CN111900753B - Emergency optimal regulation and control method for urban energy storage emergency vehicle - Google Patents

Emergency optimal regulation and control method for urban energy storage emergency vehicle Download PDF

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CN111900753B
CN111900753B CN202010638400.0A CN202010638400A CN111900753B CN 111900753 B CN111900753 B CN 111900753B CN 202010638400 A CN202010638400 A CN 202010638400A CN 111900753 B CN111900753 B CN 111900753B
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emergency vehicle
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CN111900753A (en
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沈源
王晨晖
陈成
王雨薇
闻韬
周晨
陶以彬
余豪杰
李官军
杨波
刘欢
冯鑫振
崔红芬
薛金花
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State Grid Jiangsu Electric Power Co ltd Zhenjiang Power Supply Branch
China Electric Power Research Institute Co Ltd CEPRI
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China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The invention discloses an emergency optimal regulation and control method for an urban energy storage emergency vehicle, which comprehensively considers the power of the energy storage emergency vehicle and the distance between an emergency center and a user and adopts a genetic algorithm to carry out optimal scheduling on all energy storage emergency vehicles. The method not only can better take the self characteristics of the energy storage emergency vehicle into consideration, carries out optimization regulation and control by taking the minimum power loss as a target, and has strong practicability, but also determines the output power of the energy storage emergency vehicle according to the real-time condition, constructs an optimization model by taking the minimum power failure loss as the target, and solves the optimal distribution result of the energy storage emergency vehicle through a genetic algorithm based on the type and the number of the existing energy storage emergency vehicles of each emergency center, thereby economically and effectively ensuring the emergency power supply of important users.

Description

Emergency optimal regulation and control method for urban energy storage emergency vehicle
Technical Field
The invention relates to the field of electric power system recovery, in particular to an emergency optimization regulation and control method for an urban energy storage emergency vehicle.
Background
With the continuous development of social economy in China, the power utilization demand is rapidly increased, the power utilization load diversity trend is more obvious, and higher requirements are provided for the power supply capacity and the power supply quality of a power distribution network. In emergency power supply, for important loads, especially for the first-order loads, a significant political impact or economic loss may occur in case of power interruption. Therefore, emergency power supply to important loads has been an important task for power companies.
At present, different solving methods are adopted for solving the problems, and a diesel generator set and a flywheel emergency power supply system are adopted for emergency power supply. However, the conventional diesel generator needs 5-30 s for a long starting time when being used as an emergency power supply, the power supply voltage and frequency fluctuation are large, the efficiency is low, and the use of the diesel generator inevitably brings environmental and noise pollution. And the cost of the flywheel emergency power supply system is high, and the flywheel emergency power supply system is difficult to popularize and apply in a large area. Compared with the mobile energy storage system, the mobile energy storage system is started quickly and is mostly in ms level, the power supply voltage/frequency fluctuation is small, the limitation can be within 1V/0.1Hz, the cost is lower compared with a flywheel, and the mobile energy storage system is more suitable for large-scale popularization and application.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an emergency optimization regulation and control method for an urban energy storage emergency vehicle, which can solve the problems that the existing method is time-consuming and labor-consuming and has large environmental pollution.
The purpose of the invention is realized by adopting the following technical scheme:
an emergency optimization regulation and control method for an urban energy storage emergency vehicle is characterized by comprising the following steps:
step 1, inputting load data of a power-off user and the distance between the power-off user and an emergency center according to the serial number of the power-off user;
step 2, analyzing based on the power shortage power and distance data of the power-off user; according to the requirement of power supply reliability and the degree of economic loss caused by power failure, dividing the power load into a first-level load, a second-level load and a third-level load; calculating the primary load, the secondary load and the tertiary load power of each power-losing user and the running time of the energy-storage emergency vehicle of different emergency centers reaching the power-losing users;
step 3, calculating the interval delta T of the running time of the energy storage emergency vehicle reaching each power-off user according to the running time of the energy storage emergency vehicle js ，ΔT js The formula of (1) is:
Figure GDA0003690174280000021
in the formula, T js Representing the running time of the energy storage emergency vehicle reaching the jth power-loss user in the s batch; t is outage And T 0 Respectively representing the power failure time and the emergency center response time; s is the total batch number of the energy storage emergency vehicles which successively reach a certain user;
step 4, calculating the electric quantity pool of each power-losing user, and defining the electric quantity pool of the energy storage emergency vehicle as the available electric quantity of the energy storage emergency vehicle in the power failure scheduling, wherein the electric quantity pool of the power-losing user is the sum of the electric quantity pools of the energy storage emergency vehicles reaching the user; if the energy storage emergency vehicle outputs the rated power but cannot use the stored electric quantity in the process from the power loss user to the end of power failure, calculating the available electric quantity according to the product of the rated power and the power supply time, and calculating the electric quantity battery of the power loss user by the calculation method can be expressed as:
Figure GDA0003690174280000022
wherein I, J and K respectively represent the total number of types of the emergency center, the power-off user and the energy storage emergency vehicle,
Figure GDA0003690174280000023
battery, x, representing the jth power-down user ijk The quantity of the kth energy storage emergency vehicles dispatched from the ith emergency center to the jth user in the dispatching process is represented;
Figure GDA0003690174280000031
and P k Respectively representing the stored electric quantity and rated power of the kth type energy storage emergency vehicle;
step 5, calculating the output power of the energy storage emergency vehicle of each power-off user in different time periods according to the electric quantity pool of each power-off user; output power of energy storage emergency vehicle to jth important user in s time period
Figure GDA0003690174280000032
Comprises the following steps:
Figure GDA0003690174280000033
step 6, calculating the output power of the energy storage emergency vehicle of each power-off user to different grades of loads at different time periods
Figure GDA0003690174280000034
The solution can be solved by the following formula:
Figure GDA0003690174280000035
Figure GDA0003690174280000036
in the formula, m jsl Is a variable from 0 to 1, and is,
Figure GDA0003690174280000037
representing the power of the c-th load of the j power-loss user;
step 7, the quantity x of the emergency energy storage vehicles allocated by each emergency center ijk And (3) constructing an optimized dispatching model of the energy storage emergency vehicle for decision variables, and calculating the total power failure loss, wherein the calculation formula of the power failure loss is as follows:
Figure GDA0003690174280000038
in the formula, the first term represents the power failure loss of a user when power supply is not recovered, the second term represents the power failure loss of the energy storage emergency vehicle after the energy storage emergency vehicle reaches the user, and S l Represents the power failure loss, mu, of the class I load l Represents the proportion of the user's class i load,
Figure GDA0003690174280000039
indicating the power shortage of the jth power-losing user;
and 8, solving the optimized scheduling model by using a genetic algorithm to obtain an optimal emergency energy storage vehicle allocation scheme which meets constraint conditions and minimizes total power failure loss.
Further, in step 8, the constraint condition is:
Figure GDA0003690174280000041
Figure GDA0003690174280000042
Figure GDA0003690174280000043
wherein, formula 7 shows the number x of the kth energy storage emergency vehicles provided by the ith power supply station to the user j ijk Should be smallThe total number y of the energy storage emergency vehicles in the power supply station ik (ii) a In formula 8, T ij Representing the driving time from the power supply station i to the user j, wherein the formula 8 shows that for the j-th power-loss user, the electric quantity of the energy storage emergency vehicle provided by the power supply station is not less than the power shortage electric quantity of the first-level load of the user; in the case of the formula 9, the compound,
Figure GDA0003690174280000044
and
Figure GDA0003690174280000045
the maximum value and the minimum value of the charge state of the kth type energy storage emergency vehicle are respectively shown, and the formula 9 represents the charge state constraint of the battery of the energy storage emergency vehicle.
Further, in step 8, a genetic algorithm is used to solve the optimal solution of the optimized scheduling model, and the solving process of the algorithm is as follows:
s81, generating an initial generation population meeting constraint conditions;
s82, generating offspring through crossing, mutation and selection operations;
s83, selecting the filial generation meeting the constraint condition, and calculating the corresponding power failure loss, and leaving the generation with smaller power failure loss;
and S84, repeating the steps S81-S83 until the iterative algebra meets the requirement of outputting the filial generation with the minimum power failure loss, namely the emergency energy storage vehicle allocation scheme, namely the optimal solution of the optimal scheduling model.
Compared with the prior art, the invention has the beneficial effects that: the method is based on the load data of important urban users in a recent period of time, then calculates the proportion of primary, secondary and tertiary loads, constructs an optimization model with the aim of minimizing power failure loss, and solves the optimal distribution result of the energy storage vehicles through a genetic algorithm based on the types and the number of the existing energy storage emergency vehicles of each emergency center, so that the emergency power supply of the important users can be economically and effectively ensured.
Drawings
Fig. 1 is a flow chart of emergency regulation and control of the urban mobile energy storage vehicle.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
An emergency optimization regulation and control method for an urban energy storage emergency vehicle is shown in fig. 1 and comprises the following steps.
Step 1, inputting load data of a power-losing user and a distance between a power transmission user and an emergency center according to a power-losing user serial number;
specifically, in step 1, the importance level of each user is determined according to the user property, and load data of all important power-losing users in the same city (or in the same area) is collected, wherein the load data has the basic characteristics of instantaneity and richness, and the sampling span of each user is large without making a requirement on the collection time.
The load data is load data of all power-losing users in the same city, wherein the load data is randomly acquired in the same area.
And 2, analyzing based on the power shortage power and distance data of the power-losing user. The power load is classified into three classes, i.e., a primary load, a secondary load, and a tertiary load, according to the requirement of reliability of power supply and the degree of economic loss caused by power outage. Calculating the primary load, the secondary load and the tertiary load power of each power-losing user and the running time of the energy-storage emergency vehicle of different emergency centers reaching the power-losing users;
the primary load means that the human body is injured or killed due to the interruption of power supply, or main equipment is damaged and is difficult to repair for a long time, or huge loss is brought to national economy. Such as schools, government offices, hospitals, business centers, stadiums, television stations, railways, water utility companies, subway systems, military heavy sites, and the like.
The importance of the power-losing users of the secondary load and the power-losing users of the tertiary load is reduced in sequence compared with the power-losing users of the primary load.
For the first-level load, the power supply system is required to still ensure continuous power supply when the line fails and has power failure. And (3) the power of the first-level load of each user calculated in the step 2 is used for measuring the running time of the mobile energy storage vehicle on the road, and the power and the electric quantity of the self-contained energy storage are allocated.
Step 3, calculating the interval delta T of the running time of the energy storage emergency vehicle reaching each power-off user according to the running time of the energy storage emergency vehicle js ，ΔT js The formula of (1) is:
Figure GDA0003690174280000061
in the formula, T js Representing the running time of the energy storage emergency vehicle reaching the jth power-loss user in the s batch; t is outage And T 0 Respectively representing the power failure time and the emergency center response time; and S is the total batch number of the energy storage emergency vehicles which successively reach a certain user.
Step 4, calculating the electric quantity pool of each power-losing user, and defining the electric quantity pool of the energy storage emergency vehicle as the available electric quantity of the energy storage emergency vehicle in the power failure scheduling, wherein the electric quantity pool of the power-losing user is the sum of the electric quantity pools of the energy storage emergency vehicles reaching the user; if the energy storage emergency vehicle outputs the rated power but cannot use the stored electric quantity in the process from the power loss user to the end of power failure, calculating the available electric quantity according to the product of the rated power and the power supply time, and calculating the electric quantity battery of the power loss user by the calculation method can be expressed as:
Figure GDA0003690174280000062
wherein I, J and K respectively represent the total number of types of the emergency center, the power-off user and the energy storage emergency vehicle,
Figure GDA0003690174280000063
battery, x, representing the jth power-down user ijk Indicates the ith in the scheduling processThe number of the kth energy storage emergency vehicles allocated to the jth user by the emergency center;
Figure GDA0003690174280000071
and P k Respectively representing the stored electric quantity and rated power of the kth type energy storage emergency vehicle;
step 5, calculating the output power of the energy storage emergency vehicle of each power-off user in different time periods according to the electric quantity pool of each power-off user; output power of energy storage emergency vehicle to jth important user in s time period
Figure GDA0003690174280000072
Comprises the following steps:
Figure GDA0003690174280000073
and 5, dividing the energy storage vehicles into a plurality of time periods according to the time sequence of the different energy storage vehicles reaching the user, calculating the actual power and the electric quantity of the energy storage vehicles in each time period, and continuously updating.
Step 6, calculating the output power of the energy storage emergency vehicle of each power-off user to different grades of loads at different time periods
Figure GDA0003690174280000074
The solution can be solved by the following formula:
Figure GDA0003690174280000075
Figure GDA0003690174280000076
in the formula, m jsl Is a variable from 0 to 1, and is,
Figure GDA0003690174280000077
and the power of the class c load of the j power-loss user is represented.
In step 6, because the number and the electric quantity of the energy storage emergency vehicles are limited, the situation that the electric quantity of the energy storage emergency vehicles is not enough to be supplied to a power-off user to stop power failure may exist, the output power of the energy storage emergency vehicles needs to be changed according to the power of each stage of load, and the priority power supply of the first stage of load is ensured.
Step 7, allocating the number x of the emergency energy storage vehicles by each emergency center ijk And (3) constructing an optimized dispatching model of the energy storage emergency vehicle for decision variables, and calculating the total power failure loss, wherein the calculation formula of the power failure loss is as follows:
Figure GDA0003690174280000081
in the formula, the first term represents the power failure loss of a user when power supply is not recovered, the second term represents the power failure loss of the energy storage emergency vehicle after the energy storage emergency vehicle reaches the user, and S l Represents the power failure loss, mu, of the class I load l Represents the proportion of the user's class i load,
Figure GDA0003690174280000082
indicating the power shortage of the j-th power-losing user.
In step 7, since the load of each user is divided into the first-level, second-level, and third-level loads, the power outage loss is not a linear function of power, but a piecewise function.
And 8, solving the optimized scheduling model by using a genetic algorithm to obtain an optimal emergency energy storage vehicle allocation scheme which meets constraint conditions and minimizes total power failure loss.
In step 8, the genetic algorithm needs to initialize a population. Because the optimization results are different every time, the optimal solution needs to be screened according to the constraint.
And taking a certain city as an example, arranging the load data of the important users and the location of an emergency center and the like. Suppose that there are 3 emergency centers in the city, and the types and the number of the energy storage emergency vehicles are shown in table 1.
TABLE 1 type and quantity of energy-storage emergency vehicles in each emergency center
Figure GDA0003690174280000083
The important user load data and the road travel distance from the emergency center to each user are shown in tables 2 and 3, respectively.
Table 2 important user load data
Figure GDA0003690174280000091
TABLE 3 Emergency center to user distance traveled
Figure GDA0003690174280000092
According to historical data of a power grid company and real-time maintenance conditions, each emergency center obtains the estimated value of the power failure time of 80-100 minutes. Because the invention preferentially ensures that the power supply of the first-level load is not interrupted, the power failure time in regulation and control is determined according to the worst condition, namely the power failure time is 100 minutes. And then according to data such as load, distance, driving time and the like, taking the number of the mobile energy storage vehicles regulated and controlled by each emergency center as a decision variable, generating an initial population meeting constraint conditions by using a genetic algorithm, calculating the output power of the energy storage emergency vehicle of each power-loss user and each time period, further calculating the power failure loss, generating filial generations meeting the constraint through operations such as optimization, variation, crossing and the like, comparing the power failure losses of the parent generations and the filial generations, leaving a generation with smaller power failure loss, repeating the operations such as optimization, variation and crossing until the requirement of iterative generations is met, and outputting an emergency energy storage vehicle allocation scheme with the minimum power failure loss, namely the optimal solution of the optimal scheduling model, wherein the result is shown in table 4.
Table 4 energy storage emergency vehicle optimization regulation and control result
Figure GDA0003690174280000101
According to the regulation and control result, the optimized regulation and control method can meet the condition that the primary load is supplied with power by the energy storage emergency vehicle in the whole power failure process, and the power supply is not interrupted. Next, when the first-stage, second-stage, and third-stage load blackout loss coefficients are set to 10, 0.1, and 0.05, respectively, the total blackout loss of the important user due to blackout is reduced from 495431 yuan to 30211 yuan, thereby achieving a significant economic effect.
It should be noted that the method is not only suitable for a city, but also suitable for a certain area, such as a district, a town, etc.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. An emergency optimization regulation and control method for an urban energy storage emergency vehicle is characterized by comprising the following steps:
step 1, inputting load data of a power-off user and the distance between the power-off user and an emergency center according to the serial number of the power-off user;
step 2, analyzing based on the power shortage power and distance data of the power-off user; according to the requirement of power supply reliability and the degree of economic loss caused by power failure, dividing the power load into a first-level load, a second-level load and a third-level load; calculating the primary load, the secondary load and the tertiary load power of each power-losing user and the running time of the energy-storage emergency vehicle of different emergency centers reaching the power-losing users;
step 3, calculating the interval delta T of the running time of the energy storage emergency vehicle reaching each power-off user according to the running time of the energy storage emergency vehicle js ，ΔT js The formula of (1) is:
Figure FDA0003690174270000011
in the formula, T js Representing the running time of the energy storage emergency vehicle reaching the jth power-loss user in the s batch; t is outage And T 0 Respectively representing the power failure time and the emergency center response time; s is the total batch number of the energy storage emergency vehicles which successively reach a certain user;
step 4, calculating the electric quantity pool of each power-losing user, and defining the electric quantity pool of the energy storage emergency vehicle as the available electric quantity of the energy storage emergency vehicle in the power failure scheduling, wherein the electric quantity pool of the power-losing user is the sum of the electric quantity pools of the energy storage emergency vehicles reaching the user; if the energy storage emergency vehicle outputs the rated power but cannot use the stored electric quantity in the process from the power loss user to the end of power failure, calculating the available electric quantity according to the product of the rated power and the power supply time, and calculating the electric quantity battery of the power loss user by the calculation method can be expressed as:
Figure FDA0003690174270000012
wherein I, J and K respectively represent the total number of types of the emergency center, the power-off user and the energy storage emergency vehicle,
Figure FDA0003690174270000013
battery, x, representing the jth power-down user ijk The quantity of the kth energy storage emergency vehicles dispatched from the ith emergency center to the jth user in the dispatching process is represented;
Figure FDA0003690174270000021
and P k Respectively representing the stored electric quantity and rated power of the kth type energy storage emergency vehicle;
step 5, calculating the output power of the energy storage emergency vehicle of each power-off user in different time periods according to the electric quantity pool of each power-off user; output power of energy storage emergency vehicle to jth important user in s time period
Figure FDA0003690174270000022
Comprises the following steps:
Figure FDA0003690174270000023
step 6, calculating the output power of the energy storage emergency vehicle of each power-off user to different grades of loads at different time periods
Figure FDA0003690174270000024
The solution can be solved by the following formula:
Figure FDA0003690174270000025
Figure FDA0003690174270000026
in the formula, m jsl Is a variable from 0 to 1, and is,
Figure FDA0003690174270000027
the power of the c-level load of the j power-loss user is represented;
step 7, allocating the number x of the emergency energy storage vehicles by each emergency center ijk And (3) constructing an optimized dispatching model of the energy storage emergency vehicle for decision variables, and calculating the total power failure loss, wherein the calculation formula of the power failure loss is as follows:
Figure FDA0003690174270000028
in the formula, the first term represents the power failure loss of a user when power supply is not recovered, the second term represents the power failure loss of the energy storage emergency vehicle after the energy storage emergency vehicle reaches the user, and S l Represents the power failure loss, mu, of the class I load l Represents the proportion of the user's class i load,
Figure FDA0003690174270000029
indicating the power shortage of the jth power-losing user;
and 8, solving the optimized scheduling model by using a genetic algorithm to obtain an optimal emergency energy storage vehicle allocation scheme which meets constraint conditions and minimizes total power failure loss.
2. The method of regulating as claimed in claim 1, wherein: in step 1, load data of all power-off users in the same area are randomly collected.
3. The method of regulating as claimed in claim 1, wherein: in step 2, the proportion of the three grades of loads of the power-losing user is determined according to the requirement of the power supply reliability and the degree of economic loss caused by power failure.
4. The method of regulating as claimed in claim 1, wherein: in step 3, according to historical data and real-time maintenance conditions, the estimated value range [ A, B ] of the power failure time obtained by each power supply is obtained, and in order to ensure that the power supply of the first-stage load is not interrupted, the power failure time in the scheduling is determined according to the worst condition, namely the power failure time is assumed to be B.
5. The method of regulating as claimed in claim 1, wherein: in step 5, the time when different energy storage emergency vehicles reach the power-off user is divided into a plurality of time periods, and in each time period, the output electric quantity and the power of the energy storage emergency vehicle are changed.
6. The method of regulating as claimed in claim 1, wherein: in step 6, the graded output power is determined according to the output power of each time period, and the power supply priority is ensured to be a first-level load, a second-level load and a third-level load in sequence.
7. The method of regulating as claimed in claim 1, wherein: in step 8, the constraint conditions are:
Figure FDA0003690174270000031
Figure FDA0003690174270000032
Figure FDA0003690174270000033
wherein, formula 7 shows the number x of the kth energy storage emergency vehicles provided by the ith power supply station to the user j ijk The total number y of the energy storage emergency vehicles which are less than the power supply station ik (ii) a In formula 8, T ij Representing the driving time from the power supply station i to the user j, wherein the formula 8 shows that for the j-th power-loss user, the electric quantity of the energy storage emergency vehicle provided by the power supply station is not less than the power shortage electric quantity of the first-level load of the user; in the formula (9), the first and second groups,
Figure FDA0003690174270000041
and
Figure FDA0003690174270000042
the maximum value and the minimum value of the charge state of the kth type energy storage emergency vehicle are respectively shown, and the formula 9 represents the charge state constraint of the battery of the energy storage emergency vehicle.
8. The method of regulating as claimed in claim 1, wherein: in step 8, solving the optimal solution of the optimized scheduling model by using a genetic algorithm, wherein the solving process of the algorithm is as follows:
s81, generating an initial generation population meeting constraint conditions;
s82, generating offspring through crossing, mutation and selection operations;
s83, selecting the filial generation meeting the constraint condition, and calculating the corresponding power failure loss, and leaving the generation with smaller power failure loss;
and S84, repeating the steps S81-S83 until the iterative algebra meets the requirement of outputting the filial generation with the minimum power failure loss, namely the emergency energy storage vehicle allocation scheme, namely the optimal solution of the optimal scheduling model.
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