CN115660757A - Real-time electric power market clearing pricing optimization method and device and storage medium - Google Patents
Real-time electric power market clearing pricing optimization method and device and storage medium Download PDFInfo
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Abstract
The invention discloses a real-time electric power market clearing pricing optimization method and device, which are used for acquiring real-time electric power market clearing basic data; constructing a real-time power market clearing model; solving a real-time power market clearing model; obtaining unit output, section tide, electricity balance constraint relaxation quantity and network security constraint relaxation quantity; correcting the system power load and the section quota by using the service electric balance constraint relaxation quantity and the network security constraint relaxation quantity to ensure that a service electric balance constraint equation and network security constraint are strictly established and obtain a node price calculation model; based on the node price calculation model, searching for an optimal basis of the node price calculation model; directly calculating the node price by using the optimal basis and the price coefficient; and outputting the output of the unit, the section trend, the distribution electric balance constraint shadow price, the network security constraint shadow price and the node price. The invention saves the time for clearing the market and calculating the node price, and improves the real-time electric power market operation efficiency for frequently calling the function.
Description
Technical Field
The invention relates to the technical field of power dispatching automation, in particular to a method for accelerating two-step system optimization of real-time power market clearing and pricing.
Background
The safety constraint economic dispatching algorithm is used as a core algorithm for clearing and pricing the electric power spot market, provides scientific node electricity price for the spot market, fully considers the marginal cost of a generator, the system capacity, the network loss and the circuit blocking condition into the electricity price, and provides a powerful basis for the allocation of the blocking cost. Because the power spot market clearing model considers various constraints and boundary data have large uncertainty and volatility, in order to guarantee the feasibility of the problem, a slack variable is usually introduced into the power spot market clearing model to guarantee the feasibility of the problem, and the occurrence of accidents that the clearing model is infeasible in a production environment is avoided. The relaxation variables generally comprise service electric balance relaxation and network security relaxation, the current penalty coefficients of the relaxation variables are key parameters of model decision, and when the line load is too large, the load limitation can be selected, and the line limitation can also be selected, so that the optimal solution depends on the magnitude relation of the penalty coefficients. The relaxation variable has definite physical meaning different from other variables, the unit power generation cost of the generator set can be measured by currency, the penalty coefficient of the relaxation variable has no definite physical meaning, the shadow price of the clearing model has no strict physical meaning, and the shadow price of the problem is higher when the penalty value of the relaxation variable is higher. To solve the problem, the method commonly used in the industry at present is to separate the clearing from the pricing and adopt a two-step optimization method of clearing and pricing. Firstly, setting a higher penalty coefficient of a relaxation variable in a market clearing model for clearing, fixing a value of the relaxation variable if the relaxation variable of the service balance or the network security constraint is nonzero, and then carrying out secondary solution pricing.
The long-period continuous settlement test operation is carried out along with the successive entering of the spot test in China, the timeliness requirements on market clearing and node price calculation are higher and higher, and particularly, the timeliness requirements on 5-minute or 15-minute periodic rolling operation of real-time power market clearing and node electricity price calculation are temporarily not met by partial test point units. In addition, the network security constraint in the market clearing pricing is calculated by adopting a direct current flow method, and multiple rounds of iteration are required to be carried out with security check in order to reduce the alternating current-direct current deviation, so that the two-step clearing pricing algorithm has the problems of low efficiency and long consumed time in practice.
Disclosure of Invention
The invention provides a real-time electric power market clearing and pricing optimization method, aiming at solving the problems of low efficiency and overlong time consumption of the existing electric power market clearing and pricing method in practice.
In order to solve the technical problems, the invention provides a real-time electric power market clearing pricing optimization method, which comprises the following steps:
in a first aspect, the invention provides a real-time electricity market clearing pricing optimization method, comprising:
acquiring real-time electric power market clearing basic data; constructing a real-time electric power market clearing model according to the basic data;
solving a real-time power market clearing model; obtaining unit output, section tide, issue and use electric balance constraint relaxation quantity and network security constraint relaxation quantity;
correcting the system power load and the section quota by using the service electric balance constraint relaxation quantity and the network security constraint relaxation quantity to ensure that a service electric balance constraint equation and network security constraint are strictly established and obtain a node price calculation model;
based on the node price calculation model, searching for an optimal basis of the node price calculation model; directly calculating the node price by using the optimal basis and the price coefficient;
and outputting the output of the unit, the section trend, the distribution electric balance constraint shadow price, the network security constraint shadow price and the node price.
Further, comprising: the real-time power market clearing model is expressed as follows:
in the formula: n represents the total number of the units; t represents the total time period number considered, and S represents the number of price sections declared by the unit; d represents the number of the network security constraint sections; c it Representing the power generation cost of the unit i in the time period t; p i,t Representing the output of the unit in the period t;
representing the electricity generation surplus relaxation variable in the period t by the electricity balance constraint;
representing the insufficient power generation relaxation variable of the electricity balance constraint in the period t;
respectively a forward power flow relaxation variable and a reverse power flow relaxation variable of the section d; penalty 1 Representing a balance constraint penalty coefficient; penalty 2 And representing a section flow constraint penalty coefficient.
Still further, the constraint conditions of the real-time electric power market clearing model include: the system comprises a distribution electric balance constraint, a unit operation constraint, a unit cost constraint, a unit group constraint and a network security constraint.
Still further, the expression of the issue balance constraint is:
in the formula: t is j,t The active power of a tie line j in a period t is represented; d t Representing the electrical load for time period t.
Still further, the unit operation constraints include unit output upper and lower limit constraints and unit climbing and landslide constraints, and the specific expression is as follows:
in the formula:
the maximum output and the minimum output of the unit i in the time period t are obtained;
for unit i maximum upward ramp rate, O i,t-1 Representing the output of the unit i in the time period t-1;
the maximum downward climbing rate of the unit i.
Still further, the unit cost constraint includes a unit operation cost, a unit output definition and a unit segment output constraint, and the specific expression is as follows:
in the formula: m is the total number of the sections quoted by the unit; p i,t,m The power is the winning power of the unit i in the mth output interval in the t time period;
respectively reporting left and right end points of the mth output interval of the unit i; c i,t,m And (4) carrying out segmentation on the corresponding energy price of the mth output reported by the unit i in the t period.
Still further, the expression of the group constraint is:
in the formula: p is g,t The active power of the group g in the time period t;
respectively setting the upper limit and the lower limit of the active power of the machine group g in the t period; s g Is a unit set of the unit group g.
Further, the network security constraint expression is as follows:
in the formula:
respectively the forward and reverse tidal current transmission limits of the section s; g s-i The generator output power of the section s is transferred to a distribution factor for the node where the unit i is located; g s-k The output power transfer distribution factor of the node k to the section s is obtained; d k,t Is the bus load value of the node k in the time period t; g s-j Transferring distribution factors for the output power of the node where the tie line j is located to the line s;
are respectively asA forward power flow relaxation variable and a reverse power flow relaxation variable of the section s; FP s Is an intermediate parameter.
In a second aspect, the present invention provides a real-time electric power market clearing pricing optimization device, including:
the real-time electric power market clearing model building module is used for acquiring real-time electric power market clearing basic data; constructing a real-time electric power market clearing model according to the basic data;
the real-time electric power market clearing model solving module is used for solving the real-time electric power market clearing model; obtaining unit output, section tide, issue and use electric balance constraint relaxation quantity and network security constraint relaxation quantity;
the secondary solving module is used for correcting the system power load and the section quota by using the service electric balance constraint relaxation quantity and the network security constraint relaxation quantity, so that a service electric balance constraint equation and the network security constraint are strictly established, and a node price calculation model is obtained; based on the node price calculation model, searching for an optimal basis of the node price calculation model; directly calculating the node price by using the optimal basis and the price coefficient;
and the parameter output module is used for outputting the unit output, the section flow, the distribution electric balance constraint shadow price, the network security constraint shadow price and the node price.
In a third aspect, the invention provides steps of the computer program when executed by a processor, for implementing the real-time electricity market clearing pricing optimization method as provided in any one of the possible embodiments of the first aspect.
Has the advantages that: according to the electric power market clearing and pricing optimization method provided by the invention, two-step optimization solving of real-time electric power market clearing and pricing is reduced to one time, the time for calculating market clearing and node price is saved, the real-time electric power market operating efficiency for frequently calling the function is improved, the problems of low efficiency and long time consumption of the existing electric power market clearing and pricing method are solved, and algorithm support is provided for large-scale flexible resource adjustment and rapid clearing and pricing of various types of trading variety scenes of a novel electric power system in the future.
Drawings
Fig. 1 is a flowchart of an implementation of the real-time electricity market clearing pricing method provided by the present invention.
Detailed Description
Embodiments of the present invention are further described below with reference to fig. 1. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Embodiment 1 real-time electric power market clearing pricing optimization method
As shown in fig. 1, the embodiment of the present invention includes the following steps:
s1, acquiring real-time electric power market clearing basic data;
s2, constructing a real-time electric power market clearing model;
s3, solving a real-time electric power market clearing model;
s4, acquiring a node price calculation optimal base based on a market clearing result;
s5, calculating the node price by using the price coefficient of the clearing model and the optimal basis;
and S6, outputting the output force of the unit, the price of the limited section and the node, and analyzing and warehousing.
The basic data in step S1 includes: 1) And controlling the parameters. Whether constraint conditions are relaxed or not, whether network security constraint is considered or not, an auxiliary power utilization consideration mode, a load flow calculation sensitivity threshold value, a market mode, convergence precision, maximum solving time and iteration times are included; 2) And (5) system data. The method comprises the steps of optimizing time interval information, predicting system load and reserving system for standby; 3) And (4) unit data. The method comprises the steps of calculating parameters of a unit, reporting price of the unit, initial state of the unit, maximum and minimum output of the unit, climbing and landslide rate of the unit, predicted active power of new energy and maximum and minimum startup and shutdown time of the unit; 4) Tie line data. The method comprises the steps of including basic information of a tie line and planned power of the tie line; 5) Group data. The method comprises the steps of including basic information of a machine group and a power limit value of the machine group; 6) Security constraint data. The method comprises the steps of predicting bus load, and obtaining a transfer distribution factor, mode data and a power grid model of unit/load/tie line injection power to branch/section power flow, wherein the transfer distribution factor is obtained by obtaining the latest power grid physical model and operation mode data and calculating by adopting a PQ decoupling method.
Step S2, the real-time electric power market clearing model is as follows:
the minimum power generation cost and the minimum relaxation variable penalty cost are taken as optimization targets, and the specific expression is as follows:
in the formula: n represents the total number of the units; t represents the total number of considered time periods, and assuming 96 time periods are considered in one day, T is 96; s represents the number of price sections declared by the unit; d represents the number of the network security constraint sections; c it Representing the power generation cost of the unit i in the time period t;
representing a power generation surplus relaxation variable in a period t by using the electric balance constraint;
representing a power shortage relaxation variable of the electric balance constraint of the transmission during the period t;
respectively a positive tide relaxation variable and a reverse tide relaxation variable of the section d; penalty 1 A balance constraint penalty coefficient is represented, and in order to ensure that the clearing link meets the issue and use electrical balance as much as possible, the penalty coefficient is generally set to be 10^9 in clearing; dependency 2 And a section flow constraint penalty coefficient is represented, and the penalty coefficient is generally set to 10^8 in clearing in order to ensure that the clearing link meets the power grid safety as much as possible.
The constraint conditions of the real-time power market clearing model comprise distribution and utilization balance constraint, unit operation constraint, unit cost constraint, unit group constraint and network security constraint.
The expression of the hair balance constraint is:
in the formula: p i,t Representing the output of the unit in the period t; t is j,t The active power of a tie line j in a period t is represented; d t Representing the electrical load for time period t.
The unit operation constraint comprises unit output upper and lower limit constraint and unit climbing and landslide constraint, and the specific expression is as follows:
in the formula:
the maximum and minimum output of the unit i in the time period t;
the maximum rate of ascent for unit i,
the maximum downhill speed is set i.
The unit cost constraint comprises unit operation cost, unit output definition and unit subsection output constraint, and the specific expression is as follows:
in the formula: m is the total number of the sections quoted by the unit; p is i,t,m The power is the winning power of the unit i in the mth output interval in the t time period;
respectively reporting left and right end points of the mth output interval of the unit i; c i,t,m And (4) carrying out segmentation on the corresponding energy price of the mth output reported by the unit i in the t period.
The expression of the cluster constraint is:
in the formula: p is g,t The active power of the machine group g in the t period;
respectively determining the upper limit and the lower limit of the active power of the machine group g in a t period; s. the g Is a unit set of the unit group g.
The network security constraint expression is as follows:
in the formula:
respectively in the forward and reverse directions of the section sA power flow transmission limit; g s-i The generator output power of the section s is transferred to a distribution factor for the node where the unit i is located; g s-k The output power transfer distribution factor of the node k to the section s is obtained; d k,t Is the bus load value of the node k in the time period t; g s-j Transferring distribution factors for the output power of the node where the tie line j is located to the line s;
respectively the positive and reverse tide relaxation variables of the section s.
And S3, solving the real-time power market clearing model to obtain result information such as a unit bid winning result, a section blocking condition, a distribution electric balance constraint relaxation amount, a network security constraint relaxation amount and the like. To better illustrate the acceleration method of the present invention, the specific steps of the traditional real-time electric power market clearing pricing two-step optimization method are given here:
1) And calling a CPLEX optimization engine to solve the real-time electric power market clearing model constructed in the step S2 to obtain a unit bid-winning result, a section blocking condition, a balance constraint shadow price and a network constraint shadow price. Because the penalty coefficients of the release electric balance constraint and the network security constraint relaxation variable in the market clearing model are abnormally large, the shadow price of the model is abnormally large, and the shadow price cannot be used as the market settlement price. Particularly, when the distribution electric balance constraint and the network security constraint are relaxed, the shadow price is the penalty coefficient of the distribution electric balance constraint and the network security constraint relaxation variable, and the shadow price is not priced by an actual physical unit and loses the physical significance.
2) And (3) correcting the system power load and the section quota by using the transmission electric balance constraint relaxation quantity and the network security constraint relaxation quantity in the step 1), so that a transmission electric balance constraint equation and network security constraint are strictly established, and the optimization model at the moment is called as a node price calculation model.
3) And carrying out secondary solution on the node price calculation model to obtain the distribution electric balance constraint and the network security constraint shadow price, and calculating the node price based on the distribution electric balance constraint and the network security constraint shadow price. Because the quadratic solution is time-consuming, the invention reduces the calculation time and improves the calculation efficiency by searching for the optimal basis of the node price calculation model and directly calculating the node price by using the optimal basis and the price coefficient.
And S4, acquiring a node price based on the market clearing result to calculate an optimal base. And (4) acquiring an optimal base corresponding to the optimal solution of the real-time electric power market clearing model in the step (S3), keeping the original column base variable, changing the row state corresponding to any row with a relaxation variable larger than 0 into a row base variable, wherein the modified optimal base is the optimal solution of the node price calculation model in the step (S3).
The above conclusion is demonstrated next. Firstly, the market clearing model and the node price calculation model in the step S3 are abstracted into the following problems:
market clearing model original problem (Prob 1):
MincX+MS 1 +M 1 S 2 +M 2 S 3
st.A 1 X+S 1 -S 2 ＝b 1
A 2 X+S 3 ≥b 2
X,S 1 ,S 2 ,S 3 ≥0
the optimization target is the minimum cost, and the constraints comprise equality constraints (such as balance class constraints) and inequality constraints (such as upper and lower limit class constraints); s 1 And S 2 Is an equality constrained relaxation vector, S 3 The relaxation variables which are inequality constraints respectively correspond to different penalty values in the cost function; the solution X of the model is also the segmentation force output value.
Dual problem for the market clearing model (DualProb 1):
Maxb 1 Y 1 +b 2 Y 2
IY 1 ＝M 1
IY 2 ≤M 2
Y free
correspondingly, the node price calculation model can be abstracted into the following form:
node price calculation model primitive problem (Prob 2):
Min cX
X≥0
wherein, the first and the second end of the pipe are connected with each other,
is the optimal solution of the relaxation variables resulting from the first stage solution.
Node price calculation model DualProb 2:
Yfree
the equivalence of Prob1 and Prob2 solutions can be demonstrated first. Suppose X * Is the decision variable optimal solution of Prob1, S * Is the optimal solution for the relaxation variables of Prob 1. If X is not the optimal solution for Prob2, it is equivalent to: x 'is present such that cX' is ≦ cX * And X' satisfies all the constraints of Prob 2. And because X 'satisfies the constraint of Prob2, then X' also satisfies all the constraints of Prob1, i.e., there is a feasible solution (X ', S) such that cX' + MS * ≤cX * +MS * And X * The optimal solution for the decision variables of Prob1 contradicts. Thus the optimal solution of Prob2 is equivalent to the optimal solution of Prob1, i.e. X'＝X * 。
Next, the optimal basis of Prob2 is derived from the optimal solution information of Prob 1. When S is 1 ,S 2 ,S 3 When any of the slack variables in (b) is not equal to 0, it is indicated that the corresponding inequality constraint is a tight constraint (i.e., the inequality is actually an equation). According to the complementary relaxation theorem between the original problem and the dual problem, the dual variable corresponding to a tight constraint can enter the optimal base (optimal solution) of the dual problem>0) These corresponding rows are therefore designated as base variables. In summary, the revised row and column optimal basis is the optimal basis for Prob 2.
And S5, directly solving the dual optimal solution of Prob2 through the optimal base matrix and the price coefficient, namely all the constraints of the shadow price need to be provided, avoiding the secondary solution of the node price calculation model and greatly improving the node price calculation efficiency.
And S6, outputting information such as output of the unit, section flow, distribution electric balance constraint shadow price, network security constraint shadow price, node price and the like, and analyzing and warehousing.
The method is suitable for rapid calculation of provincial real-time electric power market clearing and pricing, and has the characteristics of high calculation efficiency and strong adaptability. The technical scheme of the invention is applied to the provincial power grid, and the application effect is in line with expectations. The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Compared with the prior art, the invention provides the acceleration method of the two-step system optimization of real-time electric power market clearing and pricing for meeting the timeliness requirement of the real-time electric power market clearing and pricing calculation of spot test point saving 5-minute periodic rolling operation. The method is based on a market clearing model optimal solution considering the electricity balance and the network security constraint relaxation amount, an optimal base of a node electricity price calculation model for correcting system load and section quota by using the relaxation amount is searched, and a theoretical basis for searching the optimal base is provided. The optimal solution of the dual problem is obtained by taking the inner product of the optimal basis matrix and the price coefficient, namely, the shadow price of the electricity balance constraint and the network security constraint is issued, so that the node price is obtained, the calculation efficiency is greatly improved, and the algorithm support is provided for the quick clearing and pricing of the large-scale flexible adjusting resources and the multi-type transaction variety scene of the novel power system in the future.
Example 2
Corresponding to the real-time electricity market clearing pricing optimization method provided in embodiment 1, the present embodiment provides a real-time electricity market clearing pricing optimization device, including:
the real-time electric power market clearing model building module is used for acquiring real-time electric power market clearing basic data; constructing a real-time electric power market clearing model according to the basic data;
the real-time electric power market clearing model solving module is used for solving the real-time electric power market clearing model; obtaining unit output, section tide, issue and use electric balance constraint relaxation quantity and network security constraint relaxation quantity;
the secondary solving module is used for correcting the system power load and the section quota by using the service electricity balance constraint relaxation quantity and the network security constraint relaxation quantity so as to strictly establish a service electricity balance constraint equation and network security constraint and obtain a node price calculation model; based on the node price calculation model, searching for an optimal basis of the node price calculation model; directly calculating the node price by using the optimal basis and the price coefficient;
and the parameter output module is used for outputting the unit output, the section flow, the distribution electric balance constraint shadow price, the network security constraint shadow price and the node price.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the apparatus, and the module described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that the flow diagrams may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. The real-time electric power market clearing pricing optimization method is characterized by comprising the following steps:
acquiring real-time electric power market clearing basic data; constructing a real-time electric power market clearing model according to the basic data;
solving a real-time power market clearing model; obtaining unit output, section tide, issue and use electric balance constraint relaxation quantity and network security constraint relaxation quantity;
correcting the system power load and the section quota by using the service electric balance constraint relaxation quantity and the network security constraint relaxation quantity to ensure that a service electric balance constraint equation and network security constraint are strictly established and obtain a node price calculation model;
based on the node price calculation model, searching for an optimal basis of the node price calculation model; directly calculating the node price by using the optimal basis and the price coefficient;
and outputting the output of the unit, the section trend, the distribution electric balance constraint shadow price, the network security constraint shadow price and the node price.
2. The real-time electricity market clearing pricing optimization method of claim 1, comprising: the real-time power market clearing model is expressed as follows:
in the formula: n represents the total number of the units; t represents the total time period number considered, and S represents the number of price sections declared by the unit; d represents the number of the network security constraint sections; c it Representing the power generation cost of the unit i in the time period t; p is i,t Representing the output of the unit i in a time period t;
representing a power generation surplus relaxation variable in a period t by using the electric balance constraint;
representing a power shortage relaxation variable of the electric balance constraint of the transmission during the period t;
respectively a forward power flow relaxation variable and a reverse power flow relaxation variable of the section d; dependency 1 Representing a balance constraint penalty coefficient; dependency 2 And representing a section flow constraint penalty coefficient.
3. The real-time electricity market clearing pricing optimization method of claim 2, wherein the constraints of the real-time electricity market clearing model include: the system comprises a distribution electric balance constraint, a unit operation constraint, a unit cost constraint, a unit group constraint and a network security constraint.
4. The real-time electricity market clearing pricing optimization method of claim 3, wherein the expression of the electricity consumption balance constraint is:
in the formula: t is j,t The active power of a tie line j in a period t is represented; d t Representing the electrical load for time period t.
5. The real-time electric power market clearing pricing optimization method of claim 3, wherein the unit operation constraints include unit output upper and lower limit constraints and unit climbing and landslide constraints, and the specific expression is as follows:
in the formula:
the maximum and minimum output of the unit i in the time period t;
for unit i maximum upward slope velocity, P i,t-1 Representing the output of the unit i in the time period t-1;
the maximum downward climbing rate of the unit i.
6. The real-time electricity market clearing pricing optimization method of claim 3, wherein the unit cost constraints include unit operating costs, unit output definitions and unit segment output constraints, and the specific expressions are as follows:
in the formula: m is the total number of the sections quoted by the unit; p i,t,m The power is the winning power of the unit i in the mth output interval in the t time period;
are respectively provided withThe left and right endpoints of the m output interval declared for the unit i; c i,t,m And (4) carrying out segmentation on the corresponding energy price of the mth output reported by the unit i in the t period.
7. The real-time electricity market clearing pricing optimization method of claim 3, wherein the expression of the machine group constraint is:
in the formula: p g,t The active power of the machine group g in the t period;
respectively setting the upper limit and the lower limit of the active power of the machine group g in the t period; s g Is a unit set of the unit group g.
8. The real-time electricity market clearing pricing optimization method of claim 3, wherein the network security constraint expression is as follows:
in the formula:
respectively the forward and reverse tidal current transmission limits of the section s; g s-i Generator for section s of node pair where unit i is locatedAn output power transfer profile factor; g s-k The output power transfer distribution factor of the node k to the section s is obtained; d k,t Is the bus load value of the node k in the time period t; g s-j Transferring distribution factors for the output power of the node where the tie line j is located to the line s;
respectively a forward power flow relaxation variable and a reverse power flow relaxation variable of the section s; FP s Is an intermediate parameter.
9. Real-time electric power market clearing pricing optimization device, its characterized in that includes:
the real-time electric power market clearing model building module is used for acquiring real-time electric power market clearing basic data; constructing a real-time electric power market clearing model according to the basic data;
the real-time electric power market clearing model solving module is used for solving the real-time electric power market clearing model; obtaining unit output, section tide, issue and use electric balance constraint relaxation quantity and network security constraint relaxation quantity;
the secondary solving module is used for correcting the system power load and the section quota by using the service electric balance constraint relaxation quantity and the network security constraint relaxation quantity, so that a service electric balance constraint equation and the network security constraint are strictly established, and a node price calculation model is obtained; based on the node price calculation model, searching for an optimal basis of the node price calculation model; directly calculating the node price by using the optimal basis and the price coefficient;
and the parameter output module is used for outputting the unit output, the section flow, the distribution electric balance constraint shadow price, the network security constraint shadow price and the node price.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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