) in a regional comprehensive energy system takes a gas turbine unit as a core unit, takes natural gas as fuel and has multiple energy output functions of power generation, heat supply and the like. However, the deep coupling of the electric-thermal energy in the system has the operation constraint of 'fixing the electricity by heat'. The current energy system has a single electric-heat conversion direction, cannot realize a good electric-heat decoupling effect, and particularly in winter with large heat load demand, the situation of surplus electric energy capacity is more obvious, so that the peak shaving capacity of the electric energy of a whole regional power grid is reduced, the grid-connected operation of renewable energy sources such as wind and light can be influenced, and the utilization rate of the renewable energy sources is reduced. Meanwhile, the establishment of a carbon emission model in the existing system is insufficient, quantitative analysis is not careful enough, and correlation analysis on a water source heat pump system and a clean energy consumption scene is less.

Disclosure of Invention

In order to solve the technical problems, the invention aims to provide a low-carbon optimized operation method of a regional comprehensive energy system containing a water source heat pump, which introduces a water source heat pump unit and a multi-element energy storage device so as to solve the operation restriction of 'fixing power by heat' and relieve the difference of thermoelectric ratios of two sides of supply and demand; meanwhile, a carbon emission model of each device is established, and low-carbon optimized operation of the system is further realized.

The invention provides a low-carbon optimized operation method of a regional comprehensive energy system containing a water source heat pump, which comprises the following steps:

step 1: constructing a regional comprehensive energy system overall framework comprising a water source heat pump, a fan, a gas unit and a multi-element energy storage device;

step 2: calculating the economic cost and the carbon emission cost of system operation, constructing a two-stage carbon emission model, and proposing two optimization targets: the energy purchase cost and the carbon emission cost are minimum;

and step 3: establishing the operability constraint of each equipment unit and the cold, heat, electricity and gas energy balance constraint in the system to form a multi-objective low-carbon optimization model;

and 4, step 4: and in a python environment, solving the constructed low-carbon optimization model by adopting an NSGA-III algorithm to obtain an optimal operation strategy of the system.

The low-carbon optimized operation method of the regional comprehensive energy system containing the water source heat pump has the following beneficial effects:

1) in the comprehensive energy system with the constraint of 'fixing power with heat', a water source heat pump unit and a multi-element energy storage device are introduced to form a novel operation mode of the regional comprehensive energy system, so that thermoelectric decoupling is realized, the problem of 'fixing power with heat' operation constraint is fundamentally solved, the thermoelectric ratio difference of the supply side and the demand side is effectively improved, and the overflow electric quantity is almost not existed.

2) The carbon emission model of each equipment unit is established more comprehensively and more carefully, a proper market incentive mechanism is formulated, more objects participate in, the carbon emission cost of system operation is effectively reduced, and the low-carbon optimized operation of the system is realized.

3) The examples in the patent intuitively show that the economic cost is reduced by about 50.91 percent in the low-carbon optimized operation method provided by the invention, the carbon emission cost is reduced by about 49.70 percent, and the overall remarkable reduction proves the rationality and the effectiveness of the method provided by the invention.

Drawings

FIG. 1 is a flow chart of a low-carbon optimized operation method of a regional comprehensive energy system of a water source heat pump;

FIG. 2 is a schematic diagram of a regional integrated energy system architecture of a water source heat pump according to an embodiment of the present invention;

FIG. 3 is a flow chart of solving the constructed low-carbon optimization model using the NSGA-III algorithm;

FIG. 4 is a schematic diagram of a day-ahead power dispatching plan of the system under the constraint of 'fixed power by heat';

FIG. 5 is a schematic diagram of a day-ahead thermal energy scheduling plan of the system under the constraint of "determine electricity with heat";

FIG. 6 is a schematic diagram of a day-ahead cold energy scheduling plan of the system under the constraint of 'fixed power with heat';

FIG. 7 is a schematic diagram of a day-ahead gas energy dispatching plan of the system under the constraint of 'fixed power by heat';

FIG. 8 is a schematic diagram of a day-ahead power dispatching plan according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of a thermal energy dispatch plan before the day according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of a day-ahead cooling energy dispatch plan in accordance with an embodiment of the present invention;

FIG. 11 is a schematic diagram of a day-ahead gas energy dispatch plan in accordance with an embodiment of the present invention;

FIG. 12 is a graphical representation of economic cost versus carbon emission cost for two modes.

Detailed Description

As shown in FIG. 1, the low-carbon optimized operation method of the regional comprehensive energy system of the water source heat pump comprises the following steps:

step 1: based on the operation mechanism of each equipment unit, a regional comprehensive energy system overall framework comprising a water source heat pump, a fan, a gas unit and a multi-element energy storage device is constructed, and the method comprises the following steps:

step 101: determining the energy conversion relation of various equipment units, and establishing operation mathematical models of a water source heat pump, a fan, a gas unit and a multi-element energy storage device;

1) water source heat pump mathematical model:

in the formula:respectively supplying heat and cold to the water source heat pump in a time period t; p_t ^WHPThe input electric power of the water source heat pump is t time period; eta_H,t、η_C,tRespectively the heat supply and refrigeration scheduling coefficients of the water source heat pump in the time period t; COP_HHeat supply coefficient for water source heat pump; COP_CThe water source heat pump refrigeration performance coefficient;

wherein, the heat supply and refrigeration performance coefficient of the water source heat pump is calculated according to the following formula:

COP_C＝COP_H-1

in the formula: t is_CWThe temperature of a heat source at the evaporator side; t is_HWIs the condenser side heat source temperature; A. b, C, D, E are fit coefficients calculated in the experiment.

2) The mathematical model of the fan is as follows:

in the formula:the predicted output of the fan in the time period t is obtained; v is the real-time wind speed; v. of_iTo cut into the wind speed; v. of_oCutting out the wind speed; v. of_eRated wind speed; p_e ^WTRated power for the fan;

in specific implementation, the real-time wind speed is calculated according to the following formula:

in the formula: (v) is the wind speed probability density function, v is the wind speed; m is a shape parameter; s is a scale parameter;is the average wind speed; sigma is the standard deviation of wind speed; the Γ function is a gamma function.

3) The mathematical model of the gas turbine unit is as follows:

in the formula:the natural gas amount consumed by the gas unit in the time period t;the heat production quantity of the gas turbine set in the time period t;the heat lost by the gas turbine set in the time period t; p_t ^GUGenerating power of the gas turbine set in a time period t;

in the formula: a. b is the conversion coefficient of the gas turbine generator set, which can be measured in the fuel consumption curve of the gas turbine generator set;the power generation efficiency of the gas turbine set;the value is 0, the shutdown is carried out, the value is 1, and the operation state is used for representing the operation state of the gas turbine unit;

in the formula:the heating efficiency in the gas unit;

4) mathematical model of absorption refrigerator:

in the formula:absorbing the heating capacity of the refrigerator for a period t;absorbing the heat absorption power of the refrigerator for a period t;is the energy conversion coefficient of the absorption refrigerator;

5) mathematical model of electric refrigerator:

in the formula:the heating capacity of the electric refrigerator is t time period;input electric power of the electric refrigerator for a period t;is the energy conversion coefficient of the electric refrigerator;

6) mathematical model of thermal energy storage device

In the formula:the heat storage amounts of the thermal energy storage device in the t +1 time period and the t time period are respectively;is the natural heat loss coefficient of the thermal energy storage device;the heat storage power and the heat release power of the heat energy storage device in the time period t are respectively;the heat storage efficiency and the heat release efficiency of the heat energy storage device are achieved.

7) Mathematical model of electric energy storage device

In the formula:the electric energy storage quantity of the electric energy storage device is respectively in the t +1 time period and the t time period;is the natural electrical loss coefficient of the electrical energy storage device;charging and discharging power of the electric energy storage device in a time period t;charging and discharging efficiency of the electric energy storage device;

102, constructing a regional comprehensive energy system overall architecture based on the concept of an energy hub: the system is totally input from an external power grid, an external air grid and a fan for power generation, and is correspondingly connected with four buses of electricity, heat, cold and air in the system with four loads of electricity, heat, cold and air;

step 103: according to the model established in step 101, the input end and the output end of each equipment unit are correspondingly connected to four buses of electricity, heat, cold and gas in the system, and are coupled to form the overall architecture of the regional comprehensive energy system, as shown in fig. 2.

Step 2: calculating the economic cost and the carbon emission cost of system operation, constructing a two-stage carbon emission model, and then proposing two optimization targets: the cost of energy purchase is minimal and the cost of carbon emissions is minimal. The energy purchasing cost comprises the electricity purchasing cost for purchasing electricity from the power grid and the gas purchasing cost for purchasing gas from the gas grid, and the electricity purchasing cost is related to the real-time electricity consumption and the implementation electricity price; since the natural gas price implements a stepped gas price, the gas purchase cost needs to be represented in a piecewise linearization manner. The cost coefficient of unit carbon emission in the first-stage carbon emission cost changes along with the change of the carbon emission, so that the first-stage carbon emission cost also needs to be subjected to piecewise linearization treatment; the second stage carbon emission cost needs to be compared to the intensity of the regulated carbon emission, penalized above regulation and awarded below regulation. In conclusion, the unit price of gas purchase and the unit carbon emission cost in the first stage are not fixed values, and the optimization model has nonlinearity. The step 2 specifically comprises the following steps:

step 201, constructing a carbon emission model of power consumption equipment and electric load:

in the formula:the total carbon emission of power consumption equipment and electric load;the electric load power required by a user in the system in a period t;the conversion coefficient from electric energy to standard coal; omega_tThe carbon emission coefficient of the standard coal;

step 202, constructing a two-stage carbon emission model of the gas equipment and the gas load:

1) the first stage is as follows:

in the formula:the total carbon emission of the first stage gas equipment and gas load;the total amount of natural gas required by users in the system in the period t;the conversion coefficient from natural gas to standard coal;

2) and a second stage:

in the formula:the total carbon emission of the second stage gas equipment;the carbon emission is the carbon emission generated by unit output in the operation process of the gas unit;

step 203, building multiple optimization objectives, including minimum energy purchase cost and minimum carbon emission cost:

target 1:

in the formula: c_epTotal cost of energy purchased for the system;the electricity purchasing cost for purchasing electricity from the power grid for the system in the time period t;a gas purchase cost for gas purchase from the gas network for the rees at time t;

in the formula:purchasing power for purchasing power from the power grid for the system in the period t;selling electricity price for the power grid in the period t;total gas purchasing amount for purchasing gas from the gas network in the time period t;the first, second and third step gas selling prices of the gas network are respectively;respectively the first, second and third step air consumption;

target 2:

in the formula: c_emThe total cost of carbon emission for the system;two-stage carbon emission costs for the system operating during time t. Since the cost coefficient per carbon emission increases with increasing energy consumption,can be expressed by segmented linearization as:

in the formula: s_{c_i}I is 1, 2 and 3) is a cost coefficient of unit carbon emission of each section after piecewise linearization; l is_{c_i}I is 1, 2 and 3) is the carbon emission of each section after the piecewise linearization; i is_act、I_strActual carbon emission intensity and specified carbon emission intensity respectively; s_pfIs the carbon emission cost coefficient of the fuel gas.

And step 3: establishing the operability constraint of each equipment unit and the cold, heat, electricity and gas energy balance constraint in the system to form a multi-objective low-carbon optimization model; the step 3 comprises the following steps:

step 301: establishing the motion constraint of each equipment unit:

1) the water source heat pump unit is restricted in the transportation:

in the formula:respectively the minimum input power and the maximum input power of each time period of the water source heat pump;

2) and (3) restricting the operation of the fan:

in the formula: p_t ^WTThe actual output of the fan in the t time period;

3) gas turbine unit operation constraints

In the formula:minimum and maximum output values specified for the gas turbine set respectively;the climbing rate of the gas turbine set.

4) Energy storage device transport restriction

In the formula:the maximum output power of the electrical/thermal energy storage device;maximum input power for the electrical/thermal energy storage device;the maximum installed capacities of the electric energy storage device and the thermal energy storage device are respectively set; beta is a_min、α_minThe minimum residual state coefficients of the electric energy storage device and the thermal energy storage device are respectively; beta is a_max、α_maxThe minimum residual state coefficients of the electric energy storage device and the thermal energy storage device are respectively;

step 302, establishing the energy balance constraints of electricity, heat, cold and gas in the system:

1) electric energy balance constraint:

2) and (3) heat energy balance constraint:

in the formula:the thermal load power required by users in the system in the period t;

3) and (3) gas quantity balance constraint:

4) cold energy balance constraint:

in the formula：The total amount of cooling load required by the user in the system during the period t.

And 4, step 4: in a python environment, the NSGA-III algorithm is adopted to solve the constructed low-carbon optimization model to obtain an optimal operation strategy of the system, and the solving process is shown in figure 3 and comprises the following steps:

step 401, writing the built low-carbon optimization model of the regional comprehensive energy system in a python3.8 environment;

step 402, solving the model by adopting an NSGA-III algorithm to obtain an optimal operation strategy of the system, which comprises the following steps:

step 501, setting initial parameters of the NSGA-III algorithm, including iteration times, population size, number of objective functions, cross parameters, variation parameters, cross probability and variation probability;

step 502, setting the number of consistent reference points according to the size of the population, and generating all the reference points to ensure the diversity of the solution;

step 503, generating an initial population, calculating an initial fitness value of each individual according to a set optimization target, and obtaining an initial ideal point;

step 504, ensuring the high efficiency of the genetic operator by simulating binary intersection and polynomial variation, then generating offspring individuals, calculating the fitness value of the offspring individuals and updating the ideal points;

step 505, giving the number of new generation population, dividing the population composed of parents and offspring according to a non-dominant layer, and preferentially entering the next generation by individuals with high dominant level;

step 506, judging whether the number of the population exceeds the set population number after all the individuals with a certain allocation grade enter the next generation, if not, all the individuals with the current allocation grade enter the next generation; if so, selecting proper individuals from the individuals at the domination level one by one according to the number of the reference point associated individuals and the distance from the reference point to enter the next generation until the number of the new generation of population meets the previous setting, thereby forming a new generation of population;

step 507, judging whether the current iteration times reach the set iteration times, if not, returning to the step 504; if yes, go to step 508;

step 508, obtaining a Pareto front of the optimal solution of the secondary multi-objective optimization model according to the finally obtained population individuals and the fitness value;

step 509, setting weight coefficients of the target 1 and the target 2, calculating and comparing weighted comprehensive costs of solutions in Pareto frontier, and obtaining an optimal operation strategy, a day-ahead supply and demand scheduling plan of various types of energy:

C_total＝α₁C_ep+α₂C_em

in the formula: c_totalWeighting the integrated cost; alpha is alpha₁、α₂The economic cost and the carbon emission cost are weighted coefficients, respectively.

Comparative analysis was carried out with reference to the following examples:

an example system architecture is shown in fig. 2. The heating performance coefficient of the water source heat pump is 3.50; 35.70MJ/kW is taken as a low calorific value of natural gas combustion, and 2.5 and 2 are respectively taken as conversion coefficients a and b of a gas turbine unit; the refrigeration efficiency of the electric refrigerator is 0.8, and the maximum refrigeration power is 150 kW; the refrigerating efficiency of the double-effect absorption refrigerating machine is 1.1, and the maximum refrigerating power is 200 kW.

In the traditional mode, the operation is restricted by 'fixing power with heat', namely, various types of energy supply and demand scheduling plans in the day before obtained in the mode 1 are shown in figures 4, 5, 6 and 7; the optimized operation is realized under the low-carbon optimized operation method provided by the invention, namely, various energy supply and demand dispatching plans obtained in the mode 2 are shown in the figures 8, 9, 10 and 11; the cost pairs for both modes are shown in figure 12.

As shown in fig. 5, the typical winter scenario is mainly characterized in that the heat load demand is much greater than that of other loads, and the heat is supplied by a cogeneration unit in a conventional operation mode, so that most of the natural gas from the gas network is supplied to the gas unit except for a small part of the natural gas directly supplied to the gas load, and the heat generated by the gas unit is ensured to meet the heat load demand in each period.

In the operation mode, because the gas turbine unit is used as a core of cogeneration, the constraint of 'fixing power by heat' exists, as shown in fig. 4, a large amount of available electric energy is generated in each time period while heat is generated, particularly in the early cold period, the electric energy output reaches the peak value along with the increase of heat demand, and the electric load demand is in the valley of one day in the time period; and the electric-heat ratio of the demand side and the supply side is greatly different throughout the day, only part of electric energy except the electric load is supplied to the cold load through the electric refrigerator, but the total consumed electric energy is far less than the output, and large-scale abandoned wind and electric energy overflow are generated. Meanwhile, as can be seen from fig. 7, the consumption of natural gas and the consumption of electric energy are relatively large and small in each period of operation in this mode, and the consumption of natural gas has two-stage carbon emission, and the conversion coefficient is also relatively large compared with the electric energy, so that the carbon emission cost of the operation mode 1 is relatively high.

The water source heat pump unit can generate a large amount of heat by only consuming a small amount of electric energy, and is very suitable for the typical scene with large heat load demand. As shown in fig. 8, the gas turbine engine and the gas turbine engine run in cooperation, thermoelectric decoupling can be achieved to a great extent by matching with a thermoelectric energy storage device, and compared with a traditional running mode (shown in fig. 4), the most obvious advantage of the new mode is that the overflowing electric quantity hardly exists, which means that all generated electric energy is consumed on the spot, and the constraint of thermoelectric power is fundamentally solved. The reason for solving the problem can be found by deep analysis, under the traditional operation mode, the heat supply structure is single, the thermoelectric output proportion of the gas turbine unit is relatively fixed, and a large amount of overflow electric energy is generated on the premise of meeting the heat load supply; after the water source heat pump unit is added, the cogeneration structure is enriched, and the flexibility of supplying the system electricity and heat energy is greatly improved.

As shown in fig. 10, all of the natural gas purchased from the gas grid during the period of 2:00-7:00 is used to supply the gas load required by the customer, and the gas turbine unit is shut down during this period. The reason for this is that, as shown in fig. 8, when the electricity price is at the valley, the water source heat pump unit with excellent heat supply performance can directly purchase electric energy from the power grid to generate heat energy to be supplied to the heat load in this time period, and the user electric load can also be directly supplied by the power grid; meanwhile, in order to further improve the economic benefit, the electric energy storage device is also in a charging state in the period, and electric energy is stored to supply to the peak. The wind power is fully utilized in the whole day electric energy dispatching plan, the water source heat pump unit is put into operation at each time interval, the output is highest at the electricity price valley, the clean energy utilization rate of the system is effectively improved by utilizing the water source heat pump unit and the water source heat pump unit on a large scale, and the green low-carbon operation is realized.

As shown in fig. 11, compared to mode 1, the heat energy is supplied by both the gas turbine and the water source heat pump, and the cold energy generated by the absorption refrigeration unit is supplied to the cold load in addition to the direct supply to the heat load. Due to the introduction of the carbon emission optimization target, the system increases the output of the water source heat pump unit on the premise of meeting the energy supply, improves the utilization rate of clean energy sources while not remarkably increasing the economic cost, and reduces certain carbon emission. In early morning, the heat energy storage device is in a heat charging state, because the water source heat pump unit can utilize valley hour electric energy to heat in a large scale at the time, and a part of heat energy is stored to be required by a part of heat load at the subsequent time, so that the system operation economy can be further improved.

Fig. 10 shows that most of the cold energy of the system in the mode 2 is provided by the absorption chiller, which is different from the mode in that the novel operation mode does not generate a large amount of redundant electric energy any more, the electric energy and the thermal energy are fully decoupled, the selectivity of the electric refrigeration and the absorption refrigeration is greatly improved, and in the case, the selected double-effect absorption refrigeration efficiency is obviously higher than the electric refrigeration efficiency, so that the energy supply plan is made. Meanwhile, as the selling price of the natural gas is in a stepped price, the comparison between the graph 7 and the graph 10 shows that the purchase amount of the natural gas in each period after the thermoelectric decoupling is greatly reduced and basically maintained in the prices of 1 st to 2 nd gears, and as the price of the 3 rd gear gas is higher, the 3 rd gear natural gas which is originally required to be consumed can be replaced by energy in other forms, so that the more economic operation effect is achieved.

Compared with the traditional operation mode, the low-carbon optimized operation method provided by the invention has the most obvious advantages that the overflowing electric quantity hardly exists, the local consumption of each generated electric energy including wind power is realized, and the constraint of electricity determination by heat is fundamentally solved. On the other hand, due to the introduction of the carbon emission optimization target, the system increases the output of the water source heat pump unit on the premise of meeting the energy supply, improves the utilization rate of clean energy of the system while the economic cost is not increased remarkably, and effectively reduces the carbon emission cost.

Fig. 12 visually shows that the economic cost is reduced by about 50.91% and the carbon emission cost is reduced by about 49.70% in the low-carbon optimized operation method (mode 2) compared with the conventional mode operation (mode 1), and the reasonability and the effectiveness of the method are proved.

By adopting the operation method, in regions with abundant river water systems, the water source heat pump unit is introduced into the regional comprehensive energy system, and the electricity storage and heat storage devices are matched to operate cooperatively, so that the overflowing electric quantity is almost not existed, and the problem of operation constraint of 'fixing electricity by heat' is fundamentally solved;

according to the method, a two-stage carbon emission model is constructed, the carbon emission conditions of each equipment unit and electric and gas loads are quantitatively analyzed in a more detailed manner, and the establishment of low-carbon optimization model is supported;

the invention adopts a piecewise linearization output optimization model and adopts an NSGA-III algorithm to solve the multi-objective optimization model, thereby obtaining a reasonable and high-reliability system optimal operation strategy.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, which is defined by the appended claims.