CN112600217A - Comprehensive energy system load side optimal scheduling method considering multi-energy complementation - Google Patents

Comprehensive energy system load side optimal scheduling method considering multi-energy complementation Download PDF

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CN112600217A
CN112600217A CN202011450641.9A CN202011450641A CN112600217A CN 112600217 A CN112600217 A CN 112600217A CN 202011450641 A CN202011450641 A CN 202011450641A CN 112600217 A CN112600217 A CN 112600217A
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load
load side
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formula
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杨帆
孔祥玉
沈煜
孙方圆
胡伟
卢文祺
杨志淳
赵栩
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Tianjin University
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
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Abstract

The invention discloses a comprehensive energy system load side optimal scheduling method considering multi-energy complementation, which is developed by aiming at the problem of optimal operation scheduling of a comprehensive energy system under high-proportion clean energy penetration from the perspective of an electric power Internet of things. Firstly, a basic architecture of comprehensive energy scheduling in the context of ubiquitous power Internet of things is analyzed, and a load side control model of a comprehensive energy system in the context of ubiquitous power Internet of things is established from the perspective of multi-energy complementation at the load side and user energy flexibility. Further considering cloud side architecture, information acquisition characteristics and market environment in the power internet of things environment, and based on the frequent interaction process of the heat supply network and the power grid of the comprehensive energy system, the comprehensive energy system load side optimal scheduling method considering source-load interaction and multi-energy complementation is provided. The method has the advantages that the dispatching of the whole comprehensive energy system and the dispatching of the load side are separately carried out through a layered dispatching framework, so that the flexible interaction of the multi-energy complementation and the source load is realized.

Description

Comprehensive energy system load side optimal scheduling method considering multi-energy complementation
Technical Field
The invention relates to the field of electrical information, in particular to a comprehensive energy system load side optimal scheduling method considering multi-energy complementation.
Background
In order to relieve the energy pressure, on one hand, the proportion of green clean energy is increased, and on the other hand, the primary energy utilization efficiency is improved in the production and scheduling process, so that unnecessary energy waste is reduced. Therefore, the existing mode of independent planning, independent design and independent operation of each original energy supply system is broken through, integrated planning design and operation optimization of the social energy system are carried out, and finally, a unified social comprehensive energy supply and utilization system is constructed, so that the gradual utilization of primary energy and the unified planning and scheduling of secondary energy such as electric energy, heat energy and the like are realized.
The ubiquitous power internet of things is around each link of a power system, and the modern information technology and the advanced communication technology are fully applied to realize the mutual connection and the man-machine interaction of all links of the power system. Along with the construction of the ubiquitous power internet of things, the perception capability of the comprehensive energy scheduling system to the load is gradually enhanced, information exchange among all components in the system is more frequent, and the optimal scheduling method and structure of the system are changed.
In recent years, research on optimal scheduling of an integrated energy system mainly focuses on operation control of a thermocouple element to achieve optimal economic operation and increase green clean energy consumption. At present, the traditional integrated energy system scheduling is generally performed according to a centralized-parallel architecture, that is, energy coupling elements at part of nodes are considered while electric, thermal and air networks are respectively optimized and scheduled, information and energy interaction between different networks is realized through the coupling elements, and finally, an overall optimal scheduling scheme is obtained.
However, with the construction of the ubiquitous power internet of things, the traditional scheduling method of the centralized-parallel architecture faces the following two problems: (1) the ubiquitous power internet of things construction can greatly improve the sensing capability of a dispatching center on lower-layer loads, information measuring points, information collecting frequency, information collecting types and data quality are greatly increased, the quantity of measured data on a load side is rapidly increased, and if a traditional centralized-parallel dispatching framework is still adopted, the calculation speed of the dispatching center is greatly reduced due to the increase of data and the unsmooth information transmission; (2) due to the popularization of the intelligent electric meter with the function of combining multiple meters into one meter, the problem that electricity, heat, gas and other energy sources in a traditional comprehensive energy measuring system are respectively measured and data is not communicated is solved, coupling among different energy networks is not limited to large energy coupling elements any more, information exchange and energy transmission of the different energy networks can be achieved on a load side, coupling among the energy networks is tighter, if flexibility of the load side can be fully exerted, multi-energy complementation and flexible scheduling of the load side are achieved, peak clipping and valley filling of the comprehensive energy system can be facilitated, source consumption of green and clean energy can be increased, and the traditional scheduling framework cannot support the energy networks to conduct frequent information and energy interaction on the load side.
Although much research has been conducted on the optimization scheduling of the comprehensive energy system, with the construction of ubiquitous power internet of things, the traditional comprehensive energy scheduling method is difficult to fully utilize mass data on the load side, and is also difficult to support frequent information exchange and energy transfer of different energy networks such as electricity, heat and gas on the load side. Therefore, the load-side optimized scheduling of the integrated energy system based on edge calculation is gradually popularized. The invention fully considers the flexibility of user energy using behaviors and the conversion of energy using requirements among different energy systems, and establishes the comprehensive energy system load side optimization scheduling method considering multi-energy complementation so as to realize the flexible interaction of the multi-energy complementation and the source load.
Disclosure of Invention
In order to solve the existing problems, the invention provides a comprehensive energy system load side optimal scheduling method considering multi-energy complementation, which is applied to the day-ahead load side scheduling of a comprehensive energy system, and interconnects and fully utilizes the cloud pipe side architecture and the multi-energy information of the load side so as to improve the energy utilization efficiency, reduce the operation cost and realize the global optimal operation of the whole area; interaction among different energy networks of the load side is considered in the optimization scheduling solving process of the load side, and meanwhile complementary characteristics among different energy requirements of the load side and user energy utilization behavior flexibility based on the energy conversion equipment are fully explored.
The invention provides a comprehensive energy system load side optimization scheduling method considering multi-energy complementation, which considers cloud side architecture, information acquisition characteristics and market environment under the ubiquitous power Internet of things environment, and provides the comprehensive energy system load side optimization scheduling method considering flexible control of load side equipment and multi-energy complementation based on the frequent interaction process of a heat supply network and a power grid of a comprehensive energy system, wherein an optimization scheduling model objective function comprises the cost of purchasing heat energy and electric energy by a load, the operation cost of load side energy conversion equipment and compensation obtained through load demand response, and meanwhile, the scheduling process is limited by temperature constraint, demand response constraint and energy equipment constraint in a heat load building, and the method comprises the following steps:
a comprehensive energy system load side optimal scheduling method considering multi-energy complementation comprises the following steps:
s11: modeling load side comprehensive energy controllable equipment to obtain a load side controllable equipment model, wherein the load side comprehensive energy controllable equipment comprises an energy storage device, an energy conversion device and distributed energy, and the load side controllable equipment model is used for analyzing the energy conversion characteristics of the load side comprehensive energy controllable equipment model and constraining various types of load side comprehensive energy controllable equipment;
s12: establishing a user energy consumption behavior demand response model based on price-demand elasticity on a load side, analyzing behavior characteristics of users participating in different types of demand responses, realizing interaction with an energy internet, and constraining a power grid side scheduling behavior;
s13: establishing a comprehensive energy system load side optimization scheduling objective function considering source-load interaction and multi-energy complementation so as to minimize the operation cost of the system, wherein the operation cost of the load side controllable equipment is calculated according to the load side controllable equipment model in the step S11, and the demand response cost is calculated according to the load demand response model in the step S12;
s14: according to the load side controllable equipment model established in the step S11 and the load demand response model established in the step S12, various controllable equipment on the load side and power grid side scheduling behaviors are restrained, and an optimized scheduling model of the load side of the comprehensive energy system is established;
s15: and solving the comprehensive energy system load side optimization scheduling model established in the step S14 to further obtain an optimal day-ahead scheduling result.
Further, the load side controllable device model in the step S11 and the user energy consumption behavior demand response model in the step S12 consider cloud side architecture and information acquisition characteristics in the context of the power internet of things, and expand information interaction between different energy subsystems in the integrated energy system from large-scale coupling devices on the source side to the source and load side for information interaction, so that the load side can realize energy substitution of different energy types;
in step S11, the load-side controllable device model includes an electric boiler model and an electric energy storage system model, which are specifically as follows:
the model of the electric boiler is represented by the following formula:
Figure BDA0002826700530000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000032
is the electric power input by the electric boiler,
Figure BDA0002826700530000033
is the electric heat conversion efficiency of the electric boiler,
Figure BDA0002826700530000034
the heat energy of the electric boiler is output;
the SOC of the electrical energy storage device in the electrical energy storage system model is calculated by the following formula:
Figure BDA0002826700530000035
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000036
the SOC value of the electric energy storage equipment at the node i in the period t;
Figure BDA0002826700530000037
an energy storage efficiency for the energy storage device;
Figure BDA0002826700530000038
charging efficiency for the energy storage device;
Figure BDA0002826700530000039
and
Figure BDA00028267005300000310
the charging and discharging power of the energy storage device.
The SOC of the thermal energy storage device in the electrical energy storage system model is calculated by the following equation:
Figure BDA00028267005300000311
in the formula (II)
Figure BDA00028267005300000312
The SOC value of the heat storage boiler at the node i in the t period is obtained;
Figure BDA00028267005300000313
the energy storage efficiency of the heat storage boiler is improved;
Figure BDA00028267005300000314
the energy charging efficiency of the heat storage boiler is improved;
Figure BDA00028267005300000315
and
Figure BDA00028267005300000316
the energy charging and discharging power of the heat storage boiler.
Further, the energy-consuming behavior demand response model for the user in step S12 is as follows:
the corresponding sensitivity of the load to price is generally expressed by the spring rate, and the thermal load for heating can be increased or decreased without excessively affecting the indoor temperature, but is generally not shifted to other periods, and is generally expressed by the self-spring rate:
Figure BDA0002826700530000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000042
the coefficient of self-elasticity for the thermal load at node i during time t;
Figure BDA0002826700530000043
the heat supply price in the t period;
Figure BDA0002826700530000044
the thermal load at node i is t periods before the demand response;
Figure BDA0002826700530000045
a heat load demand response price for a period of t;
Figure BDA0002826700530000046
is the amount of change in thermal load after demand response;
the thermal load variation in the user energy behavior demand response model is represented by:
Figure BDA0002826700530000047
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000048
the thermal load at node i for time period t;
part of the electrical load can be transferred to other time periods, and part of the electrical load can be reduced or increased, so that the self-elastic coefficient and the mutual elastic coefficient are commonly expressed as follows:
Figure BDA0002826700530000049
Figure BDA00028267005300000410
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000411
the self-elastic coefficient of the electrical load at the node i in the period t;
Figure BDA00028267005300000412
electricity prices for a period of t;
Figure BDA00028267005300000413
an electrical load at node i for a period t before a demand response;
Figure BDA00028267005300000414
responding price for electric load demand in t period;
Figure BDA00028267005300000415
the variation of the electrical load at the node i in the period t of the demand response;
Figure BDA00028267005300000416
the mutual elastic coefficient of the electrical load at the node i in the t period to the tau period;
the change in electrical load in the user energy behavior demand response model is represented by:
Figure BDA00028267005300000417
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000418
the electrical load at node i is time period t.
Further, in step S13, the optimal scheduling objective function calculation formula is:
Figure BDA00028267005300000419
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000051
scheduling cost of load at node i for time period t;
Figure BDA0002826700530000052
cost of purchasing heat energy and electric energy for the load at the node i at the time period t;
Figure BDA0002826700530000053
compensation obtained by load demand response for the load at node i at time period t;
Figure BDA0002826700530000054
the operation cost of the electric boiler is reduced;
the operating cost of an electric boiler is calculated by the following formula:
Figure BDA0002826700530000055
in the formula, c0,i、c1,iAnd c2,iThe coefficient is calculated for the cost, which is determined by the device itself.
The cost of the load to purchase heat and electricity is expressed as:
Figure BDA0002826700530000056
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000057
and
Figure BDA0002826700530000058
the electricity and heat purchase prices are respectively at the time t.
Further, in step S14, a minimum energy cost scheduling constraint of the load-side multipotential transformation is quantized and considered, and the scheduling constraint calculation formula is:
(1) temperature constraints in thermally loaded buildings
Load side scheduling should be premised on not affecting the in-building temperature too much, and the in-building temperature constraint can be expressed as:
Figure BDA0002826700530000059
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000510
and
Figure BDA00028267005300000511
respectively an upper limit and a lower limit of the indoor temperature after heat supply.
(2) Demand response constraints
The demand response of the load to the price signal has a certain limit, which is expressed in detail as:
Figure BDA00028267005300000512
Figure BDA00028267005300000513
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000514
and
Figure BDA00028267005300000515
respectively responding to the load participation coefficients of the thermoelectric demand of the load at the node i;
Figure BDA00028267005300000516
the load at the i node is the time period of the thermal energy and the electric energy demand before the demand response.
(3) Energy plant restraint
The load side energy equipment mainly comprises an electric heat pump and a heat storage boiler, and for the electric heat pump, because the output of the electric heat pump is more flexible, the output range constraint is only considered:
Figure BDA00028267005300000517
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000518
the maximum output of the electric boiler at the node i.
For a thermal storage boiler, the constraints are:
Figure BDA00028267005300000519
Figure BDA00028267005300000520
Figure BDA00028267005300000521
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000061
and
Figure BDA0002826700530000062
the maximum and minimum energy storage values of the heat storage boiler at the i node;
Figure BDA0002826700530000063
and
Figure BDA0002826700530000064
the maximum charging power and the maximum heat release power of the heat storage boiler at the i node at the t moment are constant respectively.
For an electrical energy storage device, the constraints are:
Figure BDA0002826700530000065
Figure BDA0002826700530000066
Figure BDA0002826700530000067
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000068
and
Figure BDA0002826700530000069
is the maximum and minimum energy storage values of the energy storage device at the i node;
Figure BDA00028267005300000610
and
Figure BDA00028267005300000611
and the maximum charging and discharging power of the energy storage equipment at the i node at the time t is respectively.
The invention has the following advantages and beneficial effects:
through load side scheduling, various energy requirements of the load can make corresponding to price signals of an upper layer under the condition that the comfort degree is not excessively influenced. Due to the introduction of the ubiquitous power internet of things, the load can not only carry out demand response by a method for changing energy consumption requirements, but also can make full use of the flexibility of the load side, and various energy consumption requirements of the load are converted by the load side energy coupling equipment, so that the influence on the energy consumption comfort level of a user is reduced, the demand response compensation of a dispatching center for the user is reduced, the consumption capacity of a system for renewable energy is improved, and the running cost of the system is reduced. By fully considering the flexibility of the load side, the scheduling method can provide theoretical guidance for the optimal scheduling of the comprehensive energy system under the high level of the Internet of things.
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FIG. 1 is a basic step of a method for providing optimized scheduling of a load side of an integrated energy system according to an embodiment of the present invention;
fig. 2 is a flowchart of a load-side optimized scheduling algorithm according to an embodiment of the present invention;
FIG. 3 is an example system topology provided by an embodiment of the present invention;
fig. 4 illustrates changes in load of each node, changes in average temperature of a region, and changes in fan output according to an embodiment of the present invention;
FIG. 5 illustrates an electrical load versus external demand variation provided by an embodiment of the present invention;
FIG. 6 illustrates a change in thermal load demand provided by an embodiment of the present invention;
fig. 7 shows the change of the scheduling cost for each period in three scenarios according to the embodiment 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 obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1 and fig. 2, in order to make a day-ahead scheduling plan of optimal economic operation of a load side of an integrated energy system and simultaneously give full play to the flexibility of multi-energy coupling of the load side, the invention provides an optimal scheduling method of the load side of the integrated energy system considering multi-energy complementation, which includes the following steps:
s11: establishing a load side comprehensive energy controllable equipment model;
s12: establishing a load side user energy behavior demand response model;
s13: establishing an optimized dispatching objective function of a load side of the comprehensive energy system;
s14: establishing an optimized scheduling constraint condition of a load side of the comprehensive energy system;
s15: and solving an optimized scheduling model at the load side of the integrated energy system.
In the step S11, the load side controllable device model includes an electric boiler model and an electric energy storage system model, specifically, the operation characteristics of the load side controllable device are analyzed, and specifically, the load side controllable device model includes an electric boiler, an electric energy storage device and the like;
the model of the electric boiler is represented by the following formula:
Figure BDA0002826700530000071
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000072
is the electric power input by the electric boiler,
Figure BDA0002826700530000073
is the electric heat conversion efficiency of the electric boiler,
Figure BDA0002826700530000074
the heat energy of the electric boiler is output;
the SOC of the electrical energy storage device in the electrical energy storage system model may be calculated by:
Figure BDA0002826700530000075
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000076
the SOC value of the electric energy storage equipment at the node i in the period t;
Figure BDA0002826700530000077
an energy storage efficiency for the energy storage device;
Figure BDA0002826700530000078
charging efficiency for the energy storage device;
Figure BDA0002826700530000079
and
Figure BDA00028267005300000710
the charging and discharging power of the energy storage device.
The SOC of the thermal energy storage device in the electrical energy storage system model may be calculated by:
Figure BDA00028267005300000711
in the formula (II)
Figure BDA00028267005300000712
The SOC value of the heat storage boiler at the node i in the t period is obtained;
Figure BDA00028267005300000713
the energy storage efficiency of the heat storage boiler is improved;
Figure BDA00028267005300000714
the energy charging efficiency of the heat storage boiler is improved;
Figure BDA00028267005300000715
and
Figure BDA00028267005300000716
the energy charging and discharging power of the heat storage boiler.
The step S12 is specifically to analyze the relation between the user energy behaviors and the price, establish a load side user energy behavior demand response model, and use the model for calculating the energy cost in the optimized scheduling model in the step S13, specifically:
the thermal load variation in the user energy behavior demand response model can be represented by:
Figure BDA00028267005300000717
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000081
the thermal load at node i is time period t.
Part of the electrical load can be transferred to other time periods, and part of the electrical load can be reduced or increased, so that the self-elastic coefficient and the mutual elastic coefficient are commonly expressed as follows:
Figure BDA0002826700530000082
Figure BDA0002826700530000083
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000084
the self-elastic coefficient of the electrical load at the node i in the period t;
Figure BDA0002826700530000085
electricity prices for a period of t;
Figure BDA0002826700530000086
an electrical load at node i for a period t before a demand response;
Figure BDA0002826700530000087
responding price for electric load demand in t period;
Figure BDA0002826700530000088
the variation of the electrical load at the node i in the period t of the demand response;
Figure BDA0002826700530000089
the mutual elastic coefficient of the electric load at the node i in the t period to the tau period.
The electrical load variation in the user energy behavior demand response model can be represented by:
Figure BDA00028267005300000810
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000811
the electrical load at node i is time period t.
The heat load in the comprehensive energy network can be supplied through a heat supply network, and heat energy can be obtained by using a local distributed electric boiler, an electric heat pump and the like of the load through a method of replacing electric energy; the mutual peak regulation of the heat load and the electric load can be realized through the flexible conversion of the electric energy and the heat energy at the load.
Step S13, specifically, analyzing the operation optimization requirements of the scheduling side and the load side of the integrated energy system, and establishing a load side optimization scheduling objective function, which is:
Figure BDA00028267005300000812
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000813
scheduling cost of load at node i for time period t;
Figure BDA00028267005300000814
cost of purchasing heat energy and electric energy for the load at the node i at the time period t;
Figure BDA00028267005300000815
compensation obtained by load demand response for the load at node i during time t.
The calculation formula of the operation cost of the electric boiler is as follows:
Figure BDA00028267005300000816
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000817
for the running cost of the electric boiler, c0,i、c1,iAnd c2,iThe coefficient is calculated for the cost, which is determined by the device itself.
The calculation formula of the cost of purchasing electricity and heat for the load purchase is as follows:
Figure BDA00028267005300000818
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000091
and
Figure BDA0002826700530000092
the electricity and heat prices are purchased at time t, and the electricity and heat loads are calculated by the demand response model in step S12.
The step S14 is specifically to analyze the operation characteristics of the load side of the integrated energy system, and establish operation constraints of the optimization model, including temperature constraints, demand response constraints, and energy equipment constraints within the thermal load building, and specifically is to:
the temperature constraint calculation formula in the thermal load building is as follows:
Figure BDA0002826700530000093
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000094
and
Figure BDA0002826700530000095
respectively an upper limit and a lower limit of the indoor temperature after heat supply.
The demand response constraint calculation formula is as follows:
Figure BDA0002826700530000096
Figure BDA0002826700530000097
in the formula (I), the compound is shown in the specification,
Figure BDA0002826700530000098
and
Figure BDA0002826700530000099
respectively responding to the load participation coefficients of the thermoelectric demand of the load at the node i;
Figure BDA00028267005300000910
the load at the i node is the time period of the thermal energy and the electric energy demand before the demand response.
The energy device constraints are:
Figure BDA00028267005300000911
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000912
the maximum output of the electric boiler at the node i.
The heat storage boiler is constrained as follows:
Figure BDA00028267005300000913
Figure BDA00028267005300000914
Figure BDA00028267005300000915
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000916
and
Figure BDA00028267005300000917
of heat-accumulating boilers at i-nodesMaximum and minimum reserve values;
Figure BDA00028267005300000918
and
Figure BDA00028267005300000919
the maximum charging power and the maximum heat release power of the heat storage boiler at the i node at the t moment are constant respectively.
The electrical energy storage device constraints are:
Figure BDA00028267005300000920
Figure BDA00028267005300000921
Figure BDA00028267005300000922
in the formula (I), the compound is shown in the specification,
Figure BDA00028267005300000923
and
Figure BDA00028267005300000924
is the maximum and minimum energy storage values of the energy storage device at the i node;
Figure BDA00028267005300000925
and
Figure BDA00028267005300000926
and the maximum charging and discharging power of the energy storage equipment at the i node at the time t is respectively.
Example analysis
The calculation system of the invention is shown in fig. 3, wherein the power grid is an improved IEEE33 node, node 0 is connected with an external large power grid, a distributed CHP unit is added at nodes 24 and 32 respectively, the distributed CHP unit is coupled with a heat supply network, and a fan unit is connected at node 31; the output of the CHP1 unit is adjusted quickly, but the unit cost is high, and the CHP1 unit is mainly used for peak shaving; the CHP2 unit has low unit cost, and is mainly used for power generation and heat production. The heat supply network in the system is a 13-node heat supply network, wherein nodes 2, 3, 4, 6, 7, 8, 9 and 10 are load nodes, distributed electric boilers are arranged at the load nodes, interaction between the power grid and the heat supply network can be realized, a fixed heat source is arranged at the node 12, and output force is unchanged. In fig. 3, the nodes in the same dashed box are regarded as being located in the same region, and the distributed electric boiler at the thermal load node is supplied with power by the grid node in the same region, and is regarded as a load for the grid node. The load of each node of the heat supply network, the external temperature, the fan output and the load of each node of the power grid are shown in FIG. 4; the correlation coefficients of the heat sources and the CHP units are shown in table 1.
Value of correlation of each energy equipment
Figure BDA0002826700530000101
Fig. 5 and 6 show the external power consumption heat demand of the load before and after scheduling in each time period, wherein the yellow area is the load demand change caused by the load side electric heating equipment, and the blue area is the load demand change caused by the demand response. As can be seen from fig. 5, because the load side schedules the control of the load side electric heating devices, the electric energy demand in the nighttime period is increased to compensate the reduction amount of the load side to the heat energy demand of the integrated energy system, although the devices convert the high-quality electric energy into the low-quality heat energy, the method can greatly reduce the air abandoning amount and avoid the high wind abandoning penalty, and a large part of the electric energy consumed by the electric heating devices comes from the electric energy increased by the wind turbine generator, so that the electric heating devices do not generate high cost on the whole, and the output in the nighttime period is not low. As can be seen from fig. 6, in some time periods when the wind curtailment is high, the reduction of the external heat energy demand of the user mainly comes from the heat energy demand compensated by the electric heating equipment on site, and the reduction of the overall heat energy demand of the user is not changed much compared with other time periods, which is mainly because the user does not like to continue to reduce the indoor temperature to obtain the demand response compensation as the comfort of the user is reduced, and the heat energy demand of the comprehensive energy system is reduced by the electric heating.
The optimal scheduling without considering the source-load interaction, without considering the source-load interaction but without considering the various devices on the load side, and the scheduling cost of the method of the present invention in each period within 24 hours are shown in fig. 7. The scheduling cost is the same under the three scenes when no wind abandon period exists, and under the condition of wind abandon, although the scene 2 and the scene 3 can increase the wind power consumption of the system, the scene 3 further considers the energy interaction between different energy networks at the load side, and converts part of heat energy requirements into electric energy requirements, so that the consumption capacity of the system on renewable energy sources is increased, and the wind abandon is further reduced; thus, scenario 3 has better economics than scenario 2.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A comprehensive energy system load side optimal scheduling method considering multi-energy complementation is characterized by comprising the following steps:
s11: modeling load side comprehensive energy controllable equipment to obtain a load side controllable equipment model, wherein the load side comprehensive energy controllable equipment comprises an energy storage device, an energy conversion device and distributed energy, and the load side controllable equipment model is used for analyzing the energy conversion characteristics of the load side comprehensive energy controllable equipment model and constraining various types of load side comprehensive energy controllable equipment;
s12: establishing a user energy consumption behavior demand response model based on price-demand elasticity on a load side, analyzing behavior characteristics of users participating in different types of demand responses, realizing interaction with an energy internet, and constraining a power grid side scheduling behavior;
s13: establishing a comprehensive energy system load side optimization scheduling objective function considering source-load interaction and multi-energy complementation so as to minimize the operation cost of the system, wherein the operation cost of the load side controllable equipment is calculated according to the load side controllable equipment model in the step S11, and the demand response cost is calculated according to the load demand response model in the step S12;
s14: according to the load side controllable equipment model established in the step S11 and the load demand response model established in the step S12, various controllable equipment on the load side and power grid side scheduling behaviors are restrained, and an optimized scheduling model of the load side of the comprehensive energy system is established;
s15: and solving the comprehensive energy system load side optimization scheduling model established in the step S14 to further obtain an optimal day-ahead scheduling result.
2. The comprehensive energy system load side optimal scheduling method considering the multi-energy complementation as claimed in claim 1, wherein the load side controllable device model in step S11 and the user energy behavior demand response model in step S12 consider the cloud side architecture and the information acquisition characteristics in the context of the power internet of things, information interaction between different energy subsystems in the comprehensive energy system is extended from a large coupling device at the source side to the source and load side for information interaction, and the load side can realize energy substitution of different energy types;
in step S11, the load-side controllable device model includes an electric boiler model and an electric energy storage system model, which are specifically as follows:
the model of the electric boiler is represented by the following formula:
Figure FDA0002826700520000014
in the formula (I), the compound is shown in the specification,
Figure FDA0002826700520000011
is the electric power input by the electric boiler,
Figure FDA0002826700520000012
is the electric heat conversion efficiency of the electric boiler,
Figure FDA0002826700520000013
the heat energy of the electric boiler is output;
the SOC of the electrical energy storage device in the electrical energy storage system model is calculated by the following formula:
Figure FDA0002826700520000021
in the formula (I), the compound is shown in the specification,
Figure FDA0002826700520000022
the SOC value of the electric energy storage equipment at the node i in the period t;
Figure FDA0002826700520000023
an energy storage efficiency for the energy storage device;
Figure FDA0002826700520000024
charging efficiency for the energy storage device;
Figure FDA0002826700520000025
and
Figure FDA0002826700520000026
the charging and discharging power of the energy storage equipment;
the SOC of the thermal energy storage device in the electrical energy storage system model is calculated by the following equation:
Figure FDA0002826700520000027
in the formula (II)
Figure FDA0002826700520000028
Is at node iThe SOC value of the heat storage boiler in a t period;
Figure FDA0002826700520000029
the energy storage efficiency of the heat storage boiler is improved;
Figure FDA00028267005200000210
the energy charging efficiency of the heat storage boiler is improved;
Figure FDA00028267005200000211
and
Figure FDA00028267005200000212
the energy charging and discharging power of the heat storage boiler.
3. The comprehensive energy system load side optimal scheduling method considering multi-energy complementation according to claim 1, wherein the user energy behavior demand response model in step S12 is as follows:
the corresponding sensitivity of the load to price is generally expressed by the elastic coefficient, and the thermal load for heating is generally expressed by the self-elastic coefficient:
Figure FDA00028267005200000213
in the formula (I), the compound is shown in the specification,
Figure FDA00028267005200000214
the coefficient of self-elasticity for the thermal load at node i during time t;
Figure FDA00028267005200000215
the heat supply price in the t period;
Figure FDA00028267005200000216
the thermal load at node i is t periods before the demand response;
Figure FDA00028267005200000217
a heat load demand response price for a period of t;
Figure FDA00028267005200000218
is the amount of change in thermal load after demand response;
the thermal load variation in the user energy behavior demand response model is represented by:
Figure FDA00028267005200000219
in the formula (I), the compound is shown in the specification,
Figure FDA00028267005200000220
the thermal load at node i for time period t;
part of the electric load can be transferred to other time periods, and part of the electric load can be reduced or increased, so that the self-elastic coefficient and the mutual elastic coefficient are jointly expressed as follows:
Figure FDA00028267005200000221
Figure FDA00028267005200000222
in the formula (I), the compound is shown in the specification,
Figure FDA00028267005200000223
the self-elastic coefficient of the electrical load at the node i in the period t;
Figure FDA00028267005200000224
electricity prices for a period of t;
Figure FDA0002826700520000031
for making a sound for demandElectrical load at node i at time t before;
Figure FDA0002826700520000032
responding price for electric load demand in t period;
Figure FDA0002826700520000033
the variation of the electrical load at the node i in the period t of the demand response;
Figure FDA0002826700520000034
the mutual elastic coefficient of the electrical load at the node i in the t period to the tau period;
the change in electrical load in the user energy behavior demand response model is represented by:
Figure FDA0002826700520000035
in the formula (I), the compound is shown in the specification,
Figure FDA0002826700520000036
the electrical load at node i is time period t.
4. The method for optimizing and scheduling on the load side of the comprehensive energy system considering the multi-energy complementation as claimed in claim 1, wherein the optimal scheduling objective function calculation formula in step S13 is as follows:
Figure FDA0002826700520000037
in the formula (I), the compound is shown in the specification,
Figure FDA0002826700520000038
scheduling cost of load at node i for time period t;
Figure FDA0002826700520000039
is at t timeThe cost for purchasing heat energy and electric energy by the load at the node i under the section;
Figure FDA00028267005200000310
compensation obtained by load demand response for the load at node i at time period t;
Figure FDA00028267005200000311
the operation cost of the electric boiler is reduced;
the operating cost of an electric boiler is calculated by the following formula:
Figure FDA00028267005200000312
in the formula, c0,i、c1,iAnd c2,iCalculating coefficients for the cost, which is determined by the condition of the equipment;
the cost of the load to purchase heat and electricity is expressed as:
Figure FDA00028267005200000313
in the formula (I), the compound is shown in the specification,
Figure FDA00028267005200000314
and
Figure FDA00028267005200000315
the electricity and heat purchase prices are respectively at the time t.
5. The method for optimal scheduling on the load side of an integrated energy system considering multi-energy complementation according to claim 1, wherein the minimum energy cost scheduling constraint of multi-energy conversion on the load side is quantized and considered in step S14, and the calculation formula of the scheduling constraint is:
(1) temperature constraints in thermally loaded buildings
Load side scheduling should be premised on not affecting the in-building temperature too much, and the in-building temperature constraint can be expressed as:
Figure FDA00028267005200000316
in the formula (I), the compound is shown in the specification,
Figure FDA00028267005200000317
and
Figure FDA00028267005200000318
respectively an upper limit and a lower limit of the indoor temperature after heat supply;
(2) demand response constraints
The demand response of the load to the price signal has a certain limit, which is expressed in detail as:
Figure FDA00028267005200000319
Figure FDA00028267005200000320
in the formula (I), the compound is shown in the specification,
Figure FDA0002826700520000041
and
Figure FDA0002826700520000042
respectively responding to the load participation coefficients of the thermoelectric demand of the load at the node i;
Figure FDA0002826700520000043
the load at the i node is required for the heat energy and the electric energy at the t time period before the demand response;
(3) energy plant restraint
The load side energy equipment mainly comprises an electric heat pump and a heat storage boiler, and for the electric heat pump, because the output of the electric heat pump is more flexible, the output range constraint is only considered:
Figure FDA0002826700520000044
in the formula (I), the compound is shown in the specification,
Figure FDA0002826700520000045
the maximum output of the electric boiler at the node i is obtained;
for a thermal storage boiler, the constraints are:
Figure FDA0002826700520000046
Figure FDA0002826700520000047
Figure FDA0002826700520000048
in the formula (I), the compound is shown in the specification,
Figure FDA0002826700520000049
and
Figure FDA00028267005200000410
the maximum and minimum energy storage values of the heat storage boiler at the i node;
Figure FDA00028267005200000411
and
Figure FDA00028267005200000412
respectively keeping the maximum charging power and the maximum heat release power of the heat storage boiler at the i node at the t moment;
for an electrical energy storage device, the constraints are:
Figure FDA00028267005200000413
Figure FDA00028267005200000414
Figure FDA00028267005200000415
in the formula (I), the compound is shown in the specification,
Figure FDA00028267005200000416
and
Figure FDA00028267005200000417
is the maximum and minimum energy storage values of the energy storage device at the i node;
Figure FDA00028267005200000418
and
Figure FDA00028267005200000419
and the maximum charging and discharging power of the energy storage equipment at the i node at the time t is respectively.
CN202011450641.9A 2020-12-09 2020-12-09 Comprehensive energy system load side optimal scheduling method considering multi-energy complementation Pending CN112600217A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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* Cited by examiner, † Cited by third party
Title
XIANGYU KONG ET: "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things", 《ELSEVIER》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114595584A (en) * 2022-03-14 2022-06-07 南方电网数字电网研究院有限公司 Multi-energy complementary regional terminal energy use configuration method and device
CN114595584B (en) * 2022-03-14 2023-06-30 南方电网数字电网研究院有限公司 Multi-energy complementary regional terminal energy utilization configuration method and device

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