CN108154309B - Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity - Google Patents

Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity Download PDF

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CN108154309B
CN108154309B CN201711491988.6A CN201711491988A CN108154309B CN 108154309 B CN108154309 B CN 108154309B CN 201711491988 A CN201711491988 A CN 201711491988A CN 108154309 B CN108154309 B CN 108154309B
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cold
heat
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CN108154309A (en
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霍现旭
赵宝国
严晶晶
项添春
徐科
赵新
李国栋
盛业宏
李淋
徐青山
孙璐
胡澄
王旭东
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State Grid Corp of China SGCC
Southeast University
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
Southeast University
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention relates to an energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity, which is technically characterized by comprising the following steps of: the method comprises the following steps: step 1, establishing a dynamic response model of a cold, heat and power load on a demand side of a micro energy internet; step 2, constructing a micro energy internet economic optimization scheduling model considering multi-load dynamic response; and 3, solving the micro energy internet economic optimization scheduling model considering the multi-load dynamic response in the step 2 based on a quantum differential evolution algorithm. The invention can greatly develop the schedulable potential of multi-source load at the energy demand side and the cooperative complementary characteristic of the schedulable potential and the energy supply side, thereby effectively reducing the energy supply and demand cost of the micro energy internet and further improving the operation economy of the system.

Description

Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity
Technical Field
The invention belongs to the technical field of energy Internet, and relates to a micro energy Internet economic dispatching method, in particular to an energy Internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity.
Background
The healthy development of national economy is not always continuous and reliable energy supply, but the traditional fossil fuel has limited resource reserves, is difficult to support the rapidly-increased energy consumption demand, and the energy shortage is increasingly obvious. Therefore, how to optimize the energy structure, improve the energy utilization efficiency and realize open source throttling has become a common concern at home and abroad. In recent years, an energy internet technology is taken as an important expression form of 'internet + intelligent energy', and has good application prospects in the aspects of large-scale renewable energy consumption, cross-regional multi-energy complementation, energy efficient utilization and the like, and the energy internet technology emphasizes the cooperative complementation and optimized scheduling among various energy resources taking electricity, gas, cold, heat and the like as main forms, so that the large-scale consumption of the renewable energy such as wind energy, solar energy, tidal energy and the like and the efficient utilization of the traditional fossil energy are realized, the stable supply and demand of a multi-energy system are ensured, and a new thought is provided for solving the problem of energy shortage faced at the present stage.
At present, intensive research is carried out on energy internet technology at home and abroad, and certain achievements are obtained in related directions. However, the combined optimization operation research of the multi-energy hybrid system still has shortcomings, and part of optimization strategies focuses on realizing comprehensive utilization of multiple types of energy through complementary output and coordination control among different energy devices in system scheduling, neglects the positive effect of demand side load resources on system optimization scheduling, or only considers the demand response of the electrical load under the excitation of real-time electricity prices, and does not consider the time lag schedulability of cold and hot loads due to the thermal inertia of a carrier medium.
Disclosure of Invention
The invention aims to provide an energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity, which can solve the operation optimization problem of a micro energy internet system with rich energy supply and storage unit resources.
The invention solves the practical problem by adopting the following technical scheme:
an energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity comprises the following steps:
step 1, establishing a dynamic response model of a cold, heat and power load on a demand side of a micro energy internet;
step 2, constructing a micro energy internet economic optimization scheduling model considering multi-load dynamic response;
and 3, solving the micro energy internet economic optimization scheduling model considering the multi-load dynamic response in the step 2 based on a quantum differential evolution algorithm.
Further, the specific steps of step 1 include:
(1) modeling the cold/heat load time-lag response characteristic;
Figure BDA0001535743380000021
Figure BDA0001535743380000022
in the formula, ci、miThe specific heat capacity and the mass of the i-type cold/heat load carrier are respectively;
Figure BDA0001535743380000023
the temperature of the carrier and the energy supply power to the load carrier at the moment t are respectively the temperature of the i-type cold/hot load; t ist,exWhich is indicative of the temperature of the external environment,
Figure BDA0001535743380000024
the dissipation power of the cold/hot load carrier at the moment t is in direct proportion to the ambient temperature difference of the carrier, and the proportionality coefficient is alpha;
integrating the left side and the right side of the differential equation in a time interval delta t, performing approximation treatment, and combining a dissipation power expression to obtain a differentiated algebraic equation:
Figure BDA0001535743380000025
in the formula (I), the compound is shown in the specification,
Figure BDA0001535743380000026
the temperature of the cold/hot load carrier at the time t + delta t, the energy supply power of the load carrier and the dissipation power of the load carrier are respectively;
for a general cold/heat load user, the influence of the small-range variation of the temperature of the carrier medium on the actual use effect can be accepted, namely, the size of the cold/heat load can be properly regulated and controlled within a reasonable range, and a schedulable domain of the cold/heat load can be obtained:
Figure BDA0001535743380000027
wherein the content of the first and second substances,
Figure BDA0001535743380000028
the temperature fluctuation limit value of the i-type cold and hot load carrier at the time t within the user use efficiency allowable range;
the above formulas are combined to obtain an adjustable model of the cold and hot load participating system response;
(2) establishing an electricity price excitation response model of two types of typical flexible electric loads;
1) for power-adjustable electrical loads, there are:
Figure BDA0001535743380000031
of formula (II) to (III)'e1,tThe total demand after the response is participated in for all the power adjustable electric loads at the time t;
Figure BDA0001535743380000032
the demand value before the power adjustable type electric load user i participates in the response; beta (c)grid,t) The power price exciting coefficient is in positive correlation with the real-time electric energy price, and reflects the active response degree of the power adjustable type electric load user to the power price; n is a radical of1Representing the number of users of the power adjustable type electric load;
2) the adjustable electric loads of the operation period comprise:
Figure BDA0001535743380000033
Figure BDA0001535743380000034
Figure BDA0001535743380000035
in the formula, Pe2,t、P′e2,tLoad demands before and after the adjustable electric load participates in response in the operation period of the t time period;
Figure BDA0001535743380000036
the load value is transferred from the load i to the operation in other time periods for the time period t;
Figure BDA0001535743380000037
the load value of the electric load i is transferred from the k time period to the t time period;
Figure BDA0001535743380000038
adjusting the maximum transferable value of the class electric load i for any k time period operation time period;
Figure BDA0001535743380000039
for the electrovalence excitation response coefficient of this type of load, when cgrid,k-cgrid,tIs more than or equal to 0
Figure BDA00015357433800000310
And has positive correlation with the electric energy price difference in time period, otherwise, the value is 0; m, N2The number of time-sharing sections and the number of adjustable electric load users in the time-sharing section are divided in the system running period.
Further, the specific steps of step 2 include:
(1) establishing an economic optimization target of a micro energy internet economic optimization scheduling model considering multi-load dynamic response;
minFtotal＝Fele+Fgas+Feq
Figure BDA00015357433800000311
Figure BDA00015357433800000312
Figure BDA00015357433800000313
wherein, FtotalRepresents the total operating cost of the system, Fele、Fgas、FeqRespectively representing electric energy interaction cost, gas purchasing cost and equipment operation and maintenance cost; c. Cgrid,t、EtFor the electric energy price of the time period t and the difference between the electric energy supply and demand of the system and the external grid, i.e. EtIf the power supply is more than 0, the system power supply is insufficient, and the electric energy is purchased to an external power grid, otherwise, the electric energy is sold to the outside; c. Cgas1、cgas2The prices of natural gas inside and outside the micro energy internet system are respectively,
Figure BDA0001535743380000041
the consumption of the natural gas of the system and the maximum hourly supply of the internal gas network in the period t; thetatThe characteristic parameter is that whether the natural gas supply in the system is sufficient or not in the period of t, and when the internal gas supply is insufficient, the characteristic parameter is
Figure BDA0001535743380000042
Taking 1 when the current value is zero, or taking 0 when the current value is zero; x is the number ofiFor the maintenance cost per unit power, P, of the energy plant ixi,tIs the output power of the energy device i in the time period t.
(2) Establishing an operation constraint condition of a micro energy internet economic optimization scheduling model considering multi-load dynamic response;
1) and (3) output constraint of energy equipment:
for non-energy storage type energy equipment, direct energy supply and auxiliary energy supply equipment of various types of energy sources are mainly provided, for example, the direct energy supply equipment comprises a micro gas turbine, a wind driven generator, a photovoltaic cell, a gas boiler, an air conditioner and the like, the auxiliary energy supply equipment comprises an absorption refrigerator, a waste heat boiler, a ground source heat pump and the like, the auxiliary energy supply equipment has the functions of converting energy in one form into energy in other forms so as to meet the requirements of various loads or energy storage, and the working characteristics have similarity:
Figure BDA0001535743380000043
Figure BDA0001535743380000044
wherein the content of the first and second substances,
Figure BDA0001535743380000045
respectively representing the output power of the non-energy storage type energy equipment i and the limit value thereof in the t period;
Figure BDA0001535743380000046
energy power input or consumed by the corresponding energy device in the time period; sigmaiThe energy conversion efficiency is improved.
For energy storage type energy equipment, such as a storage battery, a cold storage tank, a heat storage tank and the like, the operation process only relates to the storage or release of energy in the same form, and the normalization description is carried out, and the following steps are included:
Figure BDA0001535743380000047
Figure BDA0001535743380000048
Figure BDA0001535743380000049
wherein the content of the first and second substances,
Figure BDA00015357433800000410
respectively storing energy, energy supply power and respective maximum values of the energy storage device i in a time period t; wstore,i,0、Wstor,i,T、
Figure BDA00015357433800000411
The energy storage capacity and the allowed energy storage capacity of the energy storage type equipment at the initial and ending moments of the system operation cycle are obtained.
2) And (3) system power supply and demand balance constraint:
a) electric power supply and demand balance constraint:
Figure BDA00015357433800000412
in the formula (I), the compound is shown in the specification,
Figure BDA00015357433800000413
respectively the power generation power of the system photovoltaic cell and the gas turbine,
Figure BDA00015357433800000414
exchanging power for the system and the power grid;
Figure BDA00015357433800000415
the output of the storage battery is provided;
Figure BDA00015357433800000416
respectively representing the power consumption of the ground source heat pump unit and the electric refrigerator in the t time period; pe0,tAnd fixing the total demand of the non-adjustable electric load for the system in the period t.
b) Cold power supply and demand balance constraint:
Figure BDA00015357433800000417
in the formula (I), the compound is shown in the specification,
Figure BDA0001535743380000051
respectively representing the refrigerating power of the electric refrigerator and the absorption refrigerator in the t period;
Figure BDA0001535743380000052
the power of the cold storage tank is positive when cold energy is released, and negative when the cold energy is released;
Figure BDA0001535743380000053
and participating in optimizing the actual demand value after scheduling for the cold load.
c) Thermal power supply and demand balance constraint
Figure BDA0001535743380000054
In the formula (I), the compound is shown in the specification,
Figure BDA0001535743380000055
the heating power of a ground source heat pump, a gas boiler and a waste heat boiler is respectively;
Figure BDA0001535743380000056
the power of the heat storage tank is positive when the heat energy is released, and negative when the heat energy is stored; in the same way as above, the first and second,
Figure BDA0001535743380000057
to optimize the scheduled equivalent thermal load demand.
Further, the specific steps of step 3 include:
(1) initializing a quantum population Q0I.e. the amount of force and load modulation of various types of equipment, and the differentially evolved population C0Let g be 0;
(2) observation QgGenerating PgAnd converted into P'gThen preferentially update C according to the greedy mechanismg
Figure BDA0001535743380000058
If the termination condition is met, stopping the algorithm, otherwise, performing the next step;
step (4) for CgUpdating C with DE operationsg+1；
Step (5) adding Cg+1Conversion to produce C'g+1And updating the optimal quantum population, then QgQ is obtained by QEA based on optimal populationg+1；
Step (6), enabling g to be g +1, and turning to step (2);
wherein, PgIs a binary coded population, P'gFor real number encoded populations, CgIs a real number encoded population, C'gThe size of each group is the same for the binary coded group.
The invention has the advantages and beneficial effects that:
1. considering from the energy demand side, establishing a cold and hot load dynamic schedulable model based on the time lag characteristic of cold and hot load demand change caused by the heat capacity property of a cold and hot load carrier, and simultaneously carrying out modeling analysis on demand response of various electric loads under real-time electricity prices; then, on the basis, a micro energy internet optimization economic dispatching model considering dynamic response of cold, heat and electricity loads is constructed by combining the known working characteristics of the multi-type energy supply and storage units; and then, solving an optimization model based on a hybrid quantum differential evolution algorithm to obtain a system day-ahead economic dispatching scheme, and optimizing and adjusting energy equipment and multi-load response output at each time interval according to the scheme, so that the running economy of the system is promoted to be improved.
2. According to the method, a dynamic model describing time lag change of cold and hot loads and electricity price excitation response of the electric loads is established, and a supply and demand side multi-source collaborative optimization scheduling model is established by combining the working characteristics of multiple types of supply and energy storage source equipment and is used for solving an economic scheduling scheme of an actual energy interconnection network so as to improve the running economy of the system.
3. The scientific and feasible micro-energy internet multi-source economic dispatching method provided by the invention is beneficial to fully exploiting the dispatching potential of various energy supply and storage units, optimizing the balance of multi-energy supply and demand, promoting the consumption of renewable energy sources and further contributing to improving the operation economy of an energy interconnection system.
4. The invention provides an energy internet economic dispatching method considering multi-energy comprehensive energy system with abundant energy supply and storage unit resources, aiming at the problem of optimized operation of the multi-energy comprehensive energy system with abundant energy supply and storage units, and the economic dispatching method is characterized in that various load dynamic response models are established to construct an economic optimized dispatching model of a micro energy internet system, and a mixed quantum differential evolution algorithm is adopted to solve the optimized model, so that a day-ahead dispatching scheme with better economical efficiency is obtained.
Drawings
FIG. 1 is a process flow diagram of the present invention;
FIG. 2 is a schematic diagram of an optimization of the hybrid quantum differential evolution algorithm (HQEDE);
FIG. 3 is a schematic diagram of a space cooling load demand of a typical energy Internet park;
FIG. 4 is a schematic illustration of a typical energy Internet park hot water load demand;
FIG. 5 is a diagram illustrating the prediction of various electrical load demands and renewable energy output in a typical energy Internet park;
FIG. 6 is a diagram of an exemplary solar power load optimized scheduling scheme;
FIG. 7 is a diagram of an exemplary day-to-day cooling load optimized scheduling scheme;
FIG. 8 is a diagram of an exemplary daily thermal load optimized scheduling scheme.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
the invention provides an energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity, which is based on the characteristics of rich load resources, various energy supply forms and complex energy flow of an energy internet, firstly, aiming at the characteristic of load time lag change caused by thermal inertia of a cold and heat load on the demand side of the energy internet, a cold and heat load dynamic dispatchable model taking carrier temperature as a characteristic parameter is established, and meanwhile, an electricity price excitation response model of two types of flexible electric loads with adjustable power and adjustable operation time is provided from the perspective of the user's psychology of trending out; then on the basis, a multisource economic dispatching model considering cold, heat and electricity multi-load dynamic response is constructed by taking the lowest daily running cost of the micro energy Internet system as a target; and a mixed quantum differential evolution algorithm is selected to solve an optimization model by combining a typical multi-energy interconnection scene of an industrial park in Tianjin, so that a system day-ahead energy scheduling scheme is obtained, and the effectiveness and the economy of the scheme are verified.
An energy internet economic dispatching method considering multi-load dynamic response of cooling, heating and power is shown in fig. 1 and comprises the following steps:
step 1, establishing a dynamic response model of a cold, heat and power load on a demand side of a micro energy internet;
the specific steps of the step 1 comprise:
(3) modeling the cold/heat load time-lag response characteristic;
the propagation of cold and heat energy requires the use of a certain carrier medium (e.g. water, air), and the high and low carrier temperature usually reflects the magnitude of the cold and heat load demand, and if the carrier temperature demand is higher, the corresponding heat load demand is larger and the cold load demand is smaller. On the other hand, the thermal capacity property of the carrier is such that the cold energy or the heat energy supplied to the transmission medium at the present time period does not suddenly disappear in a short time, and there is inertia in the change of the temperature of the cold and hot load carrier. Therefore, unlike electromagnetic power transients, the cold and hot load variation process has time lag, and the dynamic balance equation is as follows:
Figure BDA0001535743380000071
Figure BDA0001535743380000072
in the formula, ci、miThe specific heat capacity and the mass of the i-type cold/heat load carrier are respectively;
Figure BDA0001535743380000073
the temperature of the carrier and the energy supply power to the load carrier at the moment t are respectively the temperature of the i-type cold/hot load; t ist,exWhich is indicative of the temperature of the external environment,
Figure BDA0001535743380000074
the power dissipated by the cold/hot load carrier at time t is proportional to the ambient temperature difference of the carrier, and the proportionality coefficient is alpha.
Integrating the left side and the right side of the differential equation in a time interval delta t, approximating the integrated equation, and combining a dissipation power expression to obtain a differentiated algebraic equation
Figure BDA0001535743380000075
In the formula (I), the compound is shown in the specification,
Figure BDA0001535743380000076
the cold/hot load carrier temperature at time t + Δ t, the power supplied by the load carrier and its dissipated power, respectively.
For general cold/heat load users, the influence of the small-range change of the temperature of the carrier medium on the actual use effect can be accepted, namely, the size of the cold/heat load can be properly regulated and controlled within a reasonable range, and a schedulable domain of the cold/heat load can be obtained
Figure BDA0001535743380000077
Wherein
Figure BDA0001535743380000081
And the temperature fluctuation limit value of the i-type cold and heat load carrier at the t moment within the allowable range of the user use efficiency.
And the above formulas are combined to obtain a schedulable model of the cold and hot load participating system response.
It is added here that the cold and hot loads supply power
Figure BDA0001535743380000082
I.e. the actual cold and heat load demand value for that period
Figure BDA0001535743380000083
(4) Establishing an electricity price excitation response model of two types of typical flexible electric loads;
the power loads may be classified into fixed and flexible electrical loads according to whether the system schedule can be responded to. The flexible electric load which can respond to the system scheduling has two types of fixed energy consumption power but adjustable operation time period and fixed operation time period but adjustable operation power. Under the mechanism of electricity price excitation, energy users tend to shift the adjustable electric load of the operation period to the time period of lower electricity price for supplying power from the perspective of reducing the cost of electricity consumption, and meanwhile, actively adjust the demand of the adjustable electric load of power according to the height of the electricity price, so that:
1) for power-adjustable electrical loads, there are:
Figure BDA0001535743380000084
of formula (II) to (III)'e1,tThe total demand after the response is participated in for all the power adjustable electric loads at the time t;
Figure BDA0001535743380000085
the demand value before the power adjustable type electric load user i participates in the response; beta (c)grid,t) The power price exciting coefficient is in positive correlation with the real-time electric energy price, and reflects the active response degree of the power adjustable type electric load user to the power price; n is a radical of1Indicating the number of power-adjustable electrical load users.
2) The adjustable electric loads of the operation period comprise:
Figure BDA0001535743380000086
Figure BDA0001535743380000087
Figure BDA0001535743380000088
in the formula, Pe2,t、P′e2,tLoad demands before and after the adjustable electric load participates in response in the operation period of the t time period;
Figure BDA0001535743380000089
the load value is transferred from the load i to the operation in other time periods for the time period t;
Figure BDA00015357433800000810
the load value of the electric load i is transferred from the k time period to the t time period;
Figure BDA00015357433800000811
adjusting the maximum transferable value of the class electric load i for any k time period operation time period;
Figure BDA00015357433800000812
for the electrovalence excitation response coefficient of this type of load, when cgrid,k-cgrid,tIs more than or equal to 0
Figure BDA00015357433800000813
And has positive correlation with the electric energy price difference in time period, otherwise, the value is 0; m, N2The number of time-sharing sections and the number of adjustable electric load users in the time-sharing section are divided in the system running period.
Step 2, constructing a micro energy internet economic optimization scheduling model considering multi-load dynamic response;
based on the characteristic modeling of the cooling, heating and power loads on the demand side in the step 1, referring to an economic dispatching scheme flow chart 1, and combining the working characteristics and the operation constraints of typical energy supply, supply and storage units of the micro energy Internet system, a micro energy Internet economic optimization dispatching model considering multi-load dynamic response can be constructed;
the specific steps of the step 2 comprise:
(1) establishing an economic optimization target of a micro energy internet economic optimization scheduling model considering multi-load dynamic response;
the economic optimization target provided by the invention particularly refers to the minimum comprehensive operation cost of the system including electricity purchasing cost, gas purchasing cost and equipment maintenance cost. The electricity purchasing cost is the difference value between electricity selling and electricity purchasing of the system to an external power grid, the gas purchasing cost is the natural gas cost consumed by the work of a gas turbine and a gas boiler of the system, and the equipment maintenance cost is the maintenance cost of unit power conversion within the working years of all related energy equipment in the system. The natural gas is supplied to a certain amount in the area, but the capacity scale is limited, so the natural gas can be purchased to an external gas company at a higher price when the internal gas supply is insufficient; meanwhile, if the system assumes that the resources are not renewable in the economical development area, the system prohibits selling local gas to the outside, so that:
minFtotal＝Fele+Fgas+Feq
Figure BDA0001535743380000091
Figure BDA0001535743380000092
Figure BDA0001535743380000093
wherein, FtotalRepresents the total operating cost of the system, Fele、Fgas、FeqRespectively representing electric energy interaction cost, gas purchasing cost and equipment operation and maintenance cost; c. Cgrid,t、EtFor the electric energy price of the time period t and the difference between the electric energy supply and demand of the system and the external grid, i.e. EtIf the power supply is more than 0, the system power supply is insufficient, and the electric energy is purchased to an external power grid, otherwise, the electric energy is sold to the outside; c. Cgas1、cgas2The prices of natural gas inside and outside the micro energy internet system are respectively,
Figure BDA0001535743380000094
the consumption of the natural gas of the system and the maximum hourly supply of the internal gas network in the period t; thetatThe characteristic parameter is that whether the natural gas supply in the system is sufficient or not in the period of t, and when the internal gas supply is insufficient, the characteristic parameter is
Figure BDA0001535743380000095
Taking 1 when the current value is zero, or taking 0 when the current value is zero; x is the number ofiFor the maintenance cost per unit power, P, of the energy plant ixi,tIs the output power of the energy device i in the time period t.
(2) Establishing an operation constraint condition of a micro energy internet economic optimization scheduling model considering multi-load dynamic response;
in this embodiment, the operation constraint of the micro-energy internet system can be considered from two aspects of the energy device operation constraint and the energy supply and demand balance constraint, so that:
1) energy device output constraint
For non-energy storage type energy equipment, direct energy supply and auxiliary energy supply equipment of various types of energy sources are mainly provided, for example, the direct energy supply equipment comprises a micro gas turbine, a wind driven generator, a photovoltaic cell, a gas boiler, an air conditioner and the like, the auxiliary energy supply equipment comprises an absorption refrigerator, a waste heat boiler, a ground source heat pump and the like, the auxiliary energy supply equipment has the functions of converting energy in one form into energy in other forms so as to meet the requirements of various loads or energy storage, and the working characteristics have similarity:
Figure BDA0001535743380000101
Figure BDA0001535743380000102
wherein the content of the first and second substances,
Figure BDA0001535743380000103
respectively representing the output power of the non-energy storage type energy equipment i and the limit value thereof in the t period;
Figure BDA0001535743380000104
energy power input or consumed by the corresponding energy device in the time period; sigmaiThe energy conversion efficiency is improved.
For energy storage type energy equipment, such as a storage battery, a cold storage tank, a heat storage tank and the like, the operation process only relates to the storage or release of energy in the same form, and the normalization description is carried out, and the following steps are included:
Figure BDA0001535743380000105
Figure BDA0001535743380000106
Figure BDA0001535743380000107
wherein the content of the first and second substances,
Figure BDA0001535743380000108
respectively storing energy, energy supply power and respective maximum values of the energy storage device i in a time period t; wstore,i,0、Wstor,i,T、
Figure BDA0001535743380000109
The energy storage capacity and the allowed energy storage capacity of the energy storage type equipment at the initial and ending moments of the system operation cycle are obtained.
2) System power supply and demand balance constraints
a) Electric power supply and demand balance constraint:
Figure BDA00015357433800001010
in the formula (I), the compound is shown in the specification,
Figure BDA00015357433800001011
respectively the power generation power of the system photovoltaic cell and the gas turbine,
Figure BDA00015357433800001012
exchanging power for the system and the power grid;
Figure BDA00015357433800001013
the output of the storage battery is provided;
Figure BDA00015357433800001014
respectively representing the power consumption of the ground source heat pump unit and the electric refrigerator in the t time period; pe0,tIs a period of tThe system fixes the total demand of the non-adjustable electric load.
b) Cold power supply and demand balance constraint:
Figure BDA00015357433800001015
in the formula (I), the compound is shown in the specification,
Figure BDA00015357433800001016
respectively representing the refrigerating power of the electric refrigerator and the absorption refrigerator in the t period;
Figure BDA00015357433800001017
the power of the cold storage tank is positive when cold energy is released, and negative when the cold energy is released;
Figure BDA00015357433800001018
and participating in optimizing the actual demand value after scheduling for the cold load.
c) Thermal power supply and demand balance constraint
Figure BDA00015357433800001019
In the formula (I), the compound is shown in the specification,
Figure BDA0001535743380000111
the heating power of a ground source heat pump, a gas boiler and a waste heat boiler is respectively;
Figure BDA0001535743380000112
the power of the heat storage tank is positive when the heat energy is released, and negative when the heat energy is stored; in the same way as above, the first and second,
Figure BDA0001535743380000113
to optimize the scheduled equivalent thermal load demand.
In addition, the system operation needs to satisfy the usage effectiveness constraint of the cold and hot composite dynamic change and the constraint that the total amount before and after the operation period adjustable flexible electric load period is transferred is not changed, which are mentioned in the above described various load dynamic response models, and are not described herein again.
And 3, solving the micro energy internet economic optimization scheduling model considering the multi-load dynamic response in the step 2 based on a quantum differential evolution algorithm.
In the embodiment, for the characteristics of the established optimization model, it is considered that the Quantum Evolution Algorithm (QEA) has strong search parallelism, but since the algorithm represents the solution of the problem in a probability form, it is not favorable to emphasize local search, and in addition, in order to obtain better accuracy, a quantum string with a larger length is often required to be set for encoding, which inevitably increases the storage amount. While Differential Evolution (DE) is a population evolution algorithm that directly handles the continuous optimization problem, and search is implemented using differential operations based on real number encoding. Therefore, the invention selects a mixed quantum differential evolution algorithm (HQEDE) to solve, and FIG. 2 is a schematic diagram of an optimization process of the algorithm, and the basic steps are as follows:
(1) initializing a quantum population Q0I.e. the amount of force and load modulation of various types of equipment, and the differentially evolved population C0Let g be 0;
(2) observation QgGenerating PgAnd converted into P'gThen preferentially update C according to the greedy mechanismg
Figure BDA0001535743380000114
If the termination condition is met, stopping the algorithm, otherwise, performing the next step;
step (4) for CgUpdating C with DE operationsg+1；
Step (5) adding Cg+1Conversion to produce C'g+1And updating the optimal quantum population, then QgQ is obtained by QEA based on optimal populationg+1；
Step (6), enabling g to be g +1, and turning to step (2);
wherein, PgIs a binary coded population, P'gFor real number encoded populations, CgIs a real number encoded population, C'gThe size of each group is the same for the binary coded group.
The working principle of the invention is as follows:
the energy internet economic dispatching method considering the cold, heat and electricity multi-load dynamic response selects the micro energy internet with rich energy storage unit resources as a research object, firstly models and analyzes the dynamic response characteristics of the cold, heat and electricity multi-type loads on the energy demand side, then constructs an economic dispatching model comprehensively considering the cold, heat and electricity load dynamic response and optimization and complementation among multi-energy devices, obtains an optimal economic dispatching scheme by solving the model, and adjusts the output of various load demands and various energy devices according to the optimal dispatching scheme, thereby solving the economic operation problem of the micro energy internet system.
In this embodiment, a general flow of the energy internet economic dispatching method considering multi-load dynamic response of cooling, heating and power is given in the step 1-3, in order to verify the feasibility and economy of the proposed strategy, an example analysis is performed by selecting an actual application scenario, and the steps include selecting a typical micro energy internet application scenario and an example result analysis, which are specifically as follows:
step (1) selecting a typical micro energy internet scene
The method selects the electricity, gas, cold and heat interconnection energy supply in the typical summer day of the Tianjin industrial park to perform example simulation on the actual scene, and verifies the economy and the effectiveness of the provided strategy. The cooling and heating loads in the typical summer days of the park mainly comprise two types of space cooling loads and hot water loads according to different carriers, and a table 1 shows the configuration and parameters of main energy equipment of the park. Assuming that the energy prediction error is ignored in the present example, in addition, the scale of the natural gas capacity in the park is known to be 100m3/h, which is converted to 1000kWh/h, and the internal and external selling prices are respectively 2.8 yuan/m3And 3.5 yuan/m3And the electric energy purchasing in the park is realized by a time-of-use electricity price mechanism, wherein 11: 00-18: 00 is peak electricity price: 0.85 yuan/kWh, 8: 00-11: 00 and 18: 00-22: 00 are flat-section electricity prices: 0.54 yuan/kWh, 23: 00-8: 00 is the electricity price of the valley section: 0.33 yuan/kWh.
Fig. 3 is a space cold load demand curve of an industrial park on a typical day in summer, fig. 4 is a hot water load demand curve of the industrial park on the typical day in summer, the shaded parts in fig. 3 and fig. 4 show the adjustable range of the cold/hot load within the allowable fluctuation limit range of the temperature of the energy supply carrier, and fig. 5 shows the demand of the system power load and the output prediction condition of the renewable energy source in the typical day in summer.
TABLE 1 micro energy Internet park energy device configuration and parameters thereof
Figure BDA0001535743380000121
Figure BDA0001535743380000131
Step (2), analysis of example results
1) Optimizing computational results
By optimally solving the model, the scheduling and various load response results of each unit in the typical day in summer can be obtained, as shown in the attached figures 6, 7 and 8.
FIG. 6 is the result of the electric load optimization balance of the industrial park in a typical day in summer, and can be known from the figure: under the excitation of time-of-use electricity prices, the power-adjustable flexible electric load reduces the economic dispatch of the load demand response system at each time interval to different degrees, and the power-adjustable flexible electric load at the running time interval is transferred to the running time interval from the time interval of 8: 00-18: 00 with higher electric energy price to the time interval of 23: 00-8: 00 with lower electric energy price, so that the peak time interval of the electricity prices is avoided to reduce the electricity utilization cost of the park; in a low-electricity-price period of 23: 00-8: 00, the requirement of a system on electric load is met mainly by purchasing a large amount of external electric power, and the generated power of the gas turbine is at a lower level in the period, so that the energy cost is reduced to the maximum extent.
Fig. 7 is an optimized balance result of the space cold load of the industrial park in a typical day in summer, and fig. 8 is an optimized balance result of the space hot water load of the industrial park in a typical day in summer, which can be known from fig. 7 and 8: the dotted line in the figure is a demand curve before the cold and hot load participates in the system optimization scheduling, and the solid broken line in the figure shows a demand curve after the cold and hot load responds. The supply of the cold and heat energy of the system mainly depends on the energy supply of power consumption refrigeration/heat equipment and the recycling of the power generation waste heat, the electric refrigerator and the ground source heat pump consume electric energy in a low-price period to supply cold and heat energy to the system, the cold storage tank and the heat storage tank with low operation cost are used for storing redundant energy, the output of the power consumption energy supply equipment is gradually reduced due to the rise of the price of electricity, the waste heat recycling of waste smoke and waste gas of the gas turbine is increased, and therefore the heat supply/cooling cost can be reduced.
It can be known by analyzing by combining the attached figures 6, 7 and 8, that the micro energy internet economic dispatching considering the multi-load dynamic response of the cold, heat and electricity fully takes the demand response characteristics of the two types of flexible electric loads and the time lag of the change of the space cold load and the hot water load into consideration, and converts the time lag into a global optimization that schedulable resources are unified in the system operation, so that the cooperative optimization dispatching of multiple resources on the energy demand side and the supply side is realized.
2) Economic validation
The economic advantage of the economic dispatching method provided by the invention is verified by calculating the system operation cost under different optimized dispatching strategies, and the calculation result is shown in table 2.
TABLE 2 daily operating costs of the mini-size energy Internet park under different scheduling strategies
Figure BDA0001535743380000141
As can be seen from the data in table 2, when the energy internet economic dispatching method considering the multi-load dynamic response of cooling, heating and power provided by the invention is adopted, the total daily operating cost of the system is 22933.64 yuan; if a multi-source optimization strategy of neglecting multi-load dynamic response of cold, heat and electricity is adopted, the system operation cost is 24046.81 yuan; with an unoptimized individual power strategy, the system operating cost is 30294.92 yuan. Obviously, compared with an optimized energy supply scheme for neglecting the schedulability of the load resources on the demand side, the micro energy internet economic scheduling strategy considering the multi-load dynamic response of the cooling, heating and power has better economical efficiency, and the adoption of the strategy can further reduce the system operation cost by 5.42 percent on the basis of a general optimization strategy, so that the economic advantage of the scheduling of the micro energy internet with rich energy supply and storage units is highlighted.
According to the energy internet economic dispatching method considering the cold, heat and electricity multi-load dynamic response, the invention takes multi-energy interconnection energy supply of certain garden of Tianjin as an example, and obtains a typical daily multi-energy economic dispatching scheme of the system by solving an optimization model on the basis of the known summer typical daily energy equipment configuration and various load demand characteristics, and the result shows that: the energy internet-oriented multi-source supply and demand system demand side load resources can be converted into schedulable resources to a certain extent due to the characteristic properties of the load resources, and the supply and demand balance of the multi-energy system can be promoted through the cooperative output with multi-type energy sources, so that distributed new energy sources are consumed; compared with the past scheme of single optimized energy supply at the energy supply side and independent energy supply which is not optimized, the strategy provided by the invention is more economical for the operation scheduling of the multi-energy interconnection system.
In this embodiment, an energy internet economic dispatching method considering multi-load dynamic response of cooling, heating and power is provided, which includes the following specific steps: firstly, considering the time lag change characteristic of cold and hot loads and the power price excitation response characteristic of electric loads, an energy internet economic dispatching method considering the multi-load dynamic response of cold, hot and electricity is provided, as shown in fig. 1; aiming at the characteristics of a large number of optimization variables and complex constraint conditions of the established optimization model, a mixed quantum differential evolution algorithm is adopted for solving, and the solving flow of the algorithm is shown in figure 2; and multi-energy complementary energy supply of certain Tianjin industrial park is selected as a scene to verify the provided method, and space cold load demand and hot water load demand of the industrial park in each period of a typical day in summer are respectively shown in figures 3 and 4; FIG. 5 shows the predicted output of distributed photovoltaics and the various electrical load demands on a typical summer day of the industrial park; and substituting the configuration and parameters of the park energy equipment given in the table 1 into the optimization model data to solve, and finally obtaining the optimal scheduling scheme of the park in the typical summer day, as shown in fig. 6, 7 and 8, and simultaneously performing comparison calculation with other schemes under different optimal scheduling strategies, as shown in the table 2, so as to effectively verify the economy of the method. Through analyzing the multi-energy optimization scheduling result of the micro-energy Internet in the example, the following conclusion can be obtained:
(1) the change of cold and hot loads has time lag characteristics, and the small-range fluctuation of the temperature of a cold and hot load carrier enables the cold and hot load requirements at each time interval to have adjustability, so that the decision domain of the optimal scheduling of the multi-energy hybrid system is expanded to a certain extent;
(2) under the policy of time-of-use electricity price, various flexible electric loads actively respond to real-time high and low electricity prices, the consumption cost of electric energy is reduced by adjusting the power demand of the flexible electric loads or selecting the operation time period, and the active response mechanism is beneficial to realizing peak clipping and valley filling of the system while promoting the economic operation of the system;
(3) the micro energy internet economic dispatching method considering the multi-load response of cold, heat and electricity comprehensively considers the load resource adjustability of the energy demand side and the multi-energy complementary characteristic of the energy supply side, promotes the coupling coordination among different energy resources in a large range, and has important significance for reducing the operation cost of the micro energy internet and improving the operation economy of the system.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the present invention includes, but is not limited to, those examples described in this detailed description, as well as other embodiments that can be derived from the teachings of the present invention by those skilled in the art and that are within the scope of the present invention.

Claims (1)

1. An energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a dynamic response model of the cold, heat and power load on the demand side of the energy internet;
step 2, constructing an energy internet economic optimization scheduling model considering multi-load dynamic response;
step 3, solving the energy internet economic optimization scheduling model considering the multi-load dynamic response in the step 2 based on a quantum differential evolution algorithm;
the specific steps of the step 1 comprise:
(1) modeling the cold/heat load time-lag response characteristic;
Figure FDA0003469529340000011
Figure FDA0003469529340000012
in the formula, ci、miThe specific heat capacity and the mass of the i-type cold/heat load carrier are respectively; t ist i、
Figure FDA0003469529340000013
The temperature of the carrier and the energy supply power to the load carrier at the moment t are respectively the temperature of the i-type cold/hot load; t ist,exWhich is indicative of the temperature of the external environment,
Figure FDA0003469529340000014
the dissipation power of the cold/hot load carrier at the moment t is in direct proportion to the ambient temperature difference of the carrier, and the proportionality coefficient is alpha;
will be provided with
Figure FDA0003469529340000015
Integrating the left side and the right side in a time interval delta t, approximating the integration, and combining a dissipation power expression to obtain a differentiated algebraic equation:
Figure FDA0003469529340000016
in the formula (I), the compound is shown in the specification,
Figure FDA0003469529340000017
the temperature of the cold/hot load carrier at the time t + delta t, the energy supply power of the load carrier and the dissipation power of the load carrier are respectively;
for cold/heat load users, the influence of the small-range variation of the temperature of the carrier medium on the actual use effect is accepted, namely, the size of the cold/heat load can be properly regulated and controlled within a reasonable range, and a schedulable domain of the cold/heat load is obtained:
Figure FDA0003469529340000018
wherein the content of the first and second substances,
Figure FDA0003469529340000019
the temperature fluctuation limit value of the i-type cold and hot load carrier at the time t within the user use efficiency allowable range;
combined stand
Figure FDA0003469529340000021
And
Figure FDA0003469529340000022
obtaining a schedulable model of the cold and hot load participating in the system response;
(2) establishing an electricity price excitation response model of two types of typical flexible electric loads;
1) for power-adjustable electrical loads, there are:
Figure FDA0003469529340000023
of formula (II) to (III)'e1,tThe total demand after the response is participated in for all the power adjustable electric loads at the time t;
Figure FDA0003469529340000024
the demand value before the power adjustable type electric load user i participates in the response; beta (c)grid,t) The power price exciting coefficient is in positive correlation with the real-time electric energy price, and reflects the active response degree of the power adjustable type electric load user to the power price; n is a radical of1Indicating power adjustable class of electricityThe number of load users;
2) the adjustable electric loads of the operation period comprise:
Figure FDA0003469529340000025
Figure FDA0003469529340000026
Figure FDA0003469529340000027
in the formula, Pe2,t、P′e2,tLoad demands before and after the adjustable electric load participates in response in the operation period of the t time period;
Figure FDA0003469529340000028
the load value is transferred from the load i to the operation in the time period outside the time period t for the time period t;
Figure FDA0003469529340000029
the load value of the electric load i is transferred from the k time period to the t time period;
Figure FDA00034695293400000210
the maximum transfer value of the adjustable class electric load i is set for any k time period;
Figure FDA00034695293400000211
the electrovalence excitation response coefficient of the i-th type adjustable electric load when cgrid,k-cgrid,tIs more than or equal to 0
Figure FDA00034695293400000212
And has positive correlation with the electric energy price difference in time period, otherwise, the value is 0; m, N2The time division period number and the operation time period are respectively adjustable in the system operation cycleThe number of electricity-like load users;
the specific steps of the step 2 comprise:
(1) establishing an economic optimization target of an energy internet economic optimization scheduling model considering multi-load dynamic response;
min Ftotal＝Fele+Fgas+Feq
Figure FDA00034695293400000213
Figure FDA0003469529340000031
Figure FDA0003469529340000032
wherein, FtotalRepresents the total operating cost of the system, Fele、Fgas、FeqRespectively representing electric energy interaction cost, gas purchasing cost and equipment operation and maintenance cost; c. Cgrid,t、EtFor the electric energy price of the time period t and the difference between the electric energy supply and demand of the system and the external grid, i.e. EtIf the power supply is more than 0, the system power supply is insufficient, and the electric energy is purchased to an external power grid, otherwise, the electric energy is sold to the outside; c. Cgas1、cgas2The prices of natural gas inside and outside the energy internet system respectively,
Figure FDA0003469529340000033
the consumption of the natural gas of the system and the maximum hourly supply of the internal gas network in the period t; thetatThe characteristic parameter is that whether the natural gas supply in the system is sufficient or not in the period of t, and when the internal gas supply is insufficient, the characteristic parameter is
Figure FDA0003469529340000034
Taking 1 when the current value is zero, or taking 0 when the current value is zero; x is the number ofiFor the maintenance cost per unit power, P, of the energy plant ixi,tThe output power of the energy device i in the time period t is obtained;
(2) establishing an operation constraint condition of an energy internet economic optimization scheduling model considering multi-load dynamic response;
1) and (3) output constraint of energy equipment:
to non-energy storage type energy equipment, there are direct energy supply and the supplementary energy supply equipment of the polymorphic type energy, and direct energy supply equipment has miniature gas turbine, aerogenerator, photovoltaic cell, gas boiler, air conditioner, and supplementary energy supply equipment is absorption refrigerator, exhaust-heat boiler, ground source heat pump, and its effect all is the demand that converts the energy of a form into other forms in order to satisfy all kinds of loads or energy storage, and the operating characteristic has the similarity:
Figure FDA0003469529340000035
Figure FDA0003469529340000036
wherein the content of the first and second substances,
Figure FDA0003469529340000037
respectively representing the output power of the non-energy storage type energy equipment i and the limit value thereof in the t period;
Figure FDA0003469529340000038
energy power input or consumed by the corresponding energy device in the time period; sigmaiThe energy conversion efficiency is improved;
for energy storage type energy equipment, storage batteries, cold accumulation tanks and heat accumulation tanks, the operation process of the energy storage type energy equipment only relates to the storage or release of energy in the same form, and the normalization description is carried out, and the energy storage type energy equipment comprises the following components:
Figure FDA0003469529340000039
Figure FDA00034695293400000310
Figure FDA00034695293400000311
wherein the content of the first and second substances,
Figure FDA00034695293400000312
respectively storing energy, energy supply power and respective maximum values of the energy storage device i in a time period t; wstore,i,0、Wstor,i,T、
Figure FDA00034695293400000313
The energy storage value and the allowed energy storage capacity of the energy storage equipment at the initial and ending moments of the system operation cycle are obtained;
2) and (3) system power supply and demand balance constraint:
a) electric power supply and demand balance constraint:
Figure FDA00034695293400000314
in the formula (I), the compound is shown in the specification,
Figure FDA0003469529340000041
respectively the generated power P of the photovoltaic cell and the gas turbine of the systemt GridExchanging power for the system and the power grid;
Figure FDA0003469529340000042
the output of the storage battery is provided;
Figure FDA0003469529340000043
respectively representing the power consumption of the ground source heat pump unit and the electric refrigerator in the t time period; pe0,tFixing the total demand of the non-adjustable electric load for the system at the time t;
b) cold power supply and demand balance constraint:
Figure FDA0003469529340000044
in the formula (I), the compound is shown in the specification,
Figure FDA0003469529340000045
respectively representing the refrigerating power of the electric refrigerator and the absorption refrigerator in the t period;
Figure FDA0003469529340000046
the power of the cold storage tank is positive when cold energy is released, and negative when the cold energy is released;
Figure FDA0003469529340000047
the actual demand value after the cold load participates in the optimized scheduling;
c) thermal power supply and demand balance constraint
Figure FDA0003469529340000048
In the formula (I), the compound is shown in the specification,
Figure FDA0003469529340000049
the heating power of a ground source heat pump, a gas boiler and a waste heat boiler is respectively;
Figure FDA00034695293400000410
the power of the heat storage tank is positive when the heat energy is released, and negative when the heat energy is stored; in the same way as above, the first and second,
Figure FDA00034695293400000411
to optimize the equivalent thermal load requirement after scheduling;
the specific steps of the step 3 comprise:
(1) initializing a quantum population Q0I.e. the amount of force and load modulation of various types of equipment, and the differentially evolved population C0Let g be 0;
(2) observation QgGenerating PgAnd converted into P'gThen preferentially update C according to the greedy mechanismg
Figure FDA00034695293400000412
If the termination condition is met, stopping the algorithm, otherwise, performing the next step;
step (4) for CgUpdating C with DE operationsg+1；
Step (5) adding Cg+1Conversion to produce C'g+1And updating the optimal quantum population, then QgQ is obtained by QEA based on optimal populationg+1；
Step (6), enabling g to be g +1, and turning to step (2);
wherein, PgIs a binary coded population, P'gFor real number encoded populations, CgIs a real number encoded population, C'gThe binary coding population is the same in scale;
energy supply power of cold and hot load
Figure FDA00034695293400000413
I.e. the actual cold and heat load demand value for that period
Figure FDA00034695293400000414
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