CN115879613A - Agricultural industry park load optimization scheduling method considering carbon transaction - Google Patents
Agricultural industry park load optimization scheduling method considering carbon transaction Download PDFInfo
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Abstract
The invention provides an agricultural industry park optimal scheduling method considering carbon transaction, which comprises the following steps: the method comprises the following steps: acquiring a carbon emission quota of a system and constructing a carbon emission cost model; step two: analyzing the load adjustability characteristics of the agricultural industrial park and establishing a corresponding mathematical model; step three: establishing an objective function with the lowest system operation cost and carbon transaction cost; step four: constructing constraint conditions of thermoelectric power equality constraint and agricultural industry park load time-shifting constraint; step five: and solving the objective function to obtain an agricultural industrial park load optimization scheduling scheme. On the basis of considering the carbon emission of the agricultural system and the adjustable characteristic of the load of the agricultural industry park, the agricultural multi-energy complementary system is analyzed and a corresponding mathematical model is established. And obtaining an optimized scheduling scheme under the agricultural industry park load timeshiftability considered under the carbon trading mechanism through example simulation. The electric heating multi-energy agricultural system can complement the economy and low carbon of the agricultural system in cooperation.
Description
Technical Field
The invention relates to the technical field of comprehensive energy scheduling, in particular to an agricultural industry park load optimization scheduling method considering carbon transaction.
Background
With the development of facility agriculture in China, rural energy varieties present a diversified trend, and the traditional energy system is changed from a single system to a comprehensive energy system. A large number of emerging agricultural industrial parks are generated, but the energy requirements of cold, heat, electricity and gas load in the parks are not sufficiently combined with the natural resources of clean energy resources of the agricultural parks, so that the value of energy resources such as solar energy, wind energy, biomass energy and the like cannot be exerted to the maximum extent according to local conditions. Through the adjustable load in the rational regulation facility agriculture, like warmhouse booth etc. in the garden, have very big help to improving agricultural energy utilization and renewable energy's consumption, still can further get through the energy passageway of agricultural load and other forms of garden energy demands, realize the multipotency complemental, optimize the energy structure of facility agriculture garden. Therefore, it is necessary to make an agricultural park load scheduling mode considering carbon emission constraints by combining the characteristic of adjustable load in the park with the development concept of the agricultural park under low carbon. Currently, an optimal scheduling method for an agricultural park mainly aims at the energy characteristics of various energy sources and utilizes the advantages and disadvantages of various energy sources to perform multi-energy complementation. However, the time-shifting capability of the agricultural park load is not fully integrated when considering the factor of carbon emission. In the optimized scheduling considering the load time shifting, the system carbon emission is considered less. Optimal scheduling at low carbon for agricultural parks cannot be achieved.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a load optimization scheduling method for an agricultural industrial park considering carbon transaction, which analyzes an agricultural multifunctional complementary system and establishes a corresponding mathematical model on the basis of considering the characteristics of carbon emission of an agricultural system and adjustable load of the agricultural industrial park. And obtaining an optimized scheduling scheme under the agricultural industry park load timeshiftability considered under the carbon trading mechanism through example simulation. The method can complement the economy and low carbon property of the agricultural system by cooperating with the electric heating multi-energy, and provides assistance for the low carbon economic operation of the comprehensive energy system in the agricultural industry park.
In order to achieve the purpose, the invention adopts the following technical scheme:
an agricultural industry park load optimization scheduling method considering carbon trading comprises the following steps:
the method comprises the following steps: and acquiring a carbon emission quota of the system and constructing a carbon emission cost model.
The carbon emission quota of the system is calculated by adopting a reference line method under the condition that the electric heating power generated by related units in the agricultural industry park in the production activity is considered, a carbon emission model is constructed by considering the carbon emission coefficient of the related units on the basis of determining the carbon emission quota of the system, and a carbon trading market price factor is introduced to form a carbon trading cost model.
Step two: and analyzing the load adjustability characteristics of the agricultural industrial park and establishing a corresponding mathematical model.
The time shifting analysis is carried out on part of adjustable agricultural industry park loads in the agricultural industry park, mathematical models of related equipment and units are established, the agricultural energy utilization requirements in the park are met through various energy conversion, and the purposes of improving the system operation economy and reducing the system carbon emission can be achieved through adjusting part of adjustable agricultural industry park loads.
Step three: and constructing an objective function with the lowest running cost and carbon transaction cost of the agricultural system.
The system energy purchasing cost, the operation and maintenance cost and the carbon transaction cost are fully considered in the construction of the objective function, and the load of the agricultural industrial park is scheduled under low carbon by taking the optimal system economy as the objective on the basis of considering the load adjustability of the agricultural industrial park.
Step four: and (3) constructing constraint conditions of thermoelectric power equality constraint and agricultural industry park load time-shifting constraint.
Step five: and solving the objective function to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system.
An improved sine and cosine algorithm is used for solving the objective function, in order to improve the calculation speed and the calculation precision of the method, a position updating formula of the method in the calculation process is improved, and the power supply and heat supply equipment output in the agricultural system is solved through continuous updating iteration to obtain an agricultural industry park load optimization scheduling scheme.
In the first step, the system carbon emission quota is calculated by using a baseline method, and the method comprises the following steps:
(1) Determining a carbon emission allowance: adopting a gratuitous distribution and providing a carbon emission quota for the system based on a reference line method;
in the formula: e p,t Actual carbon emission at time t; kappa is the carbon emission allocation amount of unit electric quantity of the region;
the conversion coefficient of the electric quantity; />
The electric power output by the cogeneration unit at the moment t; />
The thermal powers output by the air heat source pump, the thermoelectric energy storage unit, the greenhouse and the cogeneration unit at the moment t are respectively.
(2) Determining a carbon emissions cost model
Actual carbon emission E of system at time t ac,t According to the emission factor method, the actual carbon emission of the unit is in direct proportion to the output of the unit, and the actual carbon emission E of the system at the time t ac,t Comprises the following steps:
in the formula: kappa he 、κ kb 、κ dp 、κ rc Carbon emission coefficients of a cogeneration unit, an air heat source pump, a greenhouse and a thermoelectric energy storage unit are respectively set; the user can trade the carbon emission quota by himself, namely the actual carbon emission is smaller than the carbon emission quota, and the surplus carbon emission quota can be sold at the market price to obtain the income; conversely, extra carbon credits are purchased from the market; thus, the carbon transaction cost C at time t Ca,t Comprises the following steps:
C Ca,t ＝k Ca (E ac,t -E p,t )
in the formula: k is a radical of Ca Is the carbon trading market price.
In the second step, the adjustable characteristic of the agricultural industry park load is analyzed and a corresponding mathematical model is established, and the method comprises the following steps:
(1) The greenhouse heat load mathematical model is as follows:
Q dre ＝Q 1 +Q 2 +Q 3 ＝∑s j a j (t in -t out )+0.5kvn(t in -t out )+∑z 1 h 1 (t in -t out )
in the formula: q dre The heat load value required by the greenhouse; q 1 Is a heat transfer load; q 2 Is the osmotic heat load; q 3 Is the ground heat load; s j The heat transfer coefficient of the jth enclosure structure; t is t in Is the temperature in the greenhouse; t is t out The temperature outside the greenhouse; k is a wind factor; v is the air volume of the greenhouse; n is the number of air changes per hour; z is a radical of 1 The ground heat transfer coefficient of the agricultural greenhouse; h is 1 Is the indoor ground area of the agricultural greenhouse.
(2) Combined heat and power generation model
The cogeneration system mainly comprises a small-sized methane power generation device, a methane tank and an absorption heat pump, and the mathematical model is as follows:
in the formula: q e (t) recovering the residual heat quantity of the flue gas of the small-sized methane power generation device at the moment t; p is MBT (t) is the output electric power of the small-sized methane power generation device at the moment t; eta e The power generation efficiency of the small-sized methane power generation device is improved; eta 1 The loss coefficient of the heat dissipation part of the small-sized methane power generation device is reduced; q AP (t) is the thermal power of the absorption heat pump at the moment t; eta AP The heat recovery efficiency of the absorption heat pump is improved.
(3) Air source heat pump heat exchange model
The air source heat pump takes the energy of air as power, the compressor is driven by electric energy to realize the conversion of heat energy, complex and expensive equipment configuration is not needed, the emission of pollutants is reduced, the economy and the environmental protection are outstanding, and a mathematical model can be expressed as follows:
Q HP (t)＝C HP P EHP (t)
in the formula: q HP (t) the output thermal power of the air source heat pump at the moment t; c HP Is the heating coefficient of the air source heat pump; p is EHP And (t) is the electric power of the air source heat pump at the time t.
(4) Thermoelectric energy storage system model
Use the lithium cell as power storage device, the phase change heat reservoir is as heat-retaining device, because 2 kinds of devices fill and discharge can the characteristics similar, the effect is similar, so adopt unified mathematical model to be:
E(t)＝E(t-1)(1-δ)+ΔTP ch (t)η ch -ΔTP dis (t)/η dis
in the formula: e (t) is the total energy of the thermoelectric energy storage device at the moment t; delta is the self-discharge coefficient of the thermoelectric energy storage device; p ch (t) the charging efficiency of the thermoelectric energy storage device at the moment t; p dis (t) the discharge power of the thermoelectric energy storage device at the moment t; eta ch Charging the thermoelectric energy storage device with an energy efficiency coefficient; eta dis The energy discharge efficiency coefficient of the thermoelectric energy storage device; Δ T is a unit period.
In the third step, the objective function with the lowest operation cost and carbon transaction cost of the agricultural system is constructed, and the method comprises the following steps:
at an energy purchase cost C Buy Carbon transaction cost C Ca And operation and maintenance cost C OP The minimum sum is the objective function:
minf＝C Buy +C Ca +C OP
1) Cost of energy purchase C Buy The system can trade electric quantity with a superior power grid, and when the generated energy cannot meet the self demand, the system purchases power from the superior power grid, and correspondingly, when the generated energy is surplus, redundant electric quantity is sold to the superior power grid; in addition, the system needs to purchase natural gas to maintain the operation of the cogeneration unit and the gas boiler in the agricultural system; thus, the energy purchase cost is:
in the formula: t is an operation period:
purchasing and selling power from a superior power grid at the moment t respectively: />
The electricity price is bought and sold at the time t respectively; q b,t Buying the amount of natural gas for the moment t: kappa type g Is the unit natural gas price.
2) Carbon transaction cost C Ca : the carbon transaction cost for one operating cycle is the sum of the costs at all times:
3) Operation and maintenance cost C OP ：
In the formula: i, taking constants of 1,2 and 3, which respectively represent equipment such as a cogeneration unit, an air heat source pump and the like; omega is the operation and maintenance coefficient of the equipment i; p i,t The output of device i.
In the fourth step, the constraint conditions include the following:
1) The thermoelectric power equation is constrained to:
P LI (t)+P MBT (t)+P grid (t)+P PV (t)-P EHP (t)＝P e (t)Q HP (t)+Q AP (t)+Q BGB (t)+Q PT (t)＝Q h (t)
in the formula: p LI (t) is the electrical power of the battery at time t; p MBT (t) is the electric power of the small-sized methane power generation device at the moment t; p e (t) is the electric load of the micro energy network at the time t; p PV (t) is the electric power of the photovoltaic power generation device at the moment t; q BGB (t) heat of the biogas boiler at the time tPower; q PT (t) is the thermal power of the phase-change heat storage device at the moment t; q h And (t) is the heat load of the micro energy network at the time t.
2) Load timeshift constraints
In the formula: p after (t) is the load at time t after the load time shift; p fore (t) is the predicted load at time t before the load time shift; p in (t) load at time t for planned transitions; p out (t) load at time t of planned transfer;
the power value of the kth load at the time when t is changed into t' from the time when t is changed; />
The power value of the kth load converted from the t' moment to the t moment is obtained; s is the load type.
In the fifth step, an improved sine and cosine algorithm is used for solving the objective function to obtain the output of power supply and heat supply equipment in the agricultural industry park, and a load optimization scheduling scheme of the agricultural industry park is obtained:
the improved sine and cosine algorithm is specifically as follows: inputting related equipment parameters in an agricultural system, initializing the positions of all variables, and setting a position updating formula of the algorithm as follows in order to improve the accuracy and the convergence rate of the algorithm:
in the formula:
representing the position of the j-th dimension individual i at the time of the t-th generation selection; />
Representing a target location; t is max The maximum number of generations; r is 2 Is the interval [0,2 pi]The random number of (1); r is a radical of hydrogen 3 Is [0,2 ]]The random number of (1); r is a radical of hydrogen 4 Is [0,1 ]]Random number of (1), r s 、r e Are respectively r 1 Initial and final values of (a), and r s >r e …0。
Calculating each candidate solution in the candidate solution set, comparing the fitness value of each candidate solution, then keeping the part with the higher fitness value, substituting the part into an algorithm updating formula to update the candidate solution set, and outputting the result by continuously updating iteration under the condition of meeting the maximum iteration times to obtain the optimal output scheme of each device.
Compared with the prior art, the invention has the beneficial effects that:
by researching the load optimization adjustment and the energy equipment output optimization scheduling of the adjustable agricultural industrial park in the integrated energy system of the agricultural industrial park under low carbon, a link with tighter connection can be established among the distributed renewable energy, the adjustable load in the agricultural industrial park and the energy equipment output, the multi-energy complementation and the energy cascade utilization are realized, the wind-light on-site consumption in rural areas and the energy utilization efficiency in facility agriculture are improved, the consumption space of the renewable energy is enlarged, the investment cost on rural agricultural energy is reduced, and the like, and the development concept of low carbon agriculture is realized.
Drawings
FIG. 1 is a flow chart of an agricultural industry park load optimization scheduling method of the present invention that accounts for carbon trading;
FIG. 2 is a comparison curve of power values of the system before and after time shifting of an agricultural electrical load;
FIG. 3 is a graph of actual carbon emissions versus carbon number;
FIG. 4 is a power supply equipment dispatch output curve in an agricultural system;
fig. 5 is a graph of the scheduled output of a heating plant in an agricultural system.
Detailed Description
The following detailed description of the present invention will be made with reference to the accompanying drawings.
As shown in fig. 1, a method for optimizing and scheduling a load of an agricultural industry park considering carbon trading is characterized by comprising the following steps:
the method comprises the following steps: and acquiring a carbon emission quota of the system and constructing a carbon emission cost model.
The carbon emission quota of the system is calculated by adopting a reference line method under the condition of considering the electric heating power generated by related units in the agricultural industrial park in production activities. And on the basis of determining the carbon emission quota of the system, a carbon emission model is constructed by considering the carbon emission coefficient of the related unit, and a carbon trading market price factor is introduced to form a carbon trading cost model.
Step two: and analyzing the load adjustability characteristics of the agricultural industrial park and establishing a corresponding mathematical model.
The method comprises the steps of carrying out time-shifting analysis on part of adjustable agricultural industry park loads in the agricultural industry park, establishing a mathematical model of related equipment and units, meeting agricultural energy utilization requirements in the park through various energy conversion, and achieving the purposes of improving system operation economy and reducing system carbon emission through adjusting part of adjustable agricultural industry park loads.
Step three: and constructing an objective function with the lowest running cost and carbon transaction cost of the agricultural system.
The system energy purchasing cost, the operation and maintenance cost and the carbon transaction cost are fully considered in the construction of the objective function, and the load of the agricultural industrial park is scheduled under low carbon by taking the optimal system economy as the objective on the basis of considering the load adjustability of the agricultural industrial park.
Step four: and (4) constructing constraint conditions of thermoelectric power equality constraint and time-shifting constraint of agricultural industrial park load.
Step five: and solving the objective function to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system.
The method is characterized in that the objective function is solved by utilizing a method capable of solving the output of each device in the system, in order to improve the calculation speed and the calculation precision of the method, a position updating formula of the method in the calculation process is improved, the output of power supply and heat supply devices in the agricultural system is solved through continuous updating iteration, and an agricultural industrial park load optimization scheduling scheme is obtained.
Further, the method for calculating the carbon emission quota of the system by adopting a baseline method comprises the following steps:
(1) Determining carbon emission quota
The carbon trading mechanism needs to determine the carbon emission quota, and there are 2 common ways of allocating the carbon emission quota: gratuitous distribution and paid distribution. The free distribution refers to the free carbon emission amount pre-distributed to the system so as to improve the participation enthusiasm of the system: the paid distribution requires the system to pay a corresponding fee for its own carbon emissions. The invention provides the carbon emission quota for the system by using the gratuitous distribution and based on the reference line method.
In the formula: e p,t Actual carbon emission at time t; kappa is the carbon emission allocation amount of unit electricity of the region;
the conversion coefficient of the electric quantity; />
The electric power output by the cogeneration unit at the moment t; />
Respectively outputting thermal powers of an air heat source pump, a thermoelectric energy storage unit, a greenhouse and a cogeneration unit at the time t; />
(2) Determining a carbon emission cost model
Actual carbon emission E of system at time t ac,t According to the emission factor methodThe actual carbon emission of the group is in direct proportion to the output of the unit, and the actual carbon emission E of the system at the moment t ac,t Comprises the following steps:
in the formula: kappa type he 、κ kb 、κ dp 、κ rc The carbon emission coefficients of the cogeneration unit, the air heat source pump, the greenhouse and the thermoelectric energy storage unit are respectively 0.6273 t/(MW & h), 0.6138 t/(MW & h), 0.5831 t/(MW & h) and 0.5632 t/(MW & h). The user can trade the carbon emission quota by himself, namely the actual carbon emission is smaller than the carbon emission quota, and the surplus carbon emission quota can be sold at the market price to obtain the income; conversely, excess carbon credits are purchased from the market. Thus, the carbon transaction cost C at time t Ca,t Comprises the following steps:
C Ca,t ＝k Ca (E ac,t -E p,t )
in the formula: k is a radical of Ca Is the carbon trade market price.
Further, the adjustable characteristic of the agricultural industry park load is analyzed and a corresponding mathematical model is established, and the method comprises the following steps:
in the comprehensive energy system of the agricultural industry park, wind, light and marsh gas can be converted into cold energy, heat energy and electric energy through various energy conversion devices to meet the agricultural energy demand in the park, and the purposes of improving the system operation economy and reducing the system carbon emission can be achieved by adjusting the load of the agricultural industry park.
(1) Heat load mathematical model of greenhouse
Most of loads in the greenhouse have the characteristic that a certain part can be shifted or reduced, according to the growth characteristics of different planted crops, and the quality requirement of the plants on various kinds of required energy is far lower than the requirement of human beings on the quality of the required energy, for example, when the ground temperature is high, the crops can be watered and irrigated to facilitate the crops to absorb water, when the ground temperature is lower than 10 ℃, the watering is forbidden, and in cloudy days or in the afternoon, the watering is preferably not carried out or less; the soil has certain heat preservation, so the change of the ground temperature and the environmental temperature of the greenhouse has time difference of about 4 hours, and the like. The load-adjustable characteristics are added into the optimal scheduling of the park comprehensive energy system, and positive influences are generated on the system operation, energy consumption cost and energy utilization efficiency of the park.
The heat load mathematical model of the greenhouse is as follows:
Q dre ＝Q 1 +Q 2 +Q 3 ＝∑s j a j (t in -t out )+0.5kvn(t in -t out )+∑z 1 h 1 (t in -t out )
in the formula: q dre The heat load value required by the greenhouse; q 1 Is a heat transfer load; q 2 To penetrate the thermal load; q 3 Is ground heat load; s j The heat transfer coefficients of the j-th enclosure structure, namely the heat transfer coefficients of a ceiling sunlight plate and a wall body part, are respectively 3.6W/(m) 2 K) and 0.538W/(m) 2 ·k)；a j The area of the jth enclosure structure refers to the cross section area of a ceiling sunlight plate and the area of a wall part, and the values of the areas are 916m respectively 2 And 170m 2 ；t in Is the temperature in the greenhouse; t is t out The temperature outside the greenhouse; k is a wind factor, and the value of k is 1.5; v is the air volume of the greenhouse and is 1500m 3 (ii) a n is the number of air changes per hour, and the value is 1.3; z is a radical of 1 The ground heat transfer coefficient of the agricultural greenhouse is 0.26W/(m) 2 ·k)；h 1 Is the indoor ground area of the agricultural greenhouse, and the value is 800m 2 。
(2) Combined heat and power generation model
The cogeneration system mainly comprises a small-sized methane power generation device, a methane tank and an absorption heat pump, and the mathematical model is as follows:
in the formula: q e (t) recovering the residual heat quantity of the flue gas of the small-sized methane power generation device at the moment t; p MBT (t) is the output electric power of the small-sized methane power generation device at the moment t; eta e The power generation efficiency of the small-sized methane power generation device is improved; eta 1 The loss coefficient of the heat dissipation part of the small-sized methane power generation device is reduced; q AP (t) is the thermal power of the absorption heat pump at the moment t; eta AP The heat recovery efficiency of the absorption heat pump is improved.
(3) Air source heat pump heat exchange model
The air source heat pump takes the energy of air as power, the compressor is driven by electric energy to realize the conversion of heat energy, complex and expensive equipment configuration is not needed, the emission of pollutants is reduced, the economy and the environmental protection are outstanding, and a mathematical model can be expressed as follows:
Q HP (t)＝C HP P EHP (t)
in the formula: q HP (t) is the output thermal power of the air source heat pump at the moment t; c HP Is the heating coefficient of the air source heat pump; p is EHP And (t) is the electric power of the air source heat pump at the time t.
(4) Thermoelectric energy storage system model
Use the lithium cell as power storage device, the phase change heat reservoir is as heat-retaining device, because 2 kinds of devices fill and discharge can the characteristics similar, the effect is similar, so adopt unified mathematical model to be:
E(t)＝E(t-1)(1-δ)+ΔTP ch (t)η ch -ΔTP dis (t)/η dis
in the formula: e (t) is the total energy of the thermoelectric energy storage device at the moment t; delta is the self-discharge coefficient of the thermoelectric energy storage device; p is ch (t) is the charging efficiency of the thermoelectric energy storage device at the moment t; p is dis (t) the discharge power of the thermoelectric energy storage device at the moment t; eta ch Charging the thermoelectric energy storage device with an energy efficiency coefficient; eta dis The energy discharge efficiency coefficient of the thermoelectric energy storage device; Δ T is a unit period.
Further, the method for constructing the objective function with the lowest running cost and carbon transaction cost of the agricultural system comprises the following steps:
(1) At an energy purchase cost C Buy Carbon transaction cost C Ca And operation and maintenance cost C OP The minimum sum is the objective function:
minf＝C Buy +C Ca +C OP
1) Energy purchase cost C Buy The system can trade the electric quantity with a superior power grid, and when the generated energy cannot meet the self demand, the system purchases the electric quantity from the superior power grid, and correspondingly, when the generated energy is surplus, the system sells the surplus electric quantity to the superior power grid; in addition, the system requires the purchase of natural gas to maintain the cogeneration unit and gas boiler in the agricultural system in operation. Thus, the energy purchase cost is:
in the formula: t is an operation period:
power purchased and sold from a superior power grid at the time t respectively: />
The electricity price is bought and sold at the time t respectively; q b,t Buying the amount of natural gas for the moment t: kappa g Is the unit natural gas price.
2) Carbon transaction cost C Ca : the carbon trading cost for one run cycle is the sum of the costs at all times:
3) Operation and maintenance cost C OP ：
In the formula: i, taking constants of 1,2 and 3, which respectively represent equipment such as a cogeneration unit, an air heat source pump and the like; omega is the operation and maintenance coefficient of the equipment i; p i,t Is the output of device i.
(2) Constraint conditions
1) The thermoelectric power equation is constrained to:
P LI (t)+P MBT (t)+P grid (t)+P PV (t)-P EHP (t)＝P e (t)Q HP (t)+Q AP (t)+Q BGB (t)+Q PT (t)＝Q h (t)
in the formula: p is LI (t) is the electrical power of the battery at time t; p is MBT (t) is the electric power of the small-sized methane power generation device at the moment t; p e (t) is the electric load of the micro energy network at the time t; p PV (t) is the electric power of the photovoltaic power generation device at the moment t; q BGB (t) is the thermal power of the biogas boiler at the moment t; q PT (t) is the thermal power of the phase-change heat storage device at the moment t; q h And (t) is the heat load of the micro energy network at the time t.
2) Load timeshift constraints
In the formula: p after (t) is the load at time t after the load time shift; p fore (t) is the predicted load at time t before the load time shift; p is in (t) load at time t for planned transitions; p out (t) load at time t of planned transfer;
the power value of the kth load at the time when t is changed into t' from the time when t is changed; />
The power value of the kth load converted from the t' moment to the t moment is obtained; s is the load type. Fig. 2 shows a comparison curve of the system power values before and after the time shift of the agricultural electric load.
FIG. 3 is a graph of actual carbon emissions versus carbon value. The carbon transaction price is a weight of the objective function, so that the change of the carbon transaction price affects the carbon emission, the carbon transaction cost and the energy purchase cost, and further affects the total operation cost of the system. To investigate the effect of carbon transaction prices on system operation, figure 3 plots carbon transaction prices.
Further, an improved sine and cosine algorithm is used for solving the objective function, the output of power supply and heat supply equipment in the agricultural system is solved, and an agricultural industrial park load optimization scheduling scheme is obtained.
Inputting related equipment parameters in an agricultural system, initializing the positions of all variables, and setting a position updating formula of the algorithm as follows in order to improve the accuracy and the convergence rate of the algorithm:
in the formula:
representing the position of the j-th dimension individual i at the time of the t-th generation selection; />
Representing a target location; t is max The maximum number of generations; r is 2 Is the interval [0,2 pi]The random number of (1); r is 3 Is [0,2 ]]The random number of (1); r is 4 Is [0,1 ]]Random number of (1), r s 、r e Are respectively r 1 And r is a starting value and a terminating value of s >r e …0。
Calculating each candidate solution in the candidate solution set, comparing the fitness value of each candidate solution, then keeping the part with the higher fitness value, substituting the part into an algorithm updating formula to update the candidate solution set, and outputting the result by continuously updating iteration under the condition of meeting the maximum iteration times to obtain the optimal output scheme of each device.
Fig. 4 and 5 are a power supply equipment scheduling output curve and a heat supply equipment scheduling output curve in the agricultural system, respectively, that is, the optimal output scheme of each equipment in the whole agricultural system under the consideration of the carbon trading mechanism.
The above embodiments are implemented on the premise of the technical solution of the present invention, and detailed embodiments and specific operation procedures are given, but the scope of the present invention is not limited to the above embodiments. The methods used in the above examples are conventional methods unless otherwise specified.
Claims (6)
1. An agricultural industry park load optimization scheduling method considering carbon trading is characterized by comprising the following steps:
the method comprises the following steps: acquiring a carbon emission quota of a system and constructing a carbon emission cost model;
the system carbon emission quota is calculated by adopting a reference line method under the consideration of electric heating power generated by related units in production activities in an agricultural industry park, a carbon emission model is established by considering carbon emission coefficients of the related units on the basis of determining the system carbon emission quota, and a carbon trading market price factor is introduced to form a carbon trading cost model;
step two: analyzing the load adjustability characteristics of the agricultural industrial park and establishing a corresponding mathematical model;
time-shifting analysis is carried out on part of adjustable agricultural industry park loads in the agricultural industry park, mathematical models of related equipment and units are established, agricultural energy requirements in the park are met through various energy conversion, and the purposes of improving system operation economy and reducing system carbon emission can be achieved through adjusting part of adjustable agricultural industry park loads;
step three: constructing a target function with the lowest operation cost and carbon transaction cost of the agricultural system;
the construction of the objective function fully considers the system energy purchasing cost, the operation and maintenance cost and the carbon transaction cost, and realizes the dispatching of the agricultural industrial park load under low carbon by taking the optimal system economy as the target on the basis of considering the agricultural industrial park load adjustability;
step four: constructing constraint conditions of thermoelectric power equality constraint and agricultural industry park load time-shifting constraint;
step five: solving the objective function to obtain a day-ahead optimization scheduling scheme of the comprehensive energy system;
an improved sine and cosine algorithm is used for solving the objective function, in order to improve the calculation speed and the calculation precision of the method, a position updating formula of the method in the calculation process is improved, and the power supply and heat supply equipment output in the agricultural system is solved through continuous updating iteration to obtain an agricultural industry park load optimization scheduling scheme.
2. The agricultural industry park load optimization scheduling method considering carbon trading according to claim 1, wherein in the first step, the system carbon emission quota is calculated by a benchmark method, and the method comprises the following steps:
(1) Determining a carbon emission allowance: adopting a gratuitous distribution and providing a carbon emission quota for the system based on a reference line method;
in the formula: e p,t Actual carbon emission at time t; kappa is the carbon emission allocation amount of unit electric quantity of the region;
converting the electric quantity into a conversion coefficient; />
The electric power output by the cogeneration unit at the moment t; />
Respectively outputting thermal powers of an air heat source pump, a thermoelectric energy storage unit, a greenhouse and a cogeneration unit at the time t;
(2) Determining a carbon emission cost model
Actual carbon emission E of system at time t ac,t According to the emission factor method, the actual carbon emission of the unit is in direct proportion to the output of the unit, and the actual carbon emission E of the system at the time t ac,t Comprises the following steps:
in the formula: kappa type he 、κ kb 、κ dp 、κ rc Carbon emission coefficients of a combined heat and power generation unit, an air heat source pump, a greenhouse and a heat and power energy storage unit are respectively set; the user can trade the carbon emission quota by himself, namely the actual carbon emission is smaller than the carbon emission quota, and the surplus carbon emission quota can be sold at the market price to obtain the income; conversely, extra carbon credits are purchased from the market; thus, the carbon transaction cost C at time t Ca,t Comprises the following steps:
C Ca,t ＝k Ca (E ac,t -E p,t )
in the formula: k is a radical of Ca Is the carbon trading market price.
3. The method as claimed in claim 1, wherein the second step of analyzing the adjustable characteristic of the agricultural industry park load and establishing a corresponding mathematical model comprises the following steps:
(1) The greenhouse heat load mathematical model is as follows:
Q dre ＝Q 1 +Q 2 +Q 3 ＝∑s j a j (t in -t out )+0.5kvn(t in -t out )+∑z 1 h 1 (t in -t out )
in the formula: q dre The heat load value required by the greenhouse; q 1 Is a heat transfer load; q 2 Is the osmotic heat load; q 3 Is ground heat load; s is j The heat transfer coefficient of the jth building envelope; t is t in Is the temperature in the greenhouse; t is t out The temperature outside the greenhouse; k is a wind factor; v is the air volume of the greenhouse; n is the number of air changes per hour; z is a radical of formula 1 The ground heat transfer coefficient of the agricultural greenhouse; h is 1 The indoor ground area of the agricultural greenhouse;
(2) Combined heat and power generation model
The cogeneration system mainly comprises a small-sized methane power generation device, a methane tank and an absorption heat pump, and the mathematical model is as follows:
in the formula: q e (t) recovering the residual heat quantity of the flue gas of the small-sized methane power generation device at the moment t; p MBT (t) is the output electric power of the small-sized methane power generation device at the moment t; eta e The power generation efficiency of the small-sized methane power generation device is improved; eta 1 The loss coefficient of the heat dissipation part of the small-sized methane power generation device is reduced; q AP (t) is the thermal power of the absorption heat pump at the moment t; eta AP The heat recovery efficiency of the absorption heat pump is improved;
(3) Air source heat pump heat exchange model
The air source heat pump takes the energy of air as power, the compressor is driven by electric energy to realize the conversion of heat energy, complex and expensive equipment configuration is not needed, the emission of pollutants is reduced, the economy and the environmental protection are outstanding, and a mathematical model can be expressed as follows:
Q HP (t)＝C HP P EHP (t)
in the formula: q HP (t) is the output thermal power of the air source heat pump at the moment t; c HP Is the heating coefficient of the air source heat pump; p is EHP (t) the power consumption of the air source heat pump at the time t;
(4) Thermoelectric energy storage system model
Use the lithium cell as power storage device, the phase change heat reservoir is as heat-retaining device, because 2 kinds of devices fill and discharge can the characteristics similar, the effect is similar, so adopt unified mathematical model to be:
E(t)＝E(t-1)(1-δ)+ΔTP ch (t)η ch -ΔTP dis (t)/η dis
in the formula: e (t) is the total energy of the thermoelectric energy storage device at the moment t; delta is the self-discharge coefficient of the thermoelectric energy storage device; p is ch (t) is the charging efficiency of the thermoelectric energy storage device at the moment t; p is dis (t) the discharge power of the thermoelectric energy storage device at the moment t; eta ch As a heat storageThe energy charging efficiency coefficient of the energy device; eta dis The energy discharge efficiency coefficient of the thermoelectric energy storage device; Δ T is a unit period.
4. The agricultural industrial park load optimization scheduling method considering carbon trading according to claim 1, wherein in the third step, an objective function with the lowest agricultural system operation cost and carbon trading cost is constructed, and the method comprises the following steps:
at an energy purchase cost C Buy Carbon transaction cost C Ca And operation and maintenance cost C OP The minimum sum is the objective function:
minf＝C Buy +C Ca +C OP
1) Energy purchase cost C Buy The system can trade electric quantity with a superior power grid, and when the generated energy cannot meet the self demand, the system purchases power from the superior power grid, and correspondingly, when the generated energy is surplus, redundant electric quantity is sold to the superior power grid; in addition, the system needs to purchase natural gas to maintain the operation of the cogeneration unit and the gas boiler in the agricultural system; thus, the energy purchase cost is:
in the formula: t is an operation period:
purchasing and selling power from a superior power grid at the moment t respectively: />
The electricity price is bought and sold at the time t respectively; q b,t Buying the amount of natural gas for the moment t: kappa g Is the unit natural gas price;
2) Carbon transaction cost C Ca : the carbon trading cost for one run cycle is the sum of the costs at all times:
3) Operation and maintenance cost C OP ：
In the formula: i, taking constants of 1,2 and 3, which respectively represent equipment such as a cogeneration unit, an air heat source pump and the like; omega is the operation and maintenance coefficient of the equipment i; p i,t Is the output of device i.
5. The method for agricultural industry park load optimization scheduling considering carbon trading according to claim 1, wherein in the fourth step, the constraint conditions include the following:
1) The thermoelectric power equation is constrained to:
P LI (t)+P MBT (t)+P grid (t)+P PV (t)-P EHP (t)＝P e (t)Q HP (t)+Q AP (t)+Q BGB (t)+Q PT (t)＝Q h (t)
in the formula: p LI (t) is the electrical power of the battery at time t; p MBT (t) is the electric power of the small-sized methane power generation device at the moment t; p e (t) is the electric load of the micro energy network at the time t; p PV (t) is the electric power of the photovoltaic power generation device at the moment t; q BGB (t) is the thermal power of the biogas boiler at the moment t; q PT (t) is the thermal power of the phase-change heat storage device at the moment t; q h (t) is the heat load of the micro energy network at the time t;
2) Load timeshift constraints
In the formula: p after (t) is the load at time t after the load time shift; p fore (t) is the predicted load at time t before the time of load shift; p in (t) load at time t for planned transitions; p out (t) load at time t of planned transfer;
the power value of the kth load at the time when t is changed into t' from the time when t is changed; />
The power value of the kth load converted from the t' moment to the t moment is obtained; s is the load type.
6. The agricultural industrial park load optimization scheduling method considering carbon trading according to claim 1, wherein in the fifth step, an improved sine and cosine algorithm is used for solving an objective function to obtain power supply and heat supply equipment output in the agricultural industrial park, and an agricultural industrial park load optimization scheduling scheme is obtained:
the improved sine and cosine algorithm is specifically as follows: inputting related equipment parameters in an agricultural system, initializing the positions of all variables, and setting a position updating formula of the algorithm as follows in order to improve the accuracy and the convergence rate of the algorithm:
in the formula:
representing the position of the j-th dimension individual i at the time of the t-th generation selection; p i j (t) represents a target position; t is a unit of max Is selected as the maximumThe number of times; r is 2 Is the interval [0,2 π]The random number of (1); r is 3 Is [0,2 ]]The random number of (1); r is a radical of hydrogen 4 Is [0,1 ]]Random number of (1), r s 、r e Are respectively r 1 Initial and final values of (a), and r s >r e …0；
Calculating each candidate solution in the candidate solution set, comparing the fitness value of each candidate solution, then keeping the part with the higher fitness value, substituting the part into an algorithm updating formula to update the candidate solution set, and outputting the result by continuously updating iteration under the condition of meeting the maximum iteration times to obtain the optimal output scheme of each device.
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| CN116757453A (en) * | 2023-08-18 | 2023-09-15 | 国网智能电网研究院有限公司 | Low-carbon park building cluster coordination optimization scheduling method, device, equipment and medium |
| CN117371669A (en) * | 2023-12-06 | 2024-01-09 | 江苏米特物联网科技有限公司 | Park comprehensive energy system operation method considering carbon transaction risk cost |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116757453A (en) * | 2023-08-18 | 2023-09-15 | 国网智能电网研究院有限公司 | Low-carbon park building cluster coordination optimization scheduling method, device, equipment and medium |
| CN116757453B (en) * | 2023-08-18 | 2023-10-27 | 国网智能电网研究院有限公司 | Low-carbon park building cluster coordination optimization scheduling method, device, equipment and medium |
| CN117371669A (en) * | 2023-12-06 | 2024-01-09 | 江苏米特物联网科技有限公司 | Park comprehensive energy system operation method considering carbon transaction risk cost |
| CN117371669B (en) * | 2023-12-06 | 2024-03-12 | 江苏米特物联网科技有限公司 | Park comprehensive energy system operation method considering carbon transaction risk cost |
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