CN113725915A - Rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia - Google Patents

Rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia Download PDF

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CN113725915A
CN113725915A CN202110989505.5A CN202110989505A CN113725915A CN 113725915 A CN113725915 A CN 113725915A CN 202110989505 A CN202110989505 A CN 202110989505A CN 113725915 A CN113725915 A CN 113725915A
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孙可
王蕾
王坤
万灿
武梦景
赵乐冰
刘曌煜
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Zhejiang University ZJU
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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Abstract

The invention discloses a rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia. The method fully considers the flexible resources of the source, the network and the load of the thermodynamic system, improves the consumption level of renewable energy sources and reduces the comprehensive cost of the system. The source side cogeneration unit has an adjustable heat-power ratio, simultaneously, a afterburning boiler and electric heating equipment exist in the system, the output is flexibly adjusted according to the operation condition, the network side utilizes the transmission delay and the virtual energy storage characteristic of a heating power pipe network, the load side carries out detailed modeling on the dynamic heat process of the building, flexible resources of a rural electric heating comprehensive energy system are excavated, and electric heating coordination and complementation are realized. Meanwhile, uncertainty of distributed renewable energy sources is considered, and an opportunity constraint model is adopted to provide scheduling schemes with different probability guarantees. The method fully excavates flexible resources of the thermodynamic system, improves the efficiency of the energy system through multi-energy coordination, can effectively promote the consumption of renewable energy sources, and reduces the energy cost.

Description

Rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia
Technical Field
The invention relates to a rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia, and belongs to the field of regional electric heating comprehensive energy system optimization scheduling.
Background
By 2019, the wind power photovoltaic global accumulated installed capacity respectively reaches 651GW and 627 GW. By the end of 2020, the wind power accumulation installation exceeds 2.8 hundred million kilowatts, the grid-connected photovoltaic power generation installation exceeds 2.53 hundred million kilowatts, and the accumulation installation capacity is the first in the world. Renewable energy resources are mainly distributed in the west and north of China, energy demand centers are located in the east and south of dense population and developed industry, the energy resources and load centers are obviously and reversely distributed, under the background of vigorous development of renewable energy, although provinces and cities are used for relieving the problem of consumption and utilization, relevant policies are drawn to promote the consumption of clean energy, the utilization hours of equipment are continuously improved, the reliability of a system is challenged due to the volatility and uncertainty of the renewable energy, the system is limited by the requirements on reliability, stability and power supply quality, the wind curtailment rate in Xinjiang area exceeds 14%, the light curtailment rate in Tibet area reaches 24.1%, and the energy utilization modes of centralized production and remote transmission cause high energy utilization cost and low energy utilization efficiency. The distributed energy with various forms, convenient installation and flexible operation opens up a new idea for consuming clean energy, reducing energy cost and reducing carbon emission. The distributed renewable energy is installed nearby the load and is aggregated into the micro-grid, and the consumption capacity and the energy utilization efficiency of the clean energy are effectively improved in a mode of 'spontaneous self-use, surplus internet surfing' by the distributed renewable energy represented by the distributed photovoltaic.
Often, a user has multiple energy demands, such as electric energy demand and heat energy demand, and pays electric and heat purchasing fees from an electric power system dispatcher and a thermal power system operator respectively. When the power system and the thermodynamic system respectively make respective operation plans, the adjustable equipment of the isolated energy system is limited, and the cooperative complementary characteristic and the cascade utilization characteristic between the energy sources cannot be utilized. Under the background of urban energy Internet, the rural electric heating comprehensive energy system breaks the energy barrier by utilizing energy coupling equipment, and can improve the consumption capacity of renewable energy and reduce the overall operation cost of the energy system through combined optimization and cooperative operation. The uncertainty of renewable energy sources is neglected in the operation optimization of the existing rural electric heating comprehensive energy system, so that the obtained operation scheduling result is not accurate enough and cannot adapt to the application of an actual system. Meanwhile, the operation optimization of the current rural electric heating comprehensive energy system ignores the thermal inertia of a heat supply network, which leads to the reduction of flexible scheduling resources of the system and compresses the scheduling space of the rural electric heating comprehensive energy system.
Disclosure of Invention
In order to solve the problems of insufficient renewable energy consumption capacity, low energy system operation efficiency and high energy utilization cost, the invention provides a rural electric heating integrated energy system operation optimization method considering renewable energy uncertainty and thermal inertia.
The invention adopts the following technical scheme:
a rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia comprises the following steps:
step 1: the method comprises the steps of modeling a source, a network and a load in a rural electric heating comprehensive energy system, wherein the source equipment comprises a thermoelectric cogeneration unit, distributed photovoltaic equipment, electric heating equipment, a gas boiler, an energy storage system, a small diesel generator set and the like, the network model comprises a heating power pipe network quasi-dynamic model and a radial power distribution network trend model which consider heat supply network transmission delay and heat dissipation, and a multi-region aggregated building heat storage model is established on a load side.
Step 2: and (3) combining a source equipment model, a network model and a building heat storage model, introducing standby constraint considering the uncertainty of renewable energy sources, and establishing a rural electric heating comprehensive energy system operation optimization opportunity constraint model.
And step 3: and solving the opportunity constraint model to obtain an optimal scheduling plan of the system, adjusting the output of equipment according to the plan, and purchasing electricity and gas for an upper-level power grid and a natural gas company.
As a preferred example, in step 1, the energy conversion equation of the cogeneration unit is as follows:
Figure BDA0003232005520000031
in the formula:
Figure BDA0003232005520000032
consuming natural gas power for time t;
Figure BDA0003232005520000033
generating power at the time t;
Figure BDA0003232005520000034
to obtain the power generation efficiency.
When the influence of the ambient temperature is neglected, the active power of the distributed photovoltaic is:
Figure BDA0003232005520000035
in the formula:
Figure BDA0003232005520000036
actual output active power of the photovoltaic system at the moment t;
Figure BDA0003232005520000037
the rated power of the photovoltaic under the standard test condition; i is the actual irradiation intensity; i isNIs the irradiation intensity under standard test conditions.
The photovoltaic is connected to the power grid through the inverter, so that the control of output power and current is realized, taking a constant power factor control strategy as an example, the reactive output of the photovoltaic inverter is as follows:
Figure BDA0003232005520000038
in the formula:
Figure BDA0003232005520000039
the output reactive power of the photovoltaic inverter at the time t;
Figure BDA00032320055200000310
a power angle set for the photovoltaic inverter.
The energy conversion equation of the electric heating equipment is as follows:
Figure BDA00032320055200000311
in the formula:
Figure BDA00032320055200000312
respectively outputting thermal power and inputting electric power of the electric heating equipment at the moment t; etaHPThe heating coefficient is shown.
The energy conversion equation of the gas boiler is as follows:
Figure BDA00032320055200000313
in the formula:
Figure BDA00032320055200000314
the natural gas power consumed by the gas boiler at the moment t;
Figure BDA00032320055200000315
the thermal output of the gas boiler at the time t; etaGBThe heat supply efficiency for the gas boiler is improved.
The operation model of the energy storage system is as follows:
Figure BDA0003232005520000041
in the formula:
Figure BDA0003232005520000042
the charge states of the energy storage system at the time t +1 and the time t are respectively; etaBU、ηch、ηdcRespectively representing the self-loss rate and the charging and discharging efficiency of the energy storage device; zBURated capacity for stored energy;
Figure BDA0003232005520000043
respectively representing the discharge and charge power of the energy storage system at time t.
The operation cost model of the small diesel generator set is as follows:
Figure BDA0003232005520000044
in the formula:
Figure BDA0003232005520000045
the unit running cost at the moment t is calculated;
Figure BDA0003232005520000046
to output active power, ag、bg、cgIs the cost coefficient of a small diesel generator set.
The power flow model of the radial distribution network can be linearized as follows:
Figure BDA0003232005520000047
in the formula: j → k represents the set of nodes k to which power flows from node j; pijAnd QijRespectively representing active power and reactive power from the node i to the node j; pjAnd QjRespectively the active power and the reactive power flowing to the node j; rijAnd XijThe resistance and reactance of branch ij are respectively; vi、VjThe voltage amplitudes of node i and node j, respectively.
The heat supply network quasi-dynamic model considering the transmission delay and heat dissipation of the heat supply network is as follows:
the energy transfer process of the heat exchange station comprises the following steps:
Figure BDA0003232005520000048
Figure BDA0003232005520000049
in the formula:
Figure BDA0003232005520000051
the heat power absorbed from the heat source by the first heat exchange station at the node n;
Figure BDA0003232005520000052
the thermal power transmitted to the secondary pipe network by the primary heat exchange station at the node n is provided; cwIs the specific heat capacity of water;
Figure BDA0003232005520000053
and
Figure BDA0003232005520000054
mass flows injected and discharged respectively for the node n;
Figure BDA0003232005520000055
and
Figure BDA0003232005520000056
the temperature of water supplied to the first heat exchange station at the node n and the temperature of return water flowing through the first heat exchange station are respectively;
Figure BDA0003232005520000057
and
Figure BDA0003232005520000058
respectively the temperature at the node n of the water supply pipeline and the water return pipeline of the heat supply network.
The number of transmission delay time sections of each section of pipeline is as follows:
Figure BDA0003232005520000059
in the formula: n is a positive integer; mpIs the total mass of hot water in the pipe p; ρ is the density of water; m ispIs the mass flow of the hot water in the pipeline p; dpAnd LpThe diameter and length of the pipe p, respectively; Δ t is the scheduling time interval.
Neglecting the temperature loss, the temperature at the outlet of the pipe at time t is:
Figure BDA00032320055200000510
in the formula:
Figure BDA00032320055200000511
in order to neglect the temperature loss, the temperature of the mass block at the p outlet of the pipeline at the time t;
Figure BDA00032320055200000512
and
Figure BDA00032320055200000513
are respectively provided with
Figure BDA00032320055200000514
And
Figure BDA00032320055200000515
the temperature of the injection line.
The actual outlet temperature of the pipe p when considering the transmission heat dissipation is:
Figure BDA00032320055200000516
in the formula: t isGDIs the temperature of the earth surrounding the pipeline; kpIs the heat transfer coefficient per unit length of the pipeline p and is related to the pipeline heat insulating material.
The heat distribution pipe network temperature mixing equation is as follows:
Figure BDA00032320055200000517
Figure BDA00032320055200000518
in the formula:
Figure BDA0003232005520000061
is the flow of the water supply and return pipes;
Figure BDA0003232005520000062
and
Figure BDA0003232005520000063
is the mixed temperature of the node n in the water supply and return network;
Figure BDA0003232005520000064
respectively representing the water supply temperature and the water return temperature at the outlet of the pipeline p;
Figure BDA0003232005520000065
respectively representing the supply water temperature and the return water temperature at the inlet of the pipe p.
Figure BDA0003232005520000066
And
Figure BDA0003232005520000067
respectively collecting pipelines which take the node n as a terminal point and take the node n as a starting point in the water supply/return network.
The building heat storage model of the multi-zone polymerization is as follows:
the temperature change process of each area in the building meets the following conditions:
Figure BDA0003232005520000068
Figure BDA0003232005520000069
in the formula:
Figure BDA00032320055200000610
and
Figure BDA00032320055200000611
respectively the heat capacity of the room and the wall;
Figure BDA00032320055200000612
the thermal power input for the ith room at the time t; t isj,tIs the temperature at node j at time t;
Figure BDA00032320055200000613
and
Figure BDA00032320055200000614
respectively obtaining room and wall temperatures at the moment t to be solved;
Figure BDA00032320055200000615
all nodes adjacent to the ith room;
Figure BDA00032320055200000616
all nodes adjacent to the wall between the node i and the node j are provided;
Figure BDA00032320055200000617
and
Figure BDA00032320055200000618
the thermal resistance of a wall or a window between the node i and the node j is shown; v isijThe variable is 0-1, and the variable represents whether the room has a window or not, wherein the variable is 1 when the room has the window and 0 when the room has no window;
Figure BDA00032320055200000619
respectively the window transmissivity and the wall heat absorption rate;
Figure BDA00032320055200000620
the surface area of the window body and the wall body;
Figure BDA00032320055200000621
and
Figure BDA00032320055200000622
the irradiation intensity corresponding to the window body or the wall body.
The sum of the thermal powers of all the heat supply areas is equivalent thermal load of the building:
Figure BDA00032320055200000623
in the formula: the subscript i is the building number,
Figure BDA00032320055200000624
the equivalent thermal load of the building at the moment t;
Figure BDA00032320055200000625
heat supply for the z-th room; n is a radical oftotThe number of heating rooms contained in the building.
As a preferred example, the control mode of the heat supply network adopts mass regulation, that is, the flow of the working medium in the heat pipe network is kept unchanged after being preset, and the heat supply amount of the heat supply network is regulated only by changing the temperature of the supplied water at the heat source.
As a preferred example, the cogeneration unit operates in a variable heat-to-power ratio mode, and the power output and the heat output of the cogeneration unit are limited by the maximum and minimum steam inlet amount and the maximum and minimum steam condensation amount, and can be flexibly adjusted in a feasible region.
As a preferred example, the optimization model established in step 2 takes the daily comprehensive operation cost minimum as a target function, and the comprehensive operation cost includes the external energy purchase cost
Figure BDA0003232005520000071
And equipment operating costs
Figure BDA0003232005520000072
Figure BDA0003232005520000073
Wherein:
Figure BDA0003232005520000074
Figure BDA0003232005520000075
in the formula: c. Celec、cgasRespectively purchasing electricity and natural gas from the outside;
Figure BDA0003232005520000076
respectively purchasing electric quantity and gas quantity from outside in a time period t; Ω represents a set of devices; subscripts CHP, CGU, HP,GB. PV and BU respectively indicate that the equipment types are a combined heat and power generation unit, a diesel generator set, electric heating equipment, a gas boiler and a distributed photovoltaic and energy storage system; subscript s denotes the device number;
Figure BDA0003232005520000077
represents the electrical power of the device during the time period t;
Figure BDA0003232005520000078
representing the thermal power of the device during the period t.
The system needs to satisfy backup constraints that take into account renewable energy uncertainty:
Figure BDA0003232005520000079
in the formula: ri,tThe method comprises the following steps of providing a rotary standby for the ith node of the power distribution network;
Figure BDA00032320055200000710
the active load of the node i of the power distribution network is; the confidence level α is the probability value at which the constraint holds.
In addition, the operation optimization opportunity constraint model of the rural electric heating comprehensive energy system further comprises the following constraints:
device rated power constraints:
Figure BDA00032320055200000711
Figure BDA00032320055200000712
Figure BDA0003232005520000081
Figure BDA0003232005520000082
Figure BDA0003232005520000083
Figure BDA0003232005520000084
Figure BDA0003232005520000085
in the formula:
Figure BDA0003232005520000086
the upper limit and the lower limit of the generating power of the cogeneration unit; rH、RLThe maximum and minimum electric-heat ratios are obtained;
Figure BDA0003232005520000087
the upper limit and the lower limit of the thermal output of the gas boiler;
Figure BDA0003232005520000088
the active output of the small diesel engine set is the upper limit and the lower limit;
Figure BDA0003232005520000089
Q CGUthe upper limit and the lower limit of the reactive power output of the small diesel engine set are set;
Figure BDA00032320055200000810
respectively representing the stored energy maximum discharge power and the stored energy maximum charge power.
And (3) equipment climbing capacity constraint:
Figure BDA00032320055200000811
Figure BDA00032320055200000812
Figure BDA00032320055200000813
in the formula:
Figure BDA00032320055200000814
the climbing capacity of the cogeneration unit is the climbing capacity;
Figure BDA00032320055200000815
the climbing capacity of the gas boiler is the climbing capacity up and down;
Figure BDA00032320055200000816
the climbing capability of the small diesel engine set is up and down.
And (3) energy storage system charge and discharge state constraint:
Figure BDA00032320055200000817
and (3) energy storage system charge state constraint:
Figure BDA00032320055200000818
Figure BDA00032320055200000819
in the formula:
Figure BDA00032320055200000820
E BUthe maximum charge state value and the minimum charge state value allowed in the energy storage operation process;
Figure BDA00032320055200000821
and
Figure BDA00032320055200000822
the states of charge of the stored energy at the starting and ending moments of the scheduling cycle are respectively.
Constraint of transmission capacity of distribution line:
Figure BDA0003232005520000091
Figure BDA0003232005520000092
in the formula:
Figure BDA0003232005520000093
P ijand
Figure BDA0003232005520000094
Q ijupper and lower limits for the transmission of active and reactive power for branch ij, respectively.
And (3) limiting the upper and lower limits of the node voltage:
Figure BDA0003232005520000095
in the formula:
Figure BDA0003232005520000096
V ithe upper limit value and the lower limit value of the voltage of the point i in the power distribution network are respectively.
Temperature constraint of heating power pipe network nodes:
Figure BDA0003232005520000097
Figure BDA0003232005520000098
in the formula:
Figure BDA0003232005520000099
and
Figure BDA00032320055200000910
the upper limit and the lower limit of the water supply temperature at the node n are respectively set;
Figure BDA00032320055200000911
is the lowest return water temperature at node n.
The heat transferred to the building by the primary pipe network is equivalent to the heat load of the heat exchange station:
Figure BDA00032320055200000912
the temperature of the building indoor area is allowed to fluctuate within a certain range:
Figure BDA00032320055200000913
in the formula:
Figure BDA00032320055200000914
the allowable upper limit and the allowable lower limit of the indoor temperature of the ith room are respectively set.
As a preferred example, in order to reduce the computational burden when solving the opportunistic constraint model in step 3, the opportunistic constraint is converted into a deterministic constraint:
Figure BDA00032320055200000915
in the formula:
Figure BDA00032320055200000916
and (4) a lower quantile point for photovoltaic output prediction.
During solving, the constraint equation (33) of the charge-discharge state of the energy storage system can be ignored, and the scheduling result is not influenced.
As a preferred example, in step 3, the heat supply network pipeline and flow parameter are brought into (11), the transmission delay of each section of pipeline of the heat network is obtained, the pipeline transmission delay, the equipment parameters, the building parameters, the parameters of the distribution network and the heat network, the power load, the heat load, the renewable energy output and the real-time electricity price information are substituted into the operation optimization opportunity constraint model of the rural electric heating integrated energy system, the model is directly solved by solving software, the optimal scheduling plan is obtained, the equipment output is adjusted, and the external incoming call and the external gas are purchased according to the plan.
The invention has the beneficial effects that:
the invention provides a rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia, which comprises the steps of firstly, carrying out detailed modeling on energy conversion equipment, a power distribution network, a heating power network and a building space which are contained in a rural electric heating comprehensive energy system, reflecting the transmission delay characteristic of a heating power pipe network by adopting a simplified linear quasi-dynamic model, and accurately describing the building heat transmission process and the temperature change process by utilizing a multi-region aggregated building heat storage model; secondly, an opportunity constraint optimization scheduling model is built, the influence of the uncertainty of the output of renewable energy is considered, and a selectable reference scheme is provided for a scheduling plan maker; and finally, converting the opportunity constraint in the model into the deterministic constraint, simplifying the optimization scheduling problem into a convex quadratic programming problem, and conveniently obtaining a global optimal solution.
The method considers the dynamic characteristic of a heating power pipe network and the virtual heat storage characteristic of a building as the flexible resources of the heat supply network to participate in the operation optimization of the rural electric heating comprehensive energy system, realizes the peak clipping and valley filling of a heat load curve by complementarily translating a heat energy supply and demand curve through electric heating coordination, effectively reduces the system investment and the operation cost, and improves the consumption capacity of the system to renewable energy.
Drawings
FIG. 1 is a diagram of an optimization model architecture of the present invention;
FIG. 2 is a schematic diagram of a rural electric heating integrated energy system according to an embodiment of the present invention;
FIG. 3 is a diagram of a multi-zone aggregated building thermal storage model according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a system power balance according to an embodiment of the present invention;
fig. 5 is a diagram showing the change of the room temperature of each area of the building according to the embodiment of the invention.
Detailed Description
The invention is further described with reference to the accompanying drawings and examples.
The structure of the rural electric heating comprehensive energy system adopted by the embodiment is shown in figure 2. The system comprises 1 large-scale cogeneration unit, 1 micro cogeneration unit, 2 gas boilers, 3 diesel generators, 3 light-storage integrated systems, 1 electric heat pump and 2 building buildings with flexible heat load characteristics, wherein a single-layer 4-area building is adopted in the embodiment, and a building model is shown in figure 3. And the scheduling interval delta t is 15min, and day-ahead dynamic economic scheduling is performed by taking the lowest total cost of the whole day as an optimization target.
The invention provides a rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia, which adopts the technical scheme that the method comprises the following steps:
step 1: the method comprises the steps of modeling a source, a network and a load in a rural electric heating comprehensive energy system, wherein the source equipment comprises a thermoelectric cogeneration unit, a micro cogeneration unit, distributed photovoltaic and electric heating equipment, a gas boiler, an energy storage system, a small diesel generator set and the like, the network model comprises a thermal power pipe network quasi-dynamic model and a radial power distribution network trend model which take the transmission delay and the heat dissipation of a heat supply network into consideration, and a multi-region aggregated building heat storage model is established on the load side.
Step 2: and (3) combining an equipment model, a network model and a building heat storage model, introducing a standby constraint considering the uncertainty of renewable energy sources, and establishing a rural electric-heat comprehensive energy system operation optimization opportunity constraint model (as shown in figure 1).
And step 3: and solving the opportunity constraint model to obtain an optimal scheduling plan of the system, adjusting the output of equipment according to the plan, and purchasing electricity and gas for an upper-level power grid and a natural gas company.
In the above embodiment, in step 1, the cogeneration unit is a pumped condensing cogeneration unit, the thermoelectric ratio can be flexibly adjusted in a feasible region, and the energy conversion equation is as follows:
Figure BDA0003232005520000121
in the formula:
Figure BDA0003232005520000122
consuming natural gas power for time t;
Figure BDA0003232005520000123
generating power at the time t;
Figure BDA0003232005520000124
to obtain the power generation efficiency.
Miniature coproduction unit adopts back pressure formula unit, and the thermoelectric ratio is fixed, can provide partial heat energy and electric energy demand for the user, and its operation model is:
Figure BDA0003232005520000125
Figure BDA0003232005520000126
in the formula:
Figure BDA0003232005520000127
respectively outputting electric power, thermal power and consumed natural gas power at the moment t by the micro co-production unit;
Figure BDA0003232005520000128
respectively the power generation efficiency and the heat supply efficiency of the micro co-generation unit.
When the influence of the ambient temperature is neglected, the active power of the distributed photovoltaic is:
Figure BDA0003232005520000129
in the formula: subscript i is the number of the distributed photovoltaic;
Figure BDA00032320055200001210
actual output active power of the photovoltaic system at the moment t;
Figure BDA00032320055200001211
the rated power of the photovoltaic under the standard test condition; i is the actual irradiation intensity; i isNIs the irradiation intensity under standard test conditions.
The photovoltaic is connected to the power grid through the inverter, so that the control of output power and current is realized, taking a constant power factor control strategy as an example, the reactive output of the photovoltaic inverter is as follows:
Figure BDA00032320055200001212
in the formula:
Figure BDA00032320055200001213
the output reactive power of the photovoltaic inverter at the time t;
Figure BDA00032320055200001214
a power angle set for the photovoltaic inverter.
In the embodiment, the electric heating equipment adopts an electric heat pump, and the energy conversion equation is as follows:
Figure BDA00032320055200001215
in the formula:
Figure BDA00032320055200001216
respectively the output thermal power and the input electric power of the electric heating pump at the moment t; etaHPThe heating coefficient is shown.
The energy conversion equation of the gas boiler is as follows:
Figure BDA0003232005520000131
in the formula: subscript i is the number of the gas boiler;
Figure BDA0003232005520000132
the natural gas power consumed by the gas boiler at the moment t;
Figure BDA0003232005520000133
the thermal output of the gas boiler at the time t; etaGBThe heat supply efficiency for the gas boiler is improved.
The energy storage system operation model is as follows:
Figure BDA0003232005520000134
in the formula:
Figure BDA0003232005520000135
the charge states of the ith energy storage system at the time t +1 and the time t are respectively;
Figure BDA0003232005520000136
ηch、 ηdcrespectively representing the self-loss rate, the charging efficiency and the discharging efficiency of the energy storage device;
Figure BDA0003232005520000137
rated capacity for stored energy;
Figure BDA0003232005520000138
Figure BDA0003232005520000139
respectively showing the discharging power and the charging power of the energy storage system at the time t.
The operation cost model of the small diesel generator set is as follows:
Figure BDA00032320055200001310
in the formula: subscript i is the number of the small diesel engine set;
Figure BDA00032320055200001311
the unit running cost at the moment t is calculated;
Figure BDA00032320055200001312
is the cost coefficient of the corresponding unit.
The power distribution network generally adopts a closed-loop design and open-loop operation power supply mode, generally radial operation is adopted, the branch loss of the power distribution network is generally smaller than the line power flow, and the simplified linear power flow model after the branch loss is ignored in the embodiment is as follows:
Figure BDA00032320055200001313
in the formula: j → k represents the set of nodes k to which power flows from node j; pijAnd QijRespectively representing active power and reactive power from the node i to the node j; pjAnd QjRespectively the active power and the reactive power flowing to the node j; rijAnd XijThe resistance and reactance of branch ij are respectively; vi、VjThe voltage amplitudes of node i and node j, respectively.
The heat supply network quasi-dynamic model considering the transmission delay and heat dissipation of the heat supply network is as follows:
the heat pipe network obtains heat energy from the heat source through the heat exchange first station, realizes heat exchange with the secondary pipe network through the first-level heat exchange station, provides heat for the user, and the heat exchange station energy transfer process is:
Figure BDA0003232005520000141
Figure BDA0003232005520000142
in the formula:
Figure BDA0003232005520000143
the heat power absorbed from the heat source by the first heat exchange station at the node n;
Figure BDA0003232005520000144
the thermal power transmitted to the secondary pipe network by the primary heat exchange station at the node n is provided; cwIs the specific heat capacity of water;
Figure BDA0003232005520000145
and
Figure BDA0003232005520000146
mass flows injected and discharged respectively for the node n;
Figure BDA0003232005520000147
and
Figure BDA0003232005520000148
the temperature of water supplied to the first heat exchange station at the node n and the temperature of return water flowing through the first heat exchange station are respectively;
Figure BDA0003232005520000149
and
Figure BDA00032320055200001410
respectively the temperature at the node n of the water supply pipeline and the water return pipeline of the heat supply network.
Under the quality adjusting mode, the number of transmission delay time sections of each section of pipeline is as follows:
Figure BDA00032320055200001411
in the formula: n is a positive integer; mpIs the total mass of hot water in the pipe p; ρ is the density of water; m ispIs the mass flow of the hot water in the pipeline p; dpAnd LpThe diameter and length of the pipe p, respectively; Δ t is the scheduling time interval.
Neglecting the temperature loss, the temperature at the outlet of the pipe at time t is:
Figure BDA00032320055200001412
in the formula:
Figure BDA00032320055200001413
in order to neglect the temperature loss, the temperature of the mass block at the p outlet of the pipeline at the time t;
Figure BDA00032320055200001414
and
Figure BDA00032320055200001415
are respectively provided with
Figure BDA00032320055200001416
And
Figure BDA00032320055200001417
the temperature of the injection line.
The actual outlet temperature of the pipe p when considering the transmission heat dissipation is:
Figure BDA00032320055200001418
in the formula: t isGDIs the temperature of the earth surrounding the pipeline; kpIs the heat transfer coefficient per unit length of the pipeline p and is related to the pipeline heat insulating material.
The above equations (13) - (15) are applicable to all water supply and return pipes in a heat distribution pipe network.
In the heating power pipe network, the hot water flowing out from different pipelines is fully mixed at the confluence node, the temperature of the hot water flowing out from the node is equal to the mixed temperature of the node, and the temperature mixing equation of the heating power pipe network is as follows:
Figure BDA0003232005520000151
Figure BDA0003232005520000152
in the formula:
Figure BDA0003232005520000153
is the flow of the water supply and return pipes;
Figure BDA0003232005520000154
and
Figure BDA0003232005520000155
is the mixed temperature of the node n in the water supply and return network;
Figure BDA0003232005520000156
respectively representing the water supply temperature and the water return temperature at the outlet of the pipeline p;
Figure BDA0003232005520000157
respectively representing the supply water temperature and the return water temperature at the inlet of the pipe p.
Figure BDA0003232005520000158
And
Figure BDA0003232005520000159
respectively collecting pipelines which take the node n as a terminal point and take the node n as a starting point in the water supply/return network.
The building heat storage model of the multi-zone polymerization is as follows:
the building is a building formed by aggregating a plurality of heating areas, the building heat dissipation is a slow dynamic process, and the temperature change process of each area meets the following conditions:
Figure BDA00032320055200001510
Figure BDA00032320055200001511
in the formula:
Figure BDA00032320055200001512
and
Figure BDA00032320055200001513
respectively the heat capacity of the room and the wall;
Figure BDA00032320055200001514
the thermal power input for the ith room at the time t; t isj,tIs the temperature at node j at time t;
Figure BDA00032320055200001515
and
Figure BDA00032320055200001516
respectively obtaining room and wall temperatures at the moment t to be solved;
Figure BDA00032320055200001517
all nodes adjacent to the ith room;
Figure BDA00032320055200001518
all nodes adjacent to the wall between the node i and the node j are provided;
Figure BDA00032320055200001519
and
Figure BDA00032320055200001520
the thermal resistance of a wall or a window between the node i and the node j is shown; v isijThe variable is 0-1, and the variable represents whether the room has a window or not, wherein the variable is 1 when the room has the window and 0 when the room has no window;
Figure BDA00032320055200001521
respectively the window transmissivity and the wall heat absorption rate;
Figure BDA0003232005520000161
the surface area of the window body and the wall body;
Figure BDA0003232005520000162
and
Figure BDA0003232005520000163
the irradiation intensity corresponding to the window body or the wall body.
The sum of the thermal powers of all the heat supply areas is equivalent thermal load of the building:
Figure BDA0003232005520000164
in the formula: the subscript i is the building number,
Figure BDA0003232005520000165
the equivalent thermal load of the building at the moment t;
Figure BDA0003232005520000166
heat supply for the z-th room; n is a radical oftotThe number of heating rooms contained in the building.
In the above embodiment, the optimization model established in step 2 takes the daily comprehensive operation cost minimum as an objective function, including the external energy purchase cost
Figure BDA0003232005520000167
And equipment operating costs
Figure BDA0003232005520000168
Figure BDA0003232005520000169
Wherein:
Figure BDA00032320055200001610
Figure BDA00032320055200001611
in the formula: c. Celec、cgasRespectively purchasing electricity and natural gas from the outside;
Figure BDA00032320055200001612
respectively purchasing electric quantity and gas quantity from outside in a time period t; Ω represents a set of devices; subscripts CHP, DCHP, CGU, HP, GB, PV and BU respectively indicate that the equipment types are a combined heat and power generation unit, a micro combined generation unit, a diesel generator unit, an electric heat pump, a gas boiler and a distributed photovoltaic and energy storage system; subscript s denotes the device number;
Figure BDA00032320055200001613
represents the electrical power of the device during time t;
Figure BDA00032320055200001614
representing the thermal power of the device during the period t.
Because the renewable energy output has uncertainty, a certain amount of spinning reserve needs to be left in the system, and the reserve capacity needed by the system meets the following opportunity constraint conditions:
Figure BDA00032320055200001615
in the formula: ri,tThe method comprises the following steps of providing a rotary standby for the ith node of the power distribution network;
Figure BDA00032320055200001616
the active load of the node i of the power distribution network is; the confidence level α is the probability value at which the constraint holds.
In addition, the operation optimization opportunity constraint model of the rural electric heating comprehensive energy system further comprises the following constraints:
device rated power constraints:
Figure BDA0003232005520000171
Figure BDA0003232005520000172
Figure BDA0003232005520000173
Figure BDA0003232005520000174
Figure BDA0003232005520000175
Figure BDA0003232005520000176
Figure BDA0003232005520000177
Figure BDA0003232005520000178
in the formula:
Figure BDA0003232005520000179
the upper limit and the lower limit of the generating power of the cogeneration unit; rH、RLThe maximum and minimum electric-heat ratios are obtained;
Figure BDA00032320055200001710
P DCHPthe upper limit and the lower limit of the generating power of the micro co-generation unit are respectively set;
Figure BDA00032320055200001711
H GBthe upper limit and the lower limit of the thermal output of the gas boiler;
Figure BDA00032320055200001712
P CGUthe active output of the small diesel engine set is the upper limit and the lower limit;
Figure BDA00032320055200001713
Q CGUthe upper limit and the lower limit of the reactive power output of the small diesel engine set are set;
Figure BDA00032320055200001714
respectively representing the stored energy maximum discharge power and the stored energy maximum charge power.
And (3) equipment climbing capacity constraint:
Figure BDA00032320055200001715
Figure BDA00032320055200001716
Figure BDA00032320055200001717
in the formula:
Figure BDA00032320055200001718
the climbing capacity of the cogeneration unit is the climbing capacity;
Figure BDA00032320055200001719
the climbing capacity of the gas boiler is the climbing capacity up and down;
Figure BDA00032320055200001720
the climbing capability of the small diesel engine set is up and down.
And (3) energy storage system charge and discharge state constraint:
Figure BDA0003232005520000181
and (3) energy storage system charge state constraint:
Figure BDA0003232005520000182
Figure BDA0003232005520000183
in the formula:
Figure BDA0003232005520000184
the maximum charge state value and the minimum charge state value allowed in the energy storage operation process;
Figure BDA0003232005520000185
and
Figure BDA0003232005520000186
the states of charge of the stored energy at the starting and ending moments of the scheduling cycle are respectively.
Constraint of transmission capacity of distribution line:
Figure BDA0003232005520000187
Figure BDA0003232005520000188
in the formula:
Figure BDA0003232005520000189
P ijand
Figure BDA00032320055200001810
Q ijupper and lower limits for the transmission of active and reactive power for branch ij, respectively.
And (3) limiting the upper and lower limits of the node voltage:
Figure BDA00032320055200001811
in the formula:
Figure BDA00032320055200001812
V ithe upper limit value and the lower limit value of the voltage of the point i in the power distribution network are respectively.
Temperature constraint of heating power pipe network nodes:
Figure BDA00032320055200001813
Figure BDA00032320055200001814
in the formula:
Figure BDA00032320055200001815
and
Figure BDA00032320055200001816
the upper limit and the lower limit of the water supply temperature at the node n are respectively set;
Figure BDA00032320055200001817
is the lowest return water temperature at node n.
The heat transferred to the building by the primary pipe network is equivalent to the heat load of the heat exchange station:
Figure BDA00032320055200001818
the temperature of the building indoor area is allowed to fluctuate within a certain range:
Figure BDA00032320055200001819
in the formula:
Figure BDA0003232005520000191
T i rthe allowable upper limit and the allowable lower limit of the indoor temperature of the ith room are respectively set.
In the above embodiment, in order to reduce the computational burden when solving the opportunistic constraint model in step 3, the opportunistic constraint is converted into a deterministic constraint:
Figure BDA0003232005520000192
in the formula:
Figure BDA0003232005520000193
and (4) a lower quantile point for photovoltaic output prediction.
And during solving, the constraint formula (36) of the charge-discharge state of the energy storage system is ignored, and the scheduling result is not influenced.
In the above embodiment, in step 3, the heat supply network pipeline and the flow parameter are brought into (13), the transmission delay of each section of pipeline in the thermal network is obtained, and the pipeline transmission delay, the equipment parameters, the building parameters, the power distribution network and thermal network parameters, and the information of the power load, the thermal load, the renewable energy output, and the real-time electricity price are substituted into the operation optimization opportunity constraint model of the rural electric heating integrated energy system.
To show the effectiveness of the present invention more clearly, the following four scheduling scenarios are set for comparison:
scene 1: and (4) a cooperative operation scheduling strategy is made without considering the virtual energy storage characteristics of the heat distribution pipe network and the building.
Scene 2: and only considering the building thermal inertia, and making a dispatching plan without considering the virtual energy storage characteristics of the heat supply network.
Scene 3: and only considering the virtual energy storage characteristics of the heat supply network and not considering the heat inertia of the building to make a dispatching plan.
Scene 4: and simultaneously, the virtual energy storage characteristics of the building and the heat supply network are considered for carrying out collaborative operation optimization.
After the implementation case is optimized, the system operation cost result is shown in table 1.
As can be seen from Table 1, the small-range fluctuation of the indoor temperature of building users and the temperature of hot water in a heat power pipe network is allowed, partial heat energy is stored in the pipe network in advance when the electricity price is low, the indoor temperature of the building is properly increased, the output of an electric heat pump is reduced when the electricity price is at the peak value, the indoor temperature of the users is allowed to be slightly lower than a set value, the electric heat combined dispatching is more flexible, and the total cost of the system is effectively reduced. No matter what scheduling strategy is adopted, when the output predicted value of the renewable energy source is equal to the actual value, the total cost of the system is the lowest, and along with the improvement of the confidence level, the total production cost of the system is gradually increased, mainly because more standby units which can directly output originally need to be reserved to deal with the prediction error. Therefore, the invention can provide the scheduling schemes under various confidence levels, and the scheduler reasonably selects the scheduling schemes according to the historical operating data and the operating condition, thereby realizing the reduction of the reliability and the economy of the system.
Table 1 total cost of four scheduling strategies at different confidence levels
Figure BDA0003232005520000201
The output situation of each device of the optimized system in this embodiment is shown in fig. 4. Because the temperature difference between the pipe network and the environment can produce heat loss in the hot water transmission process, so the total heat supply amount is greater than the total heat demand amount. As can be seen from fig. 4(b), the thermal output and the thermal load of the system are not balanced in real time, which is mainly caused by different lengths of pipes for the thermal energy to be transmitted from the heat source to the heat exchange station connected to the load, corresponding to different transmission delays. The heat output curve is not the simple translation of the heat load curve because the temperature of the working medium in the heat supply network and the building temperature are allowed to fluctuate within a certain range, and the heat source can flexibly adjust the heat output according to the change of the external energy purchase price in the optimization process.
The room temperature change of each area of the building after the optimization of the case is shown in figure 5. Although a part of the area within the building 2 undergoes significant temperature fluctuations, it can always be maintained within a range that satisfies user comfort. The heat energy used for space heating can be selectively supplied by a heat supply network or an electric heat pump, and can be more flexibly scheduled with a rural electric heat comprehensive energy system through electric heat coordination and complementation, so that the overall cost of the system is reduced.
The embodiments of the present invention are described above with reference to the accompanying drawings, and not limited to the scope of the present invention, and all equivalent models or equivalent algorithm flows made by using the contents of the description and the drawings of the present invention are within the scope of the present invention by being directly or indirectly applied to other related technical fields.

Claims (7)

1. A method for optimizing the operation of a rural electric-thermal integrated energy system in consideration of renewable energy uncertainty and thermal inertia, the method comprising the steps of:
step 1: modeling a source, a network and a load in a rural electric heating comprehensive energy system, wherein the source equipment comprises a cogeneration unit, distributed photovoltaic equipment, electric heating equipment, a gas boiler, an energy storage system and a diesel generator set, the network model comprises a heating power pipe network quasi-dynamic model and a radial power distribution network trend model which consider heat supply network transmission delay and heat dissipation, and a multi-region aggregated building heat storage model is established at the load side;
step 2: combining a source equipment model, a network model and a building heat storage model, introducing standby constraint considering the uncertainty of renewable energy, and establishing a rural electric heating comprehensive energy system operation optimization opportunity constraint model;
and step 3: and solving the opportunity constraint model to obtain an optimal scheduling plan of the system, adjusting the output of the equipment according to the plan, and purchasing electricity and gas for the upper-level power grid and the natural gas company.
2. The method for optimizing operation of a rural electric and heat comprehensive energy system considering renewable energy uncertainty and heat inertia according to claim 1, wherein the quasi-dynamic model of the heat distribution network considering heat distribution network transmission delay and heat dissipation in the step 1 is as follows:
the energy transfer process of the heat exchange station comprises the following steps:
Figure FDA0003232005510000011
Figure FDA0003232005510000012
in the formula:
Figure FDA0003232005510000013
the heat power absorbed from the heat source by the first heat exchange station at the node n;
Figure FDA0003232005510000014
the thermal power transmitted to the secondary pipe network by the primary heat exchange station at the node n is provided; cwIs the specific heat capacity of water;
Figure FDA0003232005510000015
and
Figure FDA0003232005510000016
mass flows injected and flowed respectively for the node n;
Figure FDA0003232005510000017
and
Figure FDA0003232005510000018
the temperature of water supplied to the first heat exchange station at the node n and the temperature of return water flowing through the first heat exchange station are respectively;
Figure FDA0003232005510000019
and
Figure FDA00032320055100000110
respectively supply and return to the heat supply networkThe temperature at water pipeline node n;
the number of transmission delay time sections of each section of pipeline is as follows:
Figure FDA0003232005510000021
in the formula: n is a positive integer; mpIs the total mass of hot water in the pipe p; ρ is the density of water; m ispIs the mass flow of the hot water in the pipeline p; dpAnd LpThe diameter and length of the pipe p, respectively; Δ t is a scheduling time interval;
neglecting the temperature loss, the temperature at the outlet of the pipe at time t is:
Figure FDA0003232005510000022
in the formula:
Figure FDA0003232005510000023
in order to neglect the temperature loss, the temperature of the mass block at the p outlet of the pipeline at the time t;
Figure FDA0003232005510000024
and
Figure FDA0003232005510000025
are respectively as
Figure FDA0003232005510000026
And
Figure FDA0003232005510000027
the temperature of the injection pipe;
the actual outlet temperature of the pipe p when considering the transmission heat dissipation is:
Figure FDA0003232005510000028
in the formula: t isGDIs the temperature of the earth surrounding the pipeline; kpIs the heat conduction coefficient of the pipeline p per unit length and is related to the pipeline heat-insulating material;
the heat distribution pipe network temperature mixing equation is as follows:
Figure FDA0003232005510000029
Figure FDA00032320055100000210
in the formula:
Figure FDA00032320055100000211
is the flow of the water supply and return pipes;
Figure FDA00032320055100000212
and
Figure FDA00032320055100000213
is the mixed temperature of the node n in the water supply and return network;
Figure FDA00032320055100000214
respectively representing the water supply temperature and the water return temperature at the outlet of the pipeline p;
Figure FDA00032320055100000215
Figure FDA00032320055100000216
respectively representing the supply water temperature and the return water temperature at the inlet of the pipe p.
Figure FDA00032320055100000217
And
Figure FDA00032320055100000218
respectively collecting pipelines which take the node n as a terminal point and take the node n as a starting point in the water supply/return network.
3. The method of optimizing operation of a rural electric heating integrated energy system considering renewable energy uncertainty and thermal inertia as claimed in claim 1, wherein said radial power distribution grid power flow model is linearized by:
Figure FDA0003232005510000031
in the formula: j → k represents the set of nodes k to which power flows from node j; pijAnd QijRespectively representing active power and reactive power from the node i to the node j; pjAnd QjRespectively the active power and the reactive power flowing to the node j; rijAnd XijThe resistance and reactance of branch ij are respectively; vi、VjThe voltage amplitudes of node i and node j, respectively.
4. The method of optimizing rural electric heating integrated energy system operation according to claim 1, wherein the multizone aggregated building thermal storage model is:
the temperature change process of each area of the building meets the following conditions:
Figure FDA0003232005510000032
Figure FDA0003232005510000033
in the formula:
Figure FDA0003232005510000034
and
Figure FDA0003232005510000035
respectively the heat capacity of the room and the wall;
Figure FDA0003232005510000036
the thermal power input for the ith room at the time t; t isj,tIs the temperature at node j at time t;
Figure FDA0003232005510000037
and
Figure FDA0003232005510000038
respectively obtaining room and wall temperatures at the moment t to be solved;
Figure FDA0003232005510000039
all nodes adjacent to the ith room;
Figure FDA00032320055100000310
all nodes adjacent to the wall between the node i and the node j are provided;
Figure FDA00032320055100000311
and
Figure FDA00032320055100000312
the thermal resistance of a wall or a window between the node i and the node j is shown; v isijThe variable is 0-1, and the variable represents whether the room has a window or not, wherein the variable is 1 when the room has the window and 0 when the room has no window;
Figure FDA00032320055100000313
respectively the window transmissivity and the wall heat absorption rate;
Figure FDA00032320055100000314
the surface area of the window body and the wall body;
Figure FDA00032320055100000315
and
Figure FDA00032320055100000316
the irradiation intensity corresponding to the window body or the wall body;
the sum of the thermal powers of all the heat supply areas is equivalent thermal load of the building:
Figure FDA0003232005510000041
in the formula: the subscript i is the building number,
Figure FDA0003232005510000042
the equivalent thermal load of the building at the moment t;
Figure FDA0003232005510000043
heat supply for the z-th room; n is a radical oftotThe number of heating rooms contained in the building.
5. A method for optimizing operation of a rural electric-thermal integrated energy system considering renewable energy uncertainty and heat inertia as claimed in claim 1, wherein in the step 1, the cogeneration unit is operated in a variable heat-to-power ratio mode, and the power output and the heat output of the cogeneration unit are limited by maximum and minimum steam admission amount and maximum and minimum steam condensation amount, and can be flexibly adjusted in a feasible region.
6. The rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia according to claim 1, wherein the rural electric heating comprehensive energy system operation optimization opportunity constraint model established in the step 2 takes daily comprehensive operation cost minimum as an objective function:
Figure FDA0003232005510000044
wherein:
Figure FDA0003232005510000045
Figure FDA0003232005510000046
in the formula: c. Celec、cgasRespectively purchasing electricity and natural gas from the outside;
Figure FDA0003232005510000047
respectively purchasing electric quantity and gas quantity from outside in a time period t; Ω represents a set of devices; subscripts CHP, CGU, HP, GB, PV and BU respectively indicate that the equipment types are a combined heat and power generation unit, a diesel generator set, electric heating equipment, a gas boiler and a distributed photovoltaic and energy storage system; subscript s denotes the device number;
Figure FDA0003232005510000048
represents the electrical power of the device during the time period t;
Figure FDA0003232005510000049
represents the thermal power of the device during the period t;
the system needs to satisfy backup constraints that take into account renewable energy uncertainty:
Figure FDA0003232005510000051
in the formula: ri,tThe method comprises the following steps of providing a rotary standby for the ith node of the power distribution network;
Figure FDA0003232005510000052
the active load of a node i of the power distribution network; the confidence level alpha is a probability value of the satisfied constraint;
in addition, the operation optimization opportunity constraint model of the rural electric heating comprehensive energy system further comprises the following constraints:
device rated power constraints:
Figure FDA0003232005510000053
Figure FDA0003232005510000054
Figure FDA0003232005510000055
Figure FDA0003232005510000056
Figure FDA0003232005510000057
Figure FDA0003232005510000058
Figure FDA0003232005510000059
in the formula:
Figure FDA00032320055100000510
the upper limit and the lower limit of the generating power of the cogeneration unit; rH、RLThe maximum and minimum electric-heat ratios are obtained;
Figure FDA00032320055100000511
H GBis burningThe upper and lower limits of the thermal output of the gas boiler;
Figure FDA00032320055100000512
P CGUthe active output of the small diesel engine set is the upper limit and the lower limit;
Figure FDA00032320055100000513
Q CGUthe upper limit and the lower limit of the reactive power output of the small diesel engine set are set;
Figure FDA00032320055100000514
respectively representing the maximum discharge and charge power of the stored energy;
and (3) equipment climbing capacity constraint:
Figure FDA00032320055100000515
Figure FDA00032320055100000516
Figure FDA00032320055100000517
in the formula:
Figure FDA00032320055100000518
the climbing capacity of the cogeneration unit is the climbing capacity;
Figure FDA00032320055100000519
the climbing capacity of the gas boiler is the climbing capacity up and down;
Figure FDA00032320055100000520
the climbing capability of the small diesel engine set is up-hill climbing and down-hill climbing;
and (3) energy storage system charge and discharge state constraint:
Figure FDA0003232005510000061
and (3) energy storage system charge state constraint:
Figure FDA0003232005510000062
Figure FDA0003232005510000063
in the formula:
Figure FDA0003232005510000064
E BUthe maximum charge state value and the minimum charge state value allowed in the energy storage operation process;
Figure FDA0003232005510000065
and
Figure FDA0003232005510000066
respectively storing the charge states of the energy at the starting time and the ending time of the scheduling period;
constraint of transmission capacity of distribution line:
Figure FDA0003232005510000067
Figure FDA0003232005510000068
in the formula:
Figure FDA0003232005510000069
Pijand
Figure FDA00032320055100000610
Q ijupper and lower limits for transmitting active and reactive power for branch ij, respectively;
and (3) limiting the upper and lower limits of the node voltage:
Figure FDA00032320055100000611
in the formula:
Figure FDA00032320055100000612
V ithe upper limit value and the lower limit value of the voltage of the point i in the power distribution network are respectively set;
temperature constraint of heating power pipe network nodes:
Figure FDA00032320055100000613
Figure FDA00032320055100000614
in the formula:
Figure FDA00032320055100000615
and
Figure FDA00032320055100000616
the upper limit and the lower limit of the water supply temperature at the node n are respectively set;
Figure FDA00032320055100000617
the lowest return water temperature at the node n is obtained;
the heat transferred to the building by the primary pipe network is equivalent to the heat load of the heat exchange station:
Figure FDA00032320055100000618
the temperature of the building indoor area is allowed to fluctuate within a certain range:
Figure FDA0003232005510000071
in the formula:
Figure FDA0003232005510000072
T i rthe allowable upper limit and the allowable lower limit of the indoor temperature of the ith room are respectively set.
7. The rural electric heating comprehensive energy system operation optimization method considering renewable energy uncertainty and thermal inertia according to claim 1, wherein the opportunistic constraint is converted into deterministic constraint for reducing the computational burden when solving the opportunistic constraint model in the step 3:
Figure FDA0003232005510000073
in the formula:
Figure FDA0003232005510000074
a lower quantile point for photovoltaic output prediction;
and directly solving the operation optimization opportunity constraint model of the rural electric heating comprehensive energy system by using solving software to obtain an optimal scheduling plan.
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