CN113793052A - Robust optimization scheduling method for regional comprehensive energy system - Google Patents
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Abstract
The invention discloses a robust optimization scheduling method for a regional integrated energy system, belonging to the technical field related to regional integrated energy system scheduling and comprising the following specific steps of: constructing a partial load performance function of each energy conversion equipment efficiency; constructing a regional comprehensive energy system model; constructing constraint conditions of a regional comprehensive energy system; constructing an optimized scheduling model; constructing a robust optimization scheduling model, and solving the robust optimization scheduling model to obtain an optimal scheduling scheme of the regional comprehensive energy system; and performing optimized dispatching on the regional comprehensive energy system according to the optimal dispatching scheme. The invention adopts the box-type uncertain set to effectively depict the fitting error and the range of the partial load performance function of the equipment, is applied to the optimization model, changes the scheduling scheme of the system, improves the operation robustness of the system and improves the reliability of heat supply.
Description
Technical Field
The invention relates to the technical field of regional integrated energy system scheduling, in particular to a robust optimization scheduling method for a regional integrated energy system.
Background
A Regional Integrated Energy System (RIES) is one of important physical carriers of an energy internet, and is an important way to realize multi-energy complementation and high-utility energy. In actual operation, the efficiency of energy conversion plants such as gas turbines, Gas Boilers (GB) and the like has a significant Part Load Performance (PLP), i.e. the efficiency of the plant changes with the load factor when it is not operating under nominal conditions. The efficiency of most energy conversion devices decreases with decreasing load rate, for example, the power generation efficiency of a gas turbine at low electrical load rate is only 80% or less of full load operation. After the equipment efficiency PLP is considered, the prediction error of the operation cost of the multi-energy system is reduced by 13% compared with the PLP without the equipment efficiency. The PLP of the equipment efficiency can more accurately and effectively depict the running characteristics of the equipment, so that the RIES scheduling scheme is more accurate and reasonable. The impact of the accurate characterization of the device efficiency PLP on the scheduling scheme is not negligible. The function describing the plant efficiency PLP is usually obtained by fitting experimental data or manufacturer supplied test data, which results in an objective presence of fitting errors. In the prior art, a linear function and a power function are mostly adopted to fit the relationship between the heat-electricity ratio and the electric load rate of a Combined Heat and Power (CHP) cogeneration unit, or a linear and quadratic function is adopted to fit a coefficient of performance (COP) of a water chilling unit, which relates to the fitting error of a PLP function, and load loss can be caused by neglecting the error. Therefore, the research on a robust optimization scheduling method of the regional integrated energy system based on the fitting error of the partial load performance function of the equipment is a problem to be solved urgently by the technical personnel in the field.
Disclosure of Invention
In view of the above, the invention provides a robust optimization scheduling method for a regional integrated energy system, which overcomes the defects in the prior art.
In order to achieve the above purpose, the invention provides the following technical scheme:
a robust optimization scheduling method for a regional integrated energy system comprises the following specific steps:
constructing a partial load performance function of each energy conversion equipment efficiency according to the regional comprehensive energy system framework;
constructing a regional comprehensive energy system model according to the regional comprehensive energy system framework and the partial load performance function of each energy conversion equipment efficiency;
constructing a constraint condition of the regional comprehensive energy system for the regional comprehensive energy system model;
constructing an optimized scheduling model according to a partial load performance function of each energy conversion equipment efficiency, a regional comprehensive energy system model and a constraint condition of the regional comprehensive energy system;
introducing fitting errors of partial load performance functions of the efficiency of each energy conversion device into an optimized scheduling model to construct a robust optimized scheduling model;
solving an optimal scheduling scheme of the regional comprehensive energy system by the robust optimization scheduling model;
and performing optimized dispatching on the regional comprehensive energy system according to the optimal dispatching scheme.
Preferably, the expression of the part load performance function of the efficiency of the energy conversion device is:
in the formula (I), the compound is shown in the specification,
to the efficiency of the plant; and N is the load rate of the equipment, namely the ratio of the current output to the rated output of the equipment.
Preferably, the PLP of the CHP unit power generation efficiency is:
in the formula, kCHP,nFitting coefficients of PLP which are the generating efficiency of the CHP unit;
thermoelectric ratio of CHP
The relationship with the electrical load rate is:
in the formula, kλ,nFitting coefficient of PLP as thermoelectric ratio of CHP;
the fitting coefficients for PLP for thermal efficiency of GB are:
in the formula, kGB,nAre fitting coefficients.
The method has the advantages that the partial load performance function of the efficiency of the energy conversion equipment is considered, and the situation that the heat load requirement cannot be met is avoided.
Preferably, based on the regional integrated energy system framework, the regional integrated energy system optimization model is established with the aim of lowest daily operation cost of the regional integrated energy system.
Preferably, the constraints include power balance constraints, energy storage device related constraints, and energy storage device related constraints.
Preferably, the robust optimization scheduling model is constructed by the following steps:
describing a fitting error and an error range of a partial load performance function of the equipment efficiency by adopting an uncertain set;
introducing the uncertain set into an optimized scheduling model to form a compact form of a robust optimized scheduling model;
and converting the compact form of the robust optimized scheduling model into a mixed integer semi-positive definite programming model through Lagrange duality and piecewise linearization, and defining the mixed integer semi-positive definite programming model as the robust optimized scheduling model.
Preferably, the uncertainty set is a box uncertainty set.
Preferably, the method further comprises verifying the heat load loss rate of the robust optimization scheduling model by adopting a Monte Carlo method under the constraint condition.
According to the technical scheme, compared with the prior art, the robust optimization scheduling method for the regional comprehensive energy system is provided, after the fitting error of the efficiency PLP function of the energy conversion equipment is considered, robust scheduling is adopted, and the risk of load loss is reduced; the robust scheduling model based on the fitting error of the PLP function of the energy conversion equipment efficiency can be used for not only counting the influence of single equipment, but also expanding and calculating the uncertainty of the fitting error of the PLP function of the energy conversion equipment efficiency, and further considering the coupling and correlation among the uncertain errors, so that the RIES optimal scheduling scheme is more practical; the box-type uncertain set is adopted to effectively depict the fitting error and the range of the partial load performance function of the equipment, and is applied to an optimization model, so that the system scheduling scheme is changed, the system operation robustness is improved, and the heat supply reliability is improved.
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, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of a framework for a regional energy complex of the present invention;
FIG. 2 is a graph of the CHP thermoelectric ratio of the present invention fitted to its electrical loading rate;
FIG. 3 is a schematic illustration of predicted electrical and thermal loading at a given day in the area of the present invention;
FIG. 4 is a schematic view of the time of day electricity prices at a certain day in the area of the present invention;
FIG. 5 is a diagram illustrating the effect of the uncertainty parameter r on scheduling cost according to the present invention;
FIG. 6 is a schematic diagram of thermal power scheduling in the baseline and robust schemes of the present invention;
FIG. 7 is a schematic diagram of the scheduling of electric power in the baseline and robust schemes of the present invention;
FIG. 8 is a schematic flow chart of the method 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.
The embodiment of the invention discloses a robust optimization scheduling method for a regional comprehensive energy system, which comprises the following steps as shown in figure 8:
constructing a partial load performance function of each energy conversion equipment efficiency according to the regional comprehensive energy system framework;
constructing a regional comprehensive energy system model according to the regional comprehensive energy system framework and the partial load performance function of each energy conversion equipment efficiency;
constructing a constraint condition of the regional comprehensive energy system for the regional comprehensive energy system model;
constructing an optimized scheduling model according to a partial load performance function of each energy conversion equipment efficiency, a regional comprehensive energy system model and a constraint condition of the regional comprehensive energy system;
introducing fitting errors of partial load performance functions of the efficiency of each energy conversion device into an optimized scheduling model to construct a robust optimized scheduling model;
solving an optimal scheduling scheme of the regional comprehensive energy system by the robust optimization scheduling model;
and performing optimized dispatching on the regional comprehensive energy system according to the optimal dispatching scheme.
Example 1:
1. establishment of partial load performance function of efficiency of each energy conversion device
1.1 Regional Integrated Energy Systems (RIES) framework:
the regional power grid, the heat supply network and the like are coupled together, so that multi-energy complementation is realized, and the energy utilization rate is improved. The rines of the present embodiment mainly includes energy conversion devices such as CHP and GB, and energy storage devices such as a storage battery and a hot water storage tank, and a specific frame is as shown in fig. 1.
1.2 PLP function of energy conversion device efficiency
Considering that each energy conversion device normally operates under a partial load condition during actual operation, PLP of each energy conversion device is calculated by the following formula:
in the formula:
to the efficiency of the plant; and N is the load rate of the equipment, namely the ratio of the current output to the rated output of the equipment.
For a CHP unit, the PLP of the generating efficiency can be obtained by fitting a fourth-order polynomial, and the method specifically comprises the following steps:
in the formula, kCHP,nIs a fitting coefficient;
is the electrical loading rate of the CHP.
Thermoelectric ratio of CHP
The relation with the electrical load rate can be obtained by fitting a quadratic polynomial, and specifically comprises the following steps:
in the formula, kλ,nAre fitting coefficients.
The relationship between the thermal efficiency and the thermal load rate of GB can be obtained by fitting a quadratic polynomial, which specifically comprises the following steps: .
In the formula, kGB,nIs a fitting coefficient;
is the heat load rate of GB.
1.3 fitting error of PLP function
The plant efficiency PLP function is typically fit from a number of data points derived from either the manufacturer of the plant or from actual measurements from physical simulations. However, regardless of the source of the data, fitting the data points to the PLP function has fitting errors, such as: taking the performance of CHP thermoelectric ratio under different electrical load rates as an example, the rated power P under different load rates provided by Cummins company is quotedNThe fitting result of the data of the relationship between the thermoelectric ratio and the load rate of a plurality of CHP units with the power of 1000-2000kW is shown in FIG. 2 through quadratic function fitting, and as can be seen from FIG. 2, the PLP fitting function of the thermoelectric ratio of the CHP units has obvious fitting error, and actual data points are distributed on two sides of each fitting curve. When the electrical load rate is 90%, PNThe CHP train hot spot ratio, 1100kW, has the greatest deviation (1.56%) of the fitted value from the actual value.
For each energy conversion device PLP function, the fitting error cannot be determined based on the source of the data set used for fitting, the capacity size of the data set, the validity of the data, and the like, and these factors all affect the fitting error. Thus, the fitting error belongs to an uncertain variable that makes it difficult to obtain an accurate probability distribution.
2 RIES optimization model based on equipment PLP function
2.1 objective function
In this embodiment, the lowest daily running cost of the rees is targeted. RIE to establish energy inputs containing only two of electricity and natural gasS, the operation cost comprises the electricity purchasing cost C1Gas purchase cost C2。
min＝(C1+C2) (5)
Wherein
C1＝CELE,tPELE,t (6)
In the formula, CELE,tAnd CNG,tRespectively purchasing electricity and gas prices at the time of t time period; pELE,tPurchasing electric power from the power grid for a period t; pCHP,tCHP electrical power for a period t; qGB,tGB thermal power in a t period;
and
the power generation efficiency of the CHP and the heat supply efficiency of the GB in a t period are respectively; l isHVThe low calorific value of natural gas is usually 9.7kWh/m3(ii) a And T belongs to T, and T is a scheduling period.
2.2 constraint Condition
1) Power balance constraint
PELE,t+PCHP,t+Pd,t＝Pc,t+PL,t (8)；
In the formula, Pc,t、Pd,tThe charging and discharging powers of the electricity storage device are respectively t time period; qc,t、Qd,tRespectively charging and discharging power of the heat storage device in a time period t; pL,t、QL,tRespectively, the electric load and the thermal load in the period t.
2) Energy storage device related constraints
The energy storage equipment for electricity storage, heat storage and the like has a universal mathematical model and is specifically expressed as follows
Wherein a first constraint ensures that the energy storage device is within a capacity range, EtRepresenting the capacity of the energy storage equipment in the t period; emin、EmaxThe minimum and maximum capacity of the energy storage device are respectively. The second and the third constraints limit the charge and discharge energy power range, wherein utThe variable is 0-1, the energy storage is only chargeable when the value is 1, and the energy storage is only dischargeable when the value is 0. PES,c,t、PES,d,tEnergy storage charging and discharging power is respectively stored for t time period; pES,c,min、PES,c,maxRespectively storing minimum energy storage power and maximum energy storage power; pES,d,min、PES,d,maxThe minimum energy storage power and the maximum energy discharge power are respectively. The fourth constraint is the relation between the energy storage capacity and the charging and discharging energy power, ec、ed、eselfRespectively charging energy efficiency, discharging energy efficiency and self-energy consumption rate for the energy storage equipment; final constraints guarantee the energy storage scheduling periodicity, Et-1The capacity of the energy storage equipment is in a t-1 time period; eTEnergy storage device capacity for a period of time T; e0Is the initial value of the energy storage capacity.
3) Device force range constraints
0≤PELE,t≤PELE,max (11)
QGB,min≤QGB,t≤QGB,max (12)
PCHP,min≤PCHP,t≤PCHP,max (13)
In the formula, PELE,maxFor the maximum power of purchasing electricity from the power grid, the RIES is specified not to allow electricity to be sold to the power grid; qGB,min、QGB,maxGB minimum and maximum thermal power respectively; pCHP,min、PCHP,maxCHP minimum and maximum electrical power, respectively.
Integrating the above objectives and constraints, a representation of the rees optimized scheduling model based on device efficiency PLP can be obtained as follows:
3 RIES robust optimization model based on equipment PLP function fitting error
3.1 Box aggregation robust optimization model
In the RIES optimization scheduling model based on the equipment efficiency PLP, fitting errors exist in the CHP unit heat-electricity ratio PLP function and variable, and the heat load requirement can not be met if the errors are not considered. And for the measurement of the error, a stochastic programming method can be adopted to solve the model formula (14). However, the accurate probability distribution of the fitting error is difficult to obtain, so that the uncertain quantity xi and the variation range of the fitting error are described by adopting an uncertain set U, and then the solution is carried out by adopting a robust optimization method. In this embodiment, a box-type uncertain set is selected to depict fitting errors and their ranges, and has the following structure:
in the formula (I), the compound is shown in the specification,
xi is the upper and lower limits of the uncertain quantity xi respectively, and a decision maker can adjust the upper and lower limits of the uncertain quantity according to actual conditions.
In another embodiment, fitting errors can be described in various forms such as an ellipsoid, a polyhedron and the like.
The compact form of the RIES robust optimization model based on the fitting error of the device PLP function corresponding to equation (14) is:
wherein x is a decision variable vector, and the specific expression is
x＝[PELE,t,PCHP,t,Pd,t,Pc,t,QGB,t,Qd,t,Qc,t,Et,ut]T (16)；
c is a coefficient column vector of the objective function formula (5); a (xi) is a coefficient matrix containing uncertain quantity xi; G. h is a coefficient matrix of x in the corresponding constraint; a. g and h are constant column vectors. Among the constraint conditions of the formula (15), the first constraint corresponds to the formula (9); the second constraint is an inequality constraint comprising the first three constraints of equation (10) and equations (11) - (13); the third is an equality constraint, which includes two constraints after the formulas (2) - (4), (8) and (10).
Because the first constraint comprises the uncertainty xi, the solution of the model in the formula (15) belongs to an NP difficult problem, and the model can be converted into a semi-definite dual model which is easy to solve through Lagrange dual.
3.2 Dual conversion and piecewise linearization processing
When the CHP electrical load factor is fixed, the thermoelectric ratio is not a definite value by the uncertain quantity xi, the thermoelectric ratio in the thermal power balance type (9) needs to be converted into the sum of the original PLP function expression and the uncertain quantity xi, and the method specifically comprises the following steps:
due to the addition of the uncertain parameters, equation (9) is relaxed as:
since the constraint of the above equation is satisfied for all possible xi ∈ U, the above equation can be equivalent to:
containing an indeterminate amount for processing formula (19)
Term, construct the corresponding lagrangian function as:
in the formula, gammat、δtThe dual variables are respectively corresponding to the upper and lower limits of the uncertain variable xi, and have no practical significance gamma, and delta is respectively a dual variable vector (the vector length is the scheduling period T) corresponding to the upper and lower limits of the uncertain variable xi.
The above equation is derived, making the derivative equal to zero.
The visible derivative function is a constant independent of xi, so when xi is zero vector, the original Lagrange function takes the minimum value, i.e.
According to the strong dual theorem, the method can be obtained,
thus, equation (19) is equivalent to:
the PLP function of the energy conversion device gives the model an intractable non-linear term. Therefore, in the case of the formula (19)
And in formula (7)
And processing the nonlinear terms by adopting a piecewise linearization method. By linearization
For example, it is divided into d segments,
the following constraints are attached:
in the formula, mkIs the slope of each segment; a iskIs the longitudinal axis intercept of each segment; omegakIs a continuous variable introduced; bkIs the kth segmentation point; bk-1Is the k-1 segment point; v. ofkIs a variable from 0 to 1, v k1 represents PCHP,tAt the k interval [ b ]k-1,bk]In (1).
And finally converting the robust optimization model of the formula (15) into the following optimization model through Lagrange duality and piecewise linearization:
performing segmentation processing on other nonlinear terms according to a method of an equation (25) to an equation (26) to obtain a robust optimization scheduling model; the model belongs to a mixed integer semi-definite programming model, a gurobi solver can be called in Matlab software to directly solve, an optimal scheduling scheme is obtained after solving, and robust optimization scheduling of the regional comprehensive energy system is completed.
According to the algorithm, the formula (27) is optimized, and an optimization model is obtained as follows:
example 2
Taking a typical RIES as an example, the area takes electricity and natural gas as energy input, and the electricity and the heat load are simultaneously supplied through an energy conversion device and a transmission network. In the regionThe predicted electric load, thermal load and time-of-use electricity price on a certain day are respectively shown in fig. 3 and fig. 4. Natural gas value 2 yuan/m3，PELE,maxThe detailed parameters of each device in 1MW, rees and the fitting parameters of the device efficiency PLP function are shown in table 1 and table 2, respectively. For the piecewise linear representation of the fitting function, considering the balance between linear approximation precision and calculation efficiency, the number d of the segments is generally between 30 and 70, so that d is taken to be 50.
Setting an uncertain parameter
The RIES scheduling problem when r is 0 is a deterministic optimization problem, i.e. the device PLP function fitting error is not considered. And setting the RIES optimization scheduling scheme as a reference scheme when r is 0, wherein the larger the r value is, the larger the uncertainty range of the fitting error of the PLP function of the equipment is shown to be, and the higher the tolerance of the RIES optimization scheduling to the error disturbance is.
TABLE 1 energy conversion device parameters
TABLE 2 energy storage device parameters
After the simulation calculation, the influence of different values of the uncertain parameter r on the scheduling cost is analyzed, and the simulation result is shown in fig. 5.
As can be seen from fig. 5, in the reference scenario, the operation cost of the ires is the lowest (13050.4 yuan), and as r increases, the cost also increases, i.e., the operation cost is positively correlated with the uncertain parameter r. The running cost of the robust scheduling scheme when r is 0.02 is increased by 0.63% compared with the reference scheme cost. This is because, after considering the error of the PLP function fitting, the RIES needs to increase the cost of energy input to reduce the sensitivity to random fitting errors and improve the robustness of the scheduling scheme.
The comparison results of the thermal power and electric power scheduling results of the reference scheme (r is 0) and the robust scheme (r is 0.02) are shown in fig. 6 and 7, respectively.
As can be seen from fig. 6, the robust scheme has a large difference from the reference scheme, and the output thermal power of the robust scheme GB in each period is slightly higher than the corresponding value of the reference scheme, because the robust scheme makes an optimal decision in the worst scenario where the negative thermal bias is the most severe (i.e., the CHP actual thermal output is the lowest) in most periods, and in this scenario, the GB thermal output needs to be increased to prevent the ches actual thermal output from being insufficient to cause the RIES thermal load loss.
Fig. 7 compares the electric power scheduling situations of the two schemes, and it can be seen that the overall difference of the electric power scheduling of the two schemes is not great, but the electric power output by the CHP under the 6 th-7 th period robust scheme is 16kW less than that of the reference scheme, and the corresponding power purchased from the power grid is 16kW more than that of the reference scheme. This is mainly because the electricity load is low valley in the period of 6-7, CHP is in the low load state, and it can be known from the formula (2) that CHP has low power generation efficiency at low electricity load rate and high power generation cost, while fig. 6 shows that GB is in the high heat load rate state at this period and high heat generation efficiency. The total energy cost by purchasing electricity at valley price from the grid, supplying electricity, respectively thermal load using GB high efficiency heating is lower during 6-7 hours compared to cogeneration with CHP, so that the power purchased from the grid during this hour is increased under a robust scheme.
Although the baseline solution runs at a slightly lower cost than the robust solution, there is a greater risk of heat load loss due to uncertainty in the fitting error not being considered, and the present embodiment employs the heat load loss probability to quantify this risk. Note that the robust scheme with r being 0.02 is only a scheduling scheme in a scene with the worst fitting error value, and actually, the value of the uncertainty ξ in each time period is a random value in the box set range. Thus, to compare the risk of heat load loss under the baseline scheme and the robust scheme (r ═ 0.02), in ξ [ -0.02,0.02]Under the constraint of (2), Monte Carlo simulation is used for randomly generating M (1000 scenes), and the actual CHP thermal power under different xi values of each scene is calculated and substituted into a scheduling scheme. For a reference or robust scheduling scheme, if the thermal energy input of a certain scene t period cannot meet the thermal load requirement, the thermal load requirement is metThere is a loss of thermal load under the scenario. The number of scenes with heat load loss is recorded as V, and the heat load loss probability R is obtained by calculating 100% V/Mh. The two scenario heat load loss probabilities are shown in table 3.
TABLE 3 probability of heat load loss for two schemes
As can be seen from Table 3, R for the robust schemehMuch smaller than the baseline scheme, i.e. the risk of heat load loss for the robust scheme is greatly reduced compared to the baseline scheme. This is because the robust scheme makes a decision to satisfy the original constraint in the scenario where the deviation of the thermoelectric ratio is the most severe, that is, it is ensured that the obtained robust scheduling scheme can satisfy the thermal load demand for any different fitting errors.
The invention discloses and provides a robust optimization scheduling method of a regional comprehensive energy system, which adopts robust scheduling after considering the fitting error of an efficiency PLP function of energy conversion equipment, thereby reducing the risk of load loss; the robust scheduling model based on the fitting error of the PLP function of the energy conversion equipment efficiency can be used for not only counting the influence of single equipment, but also expanding and calculating the uncertainty of the fitting error of the PLP function of the energy conversion equipment efficiency, and further considering the coupling and correlation among the uncertain errors, so that the RIES optimal scheduling scheme is more practical; and the system robustness and reliability can be further improved by adopting the function fitting error and applying the function fitting error to the optimization model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A robust optimization scheduling method for a regional integrated energy system is characterized by comprising the following specific steps:
constructing a partial load performance function of each energy conversion equipment efficiency according to the regional comprehensive energy system framework;
constructing a regional comprehensive energy system model according to the regional comprehensive energy system framework and the partial load performance function of each energy conversion equipment efficiency;
constructing a constraint condition of the regional comprehensive energy system for the regional comprehensive energy system model;
constructing an optimized scheduling model according to a partial load performance function of each energy conversion equipment efficiency, a regional comprehensive energy system model and a constraint condition of the regional comprehensive energy system;
introducing fitting errors of partial load performance functions of the efficiency of each energy conversion device into an optimized scheduling model to construct a robust optimized scheduling model;
solving an optimal scheduling scheme of the regional comprehensive energy system by the robust optimization scheduling model;
and performing optimized dispatching on the regional comprehensive energy system according to the optimal dispatching scheme.
2. The robust optimal scheduling method for the regional integrated energy system according to claim 1, wherein the expression of the partial load performance function of the energy conversion equipment efficiency is as follows:
in the formula，
To the efficiency of the plant; and N is the load rate of the equipment, namely the ratio of the current output to the rated output of the equipment.
3. The robust optimized dispatching method for regional integrated energy systems according to claim 2,
the PLP of the generating efficiency of the CHP unit is as follows:
in the formula, kCHP,nFitting coefficients of PLP which are the generating efficiency of the CHP unit;
thermoelectric ratio of CHP
The relationship with the electrical load rate is:
in the formula, kλ,nFitting coefficient of PLP as thermoelectric ratio of CHP;
the fitting coefficients for PLP for thermal efficiency of GB are:
in the formula, kGB,nAre fitting coefficients.
4. The robust optimal scheduling method for regional integrated energy system according to claim 1, wherein the regional integrated energy system optimization model is established based on the regional integrated energy system framework with the goal of lowest daily operation cost of the regional integrated energy system.
5. The robust optimized dispatching method for regional integrated energy systems according to claim 1, wherein the constraints comprise power balance constraints, energy storage device-related constraints, and energy storage device-related constraints.
6. The robust optimal scheduling method for the regional integrated energy system according to claim 1, wherein the robust optimal scheduling model is constructed by the following steps:
describing a fitting error and an error range of a partial load performance function of the equipment efficiency by adopting an uncertain set;
introducing the uncertain set into an optimized scheduling model to form a compact form of a robust optimized scheduling model;
and converting the compact form of the robust optimized scheduling model into a mixed integer semi-positive definite programming model through Lagrange duality and piecewise linearization, and defining the mixed integer semi-positive definite programming model as the robust optimized scheduling model.
7. The robust optimized dispatching method for regional integrated energy systems according to claim 6, wherein the uncertainty set is a box uncertainty set.
8. The robust optimized dispatching method for regional integrated energy systems according to claim 1, further comprising verifying a heat load loss rate of the robust optimized dispatching model by using a Monte Carlo method under a constraint condition.
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