CN115034587A - Inter-provincial and intra-provincial electric quantity interaction method considering risks - Google Patents

Inter-provincial and intra-provincial electric quantity interaction method considering risks Download PDF

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CN115034587A
CN115034587A CN202210579368.2A CN202210579368A CN115034587A CN 115034587 A CN115034587 A CN 115034587A CN 202210579368 A CN202210579368 A CN 202210579368A CN 115034587 A CN115034587 A CN 115034587A
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王洪良
兰洲
王一铮
胡嘉骅
李俊杰
张韦维
周涉宇
王坤
陈沁语
孙秋洁
杨侃
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Abstract

The invention discloses a provincial and provincial electric quantity interaction method considering risks. The method comprises the following steps: the inter-provincial electric quantity interaction participants use the upper-layer optimization model to solve the self electric quantity acquisition requirements and report the inter-provincial electric quantity interaction managers under the condition that the intra-provincial electric quantity interaction platform is minimum in operation metering value and risk index; the inter-provincial electric quantity interaction manager utilizes the lower-layer optimization model to calculate, so that the minimization of the inter-provincial electric quantity interaction operation metering value is realized, and the pre-interaction result of inter-provincial electric quantity interaction is solved; repeating the steps until the demand of acquiring the electric quantity among the provinces reaches the optimum; and the inter-provincial electric quantity interaction participants finally confirm the inter-provincial electric quantity acquisition requirements, report the inter-provincial electric quantity interaction managers and participate in the inter-provincial electric quantity interaction process. The method fully considers the risk brought by inter-provincial electric quantity acquisition to each intra-provincial electric quantity interaction platform, and avoids the risk. The device comprises an upper-layer optimization model calculation module, a data transmission module and a lower-layer optimization model calculation module, and is used for solving a method for obtaining the optimal electric quantity between provinces.

Description

Inter-provincial and intra-provincial electric quantity interaction method considering risks
Technical Field
The invention relates to the technical field of electric power automation, in particular to a provincial and provincial electric quantity interaction method considering risks.
Background
Under the two-stage power interaction mode, coupling and interaction between inter-provincial power interaction and intra-provincial power interaction are bound to be increasingly tighter. Under the limitation of factors such as an electric power interaction mechanism, at present, electric power companies of various provinces in China replace provincial participation individuals to participate in inter-provincial electric power interaction, solve the decisions of provincial surplus interaction electric quantity, obtain an interaction electric quantity result and the like, and develop provincial electric quantity interaction platform interaction to meet the provincial participation individual power consumption requirements of self agents. However, the inter-provincial power interaction result is regarded as a boundary condition of intra-provincial power interaction, and adverse effects are brought to the space and risks of the intra-provincial power interaction platform. Therefore, the factors such as the demand for acquiring the electric quantity and the interaction mechanism need to be comprehensively considered, and the decision method for the provincial electric quantity interaction participants to optimally acquire the electric quantity needs to be researched, so that the electric quantity metering value of the terminal participating individuals is favorably reduced, the uncertainty risk brought to an intra-provincial electric quantity interaction platform by the inter-provincial electric quantity interaction is favorably reduced, and the enthusiasm of the inter-provincial electric quantity interaction participants in the inter-provincial electric quantity interaction can be improved.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides an inter-provincial and intra-provincial electric quantity interaction method and device considering risks. The invention adopts the following technical scheme.
The method comprises the steps of considering the influence of inter-provincial power interaction on the space and the result of an intra-provincial power interaction platform, constructing a double-layer optimization model for acquiring the power of power-saving interaction participants, and finally obtaining the optimal power acquisition strategy of inter-provincial power interaction quotient through the mutual iteration of the optimization results of models of all layers. The interaction model of the provincial-provincial interaction and the provincial-provincial electric quantity interaction is divided into an upper layer model and a lower layer model according to different implementation objects, wherein the pre-interaction results of the acquired electric quantity demand and the provincial-provincial electric quantity interaction reported by the electric quantity interaction participants to the provincial-provincial electric quantity interaction managers are regarded as decision variables in the double-layer model, the decision variables are data needing to be mutually transmitted between the upper layer model and the lower layer model, and the decision variables are continuously iterated until the optimal value of a target is obtained. In the actual operation process, the implementation flow of the optimal electric quantity obtaining method is as follows:
step 1: the method comprises the steps that a power-saving interactive participant utilizes an upper-layer optimization model to obtain the power demand of the power-saving interactive participant, meeting the self condition, of inter-provincial power interaction, namely the inter-provincial power demand, under the condition that the intra-provincial interactive operation metering value expectation and the risk index are minimum;
the upper-layer optimization model in the step 1) uses the influence of new energy output fluctuation and load demand prediction error on the inter-provincial electric quantity interaction of the provincial electric quantity interaction participants to acquire the electric quantity demand as an important reference factor for decision making, the influence degree of the factor is reflected by the operation metering value of the intra-provincial electric quantity interaction platform, the minimum expected value and the minimum risk index value of the intra-provincial interaction operation metering value are finally used as the optimization target of the model, and the corresponding target function is as follows:
Figure BDA0003661738840000021
where ρ is s Representing the occurrence probability in the S-th scene, and S representing the total number of scenes; delta represents a risk aversion coefficient which is used for representing the aversion degree of each power-saving interactive participant to the risk and has individual difference;
A C,s the provincial interactive operation metering value of the power-saving interactive participants in the s-th scene is represented as follows:
Figure BDA0003661738840000022
in the formula, N represents the number of available generator sets in the region where the power-saving interactive participants act;
Figure BDA0003661738840000023
and
Figure BDA0003661738840000024
respectively representing the cost of unit electric quantity of the nth generator set in the area where the power-saving interactive participants proxy and the cost of providing unit spare capacity;
Figure BDA0003661738840000025
the method comprises the steps that the electric quantity demand of a power-saving interaction participant in a t time period on inter-provincial electric power interaction is represented;
Figure BDA0003661738840000026
an interaction result (a pre-interaction result obtained in step 3) representing inter-provincial power interaction in the time t;
Figure BDA0003661738840000027
respectively representing the interactive electric quantity and the standby service interaction quantity of the nth generator set in the area where the power-saving interactive participants act under a scene s and a time period t;
C CF the risk indicators for construction are specifically:
Figure BDA0003661738840000028
in the formula, xi represents a risk index value evaluated by the operation metering value of the provincial electric quantity interaction platform; chi shape s And the prediction quantity representing that the intra-provincial interactive operation metering value exceeds the risk index value xi under various scenes.
The constraint conditions of the upper-layer optimization model comprise real-time power balance constraint, real-time reserve capacity constraint, generator set climbing constraint, generator set output constraint and risk index constraint, and the specific expression is as follows:
the real-time power balance constraint is expressed as:
Figure BDA0003661738840000031
wherein,
Figure BDA0003661738840000032
representing the load demand of the mth participant in the province in the s scene and the t period; m represents the number of participating individuals in the area proxied by the power-saving interactive participants;
the real-time spare capacity constraint is expressed as:
Figure BDA0003661738840000033
wherein alpha represents the standby coefficient of the power grid in the current power-saving interaction participant;
the generator set climbing constraint is represented as:
Figure BDA0003661738840000034
wherein,
Figure BDA0003661738840000035
respectively representing the upper limit and the lower limit of the climbing rate of the nth generator set;
the generator set output constraint is expressed as:
Figure BDA0003661738840000036
wherein,
Figure BDA0003661738840000037
respectively limiting the maximum value and the minimum value of the output of the nth generator set;
the risk indicator constraint is expressed as:
Figure BDA0003661738840000038
the obtained variable xi result represents a risk index value evaluated by the operation metering value of the provincial electric quantity interaction platform; chi shape s And the non-negative auxiliary variable is used for predicting the amount of the intra-provincial interactive operation metering value exceeding the risk index value xi under various scenes.
Step 2: the power-saving interactive participants report the inter-provincial power demands of the power-saving interactive participants to inter-provincial power interactive managers;
and 3, step 3: and the inter-provincial electric quantity interaction manager calculates the inter-provincial electric quantity requirements of each inter-provincial electric quantity interaction participant by using the lower-layer optimization model, and obtains the pre-interaction result of inter-provincial electric quantity interaction under the condition of minimizing the inter-provincial electric quantity interaction operation metering value.
The lower layer optimization model in the step 3) is specifically
The lower-layer optimization model is applied to inter-provincial electric quantity interaction managers and aims to reduce the operation metering value of inter-provincial electric quantity interaction as much as possible. The interaction of the provincial power interaction can fully meet the requirement information of acquiring the electric quantity reported by each power-saving interaction participant, the interactive electric quantity value of the provincial transmission line sending unit is minimized, and the pre-interaction result of the provincial power interaction in the scene is finally obtained through optimized calculation.
The objective function of the underlying optimization model is expressed as:
Figure BDA0003661738840000041
in the formula, J represents the total number of sending-end provinces connected by the current provincial power grid through an inter-provincial transmission line; n is a radical of j Representing the number of generator sets owned by the province j of the sending end;
Figure BDA0003661738840000042
representing the unit electricity cost when a generator set n in the province j of the sending end participates in inter-province electricity interaction;
Figure BDA0003661738840000043
representing the result of the transmission electric quantity of the sending-end province j participating in the inter-province interaction;
Figure BDA0003661738840000044
and (4) representing the final interactive electric quantity result of the generator set n owned by the sending terminal province j in the inter-province electric power interaction process.
The constraint conditions of the lower-layer optimization model comprise inter-provincial transmission line real-time power balance constraint, inter-provincial transmission line transmission capacity constraint and inter-provincial interaction capacity constraint of generator set participation of a sending-end province, and the specific expression formula is as follows:
the inter-provincial transmission line real-time power balance constraint in the optimization model is expressed as:
Figure RE-GDA0003738122500000043
in the formula, xi j Representing the loss of electric quantity transmission on an inter-provincial transmission line connected between the power grid of the current province (receiver) and the transmitting-end province j;
the inter-provincial transmission line transmission capacity constraint in the optimization model is expressed as:
Figure BDA0003661738840000046
in the formula,
Figure BDA0003661738840000047
the maximum constraint value and the minimum constraint value of the available transmission capacity on the inter-provincial transmission line connected between the current provincial power grid and the sending end province j are represented;
the generator set participation inter-provincial interaction capacity constraint of the sending-end province j in the optimization model is expressed as follows:
Figure BDA0003661738840000048
in the formula,
Figure BDA0003661738840000049
representing the maximum value and the minimum value of capacity constraint of the power set n in the sending-end province j participating in the inter-province power interaction;
the lower optimization model is solved through the following processes:
constructing a Lagrangian function to form a KKT condition, wherein the expression is as follows:
Figure BDA0003661738840000051
wherein f (x) represents an objective function of the lower-layer optimization model; h (x), G (x) respectively represent the lower layer optimization modelEquality constraint and inequality constraint of the type; lambda t And mu t Lagrange coefficients of equality constraint and inequality constraint respectively; thus, the pre-interaction results of inter-provincial power interactions of the underlying optimization model
Figure BDA0003661738840000052
Expressed by the following expression:
Figure BDA0003661738840000053
and 4, step 4: calculating the change percentage between the pre-interaction result of the inter-provincial electric quantity interaction in the step 3 and the inter-provincial electric quantity requirement in the step 1, and judging whether the inter-provincial electric quantity requirement in the step 1 is optimal or not;
and 5: if the variation percentage is less than 0.1%, considering that the inter-provincial electric quantity requirement of the step 1) reaches the optimum; otherwise, the inter-provincial electric quantity requirement in the step 1) is not considered to be optimal, the inter-provincial electric quantity interaction manager feeds back the pre-interaction result of inter-provincial electric quantity interaction to the power-saving interaction participants, the step 1) is returned to be executed again,
step 6: and the power-saving interactive participants transmit the optimal inter-provincial power demand to the inter-provincial power interactive manager and participate in the inter-provincial power interactive process.
In addition, the invention also provides a device for optimally acquiring electric quantity by inter-provincial electric power interaction traders, which takes risks into consideration, and the device comprises:
the data transmission module is configured for data transmission between the power-saving interactive participants and the inter-provincial power interactive managers, so that the transmission speed of decision variables and decision results between the decision variables and the decision results in the iterative process of obtaining the optimal power acquisition strategy is increased;
the upper-layer optimization model calculation module is configured for predicting the influence of the error amount on the actual electric quantity acquisition strategy of the power-saving interactive participants in the inter-provincial electric power interaction according to the new energy output fluctuation and the load demand, minimizing the operation metering value and the risk index value of the intra-provincial electric quantity interaction platform as an optimization target, and finally obtaining the optimal electric quantity acquisition strategy to be declared by the power-saving interactive participants in the environment;
and the lower-layer optimization model calculation module is configured for ensuring that the power acquisition requirement of power-saving interactive participants is met, reducing the interactive power metering value of the inter-provincial transmission line sending unit as far as possible by the inter-provincial interaction center, and obtaining the pre-interaction result of inter-provincial power interaction under the environment.
The invention provides a risk-related provincial and provincial electric quantity interaction assessment method and device, and a double-layer optimization model is constructed in consideration of uncertain risk influence caused by new energy output fluctuation and demand prediction deviation. The objective of the upper layer optimization model is to minimize the intra-provincial interactive operation metric value expectation and the risk index value. Meanwhile, the lower-layer optimization model aims at minimizing the operation metering value of inter-provincial power interaction, and the optimal power acquisition strategy of the power-saving interaction participants is obtained through iteration.
The beneficial effects of the invention are as follows:
1) and constructing a double-layer optimization model to solve the optimal acquisition power demand of the power-saving interaction participants in the inter-provincial power interaction. In an upper-layer optimization model, power-saving interactive participants solve the power demand acquired by the power-saving interactive participants in inter-provincial power interaction by taking intra-provincial interactive operation measurement value expectation and risk index value minimization as optimization targets; in the lower-layer optimization model, the inter-provincial interaction center optimizes the target of minimizing the operation metering value of inter-provincial electric quantity interaction, and optimizes and solves the pre-interaction result of the inter-provincial electric quantity interaction.
2) The risk index value introducing method quantifies uncertain risks brought by new energy output and demand forecasting deviation, so that the potential risks are considered in the solved optimal electric quantity obtaining strategy, and the electric quantity saving interaction participants can be helped to effectively improve the control capability of the participants on the uncertain risks. Wherein the risk aversion coefficients reflect the difference of the aversion degree of the power saving interactive participants to the risks.
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FIG. 1 is a schematic flow diagram of an embodiment of the present invention;
fig. 2 is a schematic diagram of a decision-making double-layer optimized scheduling model for power-saving amount interaction participants to acquire power amounts in the embodiment of the present invention.
Fig. 3 is a schematic diagram of an optimal power acquisition device for inter-provincial power interaction quotient, which considers risks according to an embodiment of the present invention;
Detailed Description
In order that those skilled in the art will better understand the scheme of the present invention, the present invention will be described more clearly and completely with reference to the accompanying drawings and the implementation examples.
Example 1
As shown in FIG. 1, the present invention includes the following steps
Step 1: the power-saving interactive participants need to solve the power acquisition demands of the participants, and the corresponding provincial power acquisition demands of the power-saving interactive participants, namely surplus interactive power participating in provincial power interaction, are obtained by using an upper-layer optimization model under the condition that the operation metering value and the risk index of the provincial power interaction platform are minimum
And 2, step: the power-saving interactive participants report the power acquisition requirements of the participants to the inter-provincial power interaction manager and wait for the interactive results.
And step 3: the inter-provincial electric quantity interaction manager needs to simply analyze and process the acquired electric quantity demand of each acquired power-saving interaction participant, calculates by using a lower-layer optimization model to minimize the inter-provincial electric quantity interaction operation metering value, and calculates the pre-interaction result of the inter-provincial electric quantity interaction, namely the electric quantity of the surplus interaction electric quantity capable of participating in the inter-provincial interaction
And 4, step 4: and calculating the change percentage between the pre-interaction result of the inter-provincial electric quantity interaction and the interaction result of the original inter-provincial electric quantity interaction, and judging whether the acquired electric quantity requirement reaches the optimum. And if the variation percentage is less than 0.1%, the acquired electric quantity requirement is considered to be optimal, otherwise, the acquired electric quantity requirement is not optimal.
And 5: if the power acquisition requirement does not reach the optimum, the inter-provincial power interaction manager needs to feed back the pre-interaction result of inter-provincial power interaction to the power-saving interaction participants, and then returns to the step 1 to start execution again.
And 6: if the obtained electric quantity obtaining requirement reaches the optimum, the electric quantity saving interaction participants can obtain the final self optimum electric quantity obtaining requirement.
And 7: and the power-saving interactive participants transmit the finally confirmed acquired power demand information to the inter-provincial power interactive managers to participate in the inter-provincial power interactive process.
Example 2
The following describes a two-layer optimization model for acquiring the demand of electric quantity in an optimal provincial electric quantity acquisition method considering risks.
As shown in fig. 2, the two-layer optimization model for obtaining the power demand in the optimal provincial power obtaining method considering the risk according to the present invention may be divided into an upper layer optimization model and a lower layer optimization model. The execution objects of the two optimization models are different, the upper optimization model is executed by the power-saving interactive participant, and the lower optimization model is executed by the inter-saving power interactive manager. The optimization objectives, constraints, and the like between the two corresponding layers of the optimization model are different, and will be described in detail below.
The use object of the upper-layer optimization model is the electricity-saving quantity interaction participant, so the optimization goal of the model is to minimize the intra-provincial interaction operation metering value and the risk index, and the obtained result information is mainly the electricity-saving quantity demand information which accords with the best benefits of the electricity-saving quantity interaction participant in the inter-provincial electricity interaction. In the optimization operation process, the upper-layer optimization model needs to meet the real-time power balance constraint, the real-time reserve capacity constraint, the generator set climbing constraint and the generator set output constraint, and the optimal calculation result is obtained within the constraint condition range.
The use object of the lower-layer optimization model is an inter-provincial electric quantity interaction manager, so that the optimization goal of the model is to minimize the inter-provincial electric quantity interaction operation metering value, and the obtained result information is mainly the electric quantity obtaining result of the inter-provincial electric quantity interaction participants who interact on the inter-provincial electric power interaction. In the optimization operation process, the upper-layer optimization model needs to meet the real-time power balance constraint of the inter-provincial transmission line, the transmission capacity constraint of the inter-provincial transmission line and the inter-provincial interaction capacity constraint of the sending unit, and the optimal calculation result is obtained within the constraint condition range.
Example 3
The following describes the composition and function of an optimal power acquisition device for inter-provincial power interaction quotient considering risks in further detail.
As shown in fig. 3, the device for optimally acquiring electric quantity of inter-provincial power interaction quotient, which takes risk into consideration, in the invention comprises an upper-layer optimization model calculation module, a data transmission module and a lower-layer optimization model calculation module.
The upper optimization model calculation module is used for calculating an upper model, calculating the influence of large output fluctuation of new energy and load prediction deviation amount according to an inter-provincial electric power interaction result, reducing the operation metering value and uncertain risk factors of inter-provincial interaction as much as possible, meeting the operation conditions of real-time electric power balance constraint, real-time reserve capacity constraint, generator set climbing constraint and generator set output constraint, and calculating the optimal acquired electric quantity demand of the power-saving interaction participants in the inter-provincial electric quantity interaction under the conditions.
The data transmission module is used for quickly, accurately and safely transmitting decision information, operation results and other data between the provincial electric quantity interaction center and the provincial electric quantity interaction participants. On one hand, the information transmission speed is accelerated, and the calculation speed of the double-layer optimization model is guaranteed to meet the requirement in actual operation; on the other hand, the accuracy and the safety of information transmission are ensured, and the interactive stable operation of the actual provincial electric power interaction is ensured.
The lower optimization model calculation module is used for calculating a lower model, the used data comprise acquired electric quantity demand information and the like reported by the provincial electric quantity interaction participants, the operation metering value of the provincial electric power interaction is reduced as much as possible, the provincial electric power interaction operation under the conditions of the provincial transmission line real-time electric power balance constraint, the provincial transmission line transmission capacity constraint and the export unit participation provincial interaction capacity constraint is met, and the provincial electric power interaction pre-interaction result under the conditions is calculated.
Example 4
The risk aversion coefficient involved in an optimal provincial power acquisition method taking risk into account will be described in further detail below with reference to the embodiments.
In an upper-layer optimization model, an objective function considers risks brought by new energy and load fluctuation to an provincial electric quantity interaction platform, and an item formed by a risk aversion coefficient and a risk index value is added into an expression of the objective function. The risk index value is used for evaluating the risk existing at the moment, and the power grids of different provinces are measured by adopting a unified standard. The addition of the risk aversion coefficients shows the individual differences of different provincial power grids, and different power-saving interactive participants have different sensitivity degrees to uncertain risks in the process of participating in inter-provincial power interaction and are shown in the model in the form of the risk aversion coefficients.
In a specific embodiment, it is assumed that the risk aversion coefficient of province a is greater than that of province B, and the conditions of provinces a and B participating in inter-province power interaction are identical. However, the difference in risk aversion coefficients results in province a having a smaller demand for power during actual inter-province power interaction. The decision making will of province A can reduce the influence of province purchasing electric quantity on the interaction space and results in the province electric quantity interaction platform, and the province electric quantity interaction amount and results in the actual operation process are more stable.

Claims (5)

1. A province-province and intra-province electric quantity interaction method considering risks is characterized by comprising the following steps:
step 1: the method comprises the steps that a power-saving interactive participant utilizes an upper-layer optimization model to obtain the power demand of the power-saving interactive participant participating in inter-provincial power interaction, namely the inter-provincial power demand, under the condition that the intra-provincial interactive operation metering value expectation and the risk index are minimum;
and 2, step: the power-saving interactive participants report the inter-provincial power demands of the power-saving interactive participants to inter-provincial power interactive managers;
and step 3: and the inter-provincial electric quantity interaction manager calculates the inter-provincial electric quantity requirements of each inter-provincial electric quantity interaction participant by using the lower-layer optimization model, and obtains the pre-interaction result of inter-provincial electric quantity interaction under the condition of minimizing the inter-provincial electric quantity interaction operation metering value.
And 4, step 4: calculating the change percentage between the pre-interaction result of the inter-provincial electric quantity interaction in the step 3 and the inter-provincial electric quantity requirement in the step 1, and judging whether the inter-provincial electric quantity requirement in the step 1 is optimal or not;
and 5: if the variation percentage is less than 0.1%, considering that the inter-provincial electric quantity requirement of the step 1) reaches the optimum; otherwise, the inter-provincial electric quantity requirement in the step 1) is not considered to be optimal, the inter-provincial electric quantity interaction manager feeds back the pre-interaction result of inter-provincial electric quantity interaction to the power-saving interaction participants, the step 1) is returned to be executed again,
and 6: and the power-saving interactive participants transmit the optimal inter-provincial power demand to the inter-provincial power interactive manager and participate in the inter-provincial power interactive process.
2. The method of claim 1, wherein the upper optimization model in step 1) is specifically:
the optimization target of the upper-layer optimization model is that the intra-provincial interactive operation metering value expectation and the risk index value reach the minimum, and the corresponding objective function is expressed as:
Figure FDA0003661738830000011
where ρ is s Representing the occurrence probability in the S-th scene, and S represents the total number of scenes; δ represents a risk aversion coefficient for representing the aversion degree of each power saving amount interaction participant to the risk;
A C,s the provincial interactive operation metering value of the power-saving interactive participants in the s-th scene is represented as follows:
Figure FDA0003661738830000012
in the formula, N represents the number of available generator sets in the area where the power-saving interactive participants proxy;
Figure FDA0003661738830000013
and
Figure FDA0003661738830000014
respectively representing the cost of unit electric quantity of an nth generator set in an area where an interactive participant of the electric quantity saving acts and the cost of providing unit spare capacity;
Figure FDA0003661738830000015
representing the electric quantity demand of the inter-provincial electric quantity interaction participants in the t time period;
Figure FDA0003661738830000016
representing an interaction result of inter-provincial power interaction in the t period;
Figure FDA0003661738830000017
respectively representing the interactive electric quantity and the standby service interactive quantity of the nth generator set in the area where the power-saving interactive participant acts in a scene s and a time period t;
C CF the risk indicators for construction are specifically:
Figure FDA0003661738830000021
in the formula, xi represents a risk index value evaluated by the operation metering value of the provincial electric quantity interaction platform; chi shape s And the prediction quantity representing that the intra-provincial interactive operation metering value exceeds the risk index value xi under various scenes.
3. The method of claim 2, wherein the constraints of the upper layer optimization model include a real-time power balance constraint, a real-time spare capacity constraint, a generator set ramp constraint, a generator set output constraint, and a risk indicator constraint, and the specific expressions are as follows:
the real-time power balance constraint is expressed as:
Figure FDA0003661738830000022
wherein,
Figure FDA0003661738830000023
representing the load demand of the mth participant in the province in the s scene and the t period; m represents the number of participating individuals in the area proxied by the power-saving interactive participants;
the real-time spare capacity constraint is expressed as:
Figure FDA0003661738830000024
wherein alpha represents the standby coefficient of the power grid in the current power-saving interaction participant;
the generator set climbing constraint is represented as:
Figure FDA0003661738830000025
wherein,
Figure FDA0003661738830000026
respectively representing the upper limit and the lower limit of the climbing rate of the nth generator set;
the generator set output constraint is expressed as:
Figure FDA0003661738830000027
wherein,
Figure FDA0003661738830000028
respectively limiting the maximum value and the minimum value of the output of the nth generator set;
the risk indicator constraint is expressed as:
Figure FDA0003661738830000029
the obtained variable xi result represents a risk index value evaluated by the operation metering value of the provincial electric quantity interaction platform; chi shape s And the non-negative auxiliary variable is used for predicting the amount of the intra-provincial interactive operation metering value exceeding the risk index value xi under various scenes.
4. The method of claim 1, wherein the lower layer optimization model in step 3) is specifically:
the objective function of the lower optimization model is expressed as:
Figure FDA0003661738830000031
in the formula, J represents the total number of sending-end provinces connected with the current provincial power grid through an inter-provincial transmission line; n is a radical of j Representing the number of generator sets owned by the province j of the sending end;
Figure FDA0003661738830000032
representing the unit electricity cost when a generator set n in the province j of the sending end participates in inter-province electricity interaction;
Figure FDA0003661738830000033
representing the result of the transmission electric quantity of the sending-end province j participating in the inter-province interaction;
Figure FDA0003661738830000034
indicates the province j of the sending end is inAnd (5) the final interactive electric quantity result of some generator sets n in the inter-provincial electric power interaction process.
5. The inter-provincial and intra-provincial electric quantity interaction method considering the risk as claimed in claim 1, wherein the constraint conditions of the lower optimization model include inter-provincial transmission line real-time power balance constraint, inter-provincial transmission line transmission capacity constraint and generator set participation inter-provincial interaction capacity constraint of the sending terminal province, and the specific expression is as follows:
the inter-provincial transmission line real-time power balance constraint in the optimization model is expressed as:
Figure RE-FDA0003738122490000035
in the formula, xi j Representing the loss of electric quantity transmission on an inter-provincial transmission line connected between the power grid of the current province and a sending end province j;
the inter-provincial transmission line transmission capacity constraint in the optimization model is expressed as:
Figure RE-FDA0003738122490000036
in the formula,
Figure RE-FDA0003738122490000037
a maximum constraint value and a minimum constraint value representing the available transmission capacity on an inter-provincial transmission line connected between the current provincial power grid and the sending-end province j;
the generator set participation inter-provincial interaction capacity constraint of the sending-end province j in the optimization model is expressed as follows:
Figure RE-FDA0003738122490000038
in the formula,
Figure RE-FDA0003738122490000039
representing the maximum value and the minimum value of the capacity constraint of the generator set n in the sending-end province j participating in the inter-province power interaction;
solving the lower layer optimization model by the following process:
constructing a Lagrangian function to form a KKT condition, wherein the expression is as follows:
Figure RE-FDA0003738122490000041
wherein f (x) represents an objective function of the underlying optimization model; h (x), G (x) respectively represent equality constraint and inequality constraint of the lower layer optimization model; lambda t And mu t Lagrangian coefficients of equality constraint and inequality constraint respectively; thus, the pre-interaction results of inter-provincial power interactions of the underlying optimization model
Figure RE-FDA0003738122490000042
Expressed by the following expression:
Figure RE-FDA0003738122490000043
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