CN110751346A - Distributed energy management method based on driving speed prediction and game theory - Google Patents

Distributed energy management method based on driving speed prediction and game theory Download PDF

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CN110751346A
CN110751346A CN201911066424.7A CN201911066424A CN110751346A CN 110751346 A CN110751346 A CN 110751346A CN 201911066424 A CN201911066424 A CN 201911066424A CN 110751346 A CN110751346 A CN 110751346A
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许娟婷
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Abstract

The invention relates to the technical field of power grid energy management, in particular to a distributed energy management method based on driving speed prediction and game theory, which comprises the following steps: s1: predicting the driving speed by adopting a recurrent neural network (RNN-LSTM) model, and constructing a characteristic project to improve the accuracy of prediction; s2, establishing a multi-agent model by combining various parameters of the multi-energy device of the hybrid power system based on the predicted driving speed, wherein the multi-agent model is used for defining the utility equation and the state condition of each agent; s3: a non-cooperative game theory model based on the predicted driving speed is adopted to optimize a system energy management method, and the system efficiency is improved. The method has the advantages of high accuracy in predicting the driving speed, small battery fluctuation, more battery protection, more power consumption and more environmental protection.

Description

Distributed energy management method based on driving speed prediction and game theory
Technical Field
The invention relates to the technical field of power grid energy management, in particular to a distributed energy management method based on driving speed prediction and game theory.
Background
Due to energy crisis and global warming, electric vehicles are considered as a solution to the problem of alleviating environmental pollution. However, the distance that the pure electric vehicle can continue to drive is limited due to the limitations of the power density of the battery and the energy capacity of the battery. Therefore, hybrid power systems of engines, batteries and supercapacitors have been attracting attention. However, there are three different problems of energy, driving dynamics and demand fluctuation, and an energy management method needs to be designed for the three different problems.
In view of the above, the design of a reasonable and efficient energy management method by comprehensively considering factors such as multiple energy sources and driving demand fluctuation has a very important meaning.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a distributed energy management method based on driving speed prediction and game theory, which combines future information and the distributed energy management method and considers how to fully utilize the prediction information to improve the energy utilization rate.
In order to achieve the above object, the technical solution of the present invention is: a distributed energy management method based on driving speed prediction and game theory comprises the following steps:
s1: predicting the driving speed by adopting a recurrent neural network (RNN-LSTM) model, and constructing a characteristic project to improve the accuracy of prediction;
s2, establishing a multi-agent model by combining various parameters of the multi-energy device of the hybrid power system based on the predicted driving speed, wherein the multi-agent model is used for defining the utility equation and the state condition of each agent;
s3: a non-cooperative game theory model based on the predicted driving speed is adopted to optimize a system energy management method, and the system efficiency is improved.
Further, the S1 specifically includes: the method comprises the steps of constructing a characteristic project based on a driving speed curve under a standard working condition, extracting information such as acceleration, driving state and the like, and predicting the driving speed curve by adopting a group prediction method of an RNN-LSTM model, wherein the RNN-LSTM model outputs a predicted speed curve according to an input historical speed curve and a historical acceleration curve, and the input-output relation of the prediction model can be expressed as follows:
Figure BDA0002259505660000021
where v is velocity and α is acceleration.
Further, the S2 specifically includes: establishing a multi-agent model according to various parameters of a multi-energy device of the hybrid power system, wherein the multi-agent model comprises a generator utility function model Ug, a storage battery utility function model Ub and a super capacitor utility function model Ue:
(1) defining a generator utility function model by using the actual power of the generator, the optimal generating power of the generator and the normalization coefficient:
Figure BDA0002259505660000022
wherein P isgIs the actual power of the generator and,
Figure BDA0002259505660000023
is the optimal generating power of the generator, ngIs a normalized coefficient in the range of [0,1 ]]The interval, expressed as:
(2) defining a utility function model of the storage battery by using the actual power of the battery, the historical average power of the battery, the power of the previous control moment of the battery and the normalization parameter:
Ub＝ωb1Ub1+ωb2Ub1
Figure BDA0002259505660000025
Figure BDA0002259505660000026
wherein P isbIs the actual power of the battery, PbaveIs the historical average power of the battery,Pblastis the power of the battery at the previous control moment; n isb1nb2Are normalized parameters, each of which is represented as:
Figure BDA0002259505660000032
wherein P isbmaxIs the maximum power of the battery, PbminIs the minimum power of the battery.
(3) Defining a utility function model of the super capacitor by using the actual power of the super capacitor, the expected power of the super capacitor, the initial voltage of the super capacitor, the maximum power of the super capacitor and the normalization parameters:
wherein P iscIs the actual power of the super-capacitor,
Figure BDA0002259505660000035
is the desired power of the super capacitor. Vc，iniIs the initial voltage, V, of the supercapacitorc，maxIs the maximum voltage, P, of the supercapacitorc，maxIs the maximum power of the super capacitor.
ncIs a normalization parameter expressed as:
Figure BDA0002259505660000036
further, the non-cooperative game theory model based on the predicted driving speed converts three energy device game problems into two game party problems according to energy conservation, and the specific model is as follows:
Figure BDA0002259505660000038
Figure BDA0002259505660000039
Figure BDA00022595056600000310
in the above formula, the first and second carbon atoms are,
Ubc＝wb1Ub1+wb2Ub2+wcbUc
Ugc＝wgUg+wcgUc
Pc＝Pl-Pg-Pb
wherein P islIs the load demand, Pc，Pg，PbThe actual power of the super capacitor, the actual power of the generator and the actual power of the battery are respectively referred.
The nash equilibrium, i.e., the solution of the energy management strategy, can be obtained from the most reactive functions of the above equations 1 and 2.
The invention has the beneficial effects that:
the distributed energy management method based on the driving speed prediction and the game theory comprehensively considers factors such as multiple energy sources and driving demand fluctuation, a set of efficient energy management method is customized for a hybrid power system, and meanwhile, a vehicle-mounted battery is further protected. A set of efficient energy management method is designed by combining driving demand prediction, driving information and game theory algorithm. The method has the advantages that the prediction information and the distributed energy management method are combined, how to fully utilize the prediction information is considered to improve the energy utilization rate, the method has good use effect and higher accuracy in predicting the driving speed, and meanwhile, the engine and the battery of the hybrid power system are efficiently managed by using the non-cooperative game theory model based on the predicted driving speed, so that the battery has smaller volatility, the battery is more favorably protected, the power consumption is more, and the method is more environment-friendly.
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Fig. 1 is a schematic diagram of a speed prediction result based on test sample NEDC and UDDS driving conditions in a distributed energy management method based on driving speed prediction and game theory according to an embodiment of the present invention;
fig. 2 is a feature engineering construction of driving speed prediction in a distributed energy management method based on driving speed prediction and game theory, where fig. (a) is regarded as a single group of sequence problems, and fig. (b) is a multiple group of sequence problems;
fig. 3 is an architecture diagram of a divisional energy management method of an entire system in a distributed energy management method based on driving speed prediction and game theory according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments disclosed herein are intended to be within the scope of the present invention.
Referring to fig. 1-3, a distributed energy management method based on driving speed prediction and game theory is characterized by comprising the following steps:
s1: predicting the driving speed by adopting a recurrent neural network (RNN-LSTM) model, and constructing a characteristic project to improve the accuracy of prediction;
s2: establishing a multi-agent model by combining various parameters of a multi-energy device of a hybrid power system based on the predicted driving speed, wherein the multi-agent model is used for defining a utility equation and a state condition of each agent;
s3: a non-cooperative game theory model based on the predicted driving speed is adopted to optimize a system energy management method, and the system efficiency is improved.
In this embodiment, the driving demand prediction: the driving speed prediction is a dynamic nonlinear problem, so that the driving speed prediction is difficult to be expressed by a function, and therefore the neural network model is adopted to predict the driving speed. In addition, because it is difficult to obtain enough information such as historical driving data, weather information, traffic conditions, road information and the like, the invention adopts the standard working condition driving curve as a data set and considers the data set as a time series prediction problem. Because the long-term problem of the time sequence can be avoided by the structure of the recurrent neural network RNN-LSTM, the driving speed is predicted by the RNN-LSTM, and the prediction accuracy is improved by constructing characteristic engineering, wherein an RNN-LSTM model is shown in an attached figure 1. The input end of the model is a historical speed curve and a historical acceleration curve, and the output end of the model is a predicted speed curve. The input-output relationship of the prediction model of this figure can be expressed as:
Figure BDA0002259505660000052
where v is velocity and α is acceleration.
In this embodiment, the S2 specifically is: establishing a multi-agent model according to various parameters of a multi-energy device of the hybrid power system, wherein the multi-agent model comprises a generator utility function model Ug, a storage battery utility function model Ub and a super capacitor utility function model Ue:
(1) a generator: the generator wishes to maximise fuel economy and therefore defines a utility function model of the generator to provide power as close to optimum power as possible, thereby maximising fuel economy, which: the function model is as follows:
Figure BDA0002259505660000061
wherein P isgIs hairThe actual power of the motor is determined,
Figure BDA0002259505660000062
is the optimal generating power of the generator, ngIs a normalized coefficient in the range of [0,1 ]]The interval, expressed as:
Figure BDA0002259505660000063
(2) a storage battery:
the life of the battery is affected by various factors, such as ambient temperature, over-discharge, low current discharge, and the like. The fluctuation of the charging battery is analyzed in terms of the fluctuation, and the energy fluctuation of the battery is greatly influenced by the uncertainty of the load requirement. To extend the life of the battery, it can be varied by minimizing the amplitude and frequency of the battery power, and thus its utility function model is as follows:
Ub＝ωb1Ub1+ωb2Ub1
Figure BDA0002259505660000064
Figure BDA0002259505660000065
wherein P isbIs the actual power of the battery, PbaveIs the historical average power, P, of the batteryblastIs the power of the battery at the previous control moment; n isb1nb2Are normalized parameters, each of which is represented as:
Figure BDA0002259505660000066
Figure BDA0002259505660000067
wherein P isbmaxIs the maximum power of the battery, PbminIs the minimum power of the battery.
(3) Super capacitor:
supercapacitors are used as energy buffers, intended to maintain energy capacity. Thus, by setting the initial voltage required, we define the stored energy as close to its initial state as possible, and therefore its utility function model is as follows:
Figure BDA0002259505660000071
Figure BDA0002259505660000072
wherein P iscIs the actual power of the super-capacitor,
Figure BDA0002259505660000073
is the desired power of the super capacitor. Vc，iniIs the initial voltage, V, of the supercapacitorc，maxIs the maximum voltage, P, of the supercapacitorc，maxIs the maximum power of the super capacitor.
ncIs a normalization parameter expressed as:
Figure BDA0002259505660000074
in this embodiment, the non-cooperative game theory model based on the predicted driving speed converts three energy device game problems into two game party problems according to energy conservation, and the specific model is as follows:
Figure BDA0002259505660000075
Figure BDA0002259505660000076
Figure BDA0002259505660000077
Figure BDA0002259505660000078
in the above formula, the first and second carbon atoms are,
Ubc＝wb1Ub1+wb2Ub2+wcbUc
Ugc＝wgUg+wcgUc
Pc＝Pl-Pg-Pb
wherein P islIs the load demand, Pc，Pg，PbThe actual power of the super capacitor, the actual power of the generator and the actual power of the battery are respectively referred.
The nash equilibrium, i.e., the solution of the energy management strategy, can be obtained from the most reactive functions of the above equations 1 and 2.
Example 1
As shown in fig. 1, according to the neural network RNN-LSTM model adopted in S1, the speed prediction results are obtained by using the NEDS test standard and the UDDS test standard driving conditions as data sets and regarding the data sets as a time series prediction problem, where the green curve is an actual speed curve and the red curve is a prediction curve. According to the comparison between the actual speed curve and the predicted speed curve under the NEDS test standard in FIG. 1, the curves are basically consistent in trend and have a large number of coincident points, so that the error is small under the NEDS test standard when the neural network RNN-LSTM model is used for predicting the driving speed; according to the comparison between the actual speed curve and the predicted speed curve under the UDDS test standard in FIG. 1, the curves are basically consistent and have a large number of coincident points, so that the error of the driving speed prediction by using the neural network RNN-LSTM model under the UDDS test standard is small, and in conclusion, the prediction accuracy can be improved by using the neural network RNN-LSTM model as the prediction tool of the driving speed.
Example 2
Comparing a pure game theory method (GT-NVP) based on python simulation and a non-cooperative game theory method (GT-VP) based on speed prediction, the effect pairs of different management methods are obtained as follows:
(1) under the NEDS test standard, the pure game theory method and the game theory method based on the speed prediction are compared as follows:
(1.1) the average power of the engine, wherein the test data obtained by the game theory method based on the speed prediction is 0.03 per thousand less than the data obtained by the pure game theory method, so that the oil consumption of the engine under the game theory method based on the speed test is less than that of the engine under the pure game theory method. Therefore, the fuel-saving oil tank has the characteristic of low oil consumption and is more environment-friendly.
(1.2) the average power of the battery is 8.21% larger than that of the test data obtained by a pure game theory method based on the speed prediction, and the fact that the battery power consumption is larger under the speed test-based game theory method than under the pure game theory method is proved. Therefore, the invention has the characteristic of larger power consumption and is more environment-friendly.
(1.3) the volatility of the battery power is 6.84% less than that of the data obtained by a pure game theory method in the game theory method based on speed prediction, and the battery volatility is proved to be less than that of the battery obtained by the pure game theory method in the game theory method based on speed test. Therefore, the invention has small battery fluctuation and is beneficial to the protection of the battery.
(1.4) the difference between the power output of the super capacitor and the initial state of the capacitor is 0.143% smaller than that of the data obtained by a pure game theory method in the game theory method based on the speed test, and the buffering effect of the super capacitor is more obvious in the game theory method based on the speed prediction compared with that of the super capacitor in the pure game theory method.
(2) Under the UDDS test standard, a pure game theory method and a game theory method based on speed prediction are compared as follows:
(2.1) the average power of the engine, and the test data obtained by the game theory method based on the speed prediction is equal to the data obtained by the pure game theory method. The advantage of this solution in terms of fuel consumption of the engine cannot be demonstrated.
(2.2) the average power of the battery is 3.90% larger than that of the test data obtained by a pure game theory method based on the speed prediction, and the fact that the battery power consumption is larger under the speed test-based game theory method than under the pure game theory method is proved. Therefore, the invention has the characteristic of larger power consumption and is more environment-friendly.
(2.3) the volatility of the battery power is 1.51% less than that of the data obtained by a pure game theory method in the game theory method based on speed prediction, and the battery volatility is proved to be less than that of the battery obtained by the pure game theory method in the game theory method based on speed test. Therefore, the invention has small battery fluctuation and is beneficial to the protection of the battery.
(2.4) the difference between the power output of the super capacitor and the initial state of the capacitor is 0.465% less than that of the pure game theory method in the test data obtained in the game theory method based on the speed test, and the buffering effect of the super capacitor in the game theory method based on the speed prediction is proved to be more obvious than that in the pure game theory method.
By combining the conclusions obtained in the embodiment 1 and the embodiment 2, the driving speed is predicted based on the neural network RNN-LSTM, and then the distributed energy management method of the game theory algorithm is combined, so that the driving speed prediction method has high accuracy in predicting the driving speed, and has the advantages of small battery volatility, more contribution to protecting the battery, more power consumption and more environmental friendliness.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims (4)

1. A distributed energy management method based on driving speed prediction and game theory is characterized by comprising the following steps:
s1: predicting the driving speed by adopting a recurrent neural network (RNN-LSTM) model, and constructing a characteristic project to improve the accuracy of prediction;
s2, establishing a multi-agent model by combining various parameters of the multi-energy device of the hybrid power system based on the predicted driving speed, wherein the multi-agent model is used for defining the utility equation and the state condition of each agent;
s3: a non-cooperative game theory model based on the predicted driving speed is adopted to optimize a system energy management method, and the system efficiency is improved.
2. The distributed energy management method based on driving speed prediction and game theory as claimed in claim 1, wherein the S1 is specifically: the method comprises the steps of constructing a characteristic project based on a driving speed curve under a standard working condition, extracting information such as acceleration, driving state and the like, and predicting the driving speed curve by adopting a group prediction method of an RNN-LSTM model, wherein the RNN-LSTM model outputs a predicted speed curve according to an input historical speed curve and a historical acceleration curve, and the input-output relation of the prediction model can be expressed as follows:
Figure FDA0002259505650000011
Γk＝[vk，ak]
where v is velocity and α is acceleration.
3. The distributed energy management method based on driving speed prediction and game theory as claimed in claim 1, wherein the S2 is specifically: establishing a multi-agent model according to various parameters of a multi-energy device of the hybrid power system, wherein the multi-agent model comprises a generator utility function model Ug, a storage battery utility function model Ub and a super capacitor utility function model Ue:
(1) defining a generator utility function model by using the actual power of the generator, the optimal generating power of the generator and the normalization coefficient:
Figure FDA0002259505650000012
wherein P isgIs the actual power of the generator and,
Figure FDA0002259505650000013
is the optimal generating power of the generator, ngIs a normalized coefficient in the range of [0,1 ]]The interval, expressed as:
Figure FDA0002259505650000021
(2) defining a utility function model of the storage battery by using the actual power of the battery, the historical average power of the battery, the power of the previous control moment of the battery and the normalization parameter:
Ub＝ωb1Ub1+ωb2Ub1
Figure FDA0002259505650000022
Figure FDA0002259505650000023
wherein P isbIs the actual power of the battery, PbaveIs the historical average power, P, of the batteryblastIs the power of the battery at the previous control moment; n isb1nb2Are normalized parameters, each of which is represented as:
Figure FDA0002259505650000025
wherein P isbmaxIs the maximum power of the battery, PbminIs the minimum power of the battery.
(3) Defining a utility function model of the super capacitor by using the actual power of the super capacitor, the expected power of the super capacitor, the initial voltage of the super capacitor, the maximum power of the super capacitor and the normalization parameters:
Figure FDA0002259505650000026
Figure FDA0002259505650000027
wherein P iscIs the actual power of the super-capacitor,
Figure FDA0002259505650000028
is the desired power of the super capacitor. Vc，iniIs the initial voltage, Vc, of the supercapacitor，maxIs the maximum voltage, P, of the supercapacitorc，maxIs the maximum power of the super capacitor.
ncIs a normalization parameter expressed as:
Figure FDA0002259505650000029
4. a distributed energy management method based on driving speed prediction and game theory according to claim 1, 2 or 3, characterized in that the non-cooperative game theory model based on predicted driving speed converts three energy device game problems into two game party problems according to energy conservation, and the concrete model is as follows:
Figure FDA0002259505650000032
Figure FDA0002259505650000033
Figure FDA0002259505650000034
in the above formula, the first and second carbon atoms are,
Ubc＝wb1Ub1+wb2Ub2+wcbUc
Ugc＝wgUg+wcgUc
Pc＝Pl-Pg-Pb
wherein P islIs the load demand, Pc，Pg，PbThe actual power of the super capacitor, the actual power of the generator and the actual power of the battery are respectively referred.
The nash equilibrium, i.e., the solution of the energy management strategy, can be obtained from the most reactive functions of the above equations 1 and 2.
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