CN115841177A - Robust optimization method considering toughness of power distribution network - Google Patents
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Abstract
The invention discloses a robust optimization method considering toughness of a power distribution network. Belongs to the field of electric power energy, and comprises the following contents: acquiring the elastic response capability of the power distribution network to the fault and the recovery capability after the fault, and establishing a toughness power distribution network system performance model under an extreme event; analyzing the change trend of the system toughness curve at each stage, and constructing a distribution network toughness quantization index under an extreme event by combining the system load loss under a fault; considering the flexible regulation and control capability of the delay type data load, and establishing a data load space-time transfer model; in order to deal with the loss brought to the power distribution network by the small-probability high-loss extreme event, a robust optimization model considering the space-time flexible regulation and control of energy storage and data load is established from the perspective of improving the viability of the power distribution network; and verifying the reasonability and feasibility of the robust optimization scheme through an example. The method considers the influence of the extreme events with small probability and high loss on the power distribution network planning, provides theoretical guidance for power distribution network planning personnel, and improves the reliability and robustness of the planning scheme.
Description
Technical Field
The invention belongs to the field of electric power energy, and particularly relates to a robust optimization method considering the toughness of a power distribution network, which can provide theoretical guidance for system planning personnel, takes the influence of a small-probability-high-loss extreme event into consideration in a planning stage, cooperatively plans an energy storage and data load regulation strategy in the power distribution network, and improves the robustness and reliability of power distribution network planning.
Background
In recent years, grid blackout accidents due to extreme events such as natural disasters and network attacks often occur, and the occurrence probability of such extreme events is small, but the consequences of the extreme events are often serious once the extreme events occur. Therefore, how to take into account possible extreme event influences in the planning stage and ensure that the power distribution network not only meets the reliable operation in the normal environment, but also can improve the toughness of the power distribution network in the extreme event has very important significance.
On the other hand, along with the continuous development of intellectualization and informatization, the distributed resource access amount in the power distribution network is continuously improved, so that the power distribution network has higher flexibility and recovery capability. The rapid development of technologies such as distributed power sources, energy storage and data centers provides a chance for improving the toughness of the power distribution network, so that the power distribution network has enough flexible regulation and control resources to respond timely under the condition of a fault, and the reliability and the restoring force of the system are improved.
Disclosure of Invention
The technical problem is as follows: aiming at the problem that the conventional power distribution network planning scheme is not influenced by the extreme events with small probability-high loss, the invention aims to solve the technical problems that: based on the power regulation characteristic of energy storage and the space-time flexible regulation and control capability of data load, a robust optimization method considering the toughness of the power distribution network is provided, the elastic response capability of the power distribution network under extreme events is taken into account, and the robustness and reliability of power distribution network planning are improved.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the technical scheme that: a robust optimization method considering toughness of a power distribution network comprises the following steps:
acquiring the elastic response capability of the power distribution network to faults and the recovery capability after the faults, and establishing a tough power distribution network system performance model under extreme events based on the toughness of the power distribution network;
analyzing the change trend of the toughness curve of the power distribution network system in each stage, and constructing a toughness quantization index of the power distribution network in an extreme event by combining the load loss of the system under the fault; wherein each stage comprises a normal operation stage before failure, a disturbance stage, a response stage, a recovery stage and a final response stage;
obtaining flexible regulation and control capacity of a delay type data load, and establishing a data load space-time transfer model;
aiming at the loss brought to the power distribution network by the small-probability-high-loss extreme event, a robust optimization model considering the flexible time-space regulation and control of energy storage and data load is established from the perspective of improving the viability of the power distribution network according to a flexible power distribution network system performance model and a data load time-space transfer model;
and verifying the rationality and feasibility of the robust optimization scheme based on the actual power distribution network.
A robust optimization method considering the toughness of a power distribution network comprises the following stages:
T0-T1: and (5) normal operation stage before fault. At this stage, the system makes corresponding preparation and prevention aiming at the possible extreme events through reasonable resource configuration;
T1-T2: and (5) a disturbance stage. The system encounters disturbance fault at the moment T1, and the elasticity performance of the system is rapidly reduced at the moment because various elastic resources do not respond in time;
T2-T4: and (5) a response phase. After the system is disturbed for a period of time, the system is degraded to enter a stable response state, and all elastic resources are ready to respond to disturbance faults;
T4-T5: and (5) a recovery phase. The system elastic resource responds to the disturbance fault, the system performance is rapidly recovered, but the system performance is not recovered to a normal state before the fault;
T5-T7: and a final response phase. And the damaged infrastructure in the system is recovered, and the system performance is recovered to the normal operation state before the fault.
A robust optimization method considering toughness of a power distribution network comprises the following steps of:
for the first stage, only the influence of the extreme event on the viability of the power distribution network is considered, namely the toughness quantization indexes of the power distribution network in the disturbance stage and the response stage need to be considered. Generally speaking, the most direct influence of an extreme event on a power distribution network is system load reduction, and the time integral of the load loss of the power distribution network under the extreme event is considered as a toughness quantification index.
Wherein p is s The probability of occurrence of an extreme event S is S, and S is an extreme event set;
the active power reduction value of the node i in the extreme event in the t period; n is a power distribution network node set; t is 1 And T 4 Corresponding to the different status periods in claim 2; dt represents the integration over time t.
Converting the toughness quantitative index into a cost index capable of being quantitatively calculated, namely the annual load shedding loss cost of the power distribution network under the influence of extreme events:
wherein, T ex The average number of extreme events occurring in a year;
the cost is the unit active power loss in the distribution network.
A robust optimization method considering the toughness of a power distribution network, said delayed data load comprising:
data loads in the CPDS are generally divided into a delay sensitive type and a delay tolerant type, the former requires real-time processing in a short time, and an M/M/1 queuing model is generally adopted to model queuing delay in a time period, so that the data loads received in each time period of a data center must be processed in the time period; the latter has higher tolerance to the processing time requirement, the processing is completed within a specified time, and the delay tolerant data loads among different data centers can also realize space transfer, so the data loads have space-time regulation characteristics. To simplify the model and without loss of generality, the present invention primarily considers delay tolerant type loads.
A robust optimization method considering toughness of a power distribution network, wherein the data load flexible regulation and control capability comprises the following steps:
delay tolerant data loads in data centers have space-time flexible regulation potential, and data loads between different front-end servers and computing nodes should satisfy the following constraints:
equation (1) shows that the sum of the data loads regulated and distributed by the front-end servers should be equal to the local user requirement, wherein Load s,t Representing the local user demand, data, of the s-th front-end server at time t l,s,t Denotes the data load assigned to data center l by the S-th front-end server at time t, S isTotal number of front-end servers; formula (2) indicates that the data load of each data center should be equal to the sum of the space transfer loads of the data center and other data centers, and N is the total number of the data centers; equation (3) represents that the data load to be processed at each moment of each data center should be equal to the difference between the data load accepted by the space transfer and the time transfer load, wherein Trans l,t Representing the amount of data load transferred by data center l at time t.
The data load flexible regulation and control capability comprises the following data load time transfer model:
delay tolerant loads do not require real-time processing of the data load, allowing it to be processed after a delay, so the data load time transfer model is:
Total l,t+1 ＝Total l,t +ΔData l,t Δt (5)
0≤Total l,t ≤Total l,max (6)
formula (4) Δ Data l,t Representing the amount of Data load, data, transferred by the ith Data center at time t l,t And Trans l,t The same formula (1) is explained; the equation (5) represents the relationship of the data load storage total amount at different moments of the data center, and Δ t represents the time interval from t to t + 1; the upper limit and the lower limit of the data load storage Total amount of the data center are restricted by an equation (6), wherein Total l,max Representing an upper data load storage amount limit.
The data load flexible regulation and control capability comprises the following data load space transfer model:
the data load can be flexibly transferred among different data centers, and the space transfer model is as follows:
equation (8) is added to make the constraint because it is not possible for a single data center to both roll out and absorb load to any one data center.
A robust optimization method considering power distribution network toughness, the robust optimization model comprising:
in order to deal with the loss brought by the small-probability-high-loss extreme event to the power distribution network, the robust optimization model considering the energy storage and data load space-time flexible regulation is established from the perspective of improving the viability of the power distribution network, and comprises the following steps:
s.t.
Ax≤d
wherein P is a planning set, O is an operation set, and F is a fault set; x is a planning decision vector and comprises all decision variables participating in power distribution network planning; y is an operation decision vector and comprises decision variables which can participate in the elastic scheduling of the power distribution network in the operation stage; z is a fault scene vector; a is T 、b T 、c T Coefficient matrixes corresponding to the planning decision vector, the operation decision vector and the fault scene vector are respectively set; A. b, C, D, G are coefficient matrixes under corresponding constraint conditions respectively; f is a constant matrix corresponding to the equation constraint condition.
The optimization model is a two-stage three-layer robust optimization model; the first stage is an investment stage, a reasonable power distribution network investment scheme is determined based on limited severe scene probability distribution, and a planning decision vector comprises the position and capacity configuration of fixed energy storage; the second stage is an operation stage, the operation decision variables comprise a space-time flexible scheduling scheme of data load, and the worst scene probability distribution is sought under the known first-stage investment scheme; based on the method, the internal double-layer optimization problem is simulated, operated and decoupled and solved, and the annual comprehensive cost minimization of the system under the worst scene probability distribution is realized.
A robust optimization model, the set of plans comprising:
energy storage investment cost:
wherein,
investment cost for unit capacity of stored energy, E n For the nth energy storage capacity, N E The number is planned for energy storage, y1 is the energy storage operation life, and d is the current rate.
Investment cost of the intelligent terminal:
wherein it is present>
The investment cost of a single intelligent terminal is determined, K is the planned number of the intelligent terminals, y2 is the operation age of the intelligent terminal, and d is the discount rate.
A robust optimization model, the run set comprising:
energy storage operation cost:
wherein,
operating schedule fee for unit capacity storage>
Indicating the charging of the ith stored energy in the t periodElectric power or discharge power, T represents the total number of energy storage charge-discharge periods.
The operation cost of the data center is as follows:
wherein, MP t Represents the node marginal price of electricity, N, of the power distribution network at the moment t D As to the number of the data centers,
representing the power required by the data center to process a unit of data load per unit time.
A robust optimization model, the fault set comprising:
annual shedding load loss cost of a power distribution network under the influence of extreme events:
wherein, T e Is the average number of extreme events occurring in a year;
cost per active power loss in the distribution network, p s The probability of occurrence of an extreme event S is S, and S is an extreme event set; />
The active power reduction value of the node i in the time period t in the extreme event; n is a power distribution network node set; t is 1 And T 4 Corresponding to the different status periods in claim 2; dt represents the integration over time t.
A robust optimization model, the planning set constraints comprising:
(1) The node allows for installation of energy storage rated power and capacity constraints;
(2) The allowable installation energy storage quantity of the power distribution network is restricted;
(3) The number of intelligent terminals allowed to be installed by the node is restricted;
a robust optimization model, the run-set constraint condition comprising:
(1) Power flow constraints (active power, reactive power constraints);
(2) Safety constraints (voltage, current constraints);
(3) Energy storage constraints (energy storage charge state constraint, energy storage capacity constraint, energy storage electric quantity balance constraint);
(4) Data load constraints (data load time transfer amount constraints, data load space transfer amount constraints);
(5) Communication bandwidth constraints.
Has the advantages that: compared with the prior art, the invention has the following characteristics:
the invention provides a power distribution network energy storage and data center fusion planning method considering information physical coupling based on a power distribution network information physical system containing a data center and distributed resources. The planning scheme considers comprehensive utilization of distributed energy storage power and voltage regulation characteristics and data load space-time transfer potential of the data center, and cooperatively plans energy storage configuration at a physical side, a data load space-time transfer mode and communication network topology at an information side, so that a CPDS comprehensive planning model is provided, planning cost minimization is realized, and communication topology of the power distribution network is optimized. The invention takes the influence of information physical coupling into consideration, can improve the consumption capability of distributed energy in the power distribution network, and reduces the power operation cost of the system.
Drawings
Fig. 1 is a block diagram of a power distribution network energy storage and data center fusion planning method considering information physical coupling according to the present invention;
FIG. 2 is a graph illustrating the toughness performance of the distribution network at various stages according to the present invention;
FIG. 3 is a schematic diagram of a data center physical model and process flow according to the present invention;
FIG. 4 is a model of an IEEE-33 node power distribution network according to the present invention;
FIG. 5 is a typical solar photovoltaic output curve and load curve according to the present invention;
FIG. 6 is a typical daily data load curve for a data center according to 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, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A robust optimization method considering the toughness of a power distribution network comprises the following steps:
acquiring the elastic response capability of the power distribution network to faults and the recovery capability (power distribution network toughness) after the faults, and establishing a toughness power distribution network system performance model under extreme events;
analyzing the change trend of the toughness curve of the power distribution network at each stage, and constructing a toughness quantization index of the power distribution network under an extreme event by combining the system load loss under the fault; wherein each stage comprises a normal operation stage before failure, a disturbance stage, a response stage, a recovery stage and a final response stage;
obtaining flexible regulation and control capacity of a delay type data load, and establishing a data load space-time transfer model;
aiming at the loss brought to the power distribution network by the small-probability-high-loss extreme event, a robust optimization model considering the flexible time-space regulation and control of energy storage and data load is established from the perspective of improving the viability of the power distribution network according to a flexible power distribution network system performance model and a data load time-space transfer model;
and verifying the rationality and feasibility of the robust optimization scheme based on the actual power distribution network.
A robust optimization method considering the toughness of a power distribution network, wherein a system performance model of the tough power distribution network in an extreme event comprises the following steps:
under an extreme event, the toughness performance curve of the power distribution network at each stage is shown in fig. 2, wherein:
T0-T1: and (5) normal operation stage before fault. At this stage, the system makes corresponding preparation and prevention aiming at the possible extreme events through reasonable resource configuration;
T1-T2: and (5) a disturbance stage. The system encounters disturbance fault at the moment T1, and the elasticity performance of the system is rapidly reduced at the moment because various elastic resources do not respond in time;
T2-T4: and (5) a response phase. After the system is disturbed for a period of time, the system is degraded to enter a stable response state, and all elastic resources are ready to respond to disturbance faults;
T4-T5: and (4) a recovery phase. The system elastic resource responds to the disturbance fault, the system performance is rapidly recovered, but the system performance is not recovered to a normal state before the fault;
T5-T7: and a final response phase. And the damaged infrastructure in the system is recovered, and the system performance is recovered to the normal operation state before the fault.
A robust optimization method considering toughness of a power distribution network comprises the following steps of:
for the first stage, only the influence of the extreme event on the viability of the power distribution network is considered, namely the toughness quantization indexes of the power distribution network in the disturbance stage and the response stage need to be considered. Generally speaking, the most direct influence of an extreme event on a power distribution network is system load reduction, and a time integral of load loss of the power distribution network under the extreme event is taken as a toughness quantification index.
Wherein p is s The probability of occurrence of an extreme event S is S, and S is an extreme event set;
the active power reduction value of the node i in the extreme event in the t period; n is a power distribution network node set; t is 1 And T 4 Corresponding to different status periods in claim 2; dt represents the integration over time t.
The toughness quantitative index is converted into a cost index capable of being quantitatively calculated, namely the annual load shedding loss cost of the power distribution network under the influence of extreme events:
wherein, T e Is the average number of extreme events occurring in a year;
cost per active power loss in the distribution network, p s The probability of occurrence of an extreme event S is S, and S is an extreme event set; />
The active power reduction value of the node i in the extreme event in the t period; n is a power distribution network node set; t is 1 And T 4 Corresponding to the different status periods in claim 2; dt represents the integration over time t.
A robust optimization method considering power distribution network toughness, the delayed data load comprising:
data loads in the CPDS are generally divided into a delay sensitive type and a delay tolerant type, the former requires real-time processing in a short time, and an M/M/1 queuing model is generally adopted to model queuing delay in a time period, so that the data loads received in each time period of a data center must be processed in the time period; the latter has higher tolerance to the processing time requirement, the processing is completed within a specified time, and the delay tolerant data loads among different data centers can also realize space transfer, so the data loads have space-time regulation characteristics. To simplify the model and without loss of generality, the present invention primarily considers delay tolerant type loads.
A robust optimization method considering toughness of a power distribution network, wherein the data load flexible regulation and control capability comprises the following steps:
delay tolerant data loads in a data center have space-time flexible regulation potential, and the air-conditioning throttling mode is shown in fig. 3. Data load between different front-end servers and compute nodes and should satisfy the following constraints:
equation (1) shows that the sum of the data loads regulated and distributed by the front-end servers should be equal to the local user requirement, wherein Load s,t Representing the local user demand, data, of the s-th front-end server at time t l,s,t The data load distributed to the data center l by the S-th front-end server at the time t is represented, and S is the total number of the front-end servers; formula (2) indicates that the data load of each data center should be equal to the sum of the space transfer loads of the data center and other data centers, and N is the total number of the data centers; equation (3) represents that the data load to be processed at each moment of each data center should be equal to the difference between the data load accepted by the space transfer and the time transfer load, wherein Trans l,t Representing the amount of data load transferred by data center l at time t.
The data load flexible regulation and control capability comprises the following data load time transfer model:
delay tolerant loads do not require real-time processing of the data load, allowing it to be processed after a delay period, so the data load time transfer model is:
Total l,t+1 ＝Total l,t +ΔData l,t Δt (5)
0≤Total l,t ≤Total l,max (6)
formula (4) Δ Data l,t Representing the amount of Data load, data, transferred by the ith Data center at time t l,t And Trans l,t The same formula (1) is explained; equation (5) represents the number of different time instants of the data centerAccording to the relation of the total storage amount of the load, delta t represents the time interval from t to t + 1; the upper limit and the lower limit of the data load storage Total amount of the data center are restricted by an equation (6), wherein Total l,max Representing the upper data load storage amount limit.
The data load flexible regulation and control capability comprises the following data load space transfer model:
the data load can be flexibly transferred among different data centers, and the space transfer model is as follows:
equation (8) is added to make the constraint because it is not possible for a single data center to both roll out and absorb load to any one data center.
A robust optimization method considering power distribution network toughness, the robust optimization model comprising:
in order to deal with the loss brought by the small-probability-high-loss extreme event to the power distribution network, the robust optimization model considering the energy storage and data load space-time flexible regulation is established from the perspective of improving the viability of the power distribution network, and comprises the following steps:
s.t.
Ax≤d
whereinP is a planning set, O is an operation set, and F is a fault set; x is a planning decision vector and comprises all decision variables participating in power distribution network planning; y is an operation decision vector and comprises decision variables which can participate in elastic scheduling of the power distribution network in the operation stage; z is a fault scene vector; a is T 、b T 、c T Coefficient matrixes corresponding to the planning decision vector, the operation decision vector and the fault scene vector are respectively set; A. b, C, D, G are coefficient matrixes under corresponding constraint conditions respectively; f is a constant matrix corresponding to the equality constraint condition.
The optimization model is a two-stage three-layer robust optimization model; the first stage is an investment stage, a reasonable power distribution network investment scheme is determined based on limited severe scene probability distribution, and a planning decision vector comprises the position and capacity configuration of fixed energy storage; the second stage is an operation stage, the operation decision variables comprise a space-time flexible scheduling scheme of data load, and the worst scene probability distribution is sought under the known first-stage investment scheme; based on the method, the internal double-layer optimization problem is simulated, operated and decoupled and solved, and the annual comprehensive cost minimization of the system under the worst scene probability distribution is realized.
A robust optimization model, the set of plans comprising:
energy storage investment cost:
wherein,
investment cost for unit capacity of stored energy, E n Is the nth energy storage capacity, N E The number is planned for energy storage, y1 is the energy storage operation age, and d is the discount rate.
Investment cost of the intelligent terminal:
wherein,
the investment cost of a single intelligent terminal is determined, K is the planned number of the intelligent terminals, y2 is the operation age of the intelligent terminal, and d is the discount rate.
A robust optimization model, the run set comprising:
energy storage operation cost:
wherein,
operating schedule fee for unit capacity storage>
And the charging power or the discharging power of the ith stored energy in a T period is represented, and T represents the total number of the stored energy charging and discharging periods.
The operation cost of the data center is as follows:
wherein, MP t Representing the node marginal price of electricity, N, of the distribution network at time t D As to the number of the data centers,
representing the power required by the data center to process a unit of data load per unit time.
A robust optimization model, the fault set comprising:
annual shedding load loss cost of a power distribution network under the influence of extreme events:
wherein, T e Is the average number of extreme events occurring in a year;
cost per active power loss in the distribution network, p s The probability of occurrence of the extreme event S is S, and S is an extreme event set; />
The active power reduction value of the node i in the extreme event in the t period; n is a power distribution network node set; t is a unit of 1 And T 4 Corresponding to the different status periods in claim 2; dt represents the integration over time t.
A robust optimization model, the planning set constraints comprising:
(1) The node allows for installation of energy storage rated power and capacity constraints;
(2) The allowable installation energy storage quantity of the power distribution network is restricted;
(3) The number of intelligent terminals allowed to be installed by the node is restricted;
a robust optimization model, the running a cluster constraint condition comprising:
(1) Power flow constraints (active power, reactive power constraints);
(2) Safety constraints (voltage, current constraints);
(3) Energy storage constraints (energy storage state of charge constraint, energy storage capacity constraint, energy storage electric quantity balance constraint);
(4) Data load constraints (data load time transfer amount constraints, data load space transfer amount constraints);
(5) Communication bandwidth constraints.
An alternative embodiment of the invention is described in detail below.
In one embodiment of the invention: the topology identification method described above is applied to a modified IEEE-33 node power distribution network model as shown in fig. 4. The rated voltage is 12.66kV, and the rated active power of the power distribution network is 4000kW.
Distributed photovoltaics are installed at the nodes 2, 6, 10, 13, 18, 22, 26, 29 and 33, the maximum installation capacity of the node photovoltaics is 500kW, and the maximum installation capacity of energy storage is 200kW & h. The power curves of the photovoltaic system and the load are shown in fig. 5, and the power factor of the load is 0.95. The extreme event takes typhoon as an example, the annual average occurrence frequency of the typhoon is assumed to be 10 times, the moving speed is 30km/h, the links 13-14, 26-27 and 7-8 are attacked in sequence at the time T1 in fig. 2, and the maximum fault number of the distribution lines on a certain time section is 3.
The nodes 5, 10, 18 and 26 are respectively provided with data centers IDC1-IDC4, and the data load scheduling takes 15 minutes as a unit on the assumption that the fault recovery time of the power distribution network generally does not exceed 2 hours.
In addition, other parameter settings in the energy storage and data load collaborative robust optimization model considering the toughness improvement of the power distribution network are shown in the following table.
TABLE 1 parameter settings
The energy storage capacity configuration planning result and the data load scheduling scheme result are as follows:
table 2 energy storage capacity allocation planning results
TABLE 3 robust optimization annual combined costs
TABLE 4 data load idle shift amount for each time period
The energy storage and data load collaborative robust optimization of the toughness improvement of the power distribution network is considered, and the planning result schematic diagram shown in fig. 6 can be finally obtained through the realization of a computer simulation program.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (10)
1. A robust optimization method considering the toughness of a power distribution network is characterized by comprising the following steps:
acquiring the elastic response capability of the power distribution network to the fault and the recovery capability after the fault, and establishing a toughness power distribution network system performance model under an extreme event;
analyzing the change trend of the toughness curve of the power distribution network at each stage, and constructing a toughness quantization index of the power distribution network under an extreme event by combining the system load loss under the fault; wherein each stage comprises a normal operation stage before failure, a disturbance stage, a response stage, a recovery stage and a final response stage;
obtaining flexible regulation and control capacity of a delay type data load, and establishing a data load space-time transfer model;
aiming at the loss brought to the power distribution network by the small-probability-high-loss extreme event, a robust optimization model considering the flexible time-space regulation and control of energy storage and data load is established from the perspective of improving the viability of the power distribution network according to a flexible power distribution network system performance model and a data load time-space transfer model;
and verifying the rationality and feasibility of the robust optimization scheme based on the actual power distribution network.
2. The robust optimization method considering the toughness of the power distribution network according to claim 1, wherein the performance model of the tough power distribution network system in the extreme event comprises the following stages:
T0-T1: normal operation stage before fault; at this stage, the system makes corresponding preparation and prevention aiming at the extreme events which may occur through reasonable resource configuration;
T1-T2: a disturbance stage; the system encounters disturbance fault at the moment T1, and the elasticity performance of the system is rapidly reduced at the moment because various elastic resources do not respond in time;
T2-T4: a response phase; after the system is disturbed for a period of time, the system is degraded to enter a stable response state, and each elastic resource is ready to respond to disturbance faults;
T4-T5: a recovery phase; the system elastic resource responds to the disturbance fault, the system performance is rapidly recovered, but the system performance is not recovered to a normal state before the fault;
T5-T7: a final response stage; and the damaged infrastructure in the system is recovered, and the system performance is recovered to the normal operation state before the fault.
3. The robust optimization method considering the toughness of the power distribution network according to claim 1, wherein the quantitative indicators of the toughness of the power distribution network in the extreme event comprise:
for the first stage, only considering the influence of extreme events on the viability of the power distribution network, namely considering the toughness quantization indexes of the power distribution network in a disturbance stage and a response stage; generally speaking, the most direct influence of an extreme event on a power distribution network is system load reduction, and the time integral of the load loss of the power distribution network under the extreme event is considered as a toughness quantification index;
wherein p is s The probability of occurrence of the extreme event S is S, and S is an extreme event set;
for nodes in extreme eventsi is the active power reduction value in the t period; n is a power distribution network node set; t is 1 And T 4 Corresponding to the different status periods in claim 2; dt represents the integration over time t.
Converting the toughness quantitative index into a cost index capable of being quantitatively calculated, namely the annual load shedding loss cost of the power distribution network under the influence of extreme events:
wherein, T e Is the average number of extreme events occurring in a year;
cost per active power loss in the distribution network, p s The probability of occurrence of an extreme event S is S, and S is an extreme event set;
the active power reduction value of the node i in the time period t in the extreme event; n is a power distribution network node set; t is a unit of 1 And T 4 Corresponding to the different status periods in claim 2; dt represents the integration over time t.
4. The robust optimization method considering the toughness of the power distribution network according to claim 1, wherein the delay-type data load comprises:
data loads in a power distribution information physical system are generally divided into a delay sensitive type and a delay tolerant type, the delay sensitive type and the delay tolerant type require real-time processing in a short time, and an M/M/1 queuing model is generally adopted to model queuing delay in a time period, so that the data loads received in all the time periods of a data center must be processed in the time period; the latter has higher tolerance to the requirement of processing time, the processing is completed within a specified time, and the delay tolerant data loads among different data centers can also realize space transfer, so the data loads have space-time regulation characteristics; to simplify the model and without loss of generality, the present invention primarily considers delay tolerant type loads.
5. The robust optimization method considering the toughness of the power distribution network according to claim 1, wherein the data load flexible regulation and control capability comprises:
the delay tolerant data load in the data center has the potential of space-time flexible regulation, and the data load between different front-end servers and computing nodes should satisfy the following constraints:
equation (1) shows that the sum of the data loads regulated and distributed by the front-end servers should be equal to the local user requirement, wherein Load s,t Representing the local user demand, data, of the s-th front-end server at time t l,s,t The data load distributed to the data center l by the S-th front-end server at the time t is represented, and S is the total number of the front-end servers; formula (2) indicates that the data load of each data center should be equal to the sum of the space transfer loads of the data center and other data centers, and N is the total number of the data centers; equation (3) represents that the data load to be processed at each moment of each data center should be equal to the difference between the data load accepted by the space transfer and the time transfer load, wherein Trans l,t Representing the amount of data load transferred by data center l at time t.
6. The data load flexible regulation capability of claim 5 wherein the data load time shift model comprises:
delay tolerant loads do not require real-time processing of the data load, allowing it to be processed after a delay period, so the data load time transfer model is:
Total l,t+1 ＝Total l,t +ΔData l,t Δt (5)
0≤Total l,t ≤Total l,max (6)
formula (4) Δ Data l,t Representing the amount of Data load, data, transferred by the ith Data center at time t l,t And Trans l,t The same formula (1) is explained; the formula (5) represents the relation of the data load storage total amount at different moments of the data center, and delta t represents the time interval from t to t + 1; the upper limit and the lower limit of the data load storage Total amount of the data center are restricted by an equation (6), wherein Total l,max Representing the upper data load storage amount limit.
7. The data load flexible regulation capability of claim 5 wherein the data load spatial transition model comprises:
the data load can be flexibly transferred among different data centers, and the space transfer model is as follows:
equation (8) is added to make the constraint because it is not possible for a single data center to both roll out and absorb load to any one data center.
8. The robust optimization method considering the toughness of the power distribution network according to claim 1, wherein the robust optimization model comprises:
in order to deal with the loss brought to the power distribution network by the small-probability high-loss extreme event, the robust optimization model considering the energy storage and data load space-time flexible regulation is established from the perspective of improving the viability of the power distribution network, and comprises the following steps:
s.t.
Ax≤d
wherein P is a planning set, O is an operation set, and F is a fault set; x is a planning decision vector and comprises all decision variables participating in power distribution network planning; y is an operation decision vector and comprises decision variables which can participate in elastic scheduling of the power distribution network in the operation stage; z is a fault scene vector; a is T 、b T 、c T Coefficient matrixes corresponding to the planning decision vector, the operation decision vector and the fault scene vector are respectively set; A. b, C, D, G are coefficient matrixes under corresponding constraint conditions respectively; f is a constant matrix corresponding to the equality constraint condition.
The optimization model is a two-stage three-layer robust optimization model; the first stage is an investment stage, a reasonable power distribution network investment scheme is determined based on limited severe scene probability distribution, and a planning decision vector comprises the position and capacity configuration of fixed energy storage; the second stage is an operation stage, the operation decision variables comprise a space-time flexible scheduling scheme of data load, and the worst scene probability distribution is sought under the known first-stage investment scheme; based on the method, the internal double-layer optimization problem is simulated, operated and decoupled and solved, and the annual comprehensive cost minimization of the system under the worst scene probability distribution is realized.
The planning set includes:
energy storage investment cost:
wherein,
investment cost for unit capacity of stored energy, E n For the nth energy storage capacity, N E Planning the number of the energy storage devices, wherein y1 is the energy storage operation life, and d is the current rate;
investment cost of the intelligent terminal:
wherein,
the investment cost of a single intelligent terminal is determined, K is the planned number of the intelligent terminals, y2 is the operation age of the intelligent terminal, and d is the discount rate.
The operation set comprises:
energy storage operating cost:
wherein,
the operating and scheduling costs are stored for a unit of capacity,
the charging power or the discharging power of the ith energy storage in a T period is represented, and T represents the total number of energy storage charging and discharging periods;
the operation cost of the data center is as follows:
wherein, MP t Represents the node marginal price of electricity, N, of the power distribution network at the moment t D In order to be the number of the data centers,
representing the power required by the data center to process a unit of data load per unit time.
The set of faults includes:
annual shedding load loss cost of a power distribution network under the influence of extreme events:
wherein, T e Is the average number of extreme events occurring in a year;
cost per active power loss in the distribution network, p s The probability of occurrence of an extreme event S is S, and S is an extreme event set;
the active power reduction value of the node i in the extreme event in the t period; n is a power distribution network node set; t is 1 And T 4 Corresponding to the different status periods in claim 2; dt represents the integration over time t.
9. The robust optimization model of claim 8, wherein the planning set constraints comprise:
the node allows for installation of energy storage rated power and capacity constraints;
the allowable installation energy storage quantity of the power distribution network is restricted;
and the number of intelligent terminals allowed to be installed by the node is restricted.
10. The robust optimization model of claim 8, wherein the run set constraints comprise:
the method comprises the following steps of (1) power flow constraint, wherein the power flow constraint is divided into active power constraint and reactive power constraint;
safety constraints, wherein the safety constraints are voltage and current constraints;
energy storage constraint, wherein the energy storage constraint is divided into energy storage charge state constraint, energy storage capacity constraint and energy storage electric quantity balance constraint;
data load constraints which are divided into data load time transfer amount constraints and data load space transfer amount constraints;
communication bandwidth constraints.
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| CN117996722A (en) * | 2023-12-26 | 2024-05-07 | 北京交通大学 | Distribution system emergency resource toughness planning method and system under extreme event |
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| CN117175639A (en) * | 2023-09-08 | 2023-12-05 | 国网浙江省电力有限公司绍兴供电公司 | Power distribution automation method and system matched with energy storage unit in coordination |
| CN117175639B (en) * | 2023-09-08 | 2024-05-31 | 国网浙江省电力有限公司绍兴供电公司 | Power distribution automation method and system matched with energy storage unit in coordination |
| CN117996722A (en) * | 2023-12-26 | 2024-05-07 | 北京交通大学 | Distribution system emergency resource toughness planning method and system under extreme event |
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