Method and apparatus for distributed energy storage scheduling under typhoon conditions, device, and storage medium

Abstract

Disclosed in the present invention are a method and apparatus for distributed energy storage scheduling under typhoon conditions, a device, and a storage medium. The method comprises: calculating failure rates of circuits of a power distribution network at each moment; constructing a load scheduling cost model, a node load power constraint, a distributed energy storage constraint, and a power distribution network operation constraint; constructing a first objective function by using lowest distributed energy storage scheduling cost as an objective, and constructing a second objective function by using a maximum power distribution network critical load power supply rate as an objective; under the node load power constraint, the distributed energy storage constraint and the power distribution network operation constraint, solving the first objective function and the second objective function to obtain a distributed energy storage scheduling solution achieving the lowest distributed energy storage scheduling cost and the maximum power distribution network critical load power supply rate, so as to schedule distributed energy storage under typhoon conditions on the basis of the distributed energy storage scheduling solution. The present invention can make distributed energy storage scheduling more rational.

Description

A distributed energy storage scheduling method in a typhoon situation, device, equipment and storage medium TECHNICAL FIELD

[0001] The present application relates to the field of power systems and their automation, and in particular to a distributed energy storage scheduling method in a typhoon situation, device, equipment and storage medium. BACKGROUND

[0002] The power system is the basis of the development of the national economy, and its stable operation is related to the production of the people and the safety of the country. However, many extreme disaster accidents have a great impact on the safe and stable operation of the power system. In recent years, the global climate has changed rapidly, and typhoons and other extreme disaster weather occur from time to time. Extreme disaster weather can cause large-scale failure of the distribution network, resulting in large-scale power outages and causing serious economic losses and social impact. The distribution network is a key infrastructure connecting the power grid and the user, and as a key link directly serving the user, the normal operation of the distribution network in extreme disaster weather is of great significance to the protection of people's production and life. Once the distribution network fails, it will cause a large number of load power outages and affect normal life and commercial production.

[0003] Distributed energy storage has the characteristics of fast response and strong flexibility, and not only has the functions of peak shaving and power quality improvement, but also has the ability to supply power to the load after the failure of the distribution network. In the event of extreme disasters causing distribution network line failure and unable to normally supply power to the load, distributed energy storage can supply power to the load by discharging itself, thereby reducing the amount of user load shedding during extreme disasters and allowing more important loads to be normally powered.

[0004] In the event of extreme disasters causing distribution network failure, reasonable scheduling of distributed energy storage to supply power to the load allows important loads to be restored to normal power supply as soon as possible and reduces the losses caused by disasters, which is the focus of the research. However, in existing research, many scholars only consider scheduling distributed energy storage to maximize the restoration of load power supply or maximize the restoration of important load power supply, and only considering this goal will make the research have certain limitations. SUMMARY

[0005] The present application provides a distributed energy storage scheduling method in a typhoon situation, device, equipment and storage medium to solve the technical problem that existing research only considers scheduling distributed energy storage to maximize the restoration of load power supply or maximize the restoration of important load power supply, and only considers one goal, which has certain limitations.

[0006] To solve the above technical problems, the present application provides a distributed energy storage scheduling method in a typhoon situation, comprising:

[0007] A typhoon wind field model reflecting the wind speed of each line of the power distribution network after a typhoon landing is constructed;

[0008] According to the typhoon wind field model, a power distribution network line failure rate model reflecting the failure rate of each line of the power distribution network at each moment is constructed;

[0009] According to the power distribution network line failure rate model, the failure rate of each line of the power distribution network at each moment is calculated, and the failure state of each line of the power distribution network at each moment is determined according to the failure rate, and then a plurality of groups of failure scenarios corresponding to the failure state are generated;

[0010] The power distribution system entropy value of the power distribution network is calculated, and a constraint condition of the power distribution system entropy value is constructed. According to the constraint condition, a failure scenario is selected as a target failure scenario from the plurality of groups of failure scenarios, and a load scheduling cost model, a node load power constraint, a distributed energy storage constraint, and a power distribution network operation constraint under the target failure scenario are constructed;

[0011] According to the load scheduling cost model, a first objective function is constructed with the minimum distributed energy storage scheduling cost as the target, and a second objective function is constructed with the maximum power supply rate of important loads of the power distribution network as the target;

[0012] Under the constraints of the node load power constraint, the distributed energy storage constraint, and the power distribution network operation constraint, the first objective function and the second objective function are solved to obtain a distributed energy storage scheduling scheme when the distributed energy storage scheduling cost is minimum and the power supply rate of important loads of the power distribution network is maximum, and then the distributed energy storage under the typhoon condition is scheduled according to the distributed energy storage scheduling scheme.

[0013] As a preferred solution, the typhoon wind field model is:

[0014] Wherein, v lk,t is the wind speed of the line of the kth small grid of the power distribution network line l at t moment; r lk,t is the distance between the line of the kth small grid of the power distribution network line l at t moment and the center of the typhoon; R max is the maximum wind speed radius of the typhoon; v T is the distribution parameter of the wind speed of the typhoon; v max is the maximum wind speed of the typhoon; c v is a correction coefficient.

[0015] As a preferred solution, the power distribution network line failure rate model is: F l,t =max(p lk,t );

[0016] Wherein, p lk,tF represents the failure rate of the k-th small grid of the distribution network line l at time t; l,t Let be the failure rate of line l at time t.

[0017] As a preferred embodiment, the entropy value of the power distribution system is:

[0018] Where E is the entropy value of the power distribution system; N B For a collection of distribution network lines; N T This is a timeline of events from the impact of a typhoon on the power distribution network to the repair of power lines.

[0019] The constraint condition for the entropy value of the power distribution system is:

[0020] As a preferred embodiment, the load dispatching cost model includes: a load dispatching cost model for adjustable loads and a load dispatching cost model for loads that can be shifted; the load dispatching cost model for adjustable loads is as follows:

[0021] The load scheduling cost model for the shiftable load is as follows: i'∪i”∪i”'=i;

[0022] Among them, P i',t Q represents the adjustable load active power that is normally supplied to node i' in the distribution network at time t. i',t The adjustable load reactive power of the distribution network node i' at time t is the normal power supply of the load. and Let be the actual active power and reactive power required by node i' after the adjustable load adjustment at time t; t is the active power adjustment coefficient variable and reactive power adjustment coefficient variable of the adjustable load, with values ​​between [0,1]. These are the active and reactive power adjustment coefficients for the transferable load, and are 0-1 variables; and They are nodes exist The actual active power and actual reactive power required by the adjustable load at all times; and These represent the actual active power and actual reactive power required for the load to be shifted at node i” at time t, respectively. and Let be the active power scheduling cost and reactive power scheduling cost of the adjustable load at node i' at time t, respectively. and These are the unit active power dispatch cost and unit reactive power dispatch cost of the adjustable load at node i'i', respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; i”,t and Q i”,t Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively;

[0023] As a preferred solution, the node load power constraint is:

[0024] Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; i,t and Q i,t Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; i”',t and Q i”',t Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively;

[0025] The distributed energy storage constraint includes: distributed energy storage charging and discharging constraint, distributed energy storage state of charge constraint, and distributed energy storage charging and discharging cost constraint;

[0026] The distributed energy storage charging and discharging constraint is:

[0027] Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively; and Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively;

[0028] The distributed energy storage state of charge constraint is: Pi(t) and Qi(t) are the active and reactive power of the controllable load at node i' at time t, respectively;

[0029] wherein SOC i,t and SOC i,t+1 are the state of charge of the energy storage at node i at time t and t+1 respectively; η c and η d are the charge and discharge efficiency of the energy storage; SOC i,max and SOC i,min are the upper and lower limits of the capacity of the energy storage at node i;

[0030] The distributed energy storage charge and discharge cost constraint is:

[0031] wherein, and are the active and reactive charge cost of the energy storage at node i at time t; and are the unit active and reactive charge cost of the energy storage at node i; and are the active and reactive discharge cost of the energy storage at node i at time t; and are the unit active and reactive discharge cost of the energy storage at node i;

[0032] The power distribution network operation constraint is:

[0033] wherein ， δ(i) and π(i) are the child and parent node set of node i respectively; is the square of the branch current modulus from node i to node j at time t ； and are the active and reactive output of the generator at node i at time t; r ij and x ij are the resistance and reactance of the branch from node i to node j; and are the upper and lower limits of the voltage modulus square at node i; is the upper limit of the branch current modulus square from node i to node j; M is a large enough constant.

[0034] As a preferred solution, the first objective function is:

[0035] The second objective function is:

[0036] wherein, ε iThe value of the importance level of the load at node i is taken.

[0037] On the basis of the above-mentioned embodiments, another embodiment of the present application provides a distributed energy storage scheduling device in a typhoon situation, comprising: a typhoon wind field model construction module, a distribution network line failure rate model construction module, a fault scenario generation module, a load scheduling cost model construction module, a target function construction module and a distributed energy storage scheduling module;

[0038] The typhoon wind field model construction module is configured to construct a typhoon wind field model reflecting the wind speed of each line of the distribution network after the typhoon lands.

[0039] The distribution network line failure rate model construction module is configured to construct a distribution network line failure rate model reflecting the failure rate of each line of the distribution network at each time according to the typhoon wind field model.

[0040] The fault scenario generation module is configured to calculate the failure rate of each line of the distribution network at each time according to the distribution network line failure rate model, determine the failure state of each line of the distribution network at each time according to the failure rate, and then generate a plurality of groups of fault scenarios according to the failure state.

[0041] The load scheduling cost model construction module is configured to calculate the distribution system entropy value of the distribution network and construct a constraint condition of the distribution system entropy value, select a fault scenario as a target fault scenario from the plurality of groups of fault scenarios according to the constraint condition, and construct a load scheduling cost model, a node load power constraint, a distributed energy storage constraint and a distribution network operation constraint under the target fault scenario.

[0042] The target function construction module is configured to construct a first target function with the lowest distributed energy storage scheduling cost as the target according to the load scheduling cost model, and construct a second target function with the maximum power supply rate of important loads of the distribution network as the target.

[0043] The distributed energy storage scheduling module is configured to solve the first target function and the second target function under the constraints of the node load power constraint, the distributed energy storage constraint and the distribution network operation constraint, obtain a distributed energy storage scheduling scheme with the lowest distributed energy storage scheduling cost and the maximum power supply rate of important loads of the distribution network, and then schedule the distributed energy storage in the typhoon situation according to the distributed energy storage scheduling scheme.

[0044] On the basis of the above-mentioned embodiment, a further embodiment of the present application provides an electronic device, the device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the distributed energy storage scheduling method in a typhoon situation as described in the above-mentioned application embodiment when executing the computer program.

[0045] On the basis of the above-mentioned embodiment, a further embodiment of the present application provides a storage medium, the storage medium comprising a stored computer program, wherein the device where the storage medium is located executes the distributed energy storage scheduling method in a typhoon situation as described in the above-mentioned application embodiment when the computer program runs.

[0046] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0047] The present application proposes a distributed energy storage scheduling method in a typhoon situation aiming at the influence of the typhoon situation on the distribution network, first constructs a distribution network line fault model to determine the fault state of each line of the distribution network at each time, and then generates a plurality of groups of fault scenes according to the fault state; selects a fault scene as a target fault scene based on the distribution system entropy value of the distribution network, and then constructs a first objective function with the minimum distributed energy storage scheduling cost as the target, and a second objective function with the maximum important load power supply rate of the distribution network as the target, and finally solves the first objective function and the second objective function by comprehensively considering the load scheduling cost model, the node load power constraint, the distributed energy storage constraint and the distribution network operation constraint, to obtain the distributed energy storage scheduling scheme with the minimum distributed energy storage scheduling cost and the maximum important load power supply rate of the distribution network, and then schedules the distributed energy storage in the typhoon situation according to the distributed energy storage scheduling scheme.

[0048] In the typhoon situation, the present application comprehensively considers the two factors of scheduling cost and important load recovery rate, respectively constructs objective functions with the minimum scheduling cost and the maximum important load power supply rate as the targets, so that the distribution network can save costs and also restore power supply to important loads as soon as possible. In general, the present application comprehensively considers the important load power supply rate and the distribution network scheduling cost when performing distributed energy storage scheduling, so that the distributed energy storage scheduling is more reasonable. BRIEF DESCRIPTION OF DRAWINGS

[0049] Fig. 1 is a flowchart of a distributed energy storage scheduling method in a typhoon situation according to an embodiment of the present application;

[0050] Fig. 2 is a structural schematic diagram of a distributed energy storage scheduling device in a typhoon situation according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objects, technical solutions and advantages of the present application clearer, the following will clearly and completely describe the technical solutions in the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort should fall into the scope of the present application.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application; the use of the terms "including," "comprising," "having" and "with" and any variations thereof in this specification and in the claims are intended to cover both the inclusive and exclusive cases.

[0053] In the description of the embodiments of the present application, the technical terms "first", "second" and the like are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0054] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, or necessarily alternatives to other embodiments. It will be explicitly and implicitly appreciated by a person of ordinary skill in the art that the embodiments described herein can be combined with other embodiments.

[0055] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects.

[0056] In the description of the embodiments of the present application, the term "a plurality of" refers to two or more (including two), and similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0057] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connection", "connecting", "fixing" and the like should be understood in a broad sense, for example, can be fixed connection, or detachable connection, or integral; can be mechanical connection, or electrical connection; can be direct connection, or indirect connection through an intermediate medium, or internal communication of two elements or interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0058] Embodiment one

[0059] Please refer to Fig. 1, which is a flowchart of a distributed energy storage scheduling method in a typhoon situation according to an embodiment of the present application, including the following specific steps:

[0060] S1, a typhoon wind field model reflecting the wind speed size of each line of the distribution network after the landing of the typhoon is constructed;

[0061] Preferably, the typhoon wind field model is:

[0062] Wherein, v lk,t is the wind speed size of the line of the kth small grid of the distribution network line l at t time; r lk,t is the distance of the line of the kth small grid of the distribution network line l from the typhoon center at t time; R max is the maximum wind speed radius of the typhoon; v T is the distribution parameter of the typhoon wind speed; v max is the maximum wind speed of the typhoon; c v is the correction coefficient.

[0063] Specifically, the present application includes the following specific implementation steps:

[0064] (1) Construct a typhoon wind field model

[0065] The maximum wind speed of the typhoon is at the maximum wind speed radius, and the present application uses an improved Rankine typhoon model to simulate the wind speed size of each line of the distribution network after the landing of the typhoon. The distribution network is projected into a coordinate system, and a small part of each line is in a small grid. Because the grid is small, it can be considered that the wind speed size of the lines in the same grid is consistent. The constructed typhoon wind field model is as follows:

[0066] Wherein, v lk,t is the wind speed size of the line of the kth small grid of the distribution network line l at t time; r lk,t is the distance of the line of the kth small grid of the distribution network line l from the typhoon center at t time; R maxRmax is the radius of the maximum wind speed of the typhoon; v T is the distribution parameter of the wind speed of the typhoon, usually 0.5; v max is the maximum wind speed of the typhoon; c v is used to convert the wind speed into the wind speed at a height of 10 m, c v is a correction coefficient.

[0067] S2, according to the typhoon wind field model, a power distribution network line failure rate model reflecting the failure rate of each line of the power distribution network at each time is constructed;

[0068] Preferably, the power distribution network line failure rate model is: F l,t = max(p lk,t );

[0069] wherein p lk,t is the failure rate of the kth small grid of the line l of the power distribution network at time t; F l,t is the failure rate of the line l at time t.

[0070] (2) Constructing a power distribution network line failure rate model

[0071] V d is the designed wind speed of the line, when the wind speed borne by the line is less than V d , the failure rate of the line is 0, when the wind speed borne by the line is greater than twice V d , the failure rate of the line is 1, and when the wind speed borne by the line is between the two, the failure rate of the line increases exponentially. The constructed power distribution network line failure rate model is as follows:

[0072] wherein p lk,t is the failure rate of the kth small grid of the line l of the power distribution network at time t.

[0073] The failure of any part of the line is considered as the failure of the line, so the maximum value of the failure rates of all the small grids of the line is taken as the failure rate of the line. F l,t = max(p lk,t );

[0074] wherein F l,t is the failure rate of the line l at time t.

[0075] S3, according to the power distribution network line failure rate model, the failure rate of each line of the power distribution network at each time is calculated, and the failure state of each line of the power distribution network at each time is determined according to the failure rate, and then a plurality of groups of failure scenarios corresponding to the failure state are generated;

[0076] (3) determining the fault state of the distribution network line

[0077] According to the fault rate model of the distribution network line, the fault rate of each line at each time is calculated, but for the line with a fault rate between (0, 1), the fault state of the line cannot be directly determined, so a sampling method is used to determine the fault state. That is, a plurality of random numbers between (0, 1) are generated for the lth line at t time, and the average value is x l,t , and then compared with F l,t , if x l,t is less than F l,t , the line fails, otherwise the line works normally. If the line fails at a certain time, the line is in a fault state until it is repaired. Repeat the above process to generate a plurality of fault scenarios.

[0078] Wherein, s l,t is the fault state of the line, and the value of 1 indicates that the line l fails at t time, and the value of 0 indicates that the line l works normally at t time; α ij,t represents the fault state of the line from node i to node j at t time, and the value of 0 indicates that the line fails, and the value of 1 indicates that the line works normally.

[0079] S4, calculate the distribution system entropy value of the distribution network and construct the constraint condition of the distribution system entropy value, select a fault scenario as a target fault scenario from the plurality of fault scenarios according to the constraint condition, and construct a load scheduling cost model, a node load power constraint, a distributed energy storage constraint and a distribution network operation constraint under the target fault scenario;

[0080] Preferably, the distribution system entropy value is:

[0081] Wherein, E is the distribution system entropy value; N B is the line set of the distribution network; N T is the set of the distribution network affected by the typhoon to the line repair time;

[0082] The constraint condition of the distribution system entropy value is:

[0083] (4) Selecting the distribution network line fault scenario based on the system information entropy method

[0084] After generating a plurality of fault scenarios, the scenarios need to be reduced, and the system information entropy method is used to reduce the scenarios. Entropy represents the uncertainty of the system, and the entropy value of the system is calculated by the following formula:

[0085] Wherein, E is the distribution system entropy value; N BN is a set of distribution network lines; N T is a set of time instants when the distribution network is influenced by typhoon and the lines are repaired.

[0086] The entropy value of the distribution network system should not be too large or too small, and should be within a proper range. That is, the following constraint is satisfied.

[0087] If there are multiple fault scenarios satisfying the above constraints, one of them is selected as the typical fault scenario in this paper.

[0088] Preferably, the load scheduling cost model comprises: a load scheduling cost model of adjustable load and a load scheduling cost model of translatable load.

[0089] The load scheduling cost model of adjustable load is:

[0090] The load scheduling cost model of translatable load is: i'∪i”∪i”'=i;

[0091] wherein, P i',t is the active power of the adjustable load of the distribution network node i' under normal power supply at time t; Q i',t is the reactive power of the adjustable load of the distribution network node i' under normal power supply at time t; and are the actual active power and reactive power required by the adjustable load of node i' at time t, respectively; t is the active adjustment coefficient variable and the reactive adjustment coefficient variable of the adjustable load, and takes a value between [0, 1]; is the active and reactive adjustment coefficient of the translatable load, and is a 0-1 variable; and are the actual active power and actual reactive power required by the adjustable load of node at time t; are the actual active power and actual reactive power required by the adjustable load of node at time t; and are the actual active power and actual reactive power required by the translatable load of node i" at time t; and are the active scheduling cost and reactive scheduling cost of the adjustable load of node i' at time t; and are the unit active scheduling cost and unit reactive scheduling cost of the adjustable load of node i'; and are the active scheduling cost and reactive scheduling cost of the translatable load of node i" at time t; andP i”,t and Q i”,t are the active power and reactive power of the movable load of the distribution network at node i' at time t, respectively; P

[0092] Preferably, the node load power constraint is:

[0093] wherein P i,t and Q i,t are the active power and reactive power of the normal supply of the distribution network at node i at time t, respectively; P i”',t and Q i”',t are the active power and reactive power of the normal supply of the distribution network at node i' at time t, respectively, and are the actual active power and reactive power of the movable load at node i' at time t, respectively;

[0094] The distributed energy storage constraint includes: a distributed energy storage charging and discharging constraint, a distributed energy storage state of charge constraint, and a distributed energy storage charging and discharging cost constraint.

[0095] The distributed energy storage charging and discharging constraint is:

[0096] wherein, and are the charging and discharging active power of the energy storage at node i at time t, respectively; and are the charging and discharging reactive power of the energy storage at node i at time t, respectively; and are 0-1 variables, respectively representing the charging and discharging flag of the energy storage, and the value 1 represents charging or discharging of the energy storage, and the value 0 represents the energy storage in an idle state; and are the upper limits of the active charging and discharging of the energy storage at node i, respectively; and are the upper limits of the reactive charging and discharging of the energy storage at node i, respectively;

[0097] The distributed energy storage state of charge constraint is:

[0098] wherein SOC i,t and SOC i,t+1respectively the state of charge of the energy storage at node i at time t and t+1; η c and η d respectively the charging and discharging efficiency of the energy storage; SOC i,max and SOC i,min respectively the upper and lower limits of the capacity of the energy storage at node i;

[0099] The distributed energy storage charging and discharging cost constraint is:

[0100] wherein, and respectively the active charging cost and the reactive charging cost of the energy storage at node i at time t; and respectively the unit active charging cost and the unit reactive charging cost of the energy storage at node i; and respectively the active discharging cost and the reactive discharging cost of the energy storage at node i at time t; and respectively the unit active discharging cost and the unit reactive discharging cost of the energy storage at node i;

[0101] The power distribution network operation constraint is:

[0102] wherein, δ(i) and π(i) are respectively the child and parent node set of node i; is the square of the branch current modulus value from node i to node j at time t; and respectively the active output and the reactive output of the generator at node i at time t; r ij and x ij respectively the resistance and the reactance value of the branch from node i to node j; and respectively the upper and lower limits of the voltage modulus value square at node i; is the upper limit of the branch current modulus square from node i to node j; M is a sufficiently large constant.

[0103] (5) Constructing a load scheduling cost model based on load characteristics

[0104] In the power distribution network, according to the load characteristics, the load can be divided into controllable load and uncontrollable load, and the controllable load can be further divided into translatable load and adjustable load. The adjustable load can be interrupted or reduced when the power distribution network fails or other special situations occur. The adjustable load scheduling cost model is as follows:

[0105] The translatable load has the same power characteristics as the conventional load, and the scheduling cost model is as follows: i'∪i”∪i”'=i;

[0106] Among them, P i',t Q represents the adjustable load active power that is normally supplied to node i' in the distribution network at time t. i',t The adjustable load reactive power of the distribution network node i' at time t is the normal power supply of the load. and Let be the actual active power and reactive power required by node i' after the adjustable load adjustment at time t; t is the active power adjustment coefficient variable and reactive power adjustment coefficient variable of the adjustable load, with values ​​between [0,1]. These are the active and reactive power adjustment coefficients for the transferable load, and are 0-1 variables; and They are nodes exist The actual active power and actual reactive power required by the adjustable load at all times; and These represent the actual active power and actual reactive power required for the load to be shifted at node i” at time t, respectively. and Let be the active power scheduling cost and reactive power scheduling cost of the adjustable load at node i' at time t, respectively. and These are the unit active power dispatch cost and unit reactive power dispatch cost of the adjustable load at node i'i', respectively; and Let be the active power scheduling cost and reactive power scheduling cost of the load that can be moved at node i” at time t, respectively. and Let P be the unit active power dispatch cost and unit reactive power dispatch cost of the load that can be moved at node i; i”,t and Q i”,t , i, i” ...

[0107] (6) Construct node load power constraints

[0108] The load power supplied by the distribution network under normal conditions should not exceed the actual power required by the nodes:

[0109] Among them, P i,t and Q i,t P represents the active and reactive power of the power distribution network at node i during normal power supply at time t. i”',t and Q i”',tPi' (t) and Qi' (t) are the uncontrollable active power and reactive power of node i' at time t, respectively, and Pi' (t) and Qi' (t) are the uncontrollable active power and reactive power of node i' at time t, respectively.

[0110] (7) Distributed energy storage constraints

[0111] Distributed energy storage charging and discharging constraints

[0112] When the distribution network fails, the distributed energy storage can supply power to the load by charging and discharging itself, and the same distributed energy storage cannot charge and discharge at the same time:

[0113] wherein, and Pi(t) and Qi(t) are the active power and reactive power of the energy storage at node i at time t, respectively; and Pi(t) and Qi(t) are the active power and reactive power of the energy storage at node i at time t, respectively; and are 0-1 variables, respectively, indicating the charging and discharging flag of the energy storage, and the value of 1 indicates that the energy storage is charging or discharging, and the value of 0 indicates that the energy storage is in an idle state; and Pi(t) and Qi(t) are the active power and reactive power of the energy storage at node i at time t, respectively; and Pi(t) and Qi(t) are the active power and reactive power of the energy storage at node i at time t, respectively.

[0114] Distributed energy storage state of charge constraints:

[0115] wherein, SOC i,t and SOC i,t+1 are the state of charge of the energy storage at node i at time t and t+1, respectively; η c and η d are the charging and discharging efficiencies of the energy storage; SOC i,max and SOC i,min are the upper and lower limits of the capacity of the energy storage at node i.

[0116] Distributed energy storage charging and discharging cost constraints:

[0117] wherein, and Pi(t) and Qi(t) are the active charging cost and reactive charging cost of the energy storage at node i at time t, respectively; and Pi(t) and Qi(t) are the active charging cost and reactive charging cost of the energy storage at node i at time t, respectively; and The active power discharge cost and the reactive power discharge cost of the energy storage of node i at time t are respectively; And The unit active power discharge cost and the unit reactive power discharge cost of the energy storage of node i are respectively.

[0118] (8) constructing power distribution network operation constraints

[0119] The DistFlow power flow equation is adopted in the application, when the line of the power distribution network is broken due to the influence of super typhoon, the line will have no current and power transmission, the Big-M method is adopted to simplify the constraint equation:

[0120] Wherein, δ(i) and π(i) are the child-parent node set of node i respectively; Is the square of the branch current modulus value from node i to node j at time t; And The active power output and the reactive power output of the generator at node i at time t are respectively; ij And x ij The resistance and reactance values of the branch from node i to node j are respectively; And The upper and lower limits of the voltage modulus square of node i are respectively; Is the upper limit of the branch current modulus square from node i to node j; M is a constant large enough.

[0121] The nonlinear power flow model is converted into the following second-order cone constraint model by adopting second-order cone relaxation:

[0122] S5, according to the load scheduling cost model, the first objective function is constructed with the minimum distributed energy storage scheduling cost as the target, and the second objective function is constructed with the maximum power distribution network important load power supply rate as the target;

[0123] Preferably, the first objective function is:

[0124] The second objective function is:

[0125] Wherein, ε i Is the importance level value of the load at node i.

[0126] (9) constructing objective functions

[0127] The minimum scheduling cost is taken as objective function one:

[0128] The maximum important load power supply rate is taken as objective function two:

[0129] The importance level of the load is evaluated by epsilon. If it is a primary load, the value is 1; if it is a secondary load, the value is 0.5; and if it is a tertiary load, the value is 0.1. The maximum important load power supply rate is taken as the objective function two:

[0130] Wherein, epsilon i is the value of the importance level of the load at node i.

[0131] S6, under the constraints of the node load power constraint, the distributed energy storage constraint and the power distribution network operation constraint, the first objective function and the second objective function are solved to obtain a distributed energy storage scheduling scheme under which the distributed energy storage scheduling cost is lowest and the power distribution network important load power supply rate is maximum, and then the distributed energy storage under the typhoon condition is scheduled according to the distributed energy storage scheduling scheme.

[0132] (10) Solving of the double objective function model based on the improved hierarchical sequence method of bisection method

[0133] First, the objective function one f1 and the objective function two f2 are solved separately, that is, two single objective function models are solved to obtain solutions and And when solving the objective function one f1, a model with the maximum cost as the objective function is additionally solved to obtain a solution In the present application, the maximum important load power supply rate is the main objective function, so the solution is converted into a constraint condition to solve the objective function two f2, which is also a single objective function, and the obtained solution is compared with the above-mentioned value, and then the bisection method is used to take the value of f1 between , and the objective function two f2 is continuously solved until the difference between the obtained value and is small, and the difference between f1 and is also small, at which time the solution of is the optimal solution of the double objective function model.

[0134] Specifically, the above solving process can be solved by YALMIP+CPLEX solver in MATLAB.

[0135] Embodiment two

[0136] Please refer to Fig. 2, which is a structural schematic diagram of a distributed energy storage scheduling device under a typhoon condition provided by an embodiment of the present application, the device comprising: a typhoon wind field model construction module, a power distribution network line fault rate model construction module, a fault scenario generation module, a load scheduling cost model construction module, an objective function construction module and a distributed energy storage scheduling module.

[0137] The typhoon wind field model construction module is configured to construct a typhoon wind field model reflecting the wind speed borne by each line of the power distribution network after the typhoon lands.

[0138] The power distribution network line failure rate model construction module is configured to construct a power distribution network line failure rate model reflecting the failure rate of each line of the power distribution network at each moment according to the typhoon wind field model.

[0139] The fault scenario generation module is configured to calculate the failure rate of each line of the power distribution network at each moment according to the power distribution network line failure rate model, determine the failure state of each line of the power distribution network at each moment according to the failure rate, and then generate a plurality of groups of fault scenarios according to the failure state.

[0140] The load scheduling cost model construction module is configured to calculate the power distribution system entropy value of the power distribution network, construct a constraint condition of the power distribution system entropy value, select a fault scenario as a target fault scenario from the plurality of groups of fault scenarios according to the constraint condition, and construct a load scheduling cost model, a node load power constraint, a distributed energy storage constraint, and a power distribution network operation constraint under the target fault scenario.

[0141] The objective function construction module is configured to construct a first objective function with the lowest distributed energy storage scheduling cost as the target according to the load scheduling cost model, and construct a second objective function with the maximum power supply rate of important loads of the power distribution network as the target.

[0142] The distributed energy storage scheduling module is configured to solve the first objective function and the second objective function under the constraint of the node load power constraint, the distributed energy storage constraint, and the power distribution network operation constraint, obtain a distributed energy storage scheduling scheme when the distributed energy storage scheduling cost is the lowest and the power supply rate of important loads of the power distribution network is the maximum, and then schedule the distributed energy storage under the typhoon condition according to the distributed energy storage scheduling scheme.

[0143] It should be noted that the apparatus embodiments described above are merely illustrative, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the apparatus embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0144] Those skilled in the art can clearly understand that, for the convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0145] Embodiment three

[0146] Correspondingly, an electronic device is provided, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the distributed energy storage scheduling method in a typhoon situation as described in the foregoing embodiments of the application when executing the computer program.

[0147] The electronic device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The device can include, but is not limited to, a processor and a memory.

[0148] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like. The processor is a control center of the device, and connects all parts of the device through various interfaces and lines.

[0149] Embodiment four

[0150] Correspondingly, a storage medium is provided, which includes a stored computer program, wherein the computer program controls a device where the storage medium is located to execute the distributed energy storage scheduling method in a typhoon situation as described in the foregoing embodiments of the application when running.

[0151] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the like; and the data storage area can store data created according to the use of the mobile phone and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0152] The storage medium is a computer readable storage medium, and the computer program is stored in the computer readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be realized. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0153] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A distributed energy storage scheduling method in typhoon situation, characterized in that, The method comprises the following steps: constructing a typhoon wind field model reflecting the wind speed borne by each line of the power distribution network after the typhoon lands; constructing a power distribution network line failure rate model reflecting the failure rate of each line of the power distribution network at each time according to the typhoon wind field model; calculating the failure rate of each line of the power distribution network at each time according to the power distribution network line failure rate model, and determining the failure state of each line of the power distribution network at each time according to the failure rate, and then generating a plurality of groups of failure scenarios corresponding to the failure state; calculating the power distribution system entropy value of the power distribution network and constructing the constraint condition of the power distribution system entropy value, selecting a failure scenario as a target failure scenario from the plurality of groups of failure scenarios according to the constraint condition, and constructing a load scheduling cost model, a node load power constraint, a distributed energy storage constraint and a power distribution network operation constraint under the target failure scenario; constructing a first objective function with the lowest distributed energy storage scheduling cost as the target according to the load scheduling cost model, and constructing a second objective function with the maximum power supply rate of the important load of the power distribution network as the target; solving the first objective function and the second objective function under the constraint of the node load power constraint, the distributed energy storage constraint and the power distribution network operation constraint to obtain the distributed energy storage scheduling scheme when the distributed energy storage scheduling cost is the lowest and the power supply rate of the important load of the power distribution network is the maximum, and then scheduling the distributed energy storage under the typhoon condition according to the distributed energy storage scheduling scheme.

2. The method of claim 1, wherein, The typhoon wind field model is: wherein v lk,t is the wind speed of the line of the kth small grid of the distribution network line l at time t; r lk,t is the distance of the line of the kth small grid of the distribution network line l at time t from the typhoon center; R max is the maximum wind speed radius of the typhoon; v T is the distribution parameter of the typhoon wind speed; v max is the maximum wind speed of the typhoon; c v is a correction coefficient.

3. The method of claim 2, wherein, The power distribution network line failure rate model is: F l,t = max(p lk,t ); wherein p lk,t is the failure rate of the line of the kth small grid of the distribution network line l at time t; F l,t is the failure rate of the line l at time t.

4. The method of claim 3, wherein, The power distribution system entropy value is: Wherein, E is the entropy value of power distribution system; N B is the set of distribution network lines; N T is the set of distribution network lines affected by typhoon to line repair time The constraint condition of the power distribution system entropy value is:

5. The method of claim 4, wherein, The load scheduling cost model comprises a load scheduling cost model of adjustable load and a load scheduling cost model of translatable load. The load scheduling cost model of the adjustable load: The load scheduling cost model of the translatable load is: i'∪i”∪i”'=i; wherein P i',t is the active power of the adjustable load of the distribution network node i' at time t under normal power supply; Q i',t is the reactive power of the adjustable load of the distribution network node i' at time t under normal power supply; and respectively are the actual active power and reactive power required by the node i' after the adjustable load is adjusted at time t; t is an active adjustment coefficient variable and a reactive adjustment coefficient variable of the adjustable load, and takes a value in [0, 1]; Kp, Kq are the active and reactive adjustment coefficients for the translatable load, being 0-1 variables; and respectively, are nodes In actual active power and actual reactive power required by the time-adjustable load; and actual real and reactive power required by the node i" to be able to translate the load at time t; and respectively, are the active scheduling cost and the reactive scheduling cost of the adjustable load of the node i' at the time t; and respectively, are the unit active scheduling cost and the unit reactive scheduling cost of the adjustable load of the node i'i'; and respectively, are the active and reactive dispatch cost of the node i" at time t for the movable load; and respectively, are the unit active and reactive dispatch cost of the movable load at node i"; P i”,t and Q i”,t respectively, are the active and reactive power of the movable load at node i" under normal power supply at time t; i' and i" are the nodes where the adjustable load and the movable load are located respectively, i'" is the node where the uncontrollable load is located, and i is the node number of the distribution network.

6. The method of distributed energy storage dispatching in typhoon situation as claimed in claim 5, wherein, The node load power constraint is: where P i,t and Q i,t are the active and reactive power of the distribution network at time t at node i, P i”',t and Q i”',t are the active and reactive power of the uncontrollable load of the distribution network at time t at node i’’, respectively, and respectively, the actual active power and reactive power required by the uncontrollable load of node i'"i'" at time t; The distributed energy storage constraint comprises a distributed energy storage charging and discharging constraint, a distributed energy storage state of charge constraint and a distributed energy storage charging and discharging cost constraint. The distributed energy storage charging and discharging constraint is: wherein, and respectively, are the charging and discharging active power of the energy storage at time t at node i; and respectively, are the charging and discharging reactive power at node i at time t; and All are 0-1 variables, respectively representing energy storage charging and discharging flags, and the value of 1 indicates that the energy storage is charging or discharging, and the value of 0 indicates that the energy storage is in an idle state; and respectively, are the upper limits of the active charging and discharging of the energy storage at node i; and respectively, the upper limit of the reactive charging and discharging of the distributed energy storage at node i; The distributed energy storage state of charge constraint is: wherein SOC i,t and SOC i,t+1 are the state of charge of the energy storage at node i at time t and t+1, respectively; η c and η d are the charge and discharge efficiencies of the energy storage, respectively; SOC i,max and SOC i,min are the upper and lower limits of the capacity of the energy storage at node i, respectively. The distributed energy storage charging and discharging cost constraint is: wherein, and respectively, are the active and reactive charging cost of the energy storage at node i at time t; and respectively, the unit active charging cost and the unit reactive charging cost of the energy storage of node i; and respectively, are the active and reactive power discharge cost of the energy storage at node i at time t; and respectively, the unit active discharging cost and unit reactive discharging cost of the energy storage at node i; The power distribution network operation constraint is: - a ij,t M < P ij,t < a ij,t M; - a ij,t M < Q ij,t ≤ a ij,t M wherein δ(i) and π(i) are the child and parent node sets of node i, respectively; is the square of the branch current from node i to node j at time t. and PQ (i, t) and QG (i, t) are the active and reactive power output of the generator at node i at time t, respectively; r ij and x ij are the resistance and reactance of the branch from node i to node j, respectively; and upper and lower limits of the square of the voltage modulus of the node i, respectively; is the upper limit of the branch current module value from node i to node j; M is a large enough constant.

7. The method of distributed energy storage dispatching in typhoon situation as claimed in claim 6, wherein, The first objective function is: The second objective function is: where ε i is the importance level of the load at node i.

8. A distributed energy storage scheduling device in typhoon situation, characterized in that, The method comprises the following steps: The typhoon wind field model construction module is configured to construct a typhoon wind field model reflecting the wind speed borne by each line of the power distribution network after the typhoon lands; The power distribution network line failure rate model construction module is configured to construct a power distribution network line failure rate model reflecting the failure rate of each line of the power distribution network at each time according to the typhoon wind field model; The failure scenario generation module is configured to calculate the failure rate of each line of the power distribution network at each time according to the power distribution network line failure rate model, and determine the failure state of each line of the power distribution network at each time according to the failure rate, and then generate a plurality of groups of failure scenarios corresponding to the failure state; The load scheduling cost model construction module is configured to calculate the power distribution system entropy value of the power distribution network and construct the constraint condition of the power distribution system entropy value, select a failure scenario as a target failure scenario from the plurality of groups of failure scenarios according to the constraint condition, and construct a load scheduling cost model, a node load power constraint, a distributed energy storage constraint and a power distribution network operation constraint under the target failure scenario. The load scheduling cost model construction module is configured to calculate an entropy value of a power distribution system of the power distribution network, construct a constraint condition of the entropy value of the power distribution system, select a fault scenario from a plurality of fault scenarios as a target fault scenario according to the constraint condition, and construct a load scheduling cost model under the target fault scenario, a node load power constraint, a distributed energy storage constraint, and a power distribution network operation constraint. The objective function construction module is configured to construct a first objective function with a minimum distributed energy storage scheduling cost as a target according to the load scheduling cost model, and construct a second objective function with a maximum power supply rate of important loads of the power distribution network as a target. The distributed energy storage scheduling module is configured to solve the first objective function and the second objective function under the constraint of the node load power constraint, the distributed energy storage constraint, and the power distribution network operation constraint, obtain a distributed energy storage scheduling scheme when the distributed energy storage scheduling cost is minimum and the power supply rate of important loads of the power distribution network is maximum, and then schedule the distributed energy storage under the typhoon condition according to the distributed energy storage scheduling scheme.

9. An electronic device, comprising: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor are included, and the processor implements the typhoon condition distributed energy storage scheduling method according to any one of claims 1 to 7 when the computer program is executed.

10. A storage medium, characterized by The storage medium includes a stored computer program, wherein the storage medium controls the device where the storage medium is located to execute the typhoon condition distributed energy storage scheduling method according to any one of claims 1 to 7 when the computer program is executed.

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