CN115857348A - Distributed energy system capacity optimization method considering comfortable energy supply of two-combined heat pump - Google Patents

Distributed energy system capacity optimization method considering comfortable energy supply of two-combined heat pump Download PDF

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CN115857348A
CN115857348A CN202211566718.8A CN202211566718A CN115857348A CN 115857348 A CN115857348 A CN 115857348A CN 202211566718 A CN202211566718 A CN 202211566718A CN 115857348 A CN115857348 A CN 115857348A
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energy
power
formula
heat pump
distributed energy
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吴栋萁
葛佳蓓
周金辉
陈超
苏毅方
王凯
李珺逸
柴卫健
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State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a capacity optimization method of a distributed energy system considering comfortable energy supply of a two-combined heat pump. The method adopted by the invention comprises the following steps: acquiring historical load data of the distributed energy system, and establishing an investment model and an operation model of flexible resources of the distributed energy system; establishing an indoor temperature control load and a two-combined heat pump model, and realizing real-time adjustment of cold and hot output of a heat pump according to outdoor temperature through a thermal comfort evaluation index; constructing a flexible resource optimization configuration model of the distributed energy system; and converting the established flexibility resource optimization configuration model of the distributed energy system into a mixed integer linear optimization configuration model, wherein the optimization result is the optimal installation capacity of each device in the distributed energy system, and the configuration cost is output. The invention aims at the optimal economy and ensures the comfort level of the user on the basis of realizing the accurate consumption of new energy and improving the load flexibility.

Description

Distributed energy system capacity optimization method considering comfortable energy supply of two-combined heat pump
Technical Field
The invention belongs to the technical field of energy system capacity optimization, and relates to a distributed energy system capacity optimization method considering comfortable energy supply of two combined heat pumps in a comprehensive energy mode.
Background
The distributed energy system has the characteristics of large energy density, high load utilization hours and diversified energy supply and utilization forms, but because of lack of uniform energy utilization analysis, the problems of energy waste, low economy and the like exist, and the operation efficiency and the economic and environmental benefits of the distributed energy system are greatly influenced. At present, a regional comprehensive energy system for realizing coupling utilization of a multi-energy system becomes one of the development and revolution directions of the energy industry. The distributed energy system based on multi-energy complementation is used as a main landing form of the regional comprehensive energy system, and can effectively improve park-level economic and environmental benefits.
The optimization strategy research of the distributed energy system relates to two dimensions on the demand response side, namely flexible load adjustment capacity, namely the distributed energy system can be regarded as a virtual energy storage system, and the flexible adjustment capacity is integrated into the optimization scheduling of the distributed energy system; and a multi-load collaborative interaction model in the distributed energy system can be constructed according to a time-sharing energy price strategy. The multi-energy source complementation and replacement capability is realized, the top space of the distributed energy source system is wide, the distributed energy source system can fully receive illumination radiation, and the permeability of renewable energy sources and the economic benefit of a park can be effectively improved; and the energy cascade utilization is considered in the system operation, and the user-side response potential can be further excavated. For distributed energy systems, such as commercial centers, residential areas, office buildings, etc., the output of energy is directly connected to the user side, and the output of energy is closely related to the comfort of the user. If the control of the energy supply output is omitted in the operation of the distributed energy system, not only the user experience is damaged, but also the operation cost of the distributed energy system is increased.
Disclosure of Invention
Aiming at the technical problems of high operation cost and poor comfort of the existing distributed energy system, the invention provides a new feasible method for optimal configuration of distributed energy system type equipment, and the method considers flexible resources such as a Heat Pump (HP), a Photovoltaic (PV), a combined heat and power unit (CHP), an Absorption refrigerator (AC), a Gas Boiler (GB), an Electric Energy Storage (EES) and a Thermal Energy Storage (TES) and the like, thereby fully utilizing renewable energy sources and realizing flexible operation and multi-energy complementation of the equipment; in the operation stage, the output of the heat pump is restricted by using the comfort evaluation index, and the most comfortable sensible temperature in the room is adjusted in real time according to the outdoor temperature; with the optimal economy as the target, guarantee user's comfort level on the basis of realizing the accurate absorption of new forms of energy, improving load flexibility.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme: a method for optimizing capacity of a distributed energy system for comfortable energy supply in view of a two-couple heat pump, comprising:
step one, acquiring historical load data of a distributed energy system, predicting profiles of a rigid electric heating base class load, outdoor temperature and illumination radiation intensity curve, obtaining load conditions of typical days in summer and typical days in winter and external temperature and solar radiation intensity curves of the typical days by using a clustering algorithm, establishing an investment model and an operation model of flexibility resources of the distributed energy system, and reflecting the relation between input power and output power;
establishing an indoor temperature control load and a two-combined heat pump model, and realizing real-time adjustment of cold and hot output of the heat pump according to outdoor temperature through a thermal comfort evaluation index;
constructing a flexible resource optimization configuration model of the distributed energy system, wherein the model takes the minimum total cost as a target function, the constraint condition meets the actual operation safety requirement, and the total cost is the sum of the flexible resource investment cost, the system gas purchase cost, the system electricity purchase cost and the light abandonment penalty cost;
step four: and converting the established flexibility resource optimization configuration model of the distributed energy system into a mixed integer linear optimization configuration model, solving by using a Matlab, a Yalmip tool box and a Gurobi solver, wherein the optimization result is the optimal installation capacity of each device in the distributed energy system, and outputting configuration cost.
Further, in step three, the objective function expression of the distributed energy system flexibility resource optimization configuration model is as follows:
minC＝C inv +C fuel +C elec +C pv
wherein C is the total cost, C inv Investing costs for flexible resources; c fuel The cost of purchasing gas for the system; c elec The cost of purchasing electricity for the system; c pv Penalizes the cost for discarding light.
Further, the flexible resource investment cost is calculated by the formula:
Figure BDA0003986819020000031
dev∈{PV,CHP,AC,GB,HP,EES,TES}
in the formula, dev is the type of equipment, PV, CHP, AC, GB, HP, EES and TES respectively represent photovoltaic and cogeneration units, absorption refrigerators, gas boilers, heat pumps, electric energy storage and thermal energy storage; the equipment area is divided into continuous equipment and discrete equipment, wherein the continuous equipment comprises PV, EES and TES; the discrete equipment is CHP, AC, GB and HP, and is configured according to the capacity corresponding to the specific model; n is dev The configuration capacity corresponding to each device is represented; w is a dev Dev investment cost per unit quantity; y is dev The life cycle of the dev type equipment is shown; r is o The current rate is the current rate;
the calculation formula of the gas purchase cost of the system is as follows:
the operation of the cogeneration unit needs to purchase gas from a superior gas network so as to ensure the normal supply of system energy;
Figure BDA0003986819020000032
in the formula: s is a typical day type;
Figure BDA0003986819020000033
the natural gas price at time t of typical day s time period; />
Figure BDA0003986819020000034
And &>
Figure BDA0003986819020000041
The natural gas amounts consumed by the cogeneration unit and the gas boiler in a typical day s period respectively; theta s Typical time of day; t is N Is the total duration of the scheduling period;
the calculation formula of the system electricity purchasing cost is as follows:
when the electricity generated by the distributed energy system is not enough to supply regional consumption, the electricity needs to be bought from the power grid, and the cost is as follows:
Figure BDA0003986819020000042
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000043
typical day s is the electricity price at time t; />
Figure BDA0003986819020000044
Power purchased from a system to a power grid for a time period t under a typical day s;
the light abandonment penalty cost is calculated according to the following formula:
Figure BDA0003986819020000045
in the formula, xi PV Discarding penalty cost for unit power;
Figure BDA0003986819020000046
respectively, the abandoned light power of the typical day s-down period tppv.
Further, in the third step, the constraint conditions include a flexible resource installation area constraint, an indoor temperature constraint, a temperature control load constraint, a heat pump operation constraint, a comfortable operation constraint, a distributed energy system operation constraint, a power balance constraint and an equipment operation constraint.
Further, the flexible resource installation area constraint is as follows:
n dev,min ≤n dev ≤n dev,max
in the formula, n dev,min And n dev,max The minimum capacity and the maximum capacity which can be installed for dev equipment of the distributed energy system; n is dev And the configuration capacity corresponding to each device is shown.
Further, the indoor temperature is constrained as follows:
Figure BDA0003986819020000047
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000051
at time t in s-season, based on the temperature in the room>
Figure BDA0003986819020000052
Supplying energy to the outdoor temperature of the season s for a period t; r and C are equivalent thermal resistance and equivalent thermal capacity of the distributed energy system respectively; h s,t The demand quantity of the temperature control load at t time interval in the energy supply season of s is provided;
Figure BDA0003986819020000053
representing the indoor temperature during the t-1 time period in the energized season of s.
The temperature control load is constrained as follows:
Figure BDA0003986819020000054
Figure BDA0003986819020000055
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000056
and &>
Figure BDA0003986819020000057
Control quantities for the thermal regulation load and the cold regulation load, respectively; />
Figure BDA0003986819020000058
Representing the room temperature heat load demand; />
Figure BDA0003986819020000059
Represents the room temperature cooling load demand;
the heat pump operation constraints are as follows:
Figure BDA00039868190200000510
Figure BDA00039868190200000511
Figure BDA00039868190200000512
Figure BDA00039868190200000513
in the formula:
Figure BDA00039868190200000514
and &>
Figure BDA00039868190200000515
The electric power consumed by the operation of the heat pump at the moment t under the scene of s, the total generated thermal power, the thermal power for meeting the rigid thermal load, the thermal power for the room temperature regulation of a user and the cold power for the room temperature regulation are respectively; />
Figure BDA00039868190200000516
And &>
Figure BDA00039868190200000517
Setting the control quantity of the heat pump heat output and the cold output as a binary variable; />
Figure BDA00039868190200000518
And &>
Figure BDA00039868190200000519
The energy conversion efficiency of the heat pump for heating and cooling respectively.
Further, the comfort run constraints are as follows:
Figure BDA00039868190200000520
in the formula, M is the human metabolism rate, and can be set when the human body does not exercise violently in the distributed energy system;
Figure BDA00039868190200000521
thermal resistance of clothes worn by human bodies under different scenes; />
Figure BDA00039868190200000522
Representing the indoor temperature in the period t in the energy supply season of s;
the recommended PMV index range according to the ISO7730 standard is as follows:
Figure BDA0003986819020000061
further, the distributed energy system operation constraints are as follows:
assuming that the operating efficiency of the CHP remains unchanged in the operating interval, the input-output functional relationship is shown as:
Figure BDA0003986819020000062
Figure BDA0003986819020000063
in the formula:
Figure BDA0003986819020000064
and &>
Figure BDA0003986819020000065
Respectively outputting electric power, natural gas consumption rate and output thermal power of the CHP at t moment in the s energy supply season; eta e 、η h The power generation efficiency and heat recovery efficiency of CHP, respectively; lambda [ alpha ] gas Is the heating value of natural gas.
The gas boiler takes natural gas as input energy, outputs heat energy to supply users, and has the input-output function relationship as follows:
Figure BDA0003986819020000066
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000067
for the output thermal power and the gas consumption rate, eta, of the gas boiler at time t in the s-supply season GB The working efficiency of the gas boiler is improved;
active power of photovoltaic output
Figure BDA0003986819020000068
And the intensity of illumination G C And an outdoor temperature in the t period in the powered season>
Figure BDA0003986819020000069
The relationship between is approximately expressed as:
Figure BDA00039868190200000610
in the formula, P STC 、G STC And T STC Rated output power, rated illumination intensity and standard operation temperature of the photovoltaic power generation system under a standard rated condition are respectively, and k is a correction coefficient; the photovoltaic system also needs to meet the following requirements in operation:
Figure BDA00039868190200000611
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000071
for the photovoltaic actual output electric power of the comprehensive energy system>
Figure BDA0003986819020000072
Discarding the optical power for the photovoltaic;
the energy storage device is capable of temporally decoupling the production and consumption of energy, including thermal and electrical energy storage; the charging and discharging energy power of the energy storage equipment is related to the energy storage capacity and the requirement that the energy storage equipment cannot be charged and discharged simultaneously is met, and in order to ensure the continuity of the scheduling of the operation stage, the state E of the energy storage equipment at the end time T every day of the scheduling is given s,T And initial time state E s,1 In line, the energy storage device operates as follows:
Figure BDA0003986819020000073
E min ≤E s,t ≤E max
E s,1 ＝E s,T
Figure BDA0003986819020000074
Figure BDA0003986819020000075
Figure BDA0003986819020000076
in the formula: e s,t+1 The energy stored by the energy storage device at the moment t +1 in the scene s; sigma is an energy storage self-attenuation coefficient;
Figure BDA0003986819020000077
and
Figure BDA0003986819020000078
charging and discharging power for energy storage; eta char And η relea Charging and discharging efficiency for energy storage; Δ t represents a time interval; e s,t Representing the energy stored by the energy storage equipment at the moment t under the scene s; e min And E max Respectively the minimum and maximum stored energy requirements of the energy storage device;
Figure BDA0003986819020000079
and &>
Figure BDA00039868190200000710
Respectively charging and discharging energy 0-1 state variable and based on the energy storage device at the moment t in the energy supply season s>
Figure BDA00039868190200000711
The indication is that the energy is being charged,
Figure BDA00039868190200000712
representing the energy release; />
Figure BDA00039868190200000713
And &>
Figure BDA00039868190200000714
And respectively charging and discharging multiplying power for the energy storage equipment.
Further, the power balance constraint is as follows:
Figure BDA00039868190200000715
Figure BDA00039868190200000716
Figure BDA00039868190200000717
Figure BDA00039868190200000718
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000081
respectively supplying energy to a rigid electric load and a thermal load at the moment t in the s energy supply season; />
Figure BDA0003986819020000082
Power purchased from a system to a power grid for a time period t under a typical day s; />
Figure BDA0003986819020000083
Representing the electric energy storage discharge power at t moment in the energy supply season s; />
Figure BDA0003986819020000084
Representing the electric energy storage charging power at the t moment in the s energy supply season; />
Figure BDA0003986819020000085
Expressing s season of energy supplyThe heat energy storage and discharge power is carried out at the next t moment; />
Figure BDA0003986819020000086
And the thermal storage charging power at the time t in the s-supply season is shown.
Further, the device operation constraints are as follows:
output of the device
Figure BDA0003986819020000087
The allowable upper and lower limits cannot be exceeded:
Figure BDA0003986819020000088
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000089
and &>
Figure BDA00039868190200000810
Respectively the minimum output proportion and the maximum output proportion of the equipment; w is a group of s dev Is the rated output of the device.
The invention has the following beneficial effects:
the invention fully considers the output of the real-time regulation heat pump to achieve the best indoor comfort level for the analysis of the urban distributed energy system, then takes the minimization of the total cost as the optimization target, and adjusts the capacity configuration of each device of the urban distributed energy system when the operation cost is at the minimum value, thereby ensuring the low-cost operation of the urban distributed energy system and effectively ensuring the thermal comfort level of a user side.
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In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the embodiments or technical solutions in the prior art are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic diagram of a topology of a distributed energy system according to an embodiment of the present invention;
FIG. 2 is a graph of a typical summer solar rigid electrical heating load in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of typical daily rigid electrical heating loads in the winter season in accordance with an embodiment of the present invention;
FIG. 4 is a graph of typical daily outdoor temperatures for various embodiments of the present invention;
FIG. 5 is a graph of representative solar radiation intensity profiles in accordance with embodiments of the present invention;
FIG. 6 is a flow chart of a distributed energy system capacity optimization method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without any inventive step, are within the scope of the present invention.
The invention relates to a capacity optimization method of a distributed energy system considering the energy supply comfort level of a heat pump air conditioner, and the specific implementation flow chart is shown in figure 6, and the topological structure of the distributed energy system is shown in figure 1.
A method for optimizing capacity of a distributed energy system for comfort powering of a cogeneration heat pump, comprising:
acquiring historical load data of a distributed energy system, predicting profiles of a rigid electric heating base class load, outdoor temperature and illumination radiation intensity curve, obtaining load conditions of typical days in spring and autumn transition seasons, typical days in summer and typical days in winter and an external temperature and solar radiation intensity curve of the typical days by using a clustering algorithm, establishing an investment model and an operation model of distributed energy system flexibility resources, and reflecting a relation between input power and output power;
establishing an indoor temperature control load and a two-combined heat pump model, and realizing real-time adjustment of cold and hot output of the heat pump according to outdoor temperature through a thermal comfort evaluation index;
constructing a flexible resource optimization configuration model of the distributed energy system, wherein the model takes the minimum total cost as a target function, the constraint condition meets the actual operation safety requirement, and the total cost is the sum of flexible resource investment cost, system gas purchase cost, system electricity purchase cost and light abandonment penalty cost;
step four: and converting the established flexible resource optimization configuration model into a mixed integer linear optimization configuration model, solving by using a Matlab, a Yalmip tool box and a Gurobi solver, wherein the optimization result is the optimal installation capacity of each device in the distributed energy system, and outputting configuration cost.
In the third step, the objective function expression of the distributed energy system flexibility resource optimization configuration model is as follows:
minC＝C inv +C fuel +C elec +C pv
wherein C is the total cost, C inv Investment costs for flexible resources; c fuel The cost of purchasing gas for the system; c elec The cost of purchasing electricity for the system; c pv Penalizes the cost for discarding light.
The calculation formula of the flexible resource investment cost is as follows:
Figure BDA0003986819020000101
dev∈{PV,MT,HB,HP,EES,TES}
in the formula, dev is the type of equipment, and PV, CHP, AC, GB, HP, EES and TES respectively represent photovoltaic power, a cogeneration unit, an absorption refrigerator, a gas boiler, a heat pump, electric energy storage and heat energy storage. The equipment area is divided into continuous equipment and discrete equipment, wherein the continuous equipment comprises PV, EES and TES; the discrete equipment is CHP, AC, GB and HP, and is configured according to the capacity corresponding to the specific model; n is dev The configuration capacity corresponding to each device is represented; w is a dev Dev investment cost per unit quantity; y is dev The life cycle of the dev type equipment is shown; r is o The current rate is the current rate;
the calculation formula of the gas purchase cost of the system is as follows:
the operation of the cogeneration unit needs to purchase gas from a superior gas network so as to ensure the normal supply of system energy;
Figure BDA0003986819020000102
in the formula: s is a typical day type;
Figure BDA0003986819020000103
the natural gas price at time t of typical day s time period; />
Figure BDA0003986819020000104
And &>
Figure BDA0003986819020000105
The natural gas amounts consumed by the cogeneration unit and the gas boiler in a typical day s period respectively; theta s Typical time of day; t is N Is the total duration of the scheduling period;
the calculation formula of the system electricity purchasing cost is as follows:
when the electricity generated by the distributed energy system is not enough to supply regional consumption, the electricity needs to be bought from the power grid, and the cost is as follows:
Figure BDA0003986819020000111
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000112
typical day s is the electricity price at time t; />
Figure BDA0003986819020000113
For a typical day s-down period t system to the power gridPower of the electricity purchase;
the light abandonment penalty cost is calculated according to the following formula:
Figure BDA0003986819020000114
in the formula, xi PV Discarding penalty cost for unit power;
Figure BDA0003986819020000115
respectively, the abandoned light power of the typical subday time tphotovoltaic.
In the third step, the constraint conditions include a flexible resource installation area constraint, an indoor temperature constraint, a temperature control load constraint, a heat pump operation constraint, a comfortable operation constraint, a distributed energy system operation constraint, a power balance constraint and an equipment operation constraint.
The flexible resource installation area constraint is as follows:
n dev,min ≤n dev ≤n dev,max
in the formula, n dev,min And n dev,max The minimum capacity and the maximum capacity which can be installed for dev equipment of the distributed energy system; n is dev And the configuration capacity corresponding to each device is shown.
The indoor temperature constraints are as follows:
Figure BDA0003986819020000116
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000117
at time t in s-season, based on the temperature in the room>
Figure BDA0003986819020000118
Supplying energy to the outdoor temperature of the season s for a period t; r and C are equivalent thermal resistance and equivalent thermal capacity of the distributed energy system respectively; h s,t The demand quantity of the temperature control load at t time interval in the energy supply season of s is provided; />
Figure BDA0003986819020000121
Representing the indoor temperature during the t-1 time period in the energized season of s.
The temperature control load is constrained as follows:
Figure BDA0003986819020000122
Figure BDA0003986819020000123
in the formula (I), the compound is shown in the specification,
Figure BDA0003986819020000124
and &>
Figure BDA0003986819020000125
Control quantities for the thermal regulation load and the cold regulation load, respectively; />
Figure BDA0003986819020000126
Representing the room temperature heat load demand; />
Figure BDA0003986819020000127
Represents the room temperature cooling load demand;
the heat pump operation constraints are as follows:
Figure BDA0003986819020000128
Figure BDA0003986819020000129
Figure BDA00039868190200001210
Figure BDA00039868190200001211
in the formula:
Figure BDA00039868190200001212
and &>
Figure BDA00039868190200001213
Electric power consumed by the operation of the heat pump at the moment t under the scene of s, total generated thermal power, thermal power for meeting rigid thermal load, thermal power for adjusting the room temperature of a user and cold power for adjusting the room temperature are respectively used; />
Figure BDA00039868190200001214
And &>
Figure BDA00039868190200001215
Setting the control quantity of the heat pump heat output and the cold output as a binary variable; />
Figure BDA00039868190200001216
And &>
Figure BDA00039868190200001217
For controlling the heat pumps not to operate simultaneously; />
Figure BDA00039868190200001218
And &>
Figure BDA00039868190200001219
The energy conversion efficiency of the heat pump for heating and cooling respectively.
In order to ensure the comfort of the indoor temperature, the indoor temperature is restrained by using the evaluation interval of the PMV index. Specific parameters of the PMV index are shown in table 1, and satisfy the following conditions with the indoor temperature:
Figure BDA00039868190200001220
in the formula: m is humanThe metabolism rate, when the human body does not do strenuous exercise in the distributed energy system, M can be a fixed value;
Figure BDA00039868190200001221
the thermal resistance of clothes worn by human bodies under different scenes. The recommended PMV index range according to the ISO7730 standard is as follows:
Figure BDA00039868190200001222
TABLE 1 indoor temperature constraint parameters
Figure BDA0003986819020000131
The operation of the distributed energy system requires the purchase of natural gas for the upper-level energy system, if the power generation economy of equipment in the system is poor, electricity can be purchased for the upper-level energy system, and the time-of-use electricity price condition is shown in table 2. Besides HP, the system equipment also comprises CHP, AC, GB, PV, EES, TES, etc., and the economic indicators of the equipment are shown in tables 3 and 4. The plant operating model is as follows:
the CHP uses natural gas as an input energy source, high-temperature and high-pressure steam is output to drive a steam turbine to generate electricity, and waste heat of fuel gas can be recovered and then supplied to a rigid heat load or can enter an AC to supply cold for users. In this embodiment, assuming that the operating efficiency of the CHP remains unchanged in the operating interval, the input-output functional relationship is as shown in the following formula:
Figure BDA0003986819020000132
Figure BDA0003986819020000133
in the formula:
Figure BDA0003986819020000134
and &>
Figure BDA0003986819020000135
Respectively outputting electric power, natural gas consumption rate and output thermal power of the CHP at t moment in the s energy supply season; eta e 、η h The power generation efficiency and heat recovery efficiency of CHP, respectively; lambda [ alpha ] gas Is the heating value of natural gas.
The gas boiler takes natural gas as input energy, outputs heat energy to supply users, and has the input-output function relationship as follows:
Figure BDA0003986819020000136
in the formula:
Figure BDA0003986819020000141
for the output thermal power and the gas consumption rate, eta, of the gas boiler at time t in the s-supply season GB The working efficiency of the gas boiler is improved.
Active power of photovoltaic output
Figure BDA0003986819020000142
And the intensity of illumination G C And outdoor temperature->
Figure BDA0003986819020000143
The relationship between can be approximated as:
Figure BDA0003986819020000144
in the formula: p is STC 、G STC And T STC The rated output power, the rated illumination intensity and the standard operation temperature of the photovoltaic power generation system under the standard rated condition are respectively, and k is a correction coefficient. The photovoltaic system also needs to meet the following requirements in operation:
Figure BDA0003986819020000145
in the formula:
Figure BDA0003986819020000146
for the photovoltaic actual output electric power of the comprehensive energy system>
Figure BDA0003986819020000147
Abandon the optical power for the photovoltaic. />
The absorption refrigerator converts the heat energy generated by the waste heat boiler or the gas boiler into the cold energy required by a cold supply user, and the input-output function relationship of the absorption refrigerator is as follows:
Figure BDA0003986819020000148
in the formula:
Figure BDA0003986819020000149
and &>
Figure BDA00039868190200001410
Output cold power and absorption heat power, eta, respectively, of an absorption chiller AC The working efficiency of the absorption refrigerator is improved.
The energy storage device is capable of temporally decoupling the production and consumption of energy, mainly including thermal energy storage, electrical energy storage. The charging and discharging energy power of the energy storage device is related to the energy storage capacity and needs to meet the requirement that the charging and discharging can not be carried out simultaneously. To ensure the continuity of the scheduling of the operating phases, the end time per day tstate E of the energy storage device scheduling is given s,T And initial time state E s,1 And (5) the consistency is achieved. The energy storage device operates as follows.
Figure BDA00039868190200001411
E min ≤E t ≤E max
E s,1 ＝E s,T
Figure BDA0003986819020000151
Figure BDA0003986819020000152
Figure BDA0003986819020000153
In the formula: e s,t+1 The energy stored by the energy storage device at the moment t +1 in the scene s; sigma is an energy storage self-attenuation coefficient;
Figure BDA0003986819020000154
and
Figure BDA0003986819020000155
charge and discharge power for energy storage; eta char And η relea Charge-discharge efficiency for energy storage; e min And E max Respectively, the minimum and maximum stored energy requirements of the energy storage device. />
Figure BDA0003986819020000156
And &>
Figure BDA0003986819020000157
Respectively charging and discharging energy 0-1 state variable and based on the energy storage device at the moment t in the energy supply season s>
Figure BDA0003986819020000158
Indicates to be charged and can be selected>
Figure BDA0003986819020000159
Representing the energy release; />
Figure BDA00039868190200001510
And &>
Figure BDA00039868190200001511
And respectively charging and discharging multiplying power for the energy storage equipment.
The power balance constraint is as follows:
Figure BDA00039868190200001512
Figure BDA00039868190200001513
Figure BDA00039868190200001514
Figure BDA00039868190200001515
in the formula (I), the compound is shown in the specification,
Figure BDA00039868190200001516
respectively supplying energy to a rigid electric load and a thermal load at the moment t in the s energy supply season; />
Figure BDA00039868190200001517
Power purchased from a system to a power grid for a time period t under a typical day s; />
Figure BDA00039868190200001518
Representing the electric energy storage discharge power at t moment in the s energy supply season; />
Figure BDA00039868190200001519
The electric energy storage charging power at t moment in the energy supply season s is represented; />
Figure BDA00039868190200001520
Representing the heat storage and discharge power at t moment in s energy supply season; />
Figure BDA00039868190200001521
And the thermal storage charging power at the time t in the energy supply season is shown. />
The equipment operation constraints are as follows:
output of the device
Figure BDA00039868190200001522
The allowable upper and lower limits cannot be exceeded:
Figure BDA00039868190200001523
in the formula (I), the compound is shown in the specification,
Figure BDA00039868190200001524
and &>
Figure BDA00039868190200001525
Respectively the minimum output proportion and the maximum output proportion of the equipment; w is a group of s dev Is the rated output of the device.
TABLE 2 time-of-use electricity price table
Figure BDA0003986819020000161
Table 3 continuous equipment information table
Figure BDA0003986819020000162
Table 4 discrete device information table
Figure BDA0003986819020000163
Constructing a flexible resource optimization configuration model of the distributed energy system, constructing a mixed integer linear optimization configuration model on Matlab, solving by using a Yalmip tool box and calling a Gurobi solver to obtain an optimal configuration result, and outputting optimization cost.
Example analysis
The distributed energy system capacity optimization method provided by the invention is verified by taking a certain cell comprising 30 residential users as an example. Suppose residential users in a cellThe house types are consistent, and the area is 150m 2 The equivalent specific heat capacity of the building is 1.2 kWh/DEG C, and the equivalent heat resistance is 6.8 ℃/kW. The residential users adopt a central heating and cooling mode, do not count the loss in the conveying process, monitor the indoor temperature and uniformly regulate and control the indoor temperature. The year is divided into 2 typical seasons, summer and winter, and typical daily rigid load curves are shown in fig. 2-5. In order to fully analyze and consider the influence of comfortable energy supply of the two combined heat pumps on the configuration result, the invention is provided with the following 5 schemes for comparative analysis:
the scheme 1 is that a combined cooling heating and power supply mode is realized by adopting a cogeneration unit, an absorption refrigerator and a heat pump heating mode;
in the scheme 2, an energy storage system is accessed on the basis of the scheme 1;
in the scheme 3, a heat pump two-combined-supply operation mode is adopted on the basis of the scheme 2;
and 4, accessing a photovoltaic system for energy supply on the basis of the scheme 3.
The capacity allocation results are shown in table 5, and the economic calculation results are shown in table 6. The configuration results are analyzed below from different aspects.
TABLE 5 various schemes configuration capacity table
Figure BDA0003986819020000171
TABLE 6 comparison table of economic efficiency of each scheme
Figure BDA0003986819020000172
Figure BDA0003986819020000181
1) Comparing the scheme 1 with the scheme 2, the influence of the stored energy on the optimal configuration of the comprehensive energy system is analyzed. As can be seen from the optimized configuration results in table 2, compared with scheme 1, in scheme 2, after the thermal energy storage is considered, the configuration capacity of the gas boiler is reduced by 500kW, because the energy storage is added, the time-span transfer of energy is realized, the additional gas purchase cost of the gas boiler is reduced, and the environmental cost of system operation is indirectly reduced.
2) Comparing the scheme 2 with the scheme 3, the influence of the heat pump cooling and heating combined supply system on the capacity configuration of the comprehensive energy system is analyzed. Compared with the scheme 2, the configuration capacity of the heat pump is not increased in the scheme 3, and the capacity configuration of the rest equipment is reduced. This is because the flexibility of the heat pump is fully utilized during the system operation phase, and the total cost of the system is significantly reduced by the separate heating radiation and cooling convection control strategies.
3) Comparing scheme 3 with scheme 4, the impact of renewable energy addition on the capacity configuration of the integrated energy system is analyzed. As can be seen from the optimized configuration results in table 2, compared to scheme 3, although the electricity purchasing cost of the system in scheme 4 is increased, and the system is additionally configured with a certain amount of electrical energy storage, which means that the stability of the system is affected to some extent. However, the addition of the photovoltaic system reduces the operation and maintenance cost and the environmental cost of the system, and can effectively promote carbon neutralization.
In conclusion, the distributed energy system optimization method considering comfortable energy supply of the heat pump realizes flexible energy supply of distributed energy at the energy consumption side, effectively improves the overall economy of construction, and fully considers the participation of users.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for optimizing the capacity of a distributed energy system considering comfortable energy supply of a two-combined heat pump is characterized by comprising the following steps:
acquiring historical load data of a distributed energy system, predicting profiles of a rigid electric heating base class load, outdoor temperature and illumination radiation intensity curve, obtaining load conditions of typical days in summer and winter and external temperature and solar radiation intensity curves of the typical days by using a clustering algorithm, establishing an investment model and an operation model of distributed energy system flexibility resources, and reflecting the relation between input power and output power;
establishing an indoor temperature control load and a two-combined heat pump model, and realizing real-time adjustment of cold and hot output of the heat pump according to outdoor temperature through a thermal comfort evaluation index;
constructing a flexible resource optimization configuration model of the distributed energy system, wherein the model takes the minimum total cost as a target function, the constraint condition meets the actual operation safety requirement, and the total cost is the sum of the flexible resource investment cost, the system gas purchase cost, the system electricity purchase cost and the light abandonment penalty cost;
step four: and converting the established flexibility resource optimization configuration model of the distributed energy system into a mixed integer linear optimization configuration model, solving by using a Matlab, a Yalmip tool box and a Gurobi solver, wherein the optimization result is the optimal installation capacity of each device in the distributed energy system, and outputting configuration cost.
2. The method for optimizing the capacity of the distributed energy system considering the comfortable energy supply of the two-combined heat pump as claimed in claim 1, wherein in the third step, the objective function expression of the distributed energy system flexibility resource optimization configuration model is as follows:
minC＝C inv +C fuel +C elec +C pv
wherein C is the total cost, C inv Investment costs for flexible resources; c fuel The cost of purchasing gas for the system; c elec The electricity purchasing cost for the system; c pv Penalizes the cost for discarding light.
3. The method of optimizing the capacity of a distributed energy system considering comfort energy supply of a two-combined heat pump according to claim 2, wherein the flexible resource investment cost is calculated by the formula:
Figure FDA0003986819010000021
dev∈{PV,CHP,AC,GB,HP,EES,TES}
in the formula, dev is the type of equipment, PV, CHP, AC, GB, HP, EES and TES respectively represent photovoltaic and cogeneration units, absorption refrigerators, gas boilers, heat pumps, electric energy storage and thermal energy storage; the equipment area is divided into continuous equipment and discrete equipment, wherein the continuous equipment comprises PV, EES and TES; the discrete equipment is CHP, AC, GB and HP, and is configured according to the capacity corresponding to the specific model; n is dev The configuration capacity corresponding to each device is represented; w is a dev Dev investment cost per unit quantity; y is dev The life cycle of the dev type equipment is set; r is o The current sticking rate is calculated;
the calculation formula of the system gas purchase cost is as follows:
the operation of the cogeneration unit needs to purchase gas from a superior gas network so as to ensure the normal supply of system energy;
Figure FDA0003986819010000022
in the formula: s is a typical day type;
Figure FDA0003986819010000023
the natural gas price at time t of typical day s time period; />
Figure FDA0003986819010000024
And &>
Figure FDA0003986819010000025
The natural gas amounts consumed by the cogeneration unit and the gas boiler in a typical day s period respectively; theta s Typical time of day; t is a unit of N Is the total duration of the scheduling period;
the calculation formula of the system electricity purchasing cost is as follows:
when the electricity generated by the distributed energy system is not enough to supply regional consumption, the electricity needs to be bought from the power grid, and the cost is as follows:
Figure FDA0003986819010000026
in the formula (I), the compound is shown in the specification,
Figure FDA0003986819010000027
typical day s is the electricity price at time t; />
Figure FDA0003986819010000028
Is a typical day s time period t system power for purchasing electricity from the power grid;
the light abandonment penalty cost is calculated according to the following formula:
Figure FDA0003986819010000031
in the formula, xi PV Discarding penalty cost for unit power;
Figure FDA0003986819010000032
respectively, the abandoned light power of the typical subday time tphotovoltaic.
4. The method for optimizing the capacity of the distributed energy system considering the comfortable energy supply of the two-combined heat pump according to any one of the claims 1 to 3, wherein the constraint conditions in the third step comprise a flexible resource installation area constraint, an indoor temperature constraint, a temperature control load constraint, a heat pump operation constraint, a comfortable operation constraint, a distributed energy system operation constraint, a power balance constraint and an equipment operation constraint.
5. The method of optimizing capacity for a distributed energy system considering comfort powering of a two-fed heat pump according to claim 4, wherein said flexible resource installation area constraints are as follows:
n dev,min ≤n dev ≤n dev,max
in the formula, n dev,min And n dev,max The minimum capacity and the maximum capacity which can be installed for dev equipment of the distributed energy system; n is dev And the configuration capacity corresponding to each device is shown.
6. The method of optimizing capacity for a distributed energy system considering comfort powering of a two-fed heat pump according to claim 4, wherein the indoor temperature constraints are as follows:
Figure FDA0003986819010000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003986819010000034
for an indoor temperature in the time period t in the powered season, and>
Figure FDA0003986819010000035
supplying energy to the outdoor temperature of the season s for a period t; r and C are equivalent thermal resistance and equivalent thermal capacity of the distributed energy system respectively; h s,t The demand quantity of the temperature control load at t time interval in the energy supply season of s is provided; />
Figure FDA0003986819010000036
Representing the indoor temperature in a t-1 time period in an s energy supply season;
the temperature control load is constrained as follows:
Figure FDA0003986819010000037
Figure FDA0003986819010000038
in the formula (I), the compound is shown in the specification,
Figure FDA0003986819010000039
and &>
Figure FDA00039868190100000310
Control amounts of the thermal regulation load and the cold regulation load, respectively; />
Figure FDA00039868190100000311
Represents the room temperature heat load demand; />
Figure FDA0003986819010000041
Indicating cold at room temperature a load demand;
the heat pump operation constraints are as follows:
Figure FDA0003986819010000042
Figure FDA0003986819010000043
Figure FDA0003986819010000044
/>
Figure FDA0003986819010000045
in the formula:
Figure FDA0003986819010000046
and &>
Figure FDA0003986819010000047
Respectively, heat at time t in s sceneThe electric power consumed by the pump operation, the total generated thermal power, the thermal power for satisfying the rigid thermal load, the thermal power for the user's room temperature adjustment, and the cold power for the room temperature adjustment; />
Figure FDA0003986819010000048
And &>
Figure FDA0003986819010000049
Setting the control quantity of the heat pump heat output and the cold output as a binary variable; />
Figure FDA00039868190100000410
And &>
Figure FDA00039868190100000411
The energy conversion efficiency of heating and refrigeration of the heat pump is respectively.
7. The method of optimizing capacity for a distributed energy system considering comfort powering of a two-fed heat pump according to claim 4, wherein the comfort operation constraints are as follows:
Figure FDA00039868190100000412
in the formula, M is the human metabolism rate, and can be set when the human body does not exercise violently in the distributed energy system;
Figure FDA00039868190100000413
thermal resistance of clothes worn by human bodies under different scenes; />
Figure FDA00039868190100000414
Representing the indoor temperature in the period t in the energy supply season of s;
the recommended PMV index range according to the ISO7730 standard is as follows:
Figure FDA00039868190100000415
8. the method of optimizing capacity for a distributed energy system in view of comfort powering of a two-couple heat pump according to claim 6, wherein the distributed energy system operating constraints are as follows:
assuming that the operation efficiency of the cogeneration unit remains unchanged in the operation interval, the input-output function relationship is as follows:
Figure FDA0003986819010000051
Figure FDA0003986819010000052
in the formula:
Figure FDA0003986819010000053
and &>
Figure FDA0003986819010000054
Respectively outputting electric power, natural gas consumption rate and output thermal power of the CHP at t moment in the s energy supply season; eta e 、η h The power generation efficiency and heat recovery efficiency of CHP, respectively; lambda [ alpha ] gas Is the heating value of natural gas.
The gas boiler takes natural gas as input energy, outputs heat energy to supply users, and has the input-output function relationship as follows:
Figure FDA0003986819010000055
in the formula (I), the compound is shown in the specification,
Figure FDA0003986819010000056
for the output thermal power and the gas consumption rate, eta, of the gas boiler at time t in the s-supply season GB The working efficiency of the gas boiler is improved;
active power of photovoltaic output
Figure FDA0003986819010000057
And the intensity of illumination G C And outdoor temperature ^ at t time period in s energy supply season>
Figure FDA0003986819010000058
The relationship between is approximately expressed as:
Figure FDA0003986819010000059
in the formula, P STC 、G STC And T STC Rated output power, rated illumination intensity and standard operation temperature of the photovoltaic power generation system under a standard rated condition are respectively, and k is a correction coefficient; the photovoltaic system also needs to meet the following requirements in operation:
Figure FDA00039868190100000510
/>
in the formula (I), the compound is shown in the specification,
Figure FDA00039868190100000511
for the photovoltaic actual output electric power of the comprehensive energy system>
Figure FDA00039868190100000512
Discarding the optical power for the photovoltaic;
the energy storage device is capable of temporally decoupling the production and consumption of energy, including thermal and electrical energy storage; the charging and discharging energy power of the energy storage equipment is related to the energy storage capacity and the requirement that the energy storage equipment cannot be charged and discharged simultaneously is met, and in order to ensure the continuity of the scheduling of the operation stage, the state E of the energy storage equipment at the end time T every day of the scheduling is given s,T And initial time state E s,1 Consistent, energy storage device operationAs shown in the following formula:
Figure FDA0003986819010000061
E min ≤E t ≤E max
E s,1 ＝E s,T
Figure FDA0003986819010000062
Figure FDA0003986819010000063
Figure FDA0003986819010000064
in the formula: e s,t+1 The energy stored by the energy storage device at the moment t +1 in the scene s; sigma is an energy storage self-attenuation coefficient;
Figure FDA0003986819010000065
and &>
Figure FDA0003986819010000066
Charging and discharging power for energy storage; eta char And η relea Charging and discharging efficiency for energy storage; Δ t represents a time interval; e s,t Representing the energy stored by the energy storage equipment at the moment t under the scene s; e min And E max Respectively the minimum and maximum stored energy requirements of the energy storage device; />
Figure FDA0003986819010000067
And &>
Figure FDA0003986819010000068
Respectively for energy storage devices in s supplyCharging and discharging energy 0-1 state variable at the moment t in season>
Figure FDA0003986819010000069
The indication is that the energy is being charged,
Figure FDA00039868190100000610
representing the energy release; />
Figure FDA00039868190100000611
And &>
Figure FDA00039868190100000612
And respectively charging and discharging multiplying power for the energy storage equipment.
9. The method of optimizing capacity for a distributed energy system considering comfort powering of a two-fed heat pump according to claim 8, wherein the power balance constraints are as follows:
Figure FDA00039868190100000613
Figure FDA00039868190100000614
Figure FDA00039868190100000615
Figure FDA00039868190100000616
in the formula (I), the compound is shown in the specification,
Figure FDA00039868190100000617
respectively supplying energy to a rigid electric load and a thermal load at the moment t in the s energy supply season;/>
Figure FDA00039868190100000618
power purchased from a system to a power grid for a time period t under a typical day s; />
Figure FDA00039868190100000619
Representing the electric energy storage discharge power at t moment in the s energy supply season; />
Figure FDA00039868190100000620
Representing the electric energy storage charging power at the t moment in the s energy supply season; />
Figure FDA00039868190100000621
Representing the heat storage and discharge power at t moment in s energy supply season; />
Figure FDA00039868190100000622
And the thermal storage charging power at the time t in the energy supply season is shown.
10. The method of optimizing capacity for a distributed energy system considering comfort powering of a two-fed heat pump according to claim 4, wherein the plant operation constraints are as follows:
output of the device
Figure FDA0003986819010000071
The allowable upper and lower limits cannot be exceeded:
Figure FDA0003986819010000072
/>
in the formula (I), the compound is shown in the specification,
Figure FDA0003986819010000073
and &>
Figure FDA0003986819010000074
Are respectively devicesThe minimum output ratio and the maximum output ratio of (1); w is a group of s dev Is the rated output of the device. />
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116339166A (en) * 2023-03-30 2023-06-27 淮阴工学院 Intelligent energy consumption regulation and control equipment for comprehensive energy building
CN116739306A (en) * 2023-07-27 2023-09-12 华北电力大学 Heat pump load flexibility quantification method, system and equipment
CN117826907A (en) * 2024-03-01 2024-04-05 中集安瑞科能源***(上海)有限公司 Control method of cascade cogeneration device
CN118014774A (en) * 2024-04-08 2024-05-10 国网浙江综合能源服务有限公司 Heating equipment configuration method, system, equipment and medium adopting air source heat pump
CN117826907B (en) * 2024-03-01 2024-06-11 中集安瑞科能源***(上海)有限公司 Control method of cascade cogeneration device

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116339166A (en) * 2023-03-30 2023-06-27 淮阴工学院 Intelligent energy consumption regulation and control equipment for comprehensive energy building
CN116339166B (en) * 2023-03-30 2023-12-19 淮阴工学院 Intelligent energy consumption regulation and control equipment for comprehensive energy building
CN116739306A (en) * 2023-07-27 2023-09-12 华北电力大学 Heat pump load flexibility quantification method, system and equipment
CN116739306B (en) * 2023-07-27 2023-12-26 华北电力大学 Heat pump load flexibility quantification method, system and equipment
CN117826907A (en) * 2024-03-01 2024-04-05 中集安瑞科能源***(上海)有限公司 Control method of cascade cogeneration device
CN117826907B (en) * 2024-03-01 2024-06-11 中集安瑞科能源***(上海)有限公司 Control method of cascade cogeneration device
CN118014774A (en) * 2024-04-08 2024-05-10 国网浙江综合能源服务有限公司 Heating equipment configuration method, system, equipment and medium adopting air source heat pump

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