CN111898806A - Electric-thermal coupling source storage and load integration multi-energy flow park operation optimization method and system - Google Patents

Electric-thermal coupling source storage and load integration multi-energy flow park operation optimization method and system Download PDF

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CN111898806A
CN111898806A CN202010674601.6A CN202010674601A CN111898806A CN 111898806 A CN111898806 A CN 111898806A CN 202010674601 A CN202010674601 A CN 202010674601A CN 111898806 A CN111898806 A CN 111898806A
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CN111898806B (en
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白保华
沈欣炜
范滢
刘海天
卜令习
夏天
殷仁鹏
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State Grid Comprehensive Energy Service Group Co ltd
Tsinghua University
State Grid Corp of China SGCC
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Tsinghua University
State Grid Corp of China SGCC
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Abstract

The invention provides an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization method, which comprises the following steps: establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model, which comprises the following steps: determining constraint conditions of an optimization model; determining an objective function of the optimization model; the determining the constraint conditions of the optimization model comprises the following steps: a cogeneration unit is used as a main output unit in the system, an electric boiler is used as an electric-heat conversion device, and an energy gap is supplemented through an external power grid to establish a source side element model; establishing an energy storage side element model by taking a battery and a heat storage tank as an electric energy storage device and a heat energy storage device respectively; building an equivalent thermal parameter model operation constraint of the building; combining the output of the absorption distributed photovoltaic, and establishing a park electric-thermal energy balance constraint; the objective function of the optimization model is the lowest operation consumption of the energy system of the multi-energy flow park; initializing model solving parameters, and solving a source charge-storage thermal collaborative optimization scheduling model to obtain a scheduling strategy.

Description

Electric-thermal coupling source storage and load integration multi-energy flow park operation optimization method and system
Technical Field
The invention belongs to the technical field of power system operation control, and particularly relates to an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization method.
Background
In the aspect of energy production, improving the consumption of renewable energy sources is an important measure for adjusting the energy source structure; in the aspect of energy consumption, a building is taken as an energy consumption main body, and the requirements of economic operation and comfort of users need to be considered; in the aspect of energy storage and conversion, the reasonable utilization of various energy storage devices for peak shaving is an important guarantee for realizing the flexibility of operation in a park.
The energy management of the traditional park mostly adopts the operation of a splitting mode, the operation of electric and thermal systems is independent, the operation flexibility of the system is poor, and the economic operation of the system is difficult to realize; the conventional multi-energy flow park has simple operation rules, adopts basic operation modes such as 'fixing heat by electricity' (the operation mode of determining the heat productivity by the size of a power supply load), 'fixing heat by heat' (the operation mode of determining the power generation capacity by the size of a heat supply load), 'grid connection without network access' (the network connection refers to the fact that the generated electricity is used by a network of a power grid, and the network access without network access refers to the fact that the electricity quantity of the power grid can only be consumed in a factory and cannot be output to the external power grid), and the like, and has rough operation regulation and control on various energy storage elements in the park, and various flexible resources of the park cannot be fully utilized. In addition, during the operation of the system, the system can easily reach the safe operation boundary due to the large fluctuation and uncertainty of the output of the renewable energy.
Therefore, the problem to be solved urgently in the field of operation control of the power system is to provide a stable and efficient multi-energy flow park operation scheme.
Disclosure of Invention
In view of the above problems, the present invention provides an operation optimization method for an electro-thermal coupling source storage and load integration multi-energy flow park, comprising:
establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model, which comprises the following steps:
determining constraint conditions of an optimization model;
determining an objective function of the optimization model;
the determining the constraint conditions of the optimization model comprises the following steps:
a cogeneration unit is used as a main output unit in the system, an electric boiler is used as an electric-heat conversion device, and an energy gap is supplemented through an external power grid to establish a source side element model;
establishing an energy storage side element model by taking a battery and a heat storage tank as an electric energy storage device and a heat energy storage device respectively;
building an equivalent thermal parameter model operation constraint of the building;
combining the output of the absorption distributed photovoltaic, and establishing a park electric-thermal energy balance constraint;
the objective function of the optimization model is the lowest operation consumption of the energy system of the multi-energy flow park;
initializing model solving parameters, and solving a source charge-storage thermal collaborative optimization scheduling model to obtain a scheduling strategy.
Further, the establishing a source-side element model includes:
step 1-1-1-1: establishing operation constraint of a cogeneration unit, wherein the expression is as follows:
Figure BDA0002583592830000021
Figure BDA0002583592830000022
Figure BDA0002583592830000023
Figure BDA0002583592830000024
wherein P in the formulae (1) and (2)CHP(t),QCHP(t) the electrical and thermal output of the CHP at time t, respectively;
Figure BDA0002583592830000025
is an inflection point, eta, of the feasible region of the output of the cogeneration unitk(t), k ═ 1.., N is the corresponding combination coefficient; (4) the formula represents the power output climbing capacity of the cogeneration unit in a time interval,
Figure BDA0002583592830000026
respectively representing the upper limit and the lower limit of the climbing capacity;
step 1-1-1-2: establishing operation constraints of the electric boiler unit, wherein the expression is as follows:
QEB(t)＝ηEBPEB(t) (5)
Figure BDA0002583592830000027
in the formula, QEB(t),PEB(t) represents heat generation power and power consumption power at time t, QEB(t-1) represents the heat generation power at time t-1, ηEBThe electric heat conversion efficiency of the electric boiler is shown, the heat energy supply is satisfied through the consumed electric energy,
Figure BDA0002583592830000031
respectively representing the upper limit and the lower limit of the thermal power climbing capability of the electric boiler unit;
step 1-1-1-3: establishing power grid operation constraints, wherein the expression is as follows:
Figure BDA0002583592830000032
in the formula, PgridAnd (t) represents the power supply amount of the power grid at the moment t, the park can purchase power from the power grid and sell power to the power grid under the grid-connected and grid-connected operation strategy, the value is negative, the system can only purchase power from the power grid at the moment, the values at all the moments are always non-negative.
Further, the establishing of the energy storage side element model comprises:
step 1-1-2-1: establishing a battery model operation constraint, wherein the expression is as follows:
Figure BDA0002583592830000033
Figure BDA0002583592830000034
αbc(t)·αbdc(t)＝0 (10)
Figure BDA0002583592830000035
Figure BDA0002583592830000036
wherein, the expressions (8) and (9) represent the charge and discharge capacity constraints of the battery, respectively, and Pbc(t),Pbdc(t) represents a charge amount and a discharge amount of the battery at time t, respectively,
Figure BDA0002583592830000037
respectively represent the upper and lower limits of the charging capability of the battery,
Figure BDA0002583592830000038
respectively representing the upper limit and the lower limit of the discharge capacity of the battery; (10) formula represents the complementary constraint of battery charge-discharge relaxation, alphabc(t),αbdc(t) is a logical variable from 0 to 1; (11) expressed by the equationb(t) represents the battery charge at time t, Eb(t-1) is the battery charge at time t-1, γb,ηbRespectively, the self-loss rate and the charge-discharge efficiency of the battery, at represents a time interval,
Figure BDA0002583592830000039
respectively representing the upper limit and the lower limit of the battery capacity;
step 1-1-2-2: establishing a heat storage tank model operation constraint, wherein the expression is as follows:
Figure BDA00025835928300000310
Hhst(t)＝(1-ηhstl)Hhst(t-1)+Qhst(t)Δt (14)
Figure BDA00025835928300000311
Figure BDA00025835928300000312
in the formula, (13) represents the charge rate constraint, Qhst(t) storage at time tThe charging rate of the hot tank is positive, which represents heat accumulation, and negative represents heat release;
Figure BDA0002583592830000041
respectively representing the upper limit and the lower limit of the energy charging rate of the heat storage tank; (14) expressed by the formula is the energy balance constraint in the heat storage state, Hhst(t) represents the heat storage capacity of the heat storage tank at time t, Hhst(t-1) represents the heat storage amount of the heat storage tank at time t-1, Hhst(t) receiving the heat storage quantity H of the heat storage tank at the moment of t-1hst(t-1) and the influence of the charging result at time t, ηhstlThe heat self-loss rate of the heat storage tank in each time period is represented, and delta t represents a time interval; (15) the formula is the change of the heat storage capacity of the heat storage tank in a time interval, the formula (16) is the capacity constraint of the heat storage tank,
Figure BDA0002583592830000042
represents the upper and lower limits of the charge-heat variation of the heat storage tank,
Figure BDA0002583592830000043
representing the upper and lower limits of the thermal storage tank capacity.
Further, the operation constraint of the building equivalent thermal parameter model comprises the following expression:
Qs(t)＝cPm(Ts(t)-Tr(t)) (17)
cb(Tb(t)-Tb(t-1))＝UAb(TAmb(t)-Tb(t-1))Δt+Qs(t)Δt (18)
Tr(t)＝(1-Nb)Ts(t)+NbTb(t) (19)
Figure BDA0002583592830000044
Figure BDA0002583592830000045
Figure BDA0002583592830000046
wherein (17) is wherein Qs(T) represents the total heat supplied by the heat source to the room at time T, Ts(t),Tr(t) shows the temperature of the supply water and the temperature of the return water injected into the room at time t, respectively, cpIs the specific heat of water, m is the flow rate into the room; (18) the formula is a discrete difference equation, T, of the building equivalent thermal parameter modelb(T) represents the indoor temperature at time T, Tb(t-1) represents the room temperature at time t-1, cbIs the heat capacity of the building; u shapeAbIs the thermal conductivity of the building to the external environment, cb、UAbIs a constant, TAmb(t) is the outdoor temperature at time t, Δ t representing a time interval; (19) the formula shows the return water temperature T of the room at the time Tr(t)，NbIs a constant related to the performance of the indoor heat exchange equipment; (20) the formula represents the restriction of the water supply temperature at the time t, (21) the formula represents the restriction of the water supply temperature and the backwater temperature at the time t, and (22) the formula represents the restriction of the indoor temperature;
Figure BDA0002583592830000047
representing the upper and lower limits permitted or achievable by the temperature of the feed water,
Figure BDA0002583592830000048
representing the upper and lower limits permitted or achievable by the return water temperature,
Figure BDA0002583592830000049
representing the upper and lower limits that the indoor temperature allows or can reach.
Further, the campus electrothermal energy balance constraints include:
Qs(t)＝QCHP(t)+QEB(t)-Qhst(t) (23)
PCHP(t)+Pgrid(t)+PPV(t)+Pbdc(t)＝PD(t)+PEB(t)+Pbc(t) (24)
equation (24) is an electric power balance equation, where PPV(t),PDAnd (t) respectively representing the photovoltaic output and the user electricity load at the moment t.
Further, the objective function of the chemistry model includes:
Figure BDA0002583592830000051
wherein M is a scheduling interval, Cgrid(t) represents the real-time electricity price of the power grid for purchasing electricity at the moment t; f (Q)CHP(t),PCHP(t)) is a cost function of the cogeneration unit, expressed by the following equation (26), which is a quadratic linear polynomial:
Figure BDA0002583592830000052
wherein, mu1,μ2,μ3,μ4,μ5,μ6Is the cost coefficient of the CHP unit.
Further, the method further comprises converting the electric-thermal coupled source storage and load integrated multi-energy flow park operation optimization model into a mixed integer quadratic programming model in a standard matrix form before solving the adoption number:
taking equations (25) to (26) as an optimization objective function, and taking equations (1) to (24) as operation constraints and energy balance constraints for limiting each element of a feasible domain of the optimization problem, setting a scheduling interval and a scheduling time point, and converting an optimization model into a mixed integer quadratic programming model in a standard matrix form;
Figure BDA0002583592830000053
wherein M is a scheduling interval, and the state variable x (t) selects an element having an energy storage property in the system, comprising: electric charge E of batteryb(t) heat storage quantity H of the heat storage tankhst(T) and indoor temperature Tb(t); the control quantity u (t) comprises the electric output P of the CHP unitCHP(t) Heat output QCHP(t), grid output Pgrid(t), electric boiler electric power PEB(t); electric load PD(t), real-time electricity price Cgrid(T) and outdoor temperature TAmb(t) preparation ofThe controlled object is input for the control amount.
Further, solving the source charge storage thermal collaborative optimization scheduling model includes:
will use the electrical load PD(t), real-time electricity price Cgrid(T) and outdoor temperature TAmbAnd (t) taking the element characteristic parameters as system input, and solving the mixed integer quadratic programming model.
Further, according to the result of the solution,
the source side carries out day-ahead scheduling according to the optimized results of the electric output, the thermal output, the power grid output and the electric boiler output of the cogeneration unit;
the energy storage side deduces the battery charge-discharge rate P according to the electric power balance equation and charge-discharge relaxation complementary constraint between the elementsbc(t),Pbdc(t) the heat storage tank calculates the result Q according to the optimizationhst(t) arranging a heat charging and discharging scheduling plan in each time period;
and the indoor temperature control of the load side ensures the comfort requirement according to the obtained supply and return water temperature result.
The invention also provides an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization system, which comprises:
the optimization model establishing module is used for establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model;
the optimization model building module comprises:
the constraint condition establishing unit is used for determining the constraint conditions of the optimization model;
the objective function establishing unit is used for determining an objective function of the optimization model;
the determining the constraint conditions of the optimization model comprises the following steps:
a cogeneration unit is used as a main output unit in the system, an electric boiler is used as an electric-heat conversion device, and an energy gap is supplemented through an external power grid to establish a source side element model;
establishing an energy storage side element model by taking a battery and a heat storage tank as an electric energy storage device and a heat energy storage device respectively;
building an equivalent thermal parameter model operation constraint of the building;
combining the output of the absorption distributed photovoltaic, and establishing a park electric-thermal energy balance constraint;
the objective function of the optimization model is the lowest operation consumption of the energy system of the multi-energy flow park;
and the model solving module is used for initializing model solving parameters and solving a source charge-storage thermal collaborative optimization scheduling model to obtain a scheduling strategy.
The invention has the technical characteristics and beneficial effects that:
the invention provides a source-storage-load integrated multi-energy flow park operation optimization method considering electric-thermal coupling. The cooperative optimization of the electro-thermal coupling multi-energy flow system is realized, and the flexibility of electro-thermal multi-time scale is fully utilized. The multi-energy flow park mainly comprises a source side, an energy storage side, a load side and an electrothermal conversion part, wherein the source side comprises a power grid, a distributed photovoltaic unit and a CHP unit, the energy storage side comprises a battery and a heat storage tank, the electrothermal conversion part selects an electric boiler, the load side comprises an energy storage element with virtual heat storage capacity, if a typical building load is selected, the building heat load is regarded as the energy storage element with the virtual heat storage capacity, and the day-ahead scheduling modeling requirement of a minute-level and above time scales is considered, so that the model comprises a building thermal dynamic model with slow change speed, and other processes adopt a steady-state model, thereby improving the flexibility of operation optimization of the park. By flexibly configuring various resources, the problems of multiple energy flows, multiple time scales and long time periods are modeled and solved, so that a reasonable day-ahead scheduling scheme is designed, and economic operation is realized. The operation optimization method of the invention avoids the problems that the traditional multi-energy flow park is uneconomical to schedule day ahead, has simple operation mode and does not fully and reasonably configure various flexible resources or reach the safe operation boundary. Through the economic dispatching scheme of source storage and load integration and electric heating cooperation, compared with the traditional method, the operation result can obviously improve the distributed photovoltaic consumption level and reduce the operation cost of the system.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 illustrates a flow diagram of a method for optimizing operation of an electrically-thermally coupled source-storage-integrated multi-energy flow park in accordance with an embodiment of the present invention;
FIG. 2 illustrates a flow diagram of a method for optimizing operation of an electrically-thermally coupled source-storage-integrated multi-energy flow park in accordance with an embodiment of the present invention;
fig. 3 shows a schematic structural diagram of an electrically-thermally coupled source-storage-integrated multi-energy-flow-park operation optimization system according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization method, which improves the flexibility and economy of park operation through the cooperation optimization of the electric-thermal coupling and the source storage and load integration. As shown in fig. 1, the method comprises:
step 1: establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model, which comprises the following steps: determining constraint conditions of an optimization model; an objective function of the optimization model is determined.
Specifically, according to physical characteristics of various source storage loads of the multi-energy flow park, a characteristic equation and operation constraints of elements are established, electric heating energy balance constraints of park operation are established, and an electric-thermal coupling source storage load integrated multi-energy flow park operation optimization model is established according to an optimization target of economic operation of the multi-energy flow park.
Step 2: the electric-thermal coupling source load storage integrated multi-energy flow park operation optimization model is converted into a mixed integer quadratic programming model in a standard matrix form, so that the model can be solved according to the mixed integer quadratic programming model in the step 3. For example, any model with linear, quadratic, mixed integer element characteristics and any form of mixed integer quadratic programming problem can be included, and can be converted into a standard form such as general linear, quadratic programming, or mixed integer programming for solving.
And step 3: initializing model solving parameters, and solving a source charge-storage thermal collaborative optimization scheduling model to obtain a scheduling strategy. The initialization parameters comprise power load, real-time electricity price, outdoor temperature, element characteristic parameters and the like, and are used as system input. The initialization parameters may be based on historical data or a predictive fit.
The following describes the steps of the optimization method for the operation of the electro-thermal coupled source storage and load integrated multi-energy flow park.
Step 1: and establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model, wherein the model consists of an objective function and constraint conditions. As shown in fig. 2, the specific steps of establishing the model are as follows:
step 1-1: determining constraint conditions of an optimization model, including determining a source storage element characteristic equation, a source storage element operation constraint and a park electric heat energy balance constraint, as follows:
step 1-1-1: a heat and power Cogeneration (CHP) unit is used as a main output unit in a system, an electric boiler is used as an electric-heat conversion device, heat energy supply is met through consumed electric energy, an energy gap is supplemented through an external power grid, and a source side element model with an electric-heat coupling element is established, wherein the coupling element refers to a heat and power cogeneration unit element. The source side element model comprises a source load element characteristic equation and a source load element operation constraint, and the details are as follows:
step 1-1-1-1: establishing a heat and power Cogeneration (CHP) unit operation constraint, wherein the expression is as follows:
Figure BDA0002583592830000091
Figure BDA0002583592830000092
Figure BDA0002583592830000093
Figure BDA0002583592830000094
wherein P in the formulae (1) and (2)CHP(t),QCHP(t) represents the electrical and thermal output of the CHP at time t, respectively.
Figure BDA0002583592830000095
Is the inflection point of the feasible region of the CHP unit output, the output at each moment can be obtained by linear combination of the inflection points, etakAnd (t), wherein k is 1, and N is a corresponding combination coefficient, and the value of N depends on the operating characteristics of a specific CHP unit. (4) The formula describes the capacity of the CHP unit to climb electrically over a time interval,
Figure BDA0002583592830000096
respectively representing the upper and lower limits of the climbing ability.
Without loss of generality, if N is 4, the cogeneration unit operating constraints are:
Figure BDA0002583592830000097
Figure BDA0002583592830000098
Figure BDA0002583592830000099
Figure BDA00025835928300000910
Figure BDA00025835928300000911
the output of the CHP unit can be linearly combined with the inflection point (the corresponding coefficient is eta)k(t), k ═ 1.., 4).
Step 1-1-1-2: establishing operation constraints of the electric boiler unit, wherein the expression is as follows:
QEB(t)＝ηEBPEB(t) (5)
Figure BDA0002583592830000101
in the formula, QEB(t),PEB(t) represents heat generation power and power consumption power at time t, QEB(t-1) represents the heat generation power at time t-1, ηEBWhich represents the efficiency of the electric-to-heat conversion of an electric boiler, i.e. the efficiency of the conversion of electric energy into heat, is generally less than but close to 1,
Figure BDA0002583592830000102
respectively representing the upper limit and the lower limit of the thermal power climbing capability of the electric boiler unit.
Step 1-1-1-3: establishing power grid operation constraints, wherein the expression is as follows:
Figure BDA0002583592830000103
in the formula, Pgrid(t) electric network at time tThe power supply quantity has values in different ranges according to different operation strategies, under the condition that a grid-connected and grid-connected operation strategy is adopted, the park can purchase power from a power grid and can sell power to the power grid, when the value is negative, the park can sell power to the power grid by a system at the moment, and under the grid-connected and grid-disconnected strategy, the park can only purchase power from the power grid, and the values at all the moments are constantly non-negative.
Step 1-1-2: establishing an energy storage side element model by taking a battery and a heat storage tank as two typical actively controllable electric energy storage devices and heat energy storage devices respectively;
step 1-1-2-1: establishing a battery model operation constraint, wherein the expression is as follows:
Figure BDA0002583592830000104
Figure BDA0002583592830000105
αbc(t)·αbdc(t)＝0 (10)
Figure BDA0002583592830000106
Figure BDA0002583592830000107
wherein, the expressions (8) and (9) represent the charge and discharge capacity constraints of the battery, respectively, and Pbc(t),Pbdc(t) represents a charge amount and a discharge amount of the battery at time t, respectively,
Figure BDA0002583592830000108
respectively represent the upper and lower limits of the charging capability of the battery,
Figure BDA0002583592830000109
respectively representing the upper and lower limits of the discharge capacity of the battery. (10) The formula represents the complementary constraint of the battery charge-discharge relaxation, and alpha is a complementary condition for preventing the battery from simultaneously charging and dischargingbc(t),αbdc(t) is a logical variable from 0 to 1. (11) Expressed by the equationb(t) represents the battery charge at time t, Eb(t-1) the battery charge at time t-1, Eb(t) is affected by the amount of charge of the battery at time t-1 and the amount of charge and discharge at time t. Gamma rayb,ηbRespectively, the self-loss rate and the charge-discharge efficiency of the battery, at represents a time interval,
Figure BDA00025835928300001010
respectively representing the upper and lower limits of the battery capacity.
Step 1-1-2-2: establishing a heat storage tank model operation constraint, wherein the expression is as follows:
Figure BDA0002583592830000111
Hhst(t)＝(1-ηhstl)Hhst(t-1)+Qhst(t)Δt (14)
Figure BDA0002583592830000112
Figure BDA0002583592830000113
in the formula, (13) represents the charge rate constraint, QhstAnd (t) represents the charging rate of the heat storage tank at the time t, and since the heat storage and release efficiency does not exist, the complementary constraint does not need to exist, and the heat storage tank is represented by a continuous variable, wherein the value is positive to represent heat storage, and the value is negative to represent heat release.
Figure BDA0002583592830000114
Respectively representing the upper and lower limits of the charging rate of the thermal storage tank. (14) Expressed by the formula is the energy balance constraint in the heat storage state, Hhst(t) represents the heat storage capacity of the heat storage tank at time t, Hhst(t-1) represents the heat storage amount of the heat storage tank at time t-1, Hhst(t) receiving the heat storage quantity H of the heat storage tank at the moment of t-1hst(t-1) and the result of charging at time tInfluence of etahstlThe heat self-loss rate of the heat storage tank in each time period is determined by equipment factory parameters, and delta t represents a time interval. (15) The formula is the change of the heat storage capacity of the heat storage tank in a time interval, the formula (16) is the capacity constraint of the heat storage tank,
Figure BDA0002583592830000115
represents the upper and lower limits of the charge-heat variation of the heat storage tank,
Figure BDA0002583592830000116
representing the upper and lower limits of the thermal storage tank capacity.
Step 1-1-3: building an equivalent thermal parameter model operation constraint of the building: considering a typical building load user, a dynamic model is built to describe the thermal load as its time scale is significantly larger than the time scale of electrical load changes. Due to the long-time scale characteristic of the thermodynamic process, the building has the heat storage capacity, and the building is called as a virtual energy storage device. Accordingly, building equivalent thermal parameter model operation constraints are established, and the expression is as follows:
Qs(t)＝cPm(Ts(t)-Tr(t)) (17)
cb(Tb(t)-Tb(t-1))＝UAb(TAmb(t)-Tb(t-1))Δt+Qs(t)Δt (18)
Tr(t)＝(1-Nb)Ts(t)+NbTb(t) (19)
Figure BDA0002583592830000117
Figure BDA0002583592830000118
Figure BDA0002583592830000119
wherein (17) is wherein Qs(t) represents the direction of heat source at time tTotal heat supplied indoors, Ts(t),Tr(t) shows the temperature of the supply water and the temperature of the return water injected into the room at time t, respectively, cpM is the specific heat of water and m is the flow rate into the room. In the current domestic common heating system, the quality regulation mode, namely constant flow and variable temperature, is mostly adopted, so that Q iss(T) the value of the supply and return water temperature T injected into the room at the time Ts(t),Tr(t) determination of the specific heat of the water and the flow rate c of the water supplied to the roompAnd m is a constant. (18) The formula is a discrete difference equation form of the building equivalent thermal parameter model and describes the change condition of the room temperature in a time interval. T isb(T) represents the indoor temperature at time T, Tb(t-1) represents the room temperature at time t-1, cbIs the heat capacity of the building and is also the concrete embodiment of the heat inertia of the building, cbTb(t) representing the virtual heat storage capacity of the building as a virtual heat energy storage device at the moment t; u shapeAbIs the thermal conductivity, T, of the building to the external environmentAmb(t) is the outdoor temperature at time t, and Δ t represents a time interval. c. Cb、UAbDepending on the thermal parameters of the building envelope, it is generally taken as a constant. (19) The formula shows the return water temperature T of the room at the time Tr(T) from the water supply temperature T at that times(T) and indoor temperature Tb(t) co-determination wherein NbThe constant is a constant related to the performance of indoor heat exchange equipment, can be obtained through known parameters such as heat transfer coefficient, heat exchange area and the like, and is a fixed value. (20) The expression represents the constraint of the water supply temperature at the time t, (21) the expression represents the constraint of the water supply temperature and the backwater temperature at the time t, and (22) the expression represents the constraint of the indoor temperature. The temperature of the supplied water must be higher than the temperature of the returned water, which means that the heat transfer must be unidirectional, i.e. the room cannot be charged back to the heat network, which is also the basic operation rule of the load heating system.
Figure BDA0002583592830000121
Representing the upper and lower limits permitted or achievable by the temperature of the feed water,
Figure BDA0002583592830000122
indicating the upper and lower limits permitted or achievable by the return water temperature，
Figure BDA0002583592830000123
Representing the upper and lower limits that the indoor temperature allows or can reach.
Step 1-1-4: combining the output of the absorption distributed photovoltaic, establishing a park electric-thermal energy balance constraint, wherein the expression is as follows:
Qs(t)＝QCHP(t)+QEB(t)-Qhst(t) (23)
PCHP(t)+Pgrid(t)+PPV(t)+Pbdc(t)＝PD(t)+PEB(t)+Pbc(t) (24)
equation (24) is an electric power balance equation, where PPV(t),PDAnd (t) respectively representing the photovoltaic output and the user electricity load at the moment t. In the embodiment of the present invention, the same symbols represent the same physical parameters, and the description of the same parameters is omitted.
Step 1-2: determining an objective function of an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model, wherein the objective function of the optimization model is the lowest operation consumption of a multi-energy flow park energy system:
the overall optimization target is to design a day-ahead scheduling scheme through flexible configuration of various units, energy storage devices and building thermal inertia so as to realize the economic operation of the whole energy system of the multi-energy flow park, and a corresponding target function is shown as the following formula (25),
Figure BDA0002583592830000124
wherein M is a scheduling interval, CgridAnd (t) represents the real-time electricity price of the power grid for purchasing electricity at the time t, and illustratively, 96 scheduling time points are designed for taking 15 minutes as a scheduling interval, namely, M is 96. The selection of the scheduling interval and the time point is consistent with the day-ahead scheduling plan of the power grid, so that the electric-thermal coupling multi-energy flow park can be effectively helped to participate in the power grid regulation and carry out internal operation optimization. F (Q)CHP(t),PCHP(t)) is a cost function of the CHP unit, which can be expressed by the following formula (26) as a quadratic linear polynomialFormula (II) is shown.
Figure BDA0002583592830000131
In the formula, mu1,μ2,μ3,μ4,μ5,μ6Is a cost coefficient of the CHP unit and has different constant values according to different equipment parameters, PCHP(t),QCHPThe units of (t) are all [ kW]。
Step 2: converting an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model into a mixed integer quadratic programming model in a standard matrix form:
taking equations (25) to (26) as an optimization objective function, setting a scheduling interval and a scheduling time point for limiting the operation constraint and the energy balance constraint of each element of a feasible domain of the optimization problem by equations (1) to (24), and converting the electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model established in the step 1) into a mixed integer quadratic programming model in a standard matrix form;
Figure BDA0002583592830000132
wherein M is a scheduling interval, and the state variable x (t) is selected as an element with energy storage property in the systemb(t) heat storage quantity H of the heat storage tankhst(T) and indoor temperature Tb(t) of (d). The control quantity u (t) comprises the electric output P of the CHP unitCHP(t) Heat output QCHP(t), grid output Pgrid(t), electric boiler electric power PEB(t) of (d). Wherein, the electrical load PD(t), real-time electricity price Cgrid(T) and outdoor temperature TAmbAnd (t) inputting the controlled object as the disturbance of the system and as the control quantity. Rate of battery charge and discharge Pbc(t),Pbdc(t), heat storage tank heat charging and discharging rate Qhst(T) building virtual energy storage charging and discharging rate, namely water supply temperature and return water temperature Ts(t),Tr(t) as a dependent variable, based on the thermal balance relationship between the elements, optimally calculating to obtain the structureBuilding heat supply, and deducing the temperature of the supplied and returned water according to the equations (17) - (19), the heat supply equation, the building equivalent thermal parameter model difference equation, the temperature drop relationship of the supplied and returned water and the coupling relationship.
For example, with 15 minutes as one scheduling interval, 96 scheduling intervals are designed, that is, scheduling time points, and the above equation is:
Figure BDA0002583592830000141
and step 3: initializing model solving parameters, and solving a source charge storage thermal collaborative optimization scheduling model to obtain a scheduling strategy: will use the electrical load PD(t), real-time electricity price Cgrid(T) and outdoor temperature TAmb(t) and element characteristic parameters are used as system input, the mixed integer quadratic programming model obtained in the step 2 is solved, the element characteristic parameters are described according to the formulas (1) to (22), and the source side is based on the electric and thermal output P of the CHP unitCHP(t),QCHP(t), grid output Pgrid(t), electric boiler electric power PEB(t) scheduling the optimization result in the day ahead; the energy storage side deduces the battery charge-discharge rate P according to the complementary constraint of the electric power balance equation and the charge-discharge relaxation between the elements, namely the formula (24) and the formula (10)bc(t),Pbdc(t) the heat storage tank calculates the result Q according to the optimizationhst(t) arranging a heat charging and discharging scheduling plan in each time period; indoor temperature T on load sideb(T) controlling the supply and return water temperature T obtained according tos(t),Tr(t) the result guarantees the comfort requirement.
Based on the same inventive concept, an embodiment of the present invention further provides an electrical-thermal coupling source storage and load integration multi-energy flow park operation optimization system, as shown in fig. 3, the system includes:
the optimization model establishing module is used for establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model;
the optimization model building module comprises:
the constraint condition establishing unit is used for determining the constraint conditions of the optimization model;
the objective function establishing unit is used for determining an objective function of the optimization model;
the determining the constraint conditions of the optimization model comprises the following steps:
a cogeneration unit is used as a main output unit in the system, an electric boiler is used as an electric-heat conversion device, and an energy gap is supplemented through an external power grid to establish a source side element model;
establishing an energy storage side element model by taking a battery and a heat storage tank as an electric energy storage device and a heat energy storage device respectively;
building an equivalent thermal parameter model operation constraint of the building;
combining the output of the absorption distributed photovoltaic, and establishing a park electric-thermal energy balance constraint;
the objective function of the optimization model is the lowest operation consumption of the energy system of the multi-energy flow park;
and the model solving module is used for initializing model solving parameters and solving a source charge-storage thermal collaborative optimization scheduling model to obtain a scheduling strategy.
In one embodiment, the system further comprises a model conversion module for converting the electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model into a mixed integer quadratic programming model in a standard matrix form, and the model solution module carries out solution according to the quadratic programming model.
The specific formulas of various constraints and functions in the system embodiment of the present invention can be obtained according to the method embodiment described above, and are not described again.
The electricity-heat coupling source storage and load integration multi-energy flow park operation optimization method and system can provide flexibility for a short time scale of a power system by utilizing the characteristic of a thermal power system in a longer time scale, can realize load peak clipping and valley filling, and simultaneously ensure the overall economic operation of a park energy management system; the integrated modeling and collaborative optimization of the source load storage element can fully exploit the space-time complementary characteristic of electric-thermal coupling, can realize the optimal configuration of various flexible resources in the park, and has significant positive effects on the stable and efficient operation of the multi-energy flow park.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. The operation optimization method for the electric-thermal coupling source storage and load integration multi-energy flow park is characterized by comprising the following steps:
establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model, which comprises the following steps:
determining constraint conditions of an optimization model;
determining an objective function of the optimization model;
the determining the constraint conditions of the optimization model comprises the following steps:
a cogeneration unit is used as a main output unit in the system, an electric boiler is used as an electric-heat conversion device, and an energy gap is supplemented through an external power grid to establish a source side element model;
establishing an energy storage side element model by taking a battery and a heat storage tank as an electric energy storage device and a heat energy storage device respectively;
building an equivalent thermal parameter model operation constraint of the building;
combining the output of the absorption distributed photovoltaic, and establishing a park electric-thermal energy balance constraint;
the objective function of the optimization model is the lowest operation consumption of the energy system of the multi-energy flow park;
initializing model solving parameters, and solving a source charge-storage thermal collaborative optimization scheduling model to obtain a scheduling strategy.
2. The method of claim 1, wherein said modeling source-side elements comprises:
step 1-1-1-1: establishing operation constraint of a cogeneration unit, wherein the expression is as follows:
Figure FDA0002583592820000011
Figure FDA0002583592820000012
Figure FDA0002583592820000013
Figure FDA0002583592820000014
wherein P in the formulae (1) and (2)CHP(t),QCHP(t) the electrical and thermal output of the CHP at time t, respectively;
Figure FDA0002583592820000015
is an inflection point, eta, of the feasible region of the output of the cogeneration unitk(t), k ═ 1.., N is the corresponding combination coefficient; (4) the formula represents the power output climbing capacity of the cogeneration unit in a time interval,
Figure FDA0002583592820000021
ΔPrespectively representing the upper limit and the lower limit of the climbing capacity;
step 1-1-1-2: establishing operation constraints of the electric boiler unit, wherein the expression is as follows:
QEB(t)＝ηEBPEB(t) (5)
Figure FDA0002583592820000022
in the formula, QEB(t),PEB(t) represents heat generation power and power consumption power at time t, QEB(t-1) represents the heat generation power at time t-1, ηEBThe electric heat conversion efficiency of the electric boiler is shown, the heat energy supply is satisfied through the consumed electric energy,
Figure FDA0002583592820000023
ΔQ EBrespectively representing the upper limit and the lower limit of the thermal power climbing capability of the electric boiler unit;
step 1-1-1-3: establishing power grid operation constraints, wherein the expression is as follows:
Figure FDA0002583592820000024
in the formula, PgridAnd (t) represents the power supply amount of the power grid at the moment t, the park can purchase power from the power grid and sell power to the power grid under the grid-connected and grid-connected operation strategy, the value is negative, the system can only purchase power from the power grid at the moment, the values at all the moments are always non-negative.
3. The method of claim 2, wherein the modeling the energy storage side element comprises:
step 1-1-2-1: establishing a battery model operation constraint, wherein the expression is as follows:
Figure FDA0002583592820000025
Figure FDA0002583592820000026
αbc(t)·αbdc(t)＝0 (10)
Figure FDA0002583592820000027
Figure FDA0002583592820000028
wherein, the expressions (8) and (9) represent the charge and discharge capacity constraints of the battery, respectively, and Pbc(t),Pbdc(t) represents a charge amount and a discharge amount of the battery at time t, respectively,
Figure FDA0002583592820000029
P bcrespectively represent the upper and lower limits of the charging capability of the battery,
Figure FDA00025835928200000210
P bdcrespectively representing the upper limit and the lower limit of the discharge capacity of the battery; (10) formula represents the complementary constraint of battery charge-discharge relaxation, alphabc(t),αbdc(t) is a logical variable from 0 to 1; (11) expressed by the equationb(t) represents the battery charge at time t, Eb(t-1) is the battery charge at time t-1, γb,ηbRespectively, the self-loss rate and the charge-discharge efficiency of the battery, at represents a time interval,
Figure FDA0002583592820000031
E brespectively representing the upper limit and the lower limit of the battery capacity;
step 1-1-2-2: establishing a heat storage tank model operation constraint, wherein the expression is as follows:
Figure FDA0002583592820000032
Hhst(t)＝(1-ηhstl)Hhst(t-1)+Qhst(t)Δt (14)
Figure FDA0002583592820000033
Figure FDA0002583592820000034
in the formula, (13) represents the charge rate constraint, Qhst(t) represents the charging rate of the heat storage tank at time t, the value is positive and represents heat storage, and the value is negative and represents heat release;
Figure FDA0002583592820000035
Q hstrespectively representing the upper limit and the lower limit of the energy charging rate of the heat storage tank; (14) expressed by the formula is the energy balance constraint in the heat storage state, Hhst(t) represents the heat storage capacity of the heat storage tank at time t, Hhst(t-1) represents the heat storage amount of the heat storage tank at time t-1, Hhst(t) receiving the heat storage quantity H of the heat storage tank at the moment of t-1hst(t-1) and the influence of the charging result at time t, ηhstlThe heat self-loss rate of the heat storage tank in each time period is represented, and delta t represents a time interval; (15) the formula is the change of the heat storage capacity of the heat storage tank in a time interval, the formula (16) is the capacity constraint of the heat storage tank,
Figure FDA0002583592820000036
ΔH hstrepresents the upper and lower limits of the charge-heat variation of the heat storage tank,
Figure FDA0002583592820000037
H hstrepresenting the upper and lower limits of the thermal storage tank capacity.
4. The method of claim 3, wherein the building equivalent thermal parameter model operating constraints comprise the expression:
Qs(t)＝cPm(Ts(t)-Tr(t)) (17)
cb(Tb(t)-Tb(t-1))＝UAb(TAmb(t)-Tb(t-1))Δt+Qs(t)Δt (18)
Tr(t)＝(1-Nb)Ts(t)+NbTb(t) (19)
Figure FDA0002583592820000038
Figure FDA0002583592820000039
Figure FDA00025835928200000310
wherein (17) is wherein Qs(T) represents the total heat supplied by the heat source to the room at time T, Ts(t),Tr(t) shows the temperature of the supply water and the temperature of the return water injected into the room at time t, respectively, cpIs the specific heat of water, m is the flow rate into the room; (18) the formula is a discrete difference equation, T, of the building equivalent thermal parameter modelb(T) represents the indoor temperature at time T, Tb(t-1) represents the room temperature at time t-1, cbIs the heat capacity of the building; u shapeAbIs the thermal conductivity of the building to the external environment, cb、UAbIs a constant, TAmb(t) is the outdoor temperature at time t, Δ t representing a time interval; (19) the formula shows the return water temperature T of the room at the time Tr(t)，NbIs a constant related to the performance of the indoor heat exchange equipment; (20) the formula represents the restriction of the water supply temperature at the time t, (21) the formula represents the restriction of the water supply temperature and the backwater temperature at the time t, and (22) the formula represents the restriction of the indoor temperature;
Figure FDA0002583592820000041
T srepresenting the upper and lower limits permitted or achievable by the temperature of the feed water,
Figure FDA0002583592820000042
T rrepresenting the upper and lower limits permitted or achievable by the return water temperature,
Figure FDA0002583592820000043
T brepresenting the upper and lower limits that the indoor temperature allows or can reach.
5. The method of claim 4, wherein the campus electrothermal energy balance constraints comprise:
Qs(t)＝QCHP(t)+QEB(t)-Qhst(t) (23)
PCHP(t)+Pgrid(t)+PPV(t)+Pbdc(t)＝PD(t)+PEB(t)+Pbc(t) (24)
equation (24) is an electric power balance equation, where PPV(t),PDAnd (t) respectively representing the photovoltaic output and the user electricity load at the moment t.
6. The method of claim 5, wherein the objective function of the model is modeled by:
Figure FDA0002583592820000044
wherein M is a scheduling interval, Cgrid(t) represents the real-time electricity price of the power grid for purchasing electricity at the moment t; f (Q)CHP(t),PCHP(t)) is a cost function of the cogeneration unit, expressed by the following equation (26), which is a quadratic linear polynomial:
Figure FDA0002583592820000045
wherein, mu1,μ2,μ3,μ4,μ5,μ6Is the cost coefficient of the CHP unit.
7. The method of claim 6, further comprising converting the electro-thermally coupled source-storage-load-integrated multi-energy flow park operational optimization model to a mixed integer quadratic programming model in a standard matrix form prior to solving for the adoption number:
taking equations (25) to (26) as an optimization objective function, and taking equations (1) to (24) as operation constraints and energy balance constraints for limiting each element of a feasible domain of the optimization problem, setting a scheduling interval and a scheduling time point, and converting an optimization model into a mixed integer quadratic programming model in a standard matrix form;
Figure FDA0002583592820000046
x(t)＝Ax(t-1)+Bu(t)
xT(t-1)＝[Eb(t-1) Hhst(t-1) Tb(t-1)]
uT(t)＝[PCHP(t) QCHP(t) Pgrid(t) PEB(t) PD(t) Cgrid(t) TAmb(t)]
Figure FDA0002583592820000051
wherein M is a scheduling interval, and the state variable x (t) selects an element having an energy storage property in the system, comprising: electric charge E of batteryb(t) heat storage quantity H of the heat storage tankhst(T) and indoor temperature Tb(t); the control quantity u (t) comprises the electric output P of the CHP unitCHP(t) Heat output QCHP(t), grid output Pgrid(t), electric boiler electric power PEB(t); electric load PD(t), real-time electricity price Cgrid(T) and outdoor temperature TAmb(t) inputting the controlled object as a control amount.
8. The method of claim 7, wherein solving a source charge-storage thermal collaborative optimization scheduling model comprises:
will use the electrical load PD(t), real-time electricity price Cgrid(T) and outdoor temperature TAmbAnd (t) taking the element characteristic parameters as system input, and solving the mixed integer quadratic programming model.
9. The method according to any one of claims 1 to 8, wherein, based on the solution result,
the source side carries out day-ahead scheduling according to the optimized results of the electric output, the thermal output, the power grid output and the electric boiler output of the cogeneration unit;
the energy storage side deduces the battery charge-discharge rate P according to the electric power balance equation and charge-discharge relaxation complementary constraint between the elementsbc(t),Pbdc(t) the heat storage tank calculates the result Q according to the optimizationhst(t) arranging a heat charging and discharging scheduling plan in each time period;
and the indoor temperature control of the load side ensures the comfort requirement according to the obtained supply and return water temperature result.
10. An electro-thermal coupling source storage and load integration multi-energy flow park operation optimization system, comprising:
the optimization model establishing module is used for establishing an electric-thermal coupling source storage and load integration multi-energy flow park operation optimization model;
the optimization model building module comprises:
the constraint condition establishing unit is used for determining the constraint conditions of the optimization model;
the objective function establishing unit is used for determining an objective function of the optimization model;
the determining the constraint conditions of the optimization model comprises the following steps:
a cogeneration unit is used as a main output unit in the system, an electric boiler is used as an electric-heat conversion device, and an energy gap is supplemented through an external power grid to establish a source side element model;
establishing an energy storage side element model by taking a battery and a heat storage tank as an electric energy storage device and a heat energy storage device respectively;
building an equivalent thermal parameter model operation constraint of the building;
combining the output of the absorption distributed photovoltaic, and establishing a park electric-thermal energy balance constraint;
the objective function of the optimization model is the lowest operation consumption of the energy system of the multi-energy flow park;
and the model solving module is used for initializing model solving parameters and solving a source charge-storage thermal collaborative optimization scheduling model to obtain a scheduling strategy.
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