CN111415068A - Power distribution decision modeling method based on relevance of transformation measures and loss load index - Google Patents
Power distribution decision modeling method based on relevance of transformation measures and loss load index Download PDFInfo
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Abstract
The invention relates to the technical field of power systems, and provides a multi-year investment planning decision modeling technology based on the relevance of power distribution network transformation measures and power grid load loss indexes, aiming at solving the problem of complex power flow calculation related to traditional power distribution network investment decision modeling and considering the improvement of the operational reliability indexes of the existing power distribution network. The modeling technology improves the investment decision model through a data mining technology, has great advantages in the aspects of finding problem potential rules, improving the calculation efficiency and the like, can avoid a complex load flow calculation process, and effectively improves the calculation efficiency. A training sample set is formed by loss load indexes and transformation measures, and corresponding incidence relation rules can be obtained through offline learning of sample data and serve as the basis of a multi-year investment decision model. In practical application, when a scheme for configuring the transformation measures is given, the deep confidence network model can quickly give a load loss index result as a constraint condition of a subsequent power distribution network investment decision model.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a power distribution decision modeling method based on relevance between modification measures and load loss indexes.
Background
The national energy agency issues a power distribution network construction transformation action plan (2015-2020) in 2015 at 8 months, and the plan requires China to accelerate the construction of modern power distribution networks, so that the power supply reliability is further improved. Therefore, how to scientifically utilize the transformation fund and reasonably make a transformation scheme is one of the key problems concerned in the field of power distribution at present. The underlying task of a power distribution network is to provide users with a safe, stable, high quality supply of power. Compared with a power transmission network, the power distribution network has numerous nodes and a complex structure, and has a large influence on the power supply reliability, so that the evaluation of performance indexes such as reliability, economy and the like is one of core problems of the research in the power distribution field. In recent years, the rapid development of intermittent renewable energy power Generation represented by wind power and photovoltaic power Generation has increased the permeability of Distributed Generation (DG) in an active power distribution network, increased the types and number of active elements, increasingly complex network structure of the active power distribution network, increasingly prominent system distribution characteristics, and new requirements for optimization investment decision of the power distribution network.
In order to establish an accurate investment decision model and ensure the operation reliability of the power distribution network, the relationship between active power distribution network modification measures and the power grid load loss indexes is of great importance. However, the related analysis in the traditional investment decision modeling involves power flow calculation, and the analysis process of the physical model is very complex, which is not beneficial to the current power distribution network planning investment decision. Therefore, applying the data association mining method to the correlation analysis of the two is a better choice. General data association mining methods include regression analysis, feature analysis, variation and deviation analysis, association rule mining, neural network methods, network data mining, fuzzy grey association degree and other chemical methods. Considering the implicit association relation between the 'transformation measures-loss load indexes' of the power distribution network, the data fitting based on regression analysis cannot meet the association analysis, and therefore the method of machine learning is adopted for solving. The machine learning has great advantages in the aspects of finding problem latent rules, improving the calculation efficiency and the like. And analyzing the association rule between the technical path configuration and the investment benefit and the applicable operation condition thereof by analyzing large-scale index data and analyzing a data mining method such as regression analysis and machine learning.
Disclosure of Invention
The invention provides a multi-year investment planning decision-making modeling technical method based on the relevance of power distribution network modification measures and power grid load loss indexes, which aims to solve the problem of complex load flow calculation involved in the traditional power distribution network investment decision-making modeling and consider the improvement of the reliability indexes of the existing power distribution network. The modeling technology improves the great advantages of the investment decision model in the aspects of finding potential rules of problems, improving the calculation efficiency and the like through a data mining technology based on a deep confidence network, can avoid a complex load flow calculation process, and can effectively improve the calculation efficiency. And forming a training sample set by the loss load amount index and the transformation measure, and obtaining a corresponding incidence relation rule by off-line learning of sample data to be used as the basis of an investment decision model. Therefore, in practical application, when a resource allocation scheme is given, the power distribution network correlation analysis model based on the deep belief network can quickly give a result of a corresponding load loss index as a constraint condition of a subsequent power distribution network investment decision model.
In order to achieve the purpose, the technical scheme of the invention is as follows: the power distribution decision modeling method based on the relevance of the transformation measures and the load loss indexes is characterized by comprising the following steps of:
s1: determining implicit relations between different configuration schemes and the power grid load loss amount indexes; determining the influence degree of the investment decision scheme on the reliability index of the system load loss amount; determining a correlation rule between investment improvement measure configuration under different operating conditions and investment benefits improved by the loss load index;
s2: optimizing the implicit relationship, the degree of influence and the association rule in the step S1 based on a Nadam optimizer;
s3: providing a power distribution network multi-year investment decision model for correlation analysis of the transformation measures and the power distribution network load loss indexes, providing a power distribution network load loss index evaluation model based on the correlation analysis, providing a correlation relation between various transformation measures of the power distribution network and the load loss indexes, and providing a traditional power distribution network planning model; wherein,
improving a first multi-year investment decision model with the maximum target according to the loss load index of the power distribution network;
a second multi-year investment decision model aiming at minimizing the investment cost of the power distribution network;
s4: and solving the first multi-year investment decision model and the second multi-year investment decision model to obtain an optimal power distribution network investment planning decision.
Preferably, in step S1, the degree of influence of the investment decision scheme on the reliability index of the system load capacity is characterized by the load loss index of the power grid based on an association rule obtained by statistical analysis or mathematical learning.
Preferably, in step S1, the association rule between the investment improvement measure configuration under different operating conditions and the loss of the load amount and the investment benefit specifically includes: and mining by using the sample data of the loss load capacity index of the power distribution network through a research and development algorithm, and dynamically updating investment benefits along with the change of a configuration scheme so as to obtain an accurate investment aid decision result.
Preferably, in step S2, the step of optimizing based on the Nadam optimizer is a generative model, which is formed by stacking a plurality of restricted boltzmann machines, has good nonlinear mapping capability, can learn and adapt to unknown information, and has a structure formed by an input and output layer and a plurality of hidden layers.
Preferably, the power distribution network multi-year investment decision model for relevance analysis of the transformation measures and the power distribution network load loss indexes in the step S3 belongs to a complex nonlinear programming problem, and a direct mapping between the power distribution network transformation measures and the load loss indexes is constructed by utilizing the nonlinear mapping capability based on the Nadam optimizer;
the model can quickly estimate the loss load value under the transformation measures when the scene of the transformation measures of the power distribution network changes, so that the influence degree of implementation of different transformation measures of the power distribution network on reliability indexes such as the loss load of the power distribution network is judged, the influence degree is used as a relevance constraint condition of later-stage power distribution network investment decision, and the time consumption of time domain simulation is saved; adopting a deep confidence network to carry out relevance mining and obtain a corresponding power grid load loss index, wherein the process comprises the following steps:
step 1: obtaining sample data of time sequence simulation in medium and long-term operation, and initially training to determine a basic solution space of each parameter of the deep confidence network;
step 2: establishing an input and output vector of a deep belief network, and determining a neural network model and a learning mode;
and step 3: calculating the input and output of each unit of the hidden layer and the output layer of the deep belief network, namely finishing the preliminary learning of the deep belief network;
and 4, step 4: adjusting the weight and the threshold of the controlled deep belief network performance by adopting a Nadam optimizer, and optimizing the convergence speed and the convergence effect of deep belief network learning;
and 5: continuously updating the learning mode and the learning times;
step 6: repeating the step 5, and continuously carrying out deep belief network training until a cutoff condition is met, namely the maximum learning times;
and 7: and (3) applying a deep confidence network association rule, inputting data such as node load, voltage, line power, distributed power supply output and telemechanical device installation under a certain transformation scheme, and calling the deep confidence network to obtain a corresponding power grid load loss index result.
Preferably, in the traditional power distribution network investment model, the voltage, power and phase angle parameters of the network are obtained through load flow calculation, and then the technical and economic index values of the network are obtained through calculation, wherein the investment decision model is as follows:
Xi∈{0,1} (3)
in the model, the maximum improvement of the loss load index representing the investment benefit of the power distribution network is an objective function, as shown in formula (1); the total investment of the power distribution network, the independence of the transformation measures and the correlation relationship between the transformation measures and the load loss index are taken as constraints, and the constraint is shown as a formula (2-4); i and
respectively representing the loss load indexes, X, of the power distribution network before and after the implementation of the reconstruction measuresiRepresenting a different type of modification, K (X)i) Representative reconstruction measureApplication of XiThe cost of (2);
representing the incidence relation between the modification measures and the load loss index; wherein y represents the vector of parameters related to power flow and grid structure in the network, such as voltage, power and phase angle; as shown in the formula (4), in the traditional investment model, the relationship between the reconstruction measures and the load loss indexes needs to be expressed by depending on the power flow parameters;
wherein, the loss load reliability index in the model can be expressed as follows; the power supply reliability index is as follows: the reliability index is generally used for evaluating the influence degree of the power failure phenomenon of the power distribution network on users; according to the difference of the power failure time, the users can be classified into 3 types after power failure: 1) if the user is located in the upstream section of the fault area, the power failure time is the sum of the inter-section fault positioning time and the fault isolation time and is recorded as T1; 2) if the user is located in the downstream section of the fault area and can be supplied through the standby supply channel, the power failure time is the sum of T1 and the fault supply time and is recorded as T2; 3) if the user is located in the downstream section of the fault area and can not be supplied through the standby supply channel, or the user is located in the fault area, the power failure time comprises inter-section fault positioning time, intra-section fault positioning time and fault repairing time, and is recorded as T3; on the basis, a plurality of reliability evaluation indexes widely used in the international range can be calculated; the calculation formula of the user average power failure time SAIDI, the expected power shortage EENS of the system and the power supply reliability RS-3 index related in the text is shown as formulas (2), (3) and (4);
ID＝λF(β1T1+β2T2+β3T3) (5)
IE＝IDPL(6)
in the formula: i isD、IE、IRSAIDI, EENS and RS-3 indexes of the feeder line are respectively; lambda [ alpha ]FThe total failure rate of the feeder line is; pLβ is the total load of the feeder line1、β2、β3For the distribution coefficient of the power failure users, the average distribution proportion of the above 3 types of users is respectively expressed when any equipment in the feeder line fails.
Preferably, the first multi-year investment decision model with the maximum target for the maximum increase of the distribution network loss load index comprises a structure similar to that of the traditional investment decision model, wherein the structure of the first multi-year investment decision model with the maximum target for the maximum increase of the distribution network loss load index is just that a constraint condition formula (4) in the traditional investment decision model is replaced by a formula (10), and a related constraint condition is modified into a total constraint of multiple years; therefore, the annual investment decision model with the maximum target of the maximum improvement of the loss load index of the power distribution network can be expressed as follows:
in the above formula, NepRepresenting the total investment cycle, equation (8) as the investment targetRepresenting the total improvement amount of the reliability index of the power distribution network in the total investment period, wherein XepFor each investment measure in an investment cycle,. DELTA.IepRepresenting the variation of the reliability index in each investment period; formula (9) is investment amount constraint, CmaxTo an upper limit value of the investment amount, Cep(Xep) Representing the amount of investment for each investment measure in each investment period; the formula (10) is the associated rule constraint condition of the investment transformation measure and the reliability index,kthe method comprises the following steps of (1) providing an interaction strategy set of energy storage and flexible load, wherein phi is an association rule function, and omega and b respectively represent training parameters of an association rule; formula (11) represents the investment measure library constraint of the ep investment period, and psi is the investment measure library; the formula (12) is the upper and lower limit constraint of the investment reconstruction measures,
and
is XepiAt the lower and upper limits of the ep period, for example, formula (13),
and
the upper limit of the number of the two-remote device and the three-remote device in the ep period and the upper limit of the capacity of the distributed power supply in the ep period are respectively,
and
is XepiLower and upper limits within the total investment period; the formula (14) shows that the total amount of investment measures in the whole investment period also needs to meet the requirements of upper and lower limits.
The above formula replaces the parameter vector y related to the power flow calculation in the formula (4) with the threshold and weight parameters for deep belief network learning, i.e. the power flow constraint is converted into the relevance constraint based on the deep belief network.
8. The power distribution decision modeling method based on transformation measures and loss load index correlation according to claim 1, wherein the second multi-year investment decision model targeting power distribution network investment cost minimization comprises the following steps:
in the model, the minimum total investment cost of the power grid is taken as a target, the lower limit of the increase of the loss load index is taken as a constraint, and the rest constraint conditions are the same as the perennial investment decision model with the maximum increase of the loss load index of the power distribution network, and are all based on the association rule of the transformation measures of the power distribution network and the loss load index for modeling; wherein, Delta IsetSet value representing improvement of reliability index, Cinvest_epAnd CEENS_epRespectively representing the fixed investment amount and the lost load compensation amount, and the sum of the fixed investment amount and the lost load compensation amount is the total investment amount of the reconstruction measures.
Compared with the prior art, the invention has the beneficial effects that: compared with the traditional investment decision modeling technology for the power distribution network, the investment decision modeling technology based on the relevance can convert an investment decision model originally related to complex nonlinear power flow calculation into a simple linear power distribution network investment decision model based on data relevance, and can consider the influence of complex and diverse power distribution network transformation measures on the load loss index of the power distribution network, so that the accurate investment planning decision of the power distribution network can be realized more quickly and efficiently. In addition, the relevance mining technology applied in the patent has great advantages in the aspects of finding problem potential rules, improving the calculation efficiency and the like, not only can the complex load flow calculation process be avoided, but also the calculation efficiency can be effectively improved.
Drawings
FIG. 1 is a deep belief network architecture of the present invention;
FIG. 2 is a model for evaluating the loss load index of the power distribution network based on correlation analysis according to the present invention;
FIG. 3 is a flow chart of the correlation analysis based on the deep belief network of the present invention;
FIG. 4 is a decision diagram of a multi-year investment planning of a power distribution network based on the relevance of a modification measure and a load loss index.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
A power distribution network investment planning decision modeling method based on a deep belief network comprises the following steps:
analyzing a recessive relation between different investment transformation measure configuration schemes and the distribution network load loss index;
describing the influence degree of the investment decision scheme on the operation reliability such as the load loss of the power distribution system;
digging a correlation rule between technical path configuration under different operating conditions and investment benefit improvement by using a power grid load loss index;
constructing a direct mapping between a power distribution network modification measure and a load loss index;
establishing a power grid load loss index;
establishing a power distribution network load loss index evaluation model based on relevance analysis;
training a deep confidence network for the loss load index of the power distribution network and various predicted reconstruction measures;
(1) analyzing a recessive relation between different configuration schemes and the power grid load loss index;
under the consideration of a differentiated operation strategy, according to a power distribution network modification measure and load loss index association rule provided by early power distribution network interaction medium-long time operation time sequence simulation and deep confidence network training, a recessive relation between different configuration schemes and power grid load loss indexes is analyzed from the aspects of statistical analysis and data learning;
(2) describing the influence degree of the investment decision scheme on the operation reliability such as the system load loss and the like;
based on the correlation rule obtained by the statistical analysis or the mathematical learning, the influence degree of the investment decision scheme on the system operation reliability is described by the loss load index of the power distribution network.
(3) Digging a correlation rule between investment modification measure configuration under different operating conditions and investment benefit improvement by using a power grid load loss index;
and mining association rules between the technical path configuration and the investment benefits under different operating conditions by using a deep belief network algorithm, and dynamically updating the investment benefits along with the change of a configuration scheme so as to obtain accurate investment aid decisions. Since an investment decision model containing correlation analysis of 'transformation measures-power grid load loss indexes' belongs to a complex nonlinear programming problem, the direct mapping between the transformation measures of the power distribution network and the load loss indexes is constructed by utilizing the nonlinear mapping capability of a deep confidence network. The deep confidence network based on the optimization of the Nadam optimizer is a generation model, is formed by stacking a plurality of restricted Boltzmann machines, has good nonlinear mapping capability, can learn and self-adapt unknown information, and is a common relevance analysis algorithm based on data mining.
The process of performing relevance mining by adopting the deep belief network and obtaining the corresponding power grid load loss index is as follows:
step 1: obtaining sample data of time sequence simulation in medium and long-term operation, and initially training to determine a basic solution space of each parameter of the deep confidence network;
step 2: establishing an input and output vector of a deep belief network, and determining a neural network model and a learning mode;
and step 3: calculating the input and output of each unit of the hidden layer and the output layer of the deep belief network, namely finishing the preliminary learning of the deep belief network;
and 4, step 4: adjusting the weight and the threshold of the controlled deep belief network performance by adopting a Nadam optimizer, and optimizing the convergence speed and the convergence effect of deep belief network learning;
and 5: continuously updating the learning mode and the learning times;
step 6: and (5) repeating the step 5, and continuously carrying out deep belief network training until a cutoff condition (maximum learning times) is met.
And 7: and (3) applying a deep confidence network association rule, inputting data such as node load, voltage, line power, distributed power supply output and telemechanical device installation under a certain transformation scheme, and calling the deep confidence network to obtain a corresponding power grid load loss index result.
(4) Establishing a power distribution network multi-year investment decision model based on relevance analysis
Traditional power distribution network investment models; the method comprises the following steps of obtaining parameters such as voltage, power and phase angle of a network through load flow calculation in a traditional power distribution network investment model, and further obtaining a technical and economic index value of the network through calculation, wherein an investment decision model is as follows:
Xi∈{0,1} (3)
in the model, the maximum improvement of the loss load index representing the investment benefit of the power distribution network is an objective function, as shown in formula (1); the total investment of the power distribution network, the independence of the transformation measures and the correlation relationship between the transformation measures and the load loss index are taken as constraints, and the constraint is shown as a formula (2-4). I and
respectively representing the distribution before and after the implementation of the retrofitting measuresNet loss load index, XiRepresenting a different type of modification, K (X)i) Representative reconstruction measure XiThe cost of (2).
Representing the incidence relation between the modification measures and the loss load index. Wherein y represents the vector of parameters related to power flow and grid structure in the network, such as voltage, power, phase angle and the like. As can be seen from the formula (4), in the traditional investment model, the relationship between the reconstruction measures and the load loss indexes needs to be expressed by means of the power flow parameters.
The load loss reliability index in the model can be expressed as follows. The power supply reliability index is as follows: the reliability index is generally used for evaluating the influence degree of the power distribution network power failure phenomenon on users. According to the difference of the power failure time, the users can be classified into 3 types after power failure: 1) if the user is located in the section upstream of the fault area, the power failure time is the sum of the inter-section fault locating time and the fault isolating time, and is recorded as T1. 2) If the user is located in the downstream section of the fault area and can transfer the power through the backup channel, the power failure time is the sum of the power failure time T1 and the fault transfer time, and is recorded as T2. 3) If the user is located in the downstream section of the fault area and cannot be supplied through the backup channel, or if the user is located in the fault area, the power outage time includes inter-section fault location time, intra-section fault location time, and fault repair time, which is denoted as T3. On the basis, a plurality of reliability evaluation indexes widely used in the international range can be calculated. The calculation formulas of the user average power failure time (SAIDI), the expected power shortage amount (EENS) of the system and the power supply reliability (RS-3) indexes related to the method are shown in the formulas (2), (3) and (4).
ID＝λF(β1T1+β2T2+β3T3) (5)
IE＝IDPL(6)
In the formula: i isD、IE、IRSAIDI, EENS and RS-3 indexes of the feeder line are respectively; lambda [ alpha ]FThe total failure rate of the feeder line is; pLβ is the total load of the feeder line1、β2、β3For the distribution coefficient of the power failure users, the average distribution proportion of the above 3 types of users is respectively expressed when any equipment in the feeder line fails.
The power distribution decision modeling method based on the correlation between the reconstruction measures and the loss load indexes is characterized in that the multi-year investment decision model with the maximum target of the maximum improvement of the loss load indexes of the power distribution network comprises a structure similar to that of the traditional investment decision model, wherein the structure of the multi-year investment decision model with the maximum target of the maximum improvement of the loss load indexes of the power distribution network is only that a constraint condition formula (4) in the traditional investment decision model is replaced by a formula (10), and related constraint conditions are modified into total constraint for many years. Therefore, the annual investment decision model with the maximum target of the maximum improvement of the loss load index of the power distribution network can be expressed as follows:
in the above formula, NepExpressing the total investment period, and expressing the reliability index promotion total amount of the power distribution network in the total investment period by using a formula (8) as an investment target, wherein XepFor each investment measure in an investment cycle,. DELTA.IepRepresenting the variation of the reliability index in each investment period; formula (9) is investment amount constraint, CmaxTo an upper limit value of the investment amount, Cep(Xep) Representing the amount of investment for each investment measure in each investment period; the formula (10) is the associated rule constraint condition of the investment transformation measure and the reliability index,kthe method comprises the following steps of (1) providing an interaction strategy set of energy storage and flexible load, wherein phi is an association rule function, and omega and b respectively represent training parameters of an association rule; formula (11) represents the investment measure library constraint of the ep investment period, and psi is the investment measure library; the formula (12) is the upper and lower limit constraint of the investment reconstruction measures,
and
is XepiAt the lower and upper limits of the ep period, for example, formula (13),
and
the upper limit of the number of the two-remote device and the three-remote device in the ep period and the upper limit of the capacity of the distributed power supply in the ep period are respectively,
and
is XepiLower and upper limits within the total investment period; the formula (14) shows that the total amount of investment measures in the whole investment period also needs to meet the upper and lower limitsAnd (6) obtaining.
The above formula replaces the parameter vector y related to the power flow calculation in the formula (4) with the threshold and weight parameters for deep belief network learning, i.e. the power flow constraint is converted into the relevance constraint based on the deep belief network.
8. The power distribution decision modeling method based on transformation measures and loss load index correlation according to claim 1, wherein the second multi-year investment decision model targeting power distribution network investment cost minimization comprises the following steps:
in the model, the minimum total investment cost of the power grid is taken as a target, the lower limit of the increase of the loss load index is taken as a constraint, and the rest constraint conditions are the same as the perennial investment decision model with the maximum increase of the loss load index of the power distribution network, and are all based on the association rule of the transformation measures of the power distribution network and the loss load index for modeling; wherein, Delta IsetSet value representing improvement of reliability index, Cinvest_epAnd CEENS_epRespectively representing the fixed investment amount and the lost load compensation amount, and the sum of the fixed investment amount and the lost load compensation amount is the total investment amount of the reconstruction measures.
Compared with the prior art, the invention has the beneficial effects that: compared with the traditional investment decision modeling technology for the power distribution network, the relevance-based investment decision-making multi-year modeling technology provided by the patent can convert an original investment decision model related to complex nonlinear power flow calculation into a simple linear power distribution network investment decision-making model based on data relevance, and can consider the influence of complex and diverse power distribution network transformation measures on the load loss index of the power distribution network, so that the accurate investment planning decision of the power distribution network can be realized more quickly and efficiently. In addition, the relevance mining technology applied in the patent has great advantages in the aspects of finding problem potential rules, improving the calculation efficiency and the like, not only can the complex load flow calculation process be avoided, but also the calculation efficiency can be effectively improved.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (8)
1. The power distribution decision modeling method based on the relevance of the transformation measures and the load loss indexes is characterized by comprising the following steps of:
s1: determining implicit relations between different configuration schemes and the power grid load loss amount indexes; determining the influence degree of the investment decision scheme on the reliability index of the system load loss amount; determining a correlation rule between investment improvement measure configuration under different operating conditions and investment benefits improved by the loss load index;
s2: optimizing the implicit relationship, the degree of influence and the association rule in the step S1 based on a Nadam optimizer;
s3: providing a power distribution network multi-year investment decision model for correlation analysis of the transformation measures and the power distribution network load loss indexes, providing a power distribution network load loss index evaluation model based on the correlation analysis, providing a correlation relation between various transformation measures of the power distribution network and the load loss indexes, and providing a traditional power distribution network planning model; wherein,
improving a first multi-year investment decision model with the maximum target according to the loss load index of the power distribution network;
a second multi-year investment decision model aiming at minimizing the investment cost of the power distribution network;
s4: and solving the first multi-year investment decision model and the second multi-year investment decision model to obtain an optimal power distribution network investment planning decision.
2. The power distribution decision modeling method based on the correlation between the improvement measure and the loss load index as claimed in claim 1, wherein in step S1, the degree of influence of the investment decision scheme on the reliability index of the system load is characterized by the loss load index of the power grid based on the correlation rule obtained by statistical analysis or mathematical learning.
3. The power distribution decision modeling method based on modification measure and loss load index correlation according to claim 1, wherein in step S1, the rule of correlation between the configuration of the modification measure for investment under different operating conditions and the investment benefit of the loss load specifically includes: and mining by using the sample data of the loss load capacity index of the power distribution network through a research and development algorithm, and dynamically updating investment benefits along with the change of a configuration scheme so as to obtain an accurate investment aid decision result.
4. The power distribution decision modeling method based on the correlation between the improvement measure and the loss load index as claimed in claim 1, wherein in step S2, the optimization step based on the Nadam optimizer is a generative model, and is formed by stacking a plurality of restricted boltzmann machines, has good nonlinear mapping capability, can learn and adapt to unknown information, and has a structure formed by an input and output layer and a plurality of hidden layers.
5. The power distribution decision modeling method based on the correlation of reformulation measures and loss load indicators as recited in claim 1,
the power distribution network multi-year investment decision model for relevance analysis of the transformation measures and the power distribution network load loss indexes in the step S3 belongs to the problem of complex nonlinear programming, and a direct mapping between the power distribution network transformation measures and the load loss indexes is constructed by utilizing the nonlinear mapping capability based on a Nadam optimizer;
the model can quickly estimate the loss load value under the transformation measures when the scene of the transformation measures of the power distribution network changes, so that the influence degree of implementation of different transformation measures of the power distribution network on reliability indexes such as the loss load of the power distribution network is judged, the influence degree is used as a relevance constraint condition of later-stage power distribution network investment decision, and the time consumption of time domain simulation is saved; adopting a deep confidence network to carry out relevance mining and obtain a corresponding power grid load loss index, wherein the process comprises the following steps:
step 1: obtaining sample data of time sequence simulation in medium and long-term operation, and initially training to determine a basic solution space of each parameter of the deep confidence network;
step 2: establishing an input and output vector of a deep belief network, and determining a neural network model and a learning mode;
and step 3: calculating the input and output of each unit of the hidden layer and the output layer of the deep belief network, namely finishing the preliminary learning of the deep belief network;
and 4, step 4: adjusting the weight and the threshold of the controlled deep belief network performance by adopting a Nadam optimizer, and optimizing the convergence speed and the convergence effect of deep belief network learning;
and 5: continuously updating the learning mode and the learning times;
step 6: repeating the step 5, and continuously carrying out deep belief network training until a cutoff condition is met, namely the maximum learning times;
and 7: and (3) applying a deep confidence network association rule, inputting data such as node load, voltage, line power, distributed power supply output and telemechanical device installation under a certain transformation scheme, and calling the deep confidence network to obtain a corresponding power grid load loss index result.
6. The power distribution decision modeling method based on the correlation between the improvement measures and the loss load index according to claim 1 is characterized in that in a traditional power distribution network investment model, voltage, power and phase angle parameters of a network are obtained through load flow calculation, and then technical and economic index values of the network are obtained through calculation, wherein the investment decision model is as follows:
Xi∈{0,1} (3)
in the model, the maximum improvement of the loss load index representing the investment benefit of the power distribution network is an objective function, as shown in formula (1); the total investment of the power distribution network, the independence of the transformation measures and the correlation relationship between the transformation measures and the load loss index are taken as constraints, and the constraint is shown as a formula (2-4); i and
respectively representing the loss load indexes, X, of the power distribution network before and after the implementation of the reconstruction measuresiRepresenting a different type of modification, K (X)i) Representative reconstruction measure XiThe cost of (2);
representing the incidence relation between the modification measures and the load loss index; wherein y represents the vector of parameters related to power flow and grid structure in the network, such as voltage, power and phase angle; as shown in the formula (4), in the traditional investment model, the relationship between the reconstruction measures and the load loss indexes needs to be expressed by depending on the power flow parameters;
wherein, the loss load reliability index in the model can be expressed as follows; the power supply reliability index is as follows: the reliability index is generally used for evaluating the influence degree of the power failure phenomenon of the power distribution network on users; according to the difference of the power failure time, the users can be classified into 3 types after power failure: 1) if the user is located in the upstream section of the fault area, the power failure time is the sum of the inter-section fault positioning time and the fault isolation time and is recorded as T1; 2) if the user is located in the downstream section of the fault area and can be supplied through the standby supply channel, the power failure time is the sum of T1 and the fault supply time and is recorded as T2; 3) if the user is located in the downstream section of the fault area and can not be supplied through the standby supply channel, or the user is located in the fault area, the power failure time comprises inter-section fault positioning time, intra-section fault positioning time and fault repairing time, and is recorded as T3; on the basis, a plurality of reliability evaluation indexes widely used in the international range can be calculated; the calculation formula of the user average power failure time SAIDI, the expected power shortage EENS of the system and the power supply reliability RS-3 index related in the text is shown as formulas (2), (3) and (4);
ID＝λF(β1T1+β2T2+β3T3) (5)
IE＝IDPL(6)
in the formula: i isD、IE、IRSAIDI, EENS and RS-3 indexes of the feeder line are respectively; lambda [ alpha ]FThe total failure rate of the feeder line is; pLβ is the total load of the feeder line1、β2、β3For the distribution coefficient of the power failure users, the average distribution proportion of the above 3 types of users is respectively expressed when any equipment in the feeder line fails.
7. The power distribution decision modeling method based on the correlation between the improvement measure and the loss load index according to claim 1 is characterized in that the first multi-year investment decision model with the maximum target of the increase of the loss load index of the power distribution network is similar to the traditional investment decision model in structure, except that the constraint condition formula (4) in the traditional investment decision model is replaced by the following formula (10), and the related constraint condition is modified into the total constraint of many years; therefore, the first multi-year investment decision model with the maximum target for increasing the loss load index of the power distribution network can be expressed as follows:
in the above formula, NepExpressing the total investment period, and expressing the reliability index promotion total amount of the power distribution network in the total investment period by using a formula (8) as an investment target, wherein XepFor each investment measure in an investment cycle,. DELTA.IepRepresenting the variation of the reliability index in each investment period; formula (9) is investment amount constraint, CmaxTo an upper limit value of the investment amount, Cep(Xep) Representing the amount of investment for each investment measure in each investment period; the formula (10) is the associated rule constraint condition of the investment transformation measure and the reliability index,kthe method comprises the following steps of (1) providing an interaction strategy set of energy storage and flexible load, wherein phi is an association rule function, and omega and b respectively represent training parameters of an association rule; formula (11) represents the investment measure library constraint of the ep investment period, and psi is the investment measure library; the formula (12) is the upper and lower limit constraint of the investment reconstruction measures,
and
is XepiAt the lower and upper limits of the ep period, for example, formula (13),
and
the upper limit of the number of the two-remote device and the three-remote device in the ep period and the upper limit of the capacity of the distributed power supply in the ep period are respectively,
and
is XepiLower and upper limits within the total investment period; the formula (14) shows that the total amount of investment measures in the whole investment period also needs to meet the requirements of upper and lower limits.
The above formula replaces the parameter vector y related to the power flow calculation in the formula (4) with the threshold and weight parameters for deep belief network learning, i.e. the power flow constraint is converted into the relevance constraint based on the deep belief network.
8. The power distribution decision modeling method based on transformation measures and loss load index correlation according to claim 1, wherein the second multi-year investment decision model targeting power distribution network investment cost minimization comprises the following steps:
in the model, the minimum total investment cost of the power grid is taken as a target, the lower limit of the increase of the loss load index is taken as a constraint, and the rest constraint conditions are the same as the perennial investment decision model with the maximum increase of the loss load index of the power distribution network, and are all based on the association rule of the transformation measures of the power distribution network and the loss load index for modeling; wherein, Delta IsetSet value representing improvement of reliability index, Cinvest_epAnd CEENS_epRespectively representing the fixed investment amount and the lost load compensation amount, and the sum of the fixed investment amount and the lost load compensation amount is the total investment amount of the reconstruction measures.
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