CN112906988A - Robust double-layer coordinated scheduling method for multi-energy building system - Google Patents

Robust double-layer coordinated scheduling method for multi-energy building system Download PDF

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CN112906988A
CN112906988A CN202110334150.6A CN202110334150A CN112906988A CN 112906988 A CN112906988 A CN 112906988A CN 202110334150 A CN202110334150 A CN 202110334150A CN 112906988 A CN112906988 A CN 112906988A
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陈丽娟
郭营
秦晓阳
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Abstract

The invention discloses a method for robust double-layer coordinated scheduling of a multi-energy building system, which specifically comprises the following steps: s1, inputting the prediction information of the electric load, the heat load and the renewable energy output of the multi-energy building system; s2, constructing an objective function for minimizing the total operation cost of the multi-energy building system; s3, considering user preference and comfort, constructing a multi-energy building system electricity-heat load demand response model; s4, constructing a deterministic mathematical model of the multi-energy building system; s5, establishing a source-load polyhedron uncertainty set, and converting the model established in the step S4 based on a two-stage robust optimization theory so as to obtain an optimal solution for coordinated operation of the multi-energy building system. The invention considers the fluctuation of the source load prediction information and the operation economy of the multi-energy building system at the same time, meets the user requirements, and develops an optimal robust energy management strategy for the multi-energy building system.

Description

Robust double-layer coordinated scheduling method for multi-energy building system
Technical Field
The invention relates to the field of energy optimization and scheduling, in particular to a robust double-layer coordinated scheduling method for a multi-energy building system.
Background
Due to global consensus of pursuing low-carbon clean energy systems, consumption modes of the energy industry are greatly changed, and installed capacity of renewable new energy sources is on a rapid increase trend. According to statistics, the total amount of the building system occupying global energy consumption is increased year by year, and the main form of terminal load is gradually changed from the traditional electric load mainly based on illumination into electricity, heat, cold and the like, so that the building system becomes a multi-energy carrier. In order to realize more efficient energy utilization, a building-level CCHP system is introduced, and an energy scheduling management strategy of a multi-energy building system is made to be a real problem which needs to be solved urgently.
For a building system integrated with renewable new energy, the invention provides a method for robust double-layer coordinated scheduling optimization of a multi-energy building system considering source load uncertainty in consideration of uncertainty of new energy output and load demand. Since conventional CCHP systems operate in either a hot or hot mode, there is limited flexibility in operating schedules. Thus, the present invention incorporates a building-grade CCHP along with an energy storage system to enhance flexibility in system operation. By optimizing and scheduling schedulable resources in the multi-energy building system, the influence of source load uncertainty on the safe operation of the system is reduced, the operation cost is reduced as much as possible, the consumption of new energy is promoted, and the economical efficiency of the system is improved. And according to the difference of the response time of each resource in the multi-energy building system, performing optimized scheduling according to two different time scales to realize the refinement and the scientization of the energy management of the multi-energy building system.
Disclosure of Invention
The invention aims to provide a robust double-layer coordinated scheduling method for a multi-energy building system, aiming at the multi-energy building system, an energy storage system is introduced to improve the scheduling flexibility of a miniature CCHP unit, and an electric heating demand response model is constructed, so that the load side resources can fully exert the scheduling advantages; in order to reduce the operating cost of a multi-energy building system and improve the consumption capacity of new energy, the total cost of a micro combined cooling heating and power unit, the operating cost of an energy storage system, the electric power transaction cost of the multi-energy building and a superior power grid, the system maintenance cost, the load scheduling cost, the new energy power generation wind and light abandoning cost and the like is minimized as a target function, the uncertainty of the output and the load demand of the new energy is calculated, and an energy management frame of the multi-energy building system is described in detail; according to the difference of the response time of the building resources, two different time scales are set to reasonably schedule the building resources, and the coordination of multiple time scales is realized through a two-stage robust optimization model, so that the optimal solution of the coordinated operation of the multi-energy building system is obtained, and the method has a reference value for practical application.
The purpose of the invention can be realized by the following technical scheme:
a method for robust double-layer coordinated scheduling of a multi-energy building system specifically comprises the following steps:
s1, inputting the prediction information of the electric load, the heat load and the renewable energy output of the multi-energy building system;
s2, constructing an objective function for minimizing the total operation cost of the multi-energy building system;
s3, considering user preference and comfort, constructing a multi-energy building system electricity-heat load demand response model;
s4, constructing a deterministic mathematical model of the multi-energy building system;
s5, establishing a source-load polyhedron uncertainty set, and converting the model established in the step S4 based on a two-stage robust optimization theory so as to obtain an optimal solution for coordinated operation of the multi-energy building system.
Further, in the step S2, constructing an objective function that minimizes the total operating cost of the multi-energy building system, the construction method is as follows:
the total operation cost of the system is reduced to the maximum extent through the coordinated operation of a micro combined cooling heating and power supply unit, a new energy power generation system, building-upper level power grid interconnection, energy storage and building load, as shown in formula (1):
min F＝CCCHP+CDG+CEX+COM+CESS+CLOAD (1)
here, the formula (1) represents the total running cost; cCCHP、CESSRespectively representing the running cost of the micro combined cooling heating and power unit and the energy storage system;
Figure BDA0002996629310000031
a penalty function is introduced for promoting the consumption of new energy; cEXCost for electric power transaction between the multi-energy building and the superior power grid; cOMCost for system maintenance; cLOADA cost is scheduled for the load.
Further, the micro combined cooling heating and power unit mainly comprises a fuel cell and a cogeneration unit, and the operation cost is expressed as follows:
Figure BDA0002996629310000032
in the formula, CfcIs the unit fuel cost; pfc,tThe output electric power of the micro CCHP unit at the time t; lambda [ alpha ]fcIs a fuel emission factor; ccar,fcIs the carbon emission unit cost of the micro CCHP unit; etaeTo the efficiency of the power generation;
Figure BDA0002996629310000035
Cψthe fixed and cold start costs of the micro-CCHP unit, respectively; t is toRefers to the time when the micro CCHP unit is in the off state; τ is a time constant; Δ t is a scheduling time interval;
the cost of wind and light abandonment in new energy power generation:
CDG＝CabaPaba,tΔt (3)
in the formula, CabaIs the unit fuel cost; paba,tAbandoning the optical power for abandoning wind at the time t;
the electricity transaction cost of the upper grid consists of the interaction cost with the upper utility grid and the corresponding carbon tax as follows:
CEX＝(CexPex,t+λexCcar,exPex,t)Δt (4)
in the formula, CexThe time-of-use electricity price is obtained; pex,tThe interactive power of the multi-energy building system and a superior power grid at the moment t; lambda [ alpha ]exA carbon emission factor for a superior utility grid; ccar,exUnit cost for carbon emissions for a superior utility grid;
the maintenance cost of the system is as follows:
Figure BDA0002996629310000033
in the formula, Com,fc、Com,hs、Com,es、Com,DGRespectively representing the unit maintenance cost of the micro CCHP unit, the heat storage, the electricity storage and the new energy power generation equipment;
Figure BDA0002996629310000034
respectively representing the charging/discharging power of the heat storage equipment at the moment t; pDG,tThe output power of the new energy power generation equipment at the moment t is represented;
Figure BDA0002996629310000041
respectively representing the charge/discharge power of the electric storage device for a smaller time interval;
the operation cost of the energy storage system is mainly the degradation cost of the energy storage equipment, including the battery replacement cost and the penalty cost; as follows:
CESS＝CREP+CPEN (6)
wherein,
Figure BDA0002996629310000042
Figure BDA0002996629310000043
Figure BDA0002996629310000044
Figure BDA0002996629310000045
in the formula, CrepReplacement cost for a battery unit; k is a radical of1、k2、k3As a curve fitting coefficient, it scales battery life xiesConverted to cycle depth ζesA function of (a);
Figure BDA0002996629310000046
respectively representing the total charging and discharging power of the electric storage equipment;
Figure BDA0002996629310000047
respectively representing charging and discharging power of a smaller time interval;
Figure BDA0002996629310000048
cost per unit penalty;
Figure BDA0002996629310000049
respectively representing the charging and discharging efficiencies of the electric storage equipment; eesRated capacity of the power storage equipment;
the load scheduling cost is generated by scheduling electricity and heat load demand response; as follows:
CLOAD＝CLOAD,E+CLOAD,H (8)
wherein,
Figure BDA00029966293100000410
CLOAD,H＝Cload,h(Tin,t-Ts)2 (9)
in the formula, Cload,e、
Figure BDA00029966293100000411
Respectively representing the unit cost of the electric load demand response and the reduced electric load quantity;Ta、Tsrespectively representing the actual temperature and the set temperature of the building system; cload,hIs the unit cost of the thermal load demand response.
Further, in the step S3, a multi-energy building system electricity-heat load demand response model is constructed in consideration of user preference and comfort, so as to enhance the scheduling potential of load resources and the flexibility of system operation;
for electrical load demand response, the relationship between the electrical load before and after the demand response point is shown as follows:
Figure BDA0002996629310000051
Figure BDA0002996629310000052
in the formula, Pe,t、
Figure BDA0002996629310000053
Respectively representing electrical loads before and after demand response;
Figure BDA0002996629310000054
respectively representing the maximum value and the minimum value of the electric load;
the relationship between the thermal load before and after the demand response point is expressed as:
Figure BDA0002996629310000055
Figure BDA0002996629310000056
in the formula,
Figure BDA0002996629310000057
a post-demand response thermal load; cairIs the specific heat capacity of air; mairFor the air quality in the building area；Tevr,tIs the outdoor temperature of the building area; rbThermal resistance of the building envelope;
Figure BDA0002996629310000058
respectively representing the maximum and minimum values of the user-set temperature.
Further, the deterministic mathematical model of the multi-energy building system is constructed in step S4, and when performing optimization scheduling, in order to ensure the effectiveness and feasibility of the optimization result, constraint conditions in the optimization process, including energy supply and demand balance constraint, equipment safe operation constraint, and operation constraint of the energy storage system, need to be considered, and the following formula is adopted:
Figure BDA0002996629310000059
Figure BDA00029966293100000510
Figure BDA00029966293100000511
Figure BDA00029966293100000512
Figure BDA0002996629310000061
Figure BDA0002996629310000062
Figure BDA0002996629310000063
Figure BDA0002996629310000064
Figure BDA0002996629310000065
wherein,
Figure BDA0002996629310000066
respectively representing the upper limit and the lower limit of the new energy output; of the closure time of the micro CCHP unit in formula (2)
Figure BDA0002996629310000067
Obtained by formula (17);
Figure BDA0002996629310000068
binary decision variables respectively representing the operation modes of the upper and lower layers of power storage equipment at t time; ees,t、Ehs,tRespectively representing the energy of the energy storage equipment at the moment t;
Figure BDA0002996629310000069
Figure BDA00029966293100000610
respectively representing the upper limit and the lower limit of energy storage of the energy storage equipment; lambda [ alpha ]es、λhsIs the self-loss rate;
Figure BDA00029966293100000611
Figure BDA00029966293100000612
respectively representing the charging efficiency and the discharging efficiency of the stored energy; hfc,tRepresents the output thermal power of the fuel cell-based micro-CCHP unit at time t;
Figure BDA00029966293100000613
shows the charging and discharging power of the heat storage equipment at the time t,
Figure BDA00029966293100000614
Figure BDA00029966293100000615
respectively as its upper and lower limits;
Figure BDA00029966293100000616
binary decision variables respectively representing the operation modes of the heat storage equipment at the moment t; ehsIs the rated capacity of the heat storage device.
Further, in the step S5, a source-charge polyhedron uncertainty set is established, and in order to counteract multiple uncertainties encountered during system operation, the model established in the step S4 is converted into a two-stage robust optimization (TSRO) model, where the source-charge polyhedron uncertainty set is first established, and the random variable new energy output and load are described as a robust interval set:
Figure BDA0002996629310000071
wherein,
Figure BDA0002996629310000072
respectively the predicted maximum value and the predicted minimum value of the new energy output and the electrical load;
Figure BDA0002996629310000073
respectively predicting values of new energy output and electric load before day;
Figure BDA0002996629310000074
ωDG,t、
Figure BDA0002996629310000075
ωe,tthe upper and lower budgets for the respective uncertainty sets.
Further, the general matrix form of the two-stage robust optimization model is represented as:
Figure BDA0002996629310000076
s.t.A(x)≥α (24)
B(x)+C(y)≤β (25)
H(z,x)+G(y)+F(z)≡γ；z∈Z (26)
the formula (23) is a mathematical problem of min-max-min form, three groups of variables are optimized by two stages; the first min problem represents that the cost of the first stage is minimized by optimizing a decision variable x of the first stage, and the part of the decision variables are deterministic variables; the decision variable x optimized in the first stage is kept unchanged during the second stage optimization; the second min problem represents that the second stage cost is minimized by optimizing the second stage decision variable y and the worst uncertainty is realized; the worst case of uncertainty is obtained by the max problem, i.e. optimizing the uncertain variables Z, which vary within their respective uncertain sets Z and are constrained by their upper and lower limits;
in the proposed solution, the decision variable of the first stage is Pex,t、Pfc,t、Hfc,t、
Figure BDA0002996629310000077
Figure BDA0002996629310000078
The decision variable of the second stage is
Figure BDA0002996629310000079
Therefore, the model targeting equation (1) is rewritten to the TSRO problem as follows:
Figure BDA00029966293100000710
wherein,
F1(x)＝CCCHP+CEX+CDG+COM+CREP+CLOAD,H (28)
F2(y,z)＝CPEN+CLOAD,E+CDG+Com,DGPDG,tΔt (29)
Figure BDA0002996629310000081
the original model has been converted into a two-stage robust model, which is divided into upper-layer main problems for solving a mathematical model with formula (27) as a target
Figure BDA0002996629310000082
And sub-problems of the lower layer
Figure BDA0002996629310000083
And (4) carrying out iterative solution, wherein the upper-layer main problem runs once every 1h, and the lower-layer sub problem runs once every 15min in consideration of the uncertainty of the source load prediction information.
The invention has the beneficial effects that:
1. aiming at a multi-energy building system, the method for coordinating and scheduling introduces an energy storage system, which is a beneficial measure for improving the scheduling flexibility of the miniature CCHP unit, and constructs an electric heating demand response model, so that the load side resources can fully exert the scheduling advantages;
2. in order to reduce the operating cost of a multi-energy building system and improve the consumption capacity of new energy, the method for coordinating and scheduling minimizes the total cost of a micro combined cooling heating and power unit, an energy storage system, the power transaction cost of the multi-energy building and a superior power grid, the system maintenance cost, the load scheduling cost, the new energy power generation wind and light abandoning cost and the like as a target function, calculates the uncertainty of the new energy output and the load demand, and describes an energy management frame of the multi-energy building system in detail;
3. the coordinated scheduling method of the invention sets two different time scales according to the difference of the reaction time of the building resources so as to reasonably schedule the building resources, and realizes the coordination of multiple time scales through the two-stage robust optimization model, thereby obtaining the optimal solution of the coordinated operation of the multi-energy building system, and having reference value for practical application.
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The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic view of the multi-energy building system of the present invention;
fig. 3 is a schematic diagram of multi-time scale double-deck energy management of the multi-energy building system of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
A method for robust double-layer coordinated scheduling of a multi-energy building system is disclosed, as shown in FIG. 1, and specifically comprises the following steps:
s1, inputting the prediction information of the electric load, the heat load and the renewable energy output of the multi-energy building system;
s2, constructing an objective function for minimizing the total operation cost of the multi-energy building system;
the method for constructing the objective function for minimizing the total operation cost of the multi-energy building system comprises the following steps:
as shown in fig. 2, the proposed objective is to minimize the total operating cost of the system through the coordinated operation of the micro Combined Cooling Heating and Power (CCHP) unit, the new energy power generation system, the building-upper grid interconnection, the energy storage (ESS) and the building load, as shown in equation (1):
min F＝CCCHP+CDG+CEX+COM+CESS+CLOAD (1)
here, the equation (1) represents the total running cost. CCCHP、CESSRespectively representing the running cost of the micro combined cooling heating and power unit and the energy storage system;
Figure BDA0002996629310000091
a penalty function is introduced for promoting the consumption of new energy; cEXCost for electric power transaction between the multi-energy building and the superior power grid; cOMCost for system maintenance; cLOADA cost is scheduled for the load.
The micro combined cooling heating and power unit mainly comprises a fuel cell and a cogeneration unit, and the operation cost can be expressed as follows:
Figure BDA0002996629310000092
in the formula, CfcIs the unit fuel cost; pfc,tThe output electric power of the micro CCHP unit at the time t; lambda [ alpha ]fcIs a fuel emission factor; ccar,fcIs the carbon emission unit cost of the micro CCHP unit; etaeTo the efficiency of the power generation;
Figure BDA0002996629310000104
Cψthe fixed and cold start costs of the micro-CCHP unit, respectively; t is toRefers to the time when the micro CCHP unit is in the off state; τ is a time constant; Δ t is the scheduling time interval.
The cost of wind and light abandonment in new energy power generation:
CDG＝CabaPaba,tΔt (3)
in the formula, CabaIs the unit fuel cost; paba,tAnd abandoning the optical power for abandoning wind at the time t.
The electricity transaction cost of the upper grid consists of the interaction cost with the upper utility grid and the corresponding carbon tax as follows:
CEX＝(CexPex,t+λexCcar,exPex,t)Δt (4)
in the formula, CexThe time-of-use electricity price is obtained; pex,tThe interactive power of the multi-energy building system and a superior power grid at the moment t; lambda [ alpha ]exA carbon emission factor for a superior utility grid; ccar,exUnit cost of carbon emissions for the upper-level utility grid.
The maintenance cost of the system is as follows:
Figure BDA0002996629310000101
in the formula, Com,fc、Com,hs、Com,es、Com,DGRespectively representing the unit maintenance cost of the micro CCHP unit, the heat storage, the electricity storage and the new energy power generation equipment;
Figure BDA0002996629310000102
respectively representing the charging/discharging power of the heat storage equipment at the moment t; pDG,tThe output power of the new energy power generation equipment at the moment t is represented;
Figure BDA0002996629310000103
respectively representing the charge/discharge power of the electric storage device for a smaller time interval (1 h).
The operating cost of the energy storage system is mainly the degradation cost of the energy storage device, including the battery replacement cost and the penalty cost. As follows:
CESS＝CREP+CPEN (6)
wherein,
Figure BDA0002996629310000111
Figure BDA0002996629310000112
Figure BDA0002996629310000113
Figure BDA0002996629310000114
in the formula, CrepFor a battery unitCost change; k is a radical of1、k2、k3As a curve fitting coefficient, it scales battery life xiesConverted to cycle depth ζesA function of (a);
Figure BDA0002996629310000115
respectively representing the total charging and discharging power of the electric storage equipment;
Figure BDA0002996629310000116
respectively representing the charging and discharging power of a smaller time interval (15 min);
Figure BDA0002996629310000117
cost per unit penalty;
Figure BDA0002996629310000118
respectively representing the charging and discharging efficiencies of the electric storage equipment; eesIs the rated capacity of the electrical storage device.
Load scheduling costs are generated by scheduling electrical, thermal load demand responses. As follows:
CLOAD＝CLOAD,E+CLOAD,H (8)
wherein,
Figure BDA0002996629310000119
CLOAD,H＝Cload,h(Tin,t-Ts)2 (9)
in the formula, Cload,e、
Figure BDA00029966293100001110
Respectively representing the unit cost of the electric load demand response and the reduced electric load quantity; t isa、TsRespectively representing the actual temperature and the set temperature of the building system; cload,hIs the unit cost of the thermal load demand response.
S3, considering user preference and comfort, constructing a multi-energy building system electricity-heat load demand response model;
and (3) constructing a multi-energy building system electricity-heat load demand response model by considering user preference and comfort so as to enhance the dispatching potential of load resources and the flexibility of system operation.
For electrical load demand response, the relationship between the electrical load before and after the demand response point is shown as follows:
Figure BDA00029966293100001111
Figure BDA00029966293100001112
in the formula, Pe,t、
Figure BDA00029966293100001113
Respectively representing electrical loads before and after demand response;
Figure BDA00029966293100001114
respectively representing the maximum and minimum values of the electrical load.
Similarly, the relationship of the thermal load before and after the demand response point is expressed as:
Figure BDA0002996629310000121
Figure BDA0002996629310000122
in the formula,
Figure BDA0002996629310000123
a post-demand response thermal load; cairIs the specific heat capacity of air; mairIs the air quality within the building area; t isevr,tIs the outdoor temperature of the building area; rbThermal resistance of the building envelope;
Figure BDA0002996629310000124
respectively representing the maximum and minimum values of the user-set temperature.
S4, constructing a deterministic mathematical model of the multi-energy building system;
constructing a deterministic mathematical model of a multi-energy building system, and during optimization scheduling, considering constraint conditions in an optimization process including energy supply and demand balance constraint, equipment safe operation constraint, operation constraint of an energy storage system and the like in order to ensure the effectiveness and feasibility of an optimization result, wherein the following are specifically respectively shown:
Figure BDA0002996629310000125
Figure BDA0002996629310000126
Figure BDA0002996629310000127
Figure BDA0002996629310000128
Figure BDA0002996629310000129
Figure BDA00029966293100001210
Figure BDA00029966293100001211
Figure BDA0002996629310000131
Figure BDA0002996629310000132
wherein,
Figure BDA0002996629310000133
respectively representing the upper limit and the lower limit of the new energy output; of the closure time of the micro CCHP unit in formula (2)
Figure BDA0002996629310000134
Obtained by formula (17);
Figure BDA0002996629310000135
binary decision variables respectively representing the operation modes (charging/discharging) of the upper and lower layers of power storage equipment at t time; ees,t、Ehs,tRespectively representing the energy of energy storage (electricity storage and heat storage) equipment at the moment t;
Figure BDA0002996629310000136
respectively representing the upper limit and the lower limit of energy storage of the energy storage equipment; lambda [ alpha ]es、λhsIs the self-loss rate;
Figure BDA0002996629310000137
respectively representing the charging efficiency and the discharging efficiency of the stored energy; hfc,tRepresents the output thermal power of the fuel cell-based micro-CCHP unit at time t;
Figure BDA0002996629310000138
shows the charging and discharging power of the heat storage equipment at the time t,
Figure BDA0002996629310000139
respectively as its upper and lower limits;
Figure BDA00029966293100001310
binary decision variables respectively representing the operation mode (charging/discharging) of the heat storage device at the moment t; ehsBeing heat-storage devicesRated capacity.
S5, establishing a source-load polyhedron uncertainty set, and converting the model established in the step S4 based on a two-stage robust optimization theory so as to obtain the optimal scheme for coordinated dispatching of the multi-energy building system.
In order to counteract multiple uncertainties encountered in system operation, the model constructed in step S4 is converted into a two-stage robust optimization (TSRO) model, for which a source-load polyhedral uncertainty set is first established, and the random variable new energy output and load can be described as a robust interval set:
Figure BDA00029966293100001311
wherein,
Figure BDA00029966293100001312
respectively the predicted maximum value and the predicted minimum value of the new energy output and the electrical load;
Figure BDA00029966293100001313
respectively predicting values of new energy output and electric load before day;
Figure BDA00029966293100001314
ω DG,t、
Figure BDA00029966293100001315
ω e,tthe upper and lower budgets for the respective uncertainty sets.
The generic matrix form of the TSRO model can be expressed as:
Figure BDA0002996629310000141
s.t.A(x)≥α (24)
B(x)+C(y)≤β (25)
H(z,x)+G(y)+F(z)≡γ；z∈Z (26)
equation (23) is a mathematical problem of the form min-max-min, with three sets of variables optimized through two stages. The first min-problem represents minimizing the cost of the first stage by optimizing the first stage decision variables x, which are part of the deterministic variables. The decision variable x for the first stage optimization remains unchanged during the second stage optimization. The second min problem represents the minimization of the second stage cost by optimizing the second stage decision variable y and achieving the worst uncertainty. The worst case of uncertainty is obtained by the max problem, i.e. by optimizing the uncertain variables Z, which vary within the respective uncertain set Z and are constrained by their upper and lower limits.
In the proposed solution, the decision variable of the first stage is Pex,t、Pfc,t、Hfc,t、
Figure BDA0002996629310000142
Figure BDA0002996629310000143
The decision variable of the second stage is
Figure BDA0002996629310000144
Thus, a model targeting equation (1) can be rewritten to the following TSRO problem:
Figure BDA0002996629310000145
wherein,
F1(x)＝CCCHP+CEX+CDG+COM+CREP+CLOAD,H (28)
F2(y,z)＝CPEN+CLOAD,E+CDG+Com,DGPDG,tΔt (29)
Figure BDA0002996629310000146
so far, the original model has been transformed into a two-stage robust modelTo solve the mathematical model targeted at equation (27), it is divided into upper-layer main problems
Figure BDA0002996629310000151
And sub-problems of the lower layer
Figure BDA0002996629310000152
Performing iterative solution, wherein the upper-layer main problem runs once every 1h, and the lower-layer sub-problem runs once every 15min in consideration of the uncertainty of source load prediction information; the framework of multi-time scale coordinated scheduling is specifically shown in fig. 3.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (7)

1. A robust double-layer coordinated scheduling method for a multi-energy building system is characterized by comprising the following steps:
s1, inputting the prediction information of the electric load, the heat load and the renewable energy output of the multi-energy building system;
s2, constructing an objective function for minimizing the total operation cost of the multi-energy building system;
s3, considering user preference and comfort, constructing a multi-energy building system electricity-heat load demand response model;
s4, constructing a deterministic mathematical model of the multi-energy building system;
s5, establishing a source-load polyhedron uncertainty set, and converting the model established in the step S4 based on a two-stage robust optimization theory so as to obtain an optimal solution for coordinated operation of the multi-energy building system.
2. The method for robust two-tier coordinated scheduling of a multi-energy building system according to claim 1, wherein the objective function for minimizing the total operating cost of the multi-energy building system is constructed in step S2 by the following steps:
the total operation cost of the system is reduced to the maximum extent through the coordinated operation of a micro combined cooling heating and power supply unit, a new energy power generation system, building-upper level power grid interconnection, energy storage and building load, as shown in formula (1):
min F＝CCCHP+CDG+CEX+COM+CESS+CLOAD (1)
here, the formula (1) represents the total running cost; cCCHP、CESSRespectively representing the running cost of the micro combined cooling heating and power unit and the energy storage system;
Figure FDA0002996629300000012
a penalty function is introduced for promoting the consumption of new energy; cEXCost for electric power transaction between the multi-energy building and the superior power grid; cOMCost for system maintenance; cLOADA cost is scheduled for the load.
3. The method for robust double-deck coordinated scheduling of a multi-energy building system according to claim 2, wherein the micro combined cooling heating and power unit is mainly composed of a fuel cell and a cogeneration unit, and the operation cost is expressed as:
Figure FDA0002996629300000011
in the formula, CfcIs the unit fuel cost; pfc,tThe output electric power of the micro CCHP unit at the time t; lambda [ alpha ]fcIs a fuel emission factor; ccar,fcIs the carbon emission unit cost of the micro CCHP unit; etaeTo the efficiency of the power generation;
Figure FDA0002996629300000024
Cψthe fixed and cold start costs of the micro-CCHP unit, respectively; t is toRefers to the time when the micro CCHP unit is in the off state; τ is a time constant; Δ t is a scheduling time interval;
the cost of wind and light abandonment in new energy power generation:
CDG＝CabaPaba,tΔt (3)
in the formula, CabaIs the unit fuel cost; paba,tAbandoning the optical power for abandoning wind at the time t;
the electricity transaction cost of the upper grid consists of the interaction cost with the upper utility grid and the corresponding carbon tax as follows:
CEX＝(CexPex,t+λexCcar,exPex,t)Δt (4)
in the formula, CexThe time-of-use electricity price is obtained; pex,tThe interactive power of the multi-energy building system and a superior power grid at the moment t; lambda [ alpha ]exA carbon emission factor for a superior utility grid; ccar,exUnit cost for carbon emissions for a superior utility grid;
the maintenance cost of the system is as follows:
Figure FDA0002996629300000021
in the formula, Com,fc、Com,hs、Com,es、Com,DGRespectively representing the unit maintenance cost of the micro CCHP unit, the heat storage, the electricity storage and the new energy power generation equipment;
Figure FDA0002996629300000022
respectively representing the charging/discharging power of the heat storage equipment at the moment t; pDG,tThe output power of the new energy power generation equipment at the moment t is represented;
Figure FDA0002996629300000023
respectively representing the charge/discharge power of the electric storage device for a smaller time interval;
the operation cost of the energy storage system is mainly the degradation cost of the energy storage equipment, including the battery replacement cost and the penalty cost; as follows:
CESS＝CREP+CPEN (6)
wherein,
Figure FDA0002996629300000031
Figure FDA0002996629300000032
Figure FDA0002996629300000033
Figure FDA0002996629300000034
in the formula, CrepReplacement cost for a battery unit; k is a radical of1、k2、k3As a curve fitting coefficient, it scales battery life xiesConverted to cycle depth ζesA function of (a);
Figure FDA0002996629300000035
respectively representing the total charging and discharging power of the electric storage equipment;
Figure FDA0002996629300000036
respectively representing charging and discharging power of a smaller time interval;
Figure FDA0002996629300000037
cost per unit penalty;
Figure FDA0002996629300000038
respectively representing the charging and discharging efficiencies of the electric storage equipment; eesRated capacity of the power storage equipment;
the load scheduling cost is generated by scheduling electricity and heat load demand response; as follows:
CLOAD＝CLOAD,E+CLOAD,H (8)
wherein,
Figure FDA0002996629300000039
CLOAD,H＝Cload,h(Tin,t-Ts)2 (9)
in the formula, Cload,e、
Figure FDA00029966293000000310
Respectively representing the unit cost of the electric load demand response and the reduced electric load quantity; t isa、TsRespectively representing the actual temperature and the set temperature of the building system; cload,hIs the unit cost of the thermal load demand response.
4. The method for robust two-tier coordinated scheduling of a multi-energy building system according to claim 1, wherein in step S3, a multi-energy building system electricity-heat load demand response model is constructed in consideration of user preference and comfort, so as to enhance scheduling potential of load resources and flexibility of system operation;
for electrical load demand response, the relationship between the electrical load before and after the demand response point is shown as follows:
Figure FDA00029966293000000311
Figure FDA00029966293000000312
in the formula, Pe,t、
Figure FDA00029966293000000313
Respectively representing electrical loads before and after demand response;
Figure FDA00029966293000000314
respectively representing the maximum value and the minimum value of the electric load;
the relationship between the thermal load before and after the demand response point is expressed as:
Figure FDA0002996629300000041
Figure FDA0002996629300000042
in the formula,
Figure FDA0002996629300000043
a post-demand response thermal load; cairIs the specific heat capacity of air; mairIs the air quality within the building area; t isevr,tIs the outdoor temperature of the building area; rbThermal resistance of the building envelope;
Figure FDA0002996629300000044
respectively representing the maximum and minimum values of the user-set temperature.
5. The method for robust double-layer coordinated scheduling of a multi-energy building system according to claim 1, wherein a deterministic mathematical model of the multi-energy building system is constructed in the step S4, and during the optimized scheduling, in order to ensure the effectiveness and feasibility of the optimized result, constraints in the optimization process, including energy supply and demand balance constraints, equipment safe operation constraints, and operation constraints of the energy storage system, are considered, and are expressed as follows:
Figure FDA0002996629300000045
Figure FDA0002996629300000046
Figure FDA0002996629300000047
Figure FDA0002996629300000048
Figure FDA0002996629300000049
Figure FDA00029966293000000410
Figure FDA00029966293000000411
Figure FDA0002996629300000051
Figure FDA0002996629300000052
wherein,
Figure FDA0002996629300000053
respectively representing the upper limit and the lower limit of the new energy output; of the closure time of the micro CCHP unit in formula (2)
Figure FDA0002996629300000054
Obtained by formula (17);
Figure FDA0002996629300000055
binary decision variables respectively representing the operation modes of the upper and lower layers of power storage equipment at t time; ees,t、Ehs,tRespectively representing the energy of the energy storage equipment at the moment t;
Figure FDA0002996629300000056
Figure FDA0002996629300000057
respectively representing the upper limit and the lower limit of energy storage of the energy storage equipment; lambda [ alpha ]es、λhsIs the self-loss rate;
Figure FDA0002996629300000058
Figure FDA0002996629300000059
respectively representing the charging efficiency and the discharging efficiency of the stored energy; hfc,tRepresents the output thermal power of the fuel cell-based micro-CCHP unit at time t;
Figure FDA00029966293000000510
shows the charging and discharging power of the heat storage equipment at the time t,
Figure FDA00029966293000000511
Figure FDA00029966293000000512
respectively as its upper and lower limits;
Figure FDA00029966293000000513
binary decision variables respectively representing the operation modes of the heat storage equipment at the moment t; ehsIs the rated capacity of the heat storage device.
6. The method for robust double-layer coordinated scheduling of a multi-energy building system according to claim 1, wherein a source-load polyhedron uncertainty set is established in the step S5, and in order to counteract multiple uncertainties encountered during system operation, the model established in the step S4 is converted into a two-stage robust optimization (TSRO) model, the source-load polyhedron uncertainty set is established first, and the random variable new energy output and load are described as a robust interval set:
Figure FDA00029966293000000514
wherein,
Figure FDA00029966293000000515
respectively the predicted maximum value and the predicted minimum value of the new energy output and the electrical load;
Figure FDA00029966293000000516
respectively predicting values of new energy output and electric load before day;
Figure FDA00029966293000000517
ω DG,t、
Figure FDA00029966293000000518
ω e,tto be corresponding toThe upper and lower budgets of the set are not determined.
7. The method for robust double-layer coordinated scheduling of the multi-energy building system according to claim 6, wherein the general matrix form of the two-stage robust optimization model is represented as:
Figure FDA0002996629300000061
s.t.A(x)≥α (24)
B(x)+C(y)≤β (25)
H(z,x)+G(y)+F(z)≡γ；z∈Z (26)
the formula (23) is a mathematical problem of min-max-min form, three groups of variables are optimized by two stages; the first min problem represents that the cost of the first stage is minimized by optimizing a decision variable x of the first stage, and the part of the decision variables are deterministic variables; the decision variable x optimized in the first stage is kept unchanged during the second stage optimization; the second min problem represents that the second stage cost is minimized by optimizing the second stage decision variable y and the worst uncertainty is realized; the worst case of uncertainty is obtained by the max problem, i.e. optimizing the uncertain variables Z, which vary within their respective uncertain sets Z and are constrained by their upper and lower limits;
in the proposed solution, the decision variable of the first stage is Pex,t、Pfc,t、Hfc,t、
Figure FDA0002996629300000062
Figure FDA0002996629300000063
The decision variable of the second stage is
Figure FDA0002996629300000064
Therefore, the model targeting equation (1) is rewritten to the TSRO problem as follows:
Figure FDA0002996629300000065
wherein,
F1(x)＝CCCHP+CEX+CDG+COM+CREP+CLOAD,H (28)
F2(y,z)＝CPEN+CLOAD,E+CDG+Com,DGPDG,tΔt (29)
Figure FDA0002996629300000066
the original model has been converted into a two-stage robust model, which is divided into upper-layer main problems for solving a mathematical model with formula (27) as a target
Figure FDA0002996629300000071
And sub-problems of the lower layer
Figure FDA0002996629300000072
And (4) carrying out iterative solution, wherein the upper-layer main problem runs once every 1h, and the lower-layer sub problem runs once every 15min in consideration of the uncertainty of the source load prediction information.
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