CN114648250A - Park comprehensive energy system planning method considering comprehensive demand response and carbon emission - Google Patents

Park comprehensive energy system planning method considering comprehensive demand response and carbon emission Download PDF

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CN114648250A
CN114648250A CN202210378037.2A CN202210378037A CN114648250A CN 114648250 A CN114648250 A CN 114648250A CN 202210378037 A CN202210378037 A CN 202210378037A CN 114648250 A CN114648250 A CN 114648250A
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孙磊
晋旭东
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Abstract

The invention discloses a park comprehensive energy system planning method considering comprehensive demand response and carbon emission, which comprises the following steps: 1. establishing a stepped carbon emission mechanism model and considering the influence of the stepped carbon emission mechanism model on the system operation; 2. establishing a comprehensive demand response model based on the real-time electric heating and cooling demand of the user and a demand elasticity demand model; 3. considering system actual constraints, establishing a park comprehensive energy system day-ahead scheduling optimization model by taking low-carbon, low-energy-consumption and high-user satisfaction weighted scheduling sum as an optimization target, and performing coordinated optimization on supply and demand sides; 4. and constructing a park comprehensive energy system planning model considering comprehensive demand response and carbon emission, considering a scheduling strategy of comprehensive energy, and solving by adopting a particle swarm algorithm to obtain an optimal equipment configuration planning scheme of the park comprehensive energy system. The invention optimizes the planning scheme of the comprehensive energy system under the conditions of meeting the user requirements and being as low as possible in environment, thereby improving the safety and the high efficiency of the comprehensive energy system.

Description

Park comprehensive energy system planning method considering comprehensive demand response and carbon emission
Technical Field
The invention belongs to the field of comprehensive energy system planning, and particularly relates to a park planning method considering comprehensive demand response and carbon emission;
background
With the further development of energy technology, Integrated Energy Systems (IES) coupled by various energy sources such as cold, hot and electricity and the like are deeply researched and widely applied, the problem of increasingly severe fossil fuel shortage and environmental pollution is brought due to the progress and development of human society, renewable energy sources are actively promoted to replace fossil energy sources, a carbon emission control commitment is made, the traditional scheduling method only optimizes the system supply side, cannot adjust the potential of the system demand side and cannot control the carbon emission, and the existing planning scheme cannot well balance the satisfaction degree of system users and the safety of system operation;
disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a park comprehensive energy system planning method considering comprehensive demand response and carbon emission so as to reduce system energy consumption and carbon emission and improve the consumption capability of renewable energy sources on the basis of meeting the energy demand of users, thereby improving the safety of system operation and obtaining an optimal planning and construction scheme.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a planning method of a park comprehensive energy system considering comprehensive demand response and carbon emission, the park comprehensive energy system comprises a supply side and a demand side, the supply side comprises a combined heat and power generation device CHP, a gas turbine GB and a P2G unit, and the demand side comprises an electric heat pump EHP, a central air conditioner AC, a heat exchanger HE and a refrigerator AF, and the planning method of the park comprehensive energy system is characterized by comprising the following steps:
step one, calculating the initial uncompensated carbon emission of the system by a reference value method:
step 1.1, calculating the discharge amount of the uncompensated carbon by using the formulas (1) to (4):
Figure BDA0003590982500000011
Figure BDA0003590982500000012
Figure BDA0003590982500000013
Figure BDA0003590982500000014
in the formulae (1) to (4),
Figure BDA0003590982500000015
representing the uncompensated initial carbon emission of applying electric quantity to a superior energy network;
Figure BDA0003590982500000016
represents the gratuitous initial carbon emission of the cogeneration plant CHP;
Figure BDA0003590982500000017
the method comprises the steps of representing the uncompensated initial carbon emission of a gas turbine unit GB;
Figure BDA0003590982500000018
represents the amount of the uncompensated initial carbon emission per unit of electricity;
Figure BDA0003590982500000021
the method comprises the steps of representing the application of electric quantity to a superior energy network within a t period;
Figure BDA0003590982500000022
represents the amount of the initial carbon emissions per unit of heat;
Figure BDA0003590982500000023
a conversion coefficient representing the conversion from the generated energy e of the cogeneration equipment to the calorific value h;
Figure BDA0003590982500000024
indicating that the heat and power cogeneration plant CHP consumes energy of natural gas for heat production in the period t;
Figure BDA0003590982500000025
indicating that the natural gas energy is consumed when the combined heat and power generation device CHP is used for generating electricity in the period t;
Figure BDA00035909825000000214
representing the natural gas energy consumed by the gas turbine unit equipment GB within the time period t; t represents a set of scheduling times;
step 1.2, calculating the actual carbon emission by using the formula (5) to the formula (6):
M＝Melcbuy+MCHP+MGB-MP2G (5)
Figure BDA0003590982500000026
in the formulae (5) to (6), MelcbuyRepresents the application of electric quantity and carbon emission to a superior energy network, MCHPDenotes the carbon emission, M, of the CHP of the cogeneration plantP2GRepresents the carbon consumption of the P2G unit; beta is agRepresents the carbon capture coefficient of the P2G unit;
Figure BDA00035909825000000215
represents the amount of power consumed by the P2G device during the period t;
step 1.3, calculating the step carbon emission C by using the formula (7)carbon：
Figure BDA0003590982500000027
In formula (7), c represents a carbon emission reference coefficient; alpha is the growth rate of the carbon emission coefficient; d is the length of the step interval;
step two, establishing a comprehensive demand response model based on the real-time electric heating and cooling demand of the user and a demand elasticity theory:
step 2.1, calculating the cold, heat and electricity substitution coefficient by using the formula (8) to the formula (10):
Figure BDA0003590982500000028
Figure BDA0003590982500000029
Figure BDA00035909825000000210
in the formulae (8) to (10),
Figure BDA00035909825000000211
and
Figure BDA00035909825000000212
is the electricity, heat and cold direct energy demand of the ith class of users at the time period t;
Figure BDA00035909825000000213
and
Figure BDA0003590982500000031
representing the energy utilization preference of the electric cooling load and the electric heating load of the ith class of users in the t period;
Figure BDA0003590982500000032
respectively represent the thermoelectric substitution coefficient and the cold electric substitution coefficient, and is prepared from
Figure BDA0003590982500000033
And
Figure BDA0003590982500000034
carrying out normalization to obtain; deltaiRepresenting the proportion of non-rigid electrical loads of the ith type of user;
step 2.2, calculating the comprehensive demand response quantity by using the formula (11) to the formula (12):
Figure BDA0003590982500000035
Figure BDA0003590982500000036
in the formulae (11) to (12),
Figure BDA0003590982500000037
representing the original electrical load of the ith type user t period; epsilontt′Representing the required mutual elastic coefficient between the t and t' periods; when t is t', epsilontt′Representing a required self-elastic coefficient;
Figure BDA0003590982500000038
a base electrical demand penalty representing a t' period;
Figure BDA0003590982500000039
representing electrical demand penalty variations for a period t';
Figure BDA00035909825000000310
and
Figure BDA00035909825000000311
representing the electric load, heat load and cold load response of the ith type user in the t period;
step 2.3, calculating comfort compensation quantity by using the formula (13):
Figure BDA00035909825000000312
in the formula (13), CcomfIndicating a comfort response compensation quantity, λcomfRepresents a comfort compensation per unit energy;
establishing a day-ahead scheduling optimization model of the park comprehensive energy system, considering practical constraints and taking the weighted scheduling sum with low carbon, low energy consumption and high user satisfaction as an optimization objective function, and performing coordinated optimization on the supply side and the demand side:
step 3.1, calculating the weighted sum of the low-carbon, low-energy-consumption and high-user satisfaction by using the formula (14) to the formula (16):
min C＝Cbuy+Ccarbon+Cpena+Ccomf (14)
Figure BDA00035909825000000313
Figure BDA00035909825000000314
in formula (14) to formula (16), CbuyThe total energy is claimed to the upper-level energy network; cpenaPunishment of wind and light abandonment;
Figure BDA00035909825000000317
and
Figure BDA00035909825000000318
respectively representing electric quantity, natural gas quantity and heat quantity of application from the t time period to the superior energy network; c. CpenaThe wind and light abandonment penalty coefficient;
Figure BDA00035909825000000315
and
Figure BDA00035909825000000316
is wind, light predicted power; ppv,tAnd Pwt,tThe photoelectric power and the wind power actually consumed by the system in the t period;
step 3.2, defining the energy supply side constraint by using the formula (17) to the formula (19):
Figure BDA0003590982500000041
Figure BDA0003590982500000042
Figure BDA0003590982500000043
in the formulae (17) to (19),
Figure BDA0003590982500000044
and
Figure BDA0003590982500000045
the output of a P2G unit, the electricity output of a CHP (combined heat and power generation) device, the CHP heat output of the CHP device and the output of a gas boiler GB in the t period of the system are represented; η represents the plant efficiency;
step 3.2, defining the energy demand side constraint by using the formula (20) to the formula (24):
Figure BDA00035909825000000415
Figure BDA00035909825000000416
Figure BDA0003590982500000046
Figure BDA0003590982500000047
Figure BDA0003590982500000048
in the formulae (20) to (24),
Figure BDA00035909825000000417
and
Figure BDA00035909825000000418
the output of an electric heat pump EHP, the output of a central air conditioner AC, the output of a heat exchanger HE and the output of a refrigerating machine AF in the t period of the system are represented;
Figure BDA00035909825000000419
and
Figure BDA00035909825000000420
representing the electric load demand, the heat load demand and the cold load demand of the system in a period t before response;
and 3.3, defining the equipment operation constraint by using the formulas (25) to (31):
Figure BDA0003590982500000049
Figure BDA00035909825000000410
Figure BDA00035909825000000411
Figure BDA00035909825000000412
Figure BDA00035909825000000413
Figure BDA00035909825000000414
Figure BDA0003590982500000051
step 3.4, defining the wind-solar force output constraint by using the formula (32) to the formula (33):
Figure BDA0003590982500000052
Figure BDA0003590982500000053
and 3.5, defining an electrical demand penalty variation constraint by using an equation (34):
Figure BDA0003590982500000054
step four, constructing a park comprehensive energy system planning model considering comprehensive demand response and carbon emission:
step 4.1, constructing an objective function of the park comprehensive energy system planning model by using the formula (35) to the formula (37):
Figure BDA0003590982500000055
Figure BDA0003590982500000056
Figure BDA0003590982500000057
in the formula (35) to the formula (37), ΩYIs a planning year set; omegaDIs a planned set of devices;
Figure BDA0003590982500000058
is the type set of the d device; omegaSA set of quarters; cinvestIs an investment consumable;
Figure BDA0003590982500000059
for the operation of the year yConsumable materials;
Figure BDA00035909825000000510
the investment supplies of the type c are the d equipment; x is the number ofc,dThe Boolean variable indicates whether to invest the type c of the type d equipment;
Figure BDA00035909825000000511
the low-carbon low-energy-consumption high-user satisfaction weighted sum of the numbers is obtained for the typical day of the s quarter in the y year; n issNumber of days of the s quarter; rho is the loss rate;
and 4.2, defining the investment type constraint of the equipment by using an equation (38):
Figure BDA00035909825000000512
formula (38) indicates that for any class d equipment, the type of investment does not exceed 1;
step 4.3, calculating the predicted value P of the load of the s-th quarter of the y year by using the formula (39)j,y,s：
Pj,y,s＝(1+γ)tPj,0,s,j∈ΩB,y∈ΩY,s∈ΩS (39)
In the formula (39), γ represents the annual load growth rate; pi,0,sIs the load value of node j in the s quarter of the current year;
step five, solving a park comprehensive energy system planning model by adopting a particle swarm algorithm:
step 5.1, inputting initial parameters, including: particle swarm size M, learning factor c1And c2Inertia weight w, particle swarm reproduction algebra McMonte Carlo simulation times MsConfidence interval beta;
step 5.2, randomly generating M initial particles and forming a particle set M ═ M1,m2,…,mk,…,mMIn which m iskKth particle, expressed from ΩDIn the k-th planning scheme, and mk＝{mk1,mk2,…,mkd,…mkDIn which m iskdRepresenting the capacity of the class d equipment selected by the kth particle;
step 5.3, calculating investment consumables according to the equipment capacity planning scheme corresponding to each particle;
step 5.4, updating the load demand in the y year according to the formula (39), and calculating the operation consumable items in the s quarter typical day in the y year; calculating total operation consumables in a planning year, and taking the sum of the calculated investment consumables and the weighted modulation sum of the low-carbon low-energy consumption high user satisfaction as the fitness of each particle;
step 5.5, updating the position and the speed of the particles so as to obtain new particles;
step 5.6, repeating the step 5.3 to the step 5.5 until a given particle swarm breeding algebra M is reachedcUntil the end;
and 5.7, taking the equipment capacity corresponding to the best particles as an optimal planning scheme of the park comprehensive energy system.
Compared with the prior art, the invention has the beneficial effects that:
the invention considers the carbon emission and the comprehensive demand response, reduces the operation consumables of system operators under the conditions of meeting the user demand and being as low as possible in environment, and provides a comprehensive energy system planning scheme, thereby controlling the carbon emission of the system, enhancing the renewable energy consumption capability of the system, and further improving the reliability and the high efficiency of the comprehensive energy system.
Drawings
FIG. 1 is a diagram of a campus complex energy system architecture;
FIG. 2 is a schematic diagram of a step carbon emission mechanism;
FIG. 3 is a flow chart of the method of the present invention.
Detailed Description
In this embodiment, a method for planning a campus comprehensive energy system considering comprehensive demand response and carbon emission is applied to a campus comprehensive energy system shown in fig. 1, where the campus comprehensive energy system includes a supply side and a demand side, the supply side includes a cogeneration facility CHP, a gas turbine GB and a P2G unit, the demand side includes an electric heat pump EHP, a central air conditioner AC, a heat exchanger HE and a chiller AF, and the method for planning a campus comprehensive energy system includes the main steps of:
1) fully transferring optimization potential of a user side through comprehensive demand response, considering carbon emission in an operation model objective function according to the graph shown in FIG. 2, abandoning carbon emission of a wind and light control system and enhancing the consumption capability of renewable energy, establishing a planning model on the basis of the operation model, wherein the proposed planning scheme can be completely suitable for an operation scheme with low carbon, low energy consumption and high user satisfaction;
2) based on the energy use historical data of the system users, the cold, heat and electricity substitution coefficients of different types of users in different time periods are obtained, so that the comprehensive demand response quantity is calculated, and the satisfaction degree of the system users can be considered;
3) establishing a garden comprehensive energy system day-ahead scheduling optimization model, wherein the objective function is weighted and adjusted by minimizing the low-carbon, low-energy and high-user satisfaction of the system, and comprises the application amount to a superior energy network, the carbon emission amount, the wind and light abandoning punishment and the comprehensive demand response comfort compensation amount; the constraint conditions comprise comprehensive energy balance constraint, equipment operation constraint and wind-solar output constraint;
4) solving an equipment capacity planning model by adopting a particle swarm algorithm according to the graph shown in FIG. 3, calling a campus comprehensive energy system day-ahead scheduling optimization model aiming at each sampling result, solving planning and operating consumables, and taking the best particles as an optimal planning scheme of an optimization problem; specifically, the method comprises the following steps:
step one, calculating the initial uncompensated carbon emission of the system by a reference value method to obtain:
step 1.1, calculating the discharge amount of the uncompensated carbon by using the formulas (1) to (4):
Figure BDA0003590982500000071
Figure BDA0003590982500000072
Figure BDA0003590982500000073
Figure BDA0003590982500000074
equation (1) represents the total initial carbon emission of the system; equations (2), (3), and (4) represent the amount of emission of the uncompensated carbon for applying electricity to the upper-level energy network, the amount of emission of the uncompensated carbon for the cogeneration plant CHP, and the amount of emission of the uncompensated carbon for the gas turbine GB, respectively;
in the formulae (1) to (4),
Figure BDA0003590982500000075
representing the uncompensated initial carbon emission of applying electric quantity to a superior energy network;
Figure BDA0003590982500000076
represents the gratuitous initial carbon emission of the cogeneration plant CHP;
Figure BDA0003590982500000077
the method comprises the steps of representing the uncompensated initial carbon emission of a gas turbine unit GB;
Figure BDA0003590982500000078
represents the amount of the uncompensated initial carbon emission per unit of electricity;
Figure BDA00035909825000000714
the method comprises the steps of representing the application of electric quantity to a superior energy network within a t period;
Figure BDA0003590982500000079
represents the amount of the initial carbon emissions per unit of heat;
Figure BDA00035909825000000710
a conversion coefficient representing the amount of power generation of the cogeneration plant CHP to the amount of heat generation;
Figure BDA00035909825000000711
indicating that the heat and power cogeneration plant CHP consumes energy of natural gas for heat production in the period t;
Figure BDA00035909825000000712
indicating that the natural gas energy is consumed when the combined heat and power generation device CHP is used for generating electricity in the period t;
Figure BDA00035909825000000715
indicating that the gas turbine unit GB consumes natural gas energy in the t period;
step 1.2, calculating the actual carbon emission by using the formula (5) to the formula (6):
M＝Melcbuy+MCHP+MGB-MP2G (5)
Figure BDA00035909825000000713
equation (5) represents the actual carbon emissions of the system; formula (6) represents the carbon consumption of the P2G unit;
equation (5) -equation (6) calculates the actual carbon emission, M, of the computing systemelcbuyRepresents the application of electric quantity and carbon emission to a superior energy network, MCHPIndicating CHP carbon emission, M, of a cogeneration plantP2GRepresenting the carbon consumption of the P2G unit; beta is a betagRepresents the carbon capture coefficient of the P2G unit;
Figure BDA00035909825000000814
representing the electric quantity consumed by the P2G unit in the t period;
step 1.3, calculating the step carbon emission by using the formula (7):
Figure BDA0003590982500000081
c in formula (7) represents a carbon emission reference coefficient; alpha is the growth rate of the carbon emission coefficient; d is the length of the step interval;
step two, establishing a comprehensive demand response model based on the real-time electric heating and cooling demand of the user and a demand elasticity theory:
step 2.1, calculating the cold, heat and electricity substitution coefficient by using the formula (8) to the formula (10):
Figure BDA0003590982500000082
Figure BDA0003590982500000083
Figure BDA0003590982500000084
the formula (8) and the formula (9) are calculation methods of the electric cooling and heating substitution coefficient; equation (10) is a constraint on the electrothermal heat substitution coefficient;
in the formulae (8) to (10),
Figure BDA0003590982500000085
and
Figure BDA0003590982500000086
is the electricity, heat and cold direct energy demand of the ith class of users at the time period t;
Figure BDA0003590982500000087
and
Figure BDA0003590982500000088
representing the energy utilization preference of the electric cooling load and the electric heating load of the ith class of users in the t period;
Figure BDA0003590982500000089
respectively represent the thermoelectric substitution coefficient and the cold electric substitution coefficient, and is prepared from
Figure BDA00035909825000000810
And
Figure BDA00035909825000000811
carrying out normalization to obtain; deltaiRepresenting i-th type userProportion of non-rigid electrical load;
step 2.2, calculating the comprehensive demand response quantity by using the formula (11) to the formula (12):
Figure BDA00035909825000000812
Figure BDA00035909825000000813
the formula (11) is used for calculating the electric load response according to the demand elasticity theory; equation (12) is the electrical cooling thermal load response;
in the formulae (11) to (12),
Figure BDA0003590982500000091
representing the original electrical load of the ith type user t period; epsilontt′Representing the required mutual elastic coefficient between the t and t' periods; when t is t', epsilontt′Representing a required self-elastic coefficient; (ii) a
Figure BDA0003590982500000092
A base electrical demand penalty representing a t' period;
Figure BDA0003590982500000093
representing electrical demand penalty variations for a period t';
Figure BDA0003590982500000094
and
Figure BDA0003590982500000095
representing the electric, heat and cold load response of the ith type user t period;
step 2.3, calculating comfort compensation quantity by using the formula (13):
Figure BDA0003590982500000096
equation (13) is used to calculate the comfort compensation amount of the system due to the comprehensive demand response;
in the formula (13), CcomfIndicating the comfort response compensation, λcomfRepresents a comfort compensation per unit energy;
establishing a park comprehensive energy system day-ahead scheduling optimization model, considering practical constraints, taking low-carbon, low-energy-consumption and high-user satisfaction weighted scheduling sum minimum as an optimization objective function, and performing coordinated optimization on the supply side and the demand side:
step 3.1, calculating the weighted sum of the low-carbon, low-energy-consumption and high-user satisfaction by using the formula (14) to the formula (16):
min C＝Cbuy+Ccarbon+Cpena+Ccomf (14)
Figure BDA0003590982500000097
Figure BDA0003590982500000098
equation (14) is the system operation model objective function; the formula (15) and the formula (16) are the application amount to the upper-level energy network and the wind and light abandonment penalty;
in formula (14) to formula (16), CbuyThe total energy is claimed to the upper-level energy network; cpenaPunishment of wind and light abandonment;
Figure BDA00035909825000000914
and
Figure BDA00035909825000000915
respectively representing electric quantity, claimed natural gas quantity and claimed heat quantity claimed to the superior energy network in the t period; c. CpenaIs a wind and light abandoning penalty coefficient;
Figure BDA0003590982500000099
and
Figure BDA00035909825000000910
is the wind and light predicted power; ppv,tAnd Pwt,tThe photoelectric power and the wind power actually consumed by the system in the t period;
step 3.2, defining the energy supply side constraint by using the formula (17) to the formula (19):
Figure BDA00035909825000000911
Figure BDA00035909825000000912
Figure BDA00035909825000000913
equation (17) is the energy supply side power balance constraint; equation (18) is the energy supply side thermal energy balance constraint; equation (19) is the energy supply side natural gas balance constraint;
in the formulae (17) to (19),
Figure BDA00035909825000001013
and
Figure BDA0003590982500000102
the output of a P2G machine set, the output of CHP electricity of cogeneration equipment, the output of CHP heat of cogeneration equipment and the output of a gas boiler GB are represented; η represents the plant efficiency;
step 3.2, defining the energy demand side constraint by using the formula (20) to the formula (24):
Figure BDA00035909825000001014
Figure BDA00035909825000001015
Figure BDA0003590982500000103
Figure BDA0003590982500000104
Figure BDA0003590982500000105
equations (20) and (21) represent the balance of electric energy and thermal energy on the energy supply side and the user side; equation (22) represents user side power balance; equations (23) and (24) represent the user-side heat and cold energy balance;
in the formulae (20) to (24),
Figure BDA00035909825000001016
and
Figure BDA00035909825000001017
the output of an electric heat pump EHP, the output of a central air conditioner AC, the output of a heat exchanger HE and the output of a refrigerating machine AF in the t period of the system are represented;
Figure BDA00035909825000001018
and
Figure BDA00035909825000001019
representing the electric load demand, the heat load demand and the cold load demand of the system in a period t before response;
and 3.3, defining the equipment operation constraint by using the formulas (25) to (31):
Figure BDA0003590982500000106
Figure BDA0003590982500000107
Figure BDA0003590982500000108
Figure BDA0003590982500000109
Figure BDA00035909825000001010
Figure BDA00035909825000001011
Figure BDA00035909825000001012
equations (25), (26) and (27) are upper and lower limit constraints for the system to claim electricity, heat and gas to the upper network; equations (28) and (29) are the CHP power generation capacity constraint and the heat generation capacity constraint; equations (30) and (31) are capacity constraints for gas turbine GB and P2G plants;
step 3.4, defining the wind-solar force output constraint by using the formula (32) to the formula (33):
Figure BDA0003590982500000111
Figure BDA0003590982500000112
equations (32) and (33) are the photovoltaic output limit and the wind power output limit;
and 3.5, defining an electrical demand penalty variation constraint by using an equation (34):
Figure BDA0003590982500000113
equation (34) is the electrical demand penalty variation upper and lower bound limits;
step four, constructing a park comprehensive energy system planning model considering comprehensive demand response and carbon emission:
step 4.1, constructing an objective function of the park comprehensive energy system planning model by using the formula (35) to the formula (37):
Figure BDA0003590982500000114
Figure BDA0003590982500000115
Figure BDA0003590982500000116
equation (35) is the objective function of the system planning model; the equations (36) and (37) are weighted sums of the calculated investment consumables, the low-carbon low-energy consumption high-user satisfaction;
in the formula (35) to the formula (37), ΩYIs a planning year set; omegaDIs a set of planned devices;
Figure BDA0003590982500000117
is the type set of the d device; omegaSA set of quarters; cinvestIs an investment consumable;
Figure BDA0003590982500000118
is a running consumable in the y year;
Figure BDA0003590982500000119
the investment supplies of the type c are the d equipment; x is the number ofc,dIs a Boolean variable; indicating whether to invest the d type equipment and the c type;
Figure BDA00035909825000001110
the weighted sum of the low-carbon, low-energy consumption and high user satisfaction of the typical day of the s quarter in the y year is calculated according to the formula (14); n issIn the s seasonDays of degree; rho is the loss rate;
and 4.2, defining the investment type constraint of the equipment by using an equation (38):
Figure BDA00035909825000001111
formula (38) indicates that for any class d equipment, the type of investment does not exceed 1;
step 4.3, calculating the predicted value P of the load of the s-th quarter of the y year by using the formula (39)j,y,s：
Pj,y,s＝(1+γ)tPj,0,s,j∈ΩB,y∈ΩY,s∈ΩS (39)
In the formula (39), γ represents the annual load growth rate; pi,0,sIs the load value of node j in the s quarter of the current year;
step five, as shown in fig. 3, solving the park comprehensive energy system planning model by adopting a particle swarm algorithm:
step 5.1, inputting initial parameters including particle swarm size M and learning factor c1And c2Inertia weight w, particle swarm reproduction algebra McMonte Carlo simulation times MsConfidence interval beta;
step 5.2, randomly generating M initial particles, wherein the particle set M is { M ═ M1,m2,…,mk,…,mMIn which m iskDenotes the kth particle, i.e. from ΩDIn the k-th planning scheme, and mk＝{mk1,mk2,…,mkd,…mkDIn which m iskdRepresenting the capacity of the class d equipment selected by the kth particle;
step 5.3, for each particle, planning the equipment capacity scheme m according to the particlekCalculating investment consumables;
step 5.4, updating the load demand of the y year according to the formula (39), and calculating the operation consumables of the s quarter typical day of the y year; calculating total operation consumables in a planning year, and taking the sum of the calculated investment consumables and the weighted modulation sum of the low-carbon low-energy consumption high user satisfaction as the fitness of each particle;
step 5.5, updating the position and the speed of the particles so as to obtain new particles;
step 5.6, repeating the step 5.3 to the step 5.5 until a given particle swarm breeding generation M is reachedc；
And 5.7, taking the equipment capacity corresponding to the best particles as an optimal planning scheme of the park comprehensive energy system.

Claims (1)

1. A park integrated energy system planning method considering integrated demand response and carbon emission, the park integrated energy system comprises a supply side and a demand side, the supply side comprises a combined heat and power generation device CHP, a gas turbine GB and a P2G unit, the demand side comprises an electric heat pump EHP, a central air conditioner AC, a heat exchanger HE and a refrigerator AF, and the park integrated energy system planning method is characterized by comprising the following steps:
step one, calculating the initial gratuitous carbon emission of the system through a reference value method:
step 1.1, calculating the emission of the gratuitous carbon by using a formula (1) to a formula (4):
Figure FDA0003590982490000011
Figure FDA0003590982490000012
Figure FDA0003590982490000013
Figure FDA0003590982490000014
in the formulae (1) to (4),
Figure FDA0003590982490000015
representing the uncompensated initial carbon emission of applying electric quantity to a superior energy network;
Figure FDA0003590982490000016
represents the gratuitous initial carbon emission of the cogeneration plant CHP;
Figure FDA0003590982490000017
the method comprises the steps of representing the uncompensated initial carbon emission of a gas turbine unit GB;
Figure FDA0003590982490000018
represents the amount of the uncompensated initial carbon emission per unit of electricity;
Figure FDA0003590982490000019
the method comprises the steps of representing the application of electric quantity to a superior energy network within a t period;
Figure FDA00035909824900000110
represents the amount of the initial carbon emissions per unit of heat;
Figure FDA00035909824900000111
a conversion coefficient representing the conversion from the generated energy e of the cogeneration equipment to the calorific value h;
Figure FDA00035909824900000112
indicating that the heat and power cogeneration plant CHP consumes energy of natural gas for heat production in the period t;
Figure FDA00035909824900000113
indicating that the natural gas energy is consumed when the combined heat and power generation device CHP is used for generating electricity in the period t;
Figure FDA00035909824900000114
representing the natural gas energy consumed by the gas turbine unit equipment GB within the time period t; t represents a set of scheduling times;
step 1.2, calculating the actual carbon emission by using the formula (5) to the formula (6):
M＝Melcbuy+MCHP+MGB-MP2G (5)
Figure FDA00035909824900000115
in the formulae (5) to (6), MelcbuyRepresents the application of electric quantity and carbon emission to a superior energy network, MCHPDenotes the carbon emission, M, of the CHP of the cogeneration plantP2GRepresents the carbon consumption of the P2G unit; beta is agRepresents the carbon capture coefficient of the P2G unit;
Figure FDA00035909824900000116
represents the amount of power consumed by the P2G device during the t period;
step 1.3, calculating the step carbon emission C by using the formula (7)carbon：
Figure FDA0003590982490000021
In the formula (7), c represents a carbon emission reference coefficient; alpha is the growth rate of the carbon emission coefficient; d is the length of the step interval;
step two, establishing a comprehensive demand response model based on the real-time electric heating and cooling demand of the user and a demand elasticity theory:
step 2.1, calculating the cold, heat and electricity substitution coefficient by using the formula (8) to the formula (10):
Figure FDA0003590982490000022
Figure FDA0003590982490000023
Figure FDA0003590982490000024
in the formulae (8) to (10),
Figure FDA0003590982490000025
and
Figure FDA0003590982490000026
is the electricity, heat and cold direct energy demand of the ith class of users at the time period t;
Figure FDA0003590982490000027
and
Figure FDA0003590982490000028
representing the energy utilization preference of the electric cooling load and the electric heating load of the ith class of users in the t period;
Figure FDA0003590982490000029
respectively represent the thermoelectric substitution coefficient and the cold electric substitution coefficient, and is prepared from
Figure FDA00035909824900000210
And
Figure FDA00035909824900000211
carrying out normalization to obtain; deltaiRepresenting the proportion of non-rigid electrical loads of the ith type of user;
step 2.2, calculating the comprehensive demand response quantity by using the formula (11) to the formula (12):
Figure FDA00035909824900000212
Figure FDA00035909824900000213
in the formulae (11) to (12),
Figure FDA00035909824900000214
representing the original electrical load of the ith type user t period; epsilontt′Representing the required mutual elastic coefficient between the t and t' periods; when t is t', epsilontt′Representing a required self-elastic coefficient;
Figure FDA00035909824900000215
a base electrical demand penalty representing a t' period;
Figure FDA00035909824900000216
representing electrical demand penalty variations for a period t';
Figure FDA00035909824900000217
and
Figure FDA00035909824900000218
representing the electric load, heat load and cold load response of the ith type user in the t period;
step 2.3, calculating comfort compensation quantity by using the formula (13):
Figure FDA0003590982490000031
in the formula (13), CcomfIndicating a comfort response compensation quantity, λcomfA comfort compensation quantity representing a unit of energy;
establishing a day-ahead scheduling optimization model of the park comprehensive energy system, considering practical constraints and taking the weighted scheduling sum with low carbon, low energy consumption and high user satisfaction as an optimization objective function, and performing coordinated optimization on the supply side and the demand side:
step 3.1, calculating the weighted sum of the low-carbon, low-energy-consumption and high-user satisfaction by using the formula (14) to the formula (16):
min C＝Cbuy+Ccarbon+Cpena+Ccomf (14)
Figure FDA0003590982490000032
Figure FDA0003590982490000033
in the formulae (14) to (16), CbuyThe total energy is claimed to the upper-level energy network; cpenaPunishment of wind and light abandonment;
Figure FDA0003590982490000034
and
Figure FDA0003590982490000035
respectively representing electric quantity, natural gas quantity and heat quantity of application from the t time period to the superior energy network; c. CpenaThe wind and light abandonment penalty coefficient;
Figure FDA0003590982490000036
and
Figure FDA0003590982490000037
is wind, light predicted power; ppv,tAnd Pwt,tThe photoelectric power and the wind power actually consumed by the system in the t period;
step 3.2, defining the energy supply side constraint by using the formula (17) to the formula (19):
Figure FDA0003590982490000038
Figure FDA0003590982490000039
Figure FDA00035909824900000310
in the formulae (17) to (19),
Figure FDA00035909824900000311
and
Figure FDA00035909824900000312
the output of a P2G unit, the electricity output of a CHP (combined heat and power generation) device, the CHP heat output of the CHP device and the output of a gas boiler GB in the t period of the system are represented; η represents the plant efficiency;
step 3.2, defining the energy demand side constraint by using the formula (20) to the formula (24):
Figure FDA00035909824900000313
Figure FDA00035909824900000314
Figure FDA00035909824900000315
Figure FDA0003590982490000041
Figure FDA0003590982490000042
in the formulae (20) to (24),
Figure FDA0003590982490000043
and
Figure FDA0003590982490000044
the output of an electric heat pump EHP, the output of a central air conditioner AC, the output of a heat exchanger HE and the output of a refrigerating machine AF in the t period of the system are represented;
Figure FDA0003590982490000045
and
Figure FDA0003590982490000046
representing the electric load demand, the heat load demand and the cold load demand of the system in a period t before response;
step 3.3, defining the equipment operation constraint by using the formula (25) to the formula (31):
Figure FDA0003590982490000047
Figure FDA0003590982490000048
Figure FDA0003590982490000049
Figure FDA00035909824900000410
Figure FDA00035909824900000411
Figure FDA00035909824900000412
Figure FDA00035909824900000413
step 3.4, defining the wind-solar output constraint by using the formula (32) to the formula (33):
Figure FDA00035909824900000414
Figure FDA00035909824900000415
and 3.5, defining an electrical demand penalty variation constraint by using an equation (34):
Figure FDA00035909824900000416
step four, constructing a park comprehensive energy system planning model considering comprehensive demand response and carbon emission:
step 4.1, constructing an objective function of the park comprehensive energy system planning model by using the formula (35) to the formula (37):
Figure FDA00035909824900000417
Figure FDA00035909824900000418
Figure FDA00035909824900000419
in the formula (35) to the formula (37), ΩYIs a planning year set; omegaDIs a set of planned devices;
Figure FDA00035909824900000420
is a set of types for the d device; omegaSA set of quarters; cinvestIs an investment consumable;
Figure FDA0003590982490000051
is a running consumable in the y year;
Figure FDA0003590982490000052
the investment supplies of the type c are the d equipment; x is the number ofc,dThe Boolean variable indicates whether to invest the type c of the type d equipment;
Figure FDA0003590982490000053
the low-carbon low-energy-consumption high-user satisfaction weighted sum of the numbers is obtained for the typical day of the s quarter in the y year; n issNumber of days of the s quarter; rho is the loss rate;
and 4.2, defining the investment type constraint of the equipment by using an equation (38):
Figure FDA0003590982490000054
formula (38) indicates that the type of investment does not exceed 1 for any class d equipment;
step 4.3, calculating the predicted value P of the load of the s-th quarter of the y year by using the formula (39)j,y,s：
Pj,y,s＝(1+γ)tPj,0,s,j∈ΩB,y∈ΩY,s∈ΩS (39)
In the formula (39), γ represents the annual load growth rate; pi,0,sIs the load value of node j in the s quarter of the current year;
step five, solving a park comprehensive energy system planning model by adopting a particle swarm algorithm:
step 5.1, inputting initial parameters, including: particle swarm size M and learning factor c1And c2Inertia weight w, particle swarm reproduction algebra McMonte Carlo simulation times MsConfidence interval beta;
step 5.2, randomly generating M initial stagesStarting particles and forming a set of particles M ═ M1,m2,…,mk,…,mMIn which m iskKth particle, expressed from ΩDIn the k-th planning scheme, and mk＝{mk1,mk2,…,mkd,…mkDIn which m iskdRepresenting the capacity of the class d equipment selected by the kth particle;
step 5.3, calculating investment consumables according to the equipment capacity planning scheme corresponding to each particle;
step 5.4, updating the load demand in the y year according to the formula (39), and calculating the operation consumable items in the s quarter typical day in the y year; calculating total operation consumables in a planning year, and taking the sum of the calculated investment consumables and the weighted modulation number with low carbon, low energy consumption and high user satisfaction as the fitness of each particle;
step 5.5, updating the position and the speed of the particles so as to obtain new particles;
step 5.6, repeating the step 5.3 to the step 5.5 until a given particle swarm breeding algebra M is reachedcUntil the end;
and 5.7, taking the equipment capacity corresponding to the best particles as an optimal planning scheme of the park comprehensive energy system.
CN202210378037.2A 2022-04-12 2022-04-12 Park comprehensive energy system planning method considering comprehensive demand response and carbon emission Pending CN114648250A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115470564A (en) * 2022-10-08 2022-12-13 江苏智慧用能低碳技术研究院有限公司 Public building energy system coordination control method and control assembly thereof
CN117035244A (en) * 2023-10-10 2023-11-10 成都市智慧蓉城研究院有限公司 Space planning information acquisition method and system based on identification analysis

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115470564A (en) * 2022-10-08 2022-12-13 江苏智慧用能低碳技术研究院有限公司 Public building energy system coordination control method and control assembly thereof
CN117035244A (en) * 2023-10-10 2023-11-10 成都市智慧蓉城研究院有限公司 Space planning information acquisition method and system based on identification analysis
CN117035244B (en) * 2023-10-10 2024-02-02 成都市智慧蓉城研究院有限公司 Space planning information acquisition method and system based on identification analysis

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