CN114614482B - Method for improving toughness of power distribution system through virtual energy storage based on continuous time scale - Google Patents
Method for improving toughness of power distribution system through virtual energy storage based on continuous time scale Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
- H02J2310/64—The condition being economic, e.g. tariff based load management
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Abstract
The invention discloses a method for improving toughness of a power distribution system based on continuous time scale virtual energy storage, and belongs to the technical field of power distribution network energy storage. The method is characterized in that: the method comprises the following steps: establishing a virtual energy storage collaborative optimization model; proposing a Bernstein polynomial based on interval normalization; a method model for improving the toughness of a power distribution system based on virtual energy storage of a continuous time scale; and solving the toughness improvement model to obtain a virtual energy storage and distribution network toughness improvement scheme. Compared with the traditional discrete optimization method, the continuous optimization method provided by the invention can accurately simulate the dynamic operation process of virtual energy storage, and on the basis, the quick response capability of the virtual energy storage is fully exerted, so that the virtual energy storage can reduce the fault loss to a greater extent and improve the toughness of the power distribution network.
Description
Technical Field
The invention discloses a method for improving toughness of a power distribution system based on continuous time scale virtual energy storage, and belongs to the technical field of power distribution network energy storage.
Background
Extreme events easily cause large-area power failure, and have disastrous consequences for the society and the economy. It is therefore essential to study the toughness of power systems to extreme events, especially for distribution networks. Distribution network toughness is defined as the ability of a distribution network to take proactive steps to ensure that loads in an emergency are powered up and quickly recover a power outage load. As flexible resources for improving the toughness of the power distribution network, Mobile Energy Storage (MES) and Demand Response (DR) have been widely applied to the field of improving the toughness of the power distribution network.
In the research in the field of toughness improvement of power distribution networks, various operation strategies related to MES or DR, such as a fault recovery strategy for MES and microgrid collaborative optimization, a pre-positioning before MES disaster, an emergency scheduling strategy after MES disaster, a multi-objective toughness improvement strategy for minimizing line aging and maximizing network reliability, and the like, are available at present. The results show that the capacity of MES and DR for carrying out space-time transfer on electric energy plays an important role in load recovery. However, in the above MES scheduling strategy, the MES spatio-temporal model neglects the coupling of the grid to the traffic network, so that the MES may not be able to reasonably simulate the routing process. In addition, researches on the synergistic improvement of toughness of MES and DR are few, and the advantages of MES and DR cannot be complemented through synergistic optimization.
Existing research on the toughness improvement of power distribution networks has mainly focused on Discrete Time (DT) models, such as 1 hour, 30 minutes, and 15 minutes. However, the DT model of VES has the following disadvantages:
(1) the DT model cannot correctly simulate the VES climbing process and cannot fully exert the quick response capability of the VES;
(2) the DT model cannot handle sub-hour variations, which may exacerbate fault losses;
(3) relatively accurate VES operation procedures can be obtained by reducing the time interval, but the excessively long calculation time reduces the timeliness of emergency scheduling.
Compared with a DT model, the Continuous Time (CT) model can well simulate the quick response capability of virtual energy storage by embedding a continuous function in an optimization problem, so that the adaptability of the power distribution network to sub-hour changes is improved. At present, a bernstein polynomial-based CT model has been successfully applied to the fields of flexibility improvement, energy storage configuration, random scheduling, and the like.
Bernstein polynomials are continuous functions that can only be approximated within a closed interval [ t, t +1 ]. However, when an extreme event occurs, in most cases, the length of the closed interval is not 1 hour. This means that conventional bernstein polynomial based CT models cannot accurately capture the dynamic process of a controllable resource. Therefore, how to apply the bernstein polynomial to establish the CT model of any closed interval is the key to improve the toughness of the power distribution network.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for improving the toughness of the power distribution system through the virtual energy storage based on the continuous time scale can overcome the defects of the prior art, can accurately simulate the dynamic operation process of the virtual energy storage, enables the virtual energy storage to reduce fault loss to a greater extent, and improves the toughness of the power distribution network.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for improving the toughness of the power distribution system through the virtual energy storage based on the continuous time scale is characterized by comprising the following steps of: the method comprises the following steps:
establishing a virtual energy storage collaborative optimization model;
proposing a Bernstein polynomial based on interval normalization;
establishing a method model for improving the toughness of a power distribution system by virtual energy storage based on a continuous time scale;
and solving the toughness improvement model to obtain a virtual energy storage and distribution network toughness improvement scheme.
Preferably, the virtual energy storage collaborative optimization model comprises a space-time model of mobile energy storage and a demand response model.
Preferably, the method further comprises the step of constructing a space-time model of the mobile energy storage by describing a path planning method and charge-discharge constraints of the mobile energy storage; the space-time model of mobile energy storage is as follows:
wherein x ∈ [ Δ t, t Δ t]Δ t is the interval length;
and
all of which are variables of a Boolean type,
in order to move the real-time location of the stored energy,
is an auxiliary variable describing the real-time location;
the system is a Boolean variable and is used for representing whether the mobile energy storage is in a running state or not;
where N is the total number of nodes in the distribution network, R b，b′ Represents traffic information between node b and node b';
according to a traffic network in a power distribution network, an optional path matrix R between nodes is as follows:
the charge and discharge constraints of the mobile energy storage are as follows:
wherein, P max Is the maximum charge-discharge power of the mobile energy storage,
for moving the charging and discharging power of the stored energy k at the b bus in the period t, P down Rising sum P up Descent is the maximum rate of ascent and descent of mobile energy storage respectively,
is the energy State (SOE) of the mobile energy storage k during the time period t,
is the state of charge of the mobile energy storage k in the period t,
and
respectively the upper and lower limits of the mobile energy storage state of charge, E k Is the capacity of the mobile energy storage k and T is the total number of time intervals.
Preferably, the method further comprises that under a price mechanism of time-of-use electricity price, the node can translate or interrupt the flexible load of the distribution network in time according to the power supply capacity of the distribution network;
the operational constraints of the translatable loads are:
wherein,
is the maximum extent to which the load can be translated,
is the total base load of node b during time t,
is the total translational load of node b during the time period t;
the operating constraints of interruptible loads are:
wherein,
is the maximum extent to which the load can be interrupted,
is the total amount of load that node b interrupted during time t.
Preferably, the method further includes compensating each node participating in the demand response by using a step compensation mechanism, where the constraint conditions of the step compensation mechanism are:
wherein,
and
respectively the translational load amount and the interruption load amount of the node b in the period t,
is the base load of node b during time t;
the compensation for a node with a demand response is:
wherein,
and
the unit compensation coefficients of the translatable load and interruptible load, h, corresponding to j, respectively j And d j Upper limits for translatable and interruptible loads of j,
and
respectively, to compensate for load shifting or load interruption during the time t.
Preferably, the bernstein polynomial based on interval normalization is:
wherein,
representing a continuous function f t (r) a bernstein polynomial based on interval normalization, n being the order of the polynomial;
by a polynomial function
For the control point, f is obtained t (r) in the interval r ∈ [0, 1]]The approximation curve of the above interval-based normalized bernstein polynomial.
Preferably, the method further comprises the steps of establishing a continuous time model-based emergency dispatching model of the power distribution network for resisting extreme events;
the optimization target of emergency dispatching is as follows:
wherein,
is the actual power demand of node b during time t, C v Is the penalty cost per unit load loss, L t The interval length of the t period.
Preferably, the method further comprises, in the emergency dispatch, the energy conservation constraints of the mobile energy storage and the translatable load during the operation cycle are:
preferably, the power flow and power balance constraints during a fault in conforming emergency are:
wherein,
is a boolean variable characterizing the operating state of the line l.
Preferably, the collaborative optimization model is solved through a Julia platform by using a Gurobi solver, so that a virtual energy storage dynamic operation process is obtained.
Compared with the prior art, the invention has the beneficial effects that:
compared with the traditional discrete optimization method, the continuous optimization method provided by the invention can accurately simulate the dynamic operation process of the virtual energy storage, and on the basis, the quick response capability of the virtual energy storage is fully exerted, so that the virtual energy storage can reduce the fault loss to a greater extent and improve the toughness of the power distribution network.
Drawings
Fig. 1 is a flowchart of a method for improving toughness of a power distribution system based on virtual energy storage on a continuous time scale.
Detailed Description
The present invention is further described with reference to the following detailed description, however, it should be understood by those skilled in the art that the detailed description given herein with respect to the accompanying drawings is for better explanation and that the present invention is not necessarily limited to the specific embodiments, but rather, for equivalent alternatives or common approaches, may be omitted from the detailed description, while still remaining within the scope of the present application.
Fig. 1 shows a preferred embodiment of the present invention, which is further described below with reference to fig. 1.
As shown in fig. 1: the method for improving the toughness of the power distribution system based on the virtual energy storage of the continuous time scale comprises the following steps:
establishing a virtual energy storage collaborative optimization model;
proposing a Bernstein polynomial based on interval normalization;
establishing a method model for improving the toughness of a power distribution system by virtual energy storage based on a continuous time scale;
and solving the toughness improvement model to obtain a virtual energy storage and distribution network toughness improvement scheme.
According to the invention, a mobile energy storage continuous charging and discharging model is constructed, the energy storage energy states under different time scales can be accurately and effectively simulated, the dynamic characteristics of mobile energy storage are considered, the optimal configuration capacity of mobile energy storage and the optimal network access position at different moments can be determined, and the problem of safe and economic operation of a power distribution network containing high-proportion photovoltaic is solved.
As a possible implementation manner of this embodiment, the process of establishing the virtual energy storage collaborative optimization model is as follows:
in order to fully exert the advantage complementation between the mobile energy storage and the demand response, a virtual energy storage collaborative optimization model is established, wherein the virtual energy storage collaborative optimization model comprises a space-time model of the mobile energy storage and a demand response model. In the collaborative optimization model, a path selection process of mobile energy storage is simulated by a path planning method with the shortest driving time.
Mobility and charge-discharge capability are important features that distinguish mobile energy storage from other flexible resources. However, in the process of improving the toughness of the power distribution network, the traveling path of the mobile energy storage is affected by the traffic network. In the embodiment, a space-time model of mobile energy storage is constructed by describing a mobile energy storage path planning method and charge-discharge constraints.
The mobile energy storage has two running states, namely a running state or a charging and discharging state. The operation state constraint of the mobile energy storage is as follows:
wherein N is the total number of nodes in the power distribution network,
the system is a Boolean variable and is used for representing whether the mobile energy storage is in a running state or not; if the mobile energy storage k runs from node b to node b' during the period t, then
If the mobile energy storage k is in a charging and discharging state in the t period, then
R b，b′ Representing traffic information between node b and node b'.
Before planning a path for mobile energy storage, an alternative path for mobile energy storage should be determined first. According to the traffic network in the power distribution network, the selectable path matrix R between the nodes is as follows:
in the traffic network, if there is a path between node b and node b', R b，b′ 1; otherwise, R b，b′ 0. The traffic information among all nodes is not constant, when the distribution line is broken due to an extreme event, the mobile energy storage cannot select the path as an optional path, and therefore the path R corresponding to the broken line b，b′ Is 0.
By describing the running state constraint and the selectable path matrix of the mobile energy storage, the space-time constraint of the mobile energy storage is as follows:
wherein x ∈ [ Δ t, t Δ t]Δ t is the interval length;
and
all of which are variables of a Boolean type,
in order to move the real-time location of the stored energy,
are auxiliary variables describing the real-time position. If the mobile energy storage k is positioned at the node b in the period t, then
If not, then,
if the mobile energy storage k is in a charge-discharge state at the node b in the period t, the mobile energy storage k is in a charge-discharge state
If not, then,
by setting the auxiliary variable, the following effects can be achieved:
if the mobile energy storage is in a running state in the period t, the position of the mobile energy storage in the period t +1 is changed, namely
If the mobile energy storage is in a charging and discharging state in the t period, the position of the mobile energy storage in the t +1 period is not changed, namely
When the mobile energy storage provides charging and discharging service for the node b, the following constraints need to be met:
wherein x ∈ [ Δ t, t Δ t]，P max Is the maximum charge and discharge power of the mobile energy storage,
for moving the charging and discharging power of the stored energy k at the b bus in the period t, P down Rising sum P up The descent is the maximum ascent and descent rate of the mobile energy storage respectively,
is the energy State (SOE) of the mobile energy storage k during the time period t,
is the state of charge of the mobile energy storage k in the period t,
and
respectively the upper and lower limits of the mobile energy storage state of charge, E k Is the capacity of the mobile energy storage k and T is the total number of time intervals.
Under the price mechanism of the time-of-use electricity price, the node can translate or interrupt the flexible load of the distribution network in time according to the power supply capacity of the distribution network. Typically, to relieve the pressure of power balance during peak hours, the node interrupts the load during peak hours of electricity prices and shifts the load to the valley hours of electricity prices.
Translatable loads, such as washing machines, water heaters, and the like, may be translated to other work periods as needed by the nodes. The operational constraints of a translatable load are:
wherein x ∈ [ Δ t, t Δ t]，
Is the maximum extent to which the load can be translated,
is the total base load of node b during time t,
is the total translational load of node b during time t.
Interruptible loads refer to loads that air conditioners, incandescent lamps, etc. do not significantly affect the quality of service of the power supply. The operating constraints of interruptible loads are:
wherein, x ∈ [ Delta t,tΔt]，
is the maximum extent to which the load can be interrupted,
is the total amount of load that node b interrupted during time t.
In the process of adjusting the flexible load of each node, the electricity utilization satisfaction of each node can be influenced to different degrees. In order to reasonably compensate each node participating in the demand response, a step compensation mechanism is adopted in the invention.
The constraints of the step compensation mechanism are as follows:
wherein x ∈ [ Δ t, t Δ t]，
And
respectively the translational load amount and the interruption load amount of the node b in the period t,
is the base load of node b during time t; based on this, the compensation for nodes with demand response is as follows:
wherein,
and
translatable and interruptible loads, respectively, of jUnit compensation coefficient of (h) j And d j Upper limits for translatable and interruptible loads of j,
and
respectively, to compensate for load shifting or load interruption during the time t.
As a possible implementation manner of this embodiment, a process of a bernstein polynomial based on interval transformation is proposed as follows:
the bernstein polynomial has the ability to approximate a continuous function over a closed interval [ (t-1) Δ t, t Δ t ] (Δ t ═ 1). However, when Δ t ≠ 1, the approximation property of bernstein polynomials is no longer valid.
If function f t (x) At x ∈ [ (t-1) Δ t, t Δ t]The above is continuous and has Δ t ≠ 1, then a ═ t (t-1) Δ t and b ═ t Δ t, which can be converted into a normalized interval according to the following formula:
x＝a(1-r)+br，r∈[0，1]；
wherein f is t (x) In the case of r ∈ [0, 1]]Upper run, f t (x) In the case of r ∈ [0, 1]]The above interval-based normalized bernstein polynomial is defined as follows:
wherein,
representing a continuous function f t (r) bernstein polynomial based on interval normalization, n is the order of the polynomial. In a polynomial function
For the control point, f is obtained t (r) in the interval r ∈ [0, 1]]The approximation curve of the above interval-based normalized bernstein polynomial.
Bernstein polynomial based on interval normalization
With approximation of continuous function f over arbitrary closed intervals t (r) ability. And when the order of the bernstein polynomial based on interval normalization is increased infinitely,
approximating a continuous function f t (r)。
As the order of the polynomial increases,
for f t The higher the approximation accuracy of (r). And from 4 th order to 10 th order, the approximation precision of the interval normalization Bernstein polynomial is obviously improved. However, when the order is higher than 10, the approximation accuracy of bernstein polynomial is hardly improved. Therefore, the 10 th order interval-based normalized bernstein polynomial can obtain sufficient approximation accuracy.
Derivative properties of interval-based normalized bernstein polynomials:
the polynomial n of the above formula is reduced to n-1.
Integral property of interval normalized bernstein polynomial:
the above equation shows that the integral value of the interval-based normalized bernstein polynomial depends on the polynomial function
And an order n.
Constrained reduction based on interval normalized bernstein polynomial: according to the definition of the interval-based normalized bernstein polynomial, the polynomial can be converted into the form of multiplication of a row vector and a column vector:
in the approximation of interval-normalized bernstein polynomials, the same column of vectors can be eliminated in equality or inequality constraints, such that equality constraints and inequality constraints are reduced to the formula of only the remaining polynomial function. As a possible implementation manner of this embodiment, the process of the virtual energy storage distribution network toughness improvement model based on continuous time scheduling is as follows:
before an extreme event occurs, the power distribution network implements a day-ahead scheduling strategy. In order to verify the performance of the toughness improvement model, an emergency dispatching model of the power distribution network for resisting extreme events based on a continuous time model is established.
The distribution network day-ahead scheduling of Bernstein polynomial based on interval normalization: the goal of day-ahead scheduling is to minimize the operating costs of the distribution grid, including the cost of power generation, the cost of purchase, and the cost of demand response compensation, as well as the cost of maintenance of mobile energy storage. Thus, the objective function of the day-ahead schedule can be expressed as:
wherein K is the number of mobile energy storage, n is the order of Bernstein polynomial based on interval normalization, and L t Is the interval length of the t period, C b Is the cost factor of the power generation of the distributed power source at node b,
is the electricity purchase price of the upper-level power grid in the period t,
is the ith order of the power output of the distributed power supply in the node b in the period tThe coefficient of the term is given,
is the ith polynomial coefficient of the electric energy purchased by the node b from the upper-level power grid in the period of t, gamma is the daily cost coefficient of mobile energy storage, C E And C P The cost coefficients of the mobile energy storage capacity and the charge and discharge power are respectively.
Constraints of the day-ahead scheduling are transformed into a continuous-time form according to the approximating nature of the interval-based normalized bernstein polynomial.
The charge and discharge constraints of the mobile energy storage are as follows:
wherein,
and
respectively the upper and lower limits of the mobile energy storage state of charge,
is an n + 1-dimensional vector formed by a polynomial coefficient of the charging and discharging power of the mobile energy storage k at the node b in the t period,
is the ith polynomial coefficient of the charge and discharge power of the mobile energy storage k in the t period at the node b,
is an n + 1-dimensional vector formed by a polynomial coefficient of the residual capacity of the mobile energy storage k in the time period t,
is the ith polynomial coefficient of the residual capacity of the mobile energy storage k in the period t.
The continuous-time constraint of demand response is:
wherein,
respectively an n + 1-dimensional vector composed of polynomial coefficients of translation load, interruption load and base load of the node b in a period of t,
and
the maximum translation and interruption amounts of the translation load and the interruption load are respectively.
The translation load, interrupt load and base load at node b during t are as follows:
power generation constraint of distributed power generation: the continuous-time power generation constraint of a Distributed Generation (DG) can be translated into:
wherein,
is the maximum power generation of the DG located at node bThe amount of the compound (A) is,
is an n + 1-dimensional vector composed of a polynomial coefficient of the generated power of the DG of the node b in the period t.
In order to ensure the continuity of the distributed power generation in the adjacent time period, the continuous power generation constraint of the distributed power generation in the adjacent time period is as follows:
the continuous constraints of continuous power and virtual energy storage purchased from the upper grid are:
the power flow and power balance constraints during the normal period are as follows:
the continuous time constraint that limits the power flow of line i during a normal cycle for a period t is:
wherein L is the number of distribution lines,
is a n + 1-dimensional vector, S, composed of polynomial coefficients of the active power transmitted by the line l during the time period t l Is the maximum transmission capacity of the line l.
In the t period, the node b generates an n + 1-dimensional vector consisting of polynomial coefficients of the actual power demand in the t period
Comprises the following steps:
the continuous-time power balance constraint during the normal period is:
wherein,
n + 1-dimensional vector composed of polynomial coefficient for purchasing electric energy from superior electric network for node, s (l) is power outflow end of line l, r (l) is power inflow end of line l,
and the n + 1-dimensional vectors are formed by polynomial coefficients of active power and reactive power transmitted by the transmission line flowing into the node b respectively.
When an extreme event occurs, making an emergency decision in time is the key for improving the disaster resistance of the power distribution network. In the present embodiment, x b Is the time of occurrence of an extreme event, [1, t c ]T in (1) is a normal period, [ t h ，T+1]T in (1) is a failure period. Each interval having a length of
Before emergency scheduling, the operation cost of the power distribution network in a normal time period is calculated in the following mode:
wherein, C m In order to be the operating cost of the normal period,
is node b at t c The total amount of electricity generated in the period,
is node b at t c Time interval from upper levelThe total amount of electricity purchased by the power grid,
respectively in the time period t c Compensation for node b translation or load interruption, gamma is the daily cost coefficient of mobile energy storage,
and
the calculation method of (2) is as follows:
in addition, the total charge-discharge power, the load translation amount, the load interruption amount and the base load of the mobile energy storage k in the node b are respectively as follows:
the initial value of the controllable resource has a great influence on the emergency scheduling. In the emergency scheduling, the initial value of the controllable resource of the power distribution network is as follows:
by calculating the initial value when the extreme event occurs, the power distribution network can make an emergency decision more accurately.
To better cope with extreme events, the goal of conforming emergency is to minimize economic losses, including cost of electricity generation, cost of electricity purchase, load compensation, and cost of lost loads during a fault. The optimization objective of the conforming emergency is modeled as follows:
wherein,
is the actual power demand of node b during time t, C v Is the penalty cost per unit load loss, L t The interval length of the t period.
The energy conservation constraints of the mobile energy storage and the translatable load in the emergency dispatching in the operation period can be respectively converted into the following models:
in conforming emergency, the power flow and power balance constraints during a fault can be modeled as:
wherein,
is a Boolean type variable for representing the running state of the line l, if the line l is disconnected, the line l is in a closed state
If not, then,
when an extreme event occurs, the power obtained by the node b in the period t is not higher than the actual power demand
As a possible implementation manner of this embodiment, solving the toughness improvement model of the power distribution network, and obtaining the virtual energy storage emergency scheduling policy, the process is as follows: and solving the collaborative optimization model by using a Gurobi solver through a Julia platform to obtain a virtual energy storage dynamic operation process.
The virtual energy storage based continuous time scale based toughness improvement power distribution system model is specifically described in an example, the power distribution network is provided with two MESs, and specific parameters of the MESs are shown in Table 1:
TABLE 1 Mobile energy storage parameter settings
The translatable load is shifted from a peak electricity rate period to a low electricity rate period, and the interruptible load is interrupted during the peak electricity rate period. On the other hand, since the distributed power generation at the node 12 has a low cost, both mobile energy storages charge at the node 12. Table 2 below gives the day-ahead scheduling cost comparison of continuous-time scheduling versus discrete-time scheduling. Table 2 below gives the day-ahead scheduling cost comparison of continuous-time scheduling versus discrete-time scheduling.
TABLE 2 comparison of day-ahead scheduling costs for continuous time scheduling and discrete time scheduling
Table 3 shows the cost comparison between the continuous time model and the discrete time model in different emergency scenarios.
TABLE 3 cost comparison of continuous time model and discrete time model under different Emergency scenarios
As can be seen from table 3, the total operating cost of the continuous-time model is lower than that of the discrete-time model in both the line 1 fault and the line 12 fault. Under the continuous time model, the nodes in the fault area can interrupt the load in time. In addition, the nodes in the fault area can also stop shifting the loads of other time periods to the fault time period in time. In contrast, in discrete time mode, the bus cannot perform load interruption until 4 o' clock, and still translates the load to the failure period. Thus, the load interruption cost required for the discrete-time model is higher than for the discrete-time model, while the load translation cost required for the continuous-time model is lower than for the discrete-time model. When the line 12 has a fault, the running state of the mobile energy storage 1 can be adjusted in time and the mobile energy storage 1 runs to a fault area to participate in the load recovery process, so that the load loss cost is greatly reduced by the continuous time model. Compared with a discrete time model, the load loss cost is reduced by 2.12% and 5.93% when the line 1 and the line 12 are in fault respectively under the continuous time model.
In conclusion, the continuous time model provided by the invention solves the problem that the discrete time model cannot carry out emergency scheduling immediately when an extreme event occurs. By adjusting the scheduling of the virtual energy storage in time, the power distribution network in the continuous time mode has lower operation cost. Therefore, the continuous time model has a good effect on improving the toughness of the power distribution system under the extreme event.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (5)
1. The method for improving the toughness of the power distribution system by virtual energy storage based on the continuous time scale is characterized by comprising the following steps of: the method comprises the following steps:
establishing a virtual energy storage collaborative optimization model;
proposing a Bernstein polynomial based on interval normalization;
establishing a method model for improving the toughness of a power distribution system by virtual energy storage based on a continuous time scale;
solving the toughness improvement model to obtain a virtual energy storage and distribution network toughness improvement scheme;
the virtual energy storage collaborative optimization model comprises a mobile energy storage space-time model and a demand response model;
the method also comprises the steps that under the price mechanism of time-of-use electricity price, the node can translate or interrupt the flexible load of the distribution network in time according to the power supply capacity of the distribution network;
the operational constraints of a translatable load are:
wherein,
is the maximum extent to which the load can be translated,
is a node
In that
The total amount of the base load of the time period,
is a node
In that
Total translational load for a time period;
the operating constraints of interruptible loads are:
wherein,
is the maximum extent to which the load can be interrupted,
is a node
In that
(ii) total outage load;
the method further comprises the steps of establishing an emergency dispatching model of the power distribution network for resisting extreme events based on the continuous time model;
the optimization target of emergency dispatching is as follows:
wherein,
is a node
In that
The actual power demand of the time period,
is the penalty cost per unit load loss,
is composed of
The interval length of the time period;
is located at a node
The power generation cost factor of the distributed power source of (1),
is located at a node
In a distributed power supply
Time interval power output
The coefficient of the order polynomial is the coefficient,
is a node
In that
Periodic purchasing of electrical energy from an upper grid
The coefficient of the order polynomial is the coefficient,
is that
On a time periodThe electricity purchase price of the primary power grid;
the method further comprises that in the emergency dispatching, the energy conservation constraints of the mobile energy storage and the translatable load in the operation period are as follows:
the power flow and power balance constraints during a fault in conforming emergency are:
wherein,
is to characterize the line
A boolean-type variable of the operating state,
is a line
The maximum transmission capacity of the network element,
is a line
In that
Formed by polynomial coefficients of active power transmitted during time intervals
A dimension vector is set to the vector of the dimension,
is a node
In that
The actual power requirements of the time period,
is a node
In that
Formed by polynomial coefficients of the actual power demand over time
A dimension vector is set to the vector of the dimension,
is mobile energy storage
At a node
Is located in
Formed by polynomial coefficient of time interval charge-discharge power
A dimension vector is set to the vector of the dimension,
is a node of inflow
Formed by polynomial coefficients of the active power transmitted by the transmission line
A dimension vector is set to the vector of the dimension,
comprising polynomial coefficients for purchasing electric energy from a superordinate network for a node
The dimension vector of the vector is calculated,
is a node
Is in
Formed by polynomial coefficients of time-interval generated power
A dimension vector is set to the vector of the dimension,
as a line
The power inflow end of the power supply system,
as a line
The power outflow end of (2).
2. The method for improving the toughness of the power distribution system based on the virtual energy storage of the continuous time scale as claimed in claim 1, wherein: the method also comprises the steps of constructing a space-time model of the mobile energy storage by describing a path planning method and charge-discharge constraints of the mobile energy storage; the space-time model of mobile energy storage is as follows:
wherein,
，
is the interval length;
、
and
all of which are variables of a Boolean type,
in order to move the real-time location of the stored energy,
is an auxiliary variable describing the real-time location;
the system is a Boolean variable and is used for representing whether the mobile energy storage is in a running state or not;
wherein,
is the total number of nodes in the distribution network,
representing nodes
And node
Traffic information between;
selectable path matrix between nodes according to traffic network in power distribution network
Comprises the following steps:
the charge and discharge constraints of the mobile energy storage are as follows:
wherein,
is the maximum charge-discharge power of the mobile energy storage,
for storing energy for movement
In that
The bus is arranged
The charging and discharging power of the time period,
and
respectively the maximum rise and fall rates of the mobile energy storage,
is mobile energy storage
In that
Energy state of a period
，
Is mobile energy storage
In that
The state of charge of the time period,
and
respectively the upper and lower limits of the mobile energy storage state of charge,
is mobile energy storage
The capacity of (a) is set to be,
is the total number of time intervals.
3. The continuous-time-scale-based virtual energy storage power distribution system toughness improvement method according to claim 1, wherein the method comprises the following steps: the method further comprises the step compensation mechanism is adopted to compensate each node participating in the demand response, and the constraint conditions of the step compensation mechanism are as follows:
wherein,
and
are respectively a node
In that
The translational load amount and the interrupt load amount of the time period,
is a node
In that
A base load for the time period;
the compensation for a node with a demand response is:
wherein,
and
are respectively
The unit compensation coefficients of the corresponding translatable and interruptible loads,
and
are respectively
And an upper limit of the interruptible load,
and
are respectively aligned at
The time period may translate the load or interrupt the compensation of the load.
4. The method for improving the toughness of the power distribution system based on the virtual energy storage of the continuous time scale as claimed in claim 1, wherein: the bernstein polynomial based on interval normalization is:
wherein,
representing continuous functions
Based on the interval normalized bernstein polynomial,
is the order of the polynomial;
in a polynomial function
To control point, obtain
In the interval
Approximation curve of the above interval-based normalized bernstein polynomial.
5. The method for improving the toughness of the power distribution system based on the virtual energy storage of the continuous time scale as claimed in claim 1, wherein: and solving the collaborative optimization model by using a Gurobi solver through a Julia platform to obtain a virtual energy storage dynamic operation process.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2503666A2 (en) * | 2011-02-01 | 2012-09-26 | Siemens Aktiengesellschaft | Power supply system for an electrical drive of a marine vessel |
| WO2015196482A1 (en) * | 2014-06-27 | 2015-12-30 | Abb Technology Ltd | Method and device of determining reference output power for energy storage system in wind power generation system |
| WO2017186178A1 (en) * | 2016-04-28 | 2017-11-02 | 中国电力科学研究院 | Adaptive dynamic planning control method and system for energy storage station, and storage medium |
| CN110890766A (en) * | 2019-11-28 | 2020-03-17 | 山东理工大学 | Power distribution network mobile energy storage configuration method based on Fourier-Legendre series |
| CN113283074A (en) * | 2021-05-21 | 2021-08-20 | 华中科技大学 | Method and system for activating potential flexibility of power system |
| WO2021244000A1 (en) * | 2020-06-03 | 2021-12-09 | 国网上海市电力公司 | Virtual aggregation system and method for regional energy source complex |
| CN114188950A (en) * | 2021-12-15 | 2022-03-15 | 国网山东省电力公司日照供电公司 | Power distribution system toughness improvement method based on standby mobile energy storage emergency dispatching |
| CN114219291A (en) * | 2021-12-15 | 2022-03-22 | 山东理工大学 | Power distribution system toughness improvement method based on P2P transaction mode and MES electric energy sharing |
-
2022
- 2022-05-12 CN CN202210511304.9A patent/CN114614482B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2503666A2 (en) * | 2011-02-01 | 2012-09-26 | Siemens Aktiengesellschaft | Power supply system for an electrical drive of a marine vessel |
| WO2015196482A1 (en) * | 2014-06-27 | 2015-12-30 | Abb Technology Ltd | Method and device of determining reference output power for energy storage system in wind power generation system |
| WO2017186178A1 (en) * | 2016-04-28 | 2017-11-02 | 中国电力科学研究院 | Adaptive dynamic planning control method and system for energy storage station, and storage medium |
| CN110890766A (en) * | 2019-11-28 | 2020-03-17 | 山东理工大学 | Power distribution network mobile energy storage configuration method based on Fourier-Legendre series |
| WO2021244000A1 (en) * | 2020-06-03 | 2021-12-09 | 国网上海市电力公司 | Virtual aggregation system and method for regional energy source complex |
| CN113283074A (en) * | 2021-05-21 | 2021-08-20 | 华中科技大学 | Method and system for activating potential flexibility of power system |
| CN114188950A (en) * | 2021-12-15 | 2022-03-15 | 国网山东省电力公司日照供电公司 | Power distribution system toughness improvement method based on standby mobile energy storage emergency dispatching |
| CN114219291A (en) * | 2021-12-15 | 2022-03-22 | 山东理工大学 | Power distribution system toughness improvement method based on P2P transaction mode and MES electric energy sharing |
Non-Patent Citations (4)
| Title |
|---|
| Minimizing Wind Power Curtailment Using a Continuous-Time Risk Based Model of Generating Units and Bulk Energy Storage;Ahmad Nikoobakht;《IEEE transaction on smart grid》;20201130;第4833-4846页 * |
| 伯恩斯坦多项式在收敛区间上的逼近度;刘勇等;《大连铁道学院学报》;20051231;第1-3页 * |
| 极端天气下的主动配电网故障恢复方法研究;高鹏;《中国优秀博硕士学位论文全文数据库》;20200315;第C042-890页 * |
| 融合Bezier遗传算法的移动机器人路径规划;李开荣等;《扬州大学学报》;20211031;第58-64页 * |
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