CN115954882A - Scheduling optimization method and device for electric-carbon fusion power distribution system - Google Patents
Scheduling optimization method and device for electric-carbon fusion power distribution system Download PDFInfo
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Abstract
The invention discloses a scheduling optimization method and equipment for an electric-carbon fusion power distribution system, which comprises the following steps: generating a random scene set; constructing a linear power distribution system load flow model, and calculating to obtain optimal load flow distribution; solving a distribution system branch carbon flow rate distribution matrix according to the power flow distribution; taking the electrical connection degree among the nodes of the power distribution system and the branch carbon flow rate distribution matrix as indexes for dividing the carbon emission area of the power distribution system, and constructing a linear power distribution system carbon emission area division model to obtain a power distribution system carbon emission area division result; and optimizing the dispatching of the power distribution system according to the carbon emission region division result of the power distribution system. The method optimizes the division of the carbon emission region of the power distribution system, and is beneficial to the carbon reduction scheduling strategy formulation and control of the power distribution system operator according to the carbon emission characteristics of the region, thereby optimizing the power distribution system scheduling and improving the efficiency of the power distribution system operator in controlling the resource equipment in the region and the effect of energy conservation and emission reduction.
Description
Technical Field
The invention relates to the field of power distribution system scheduling, in particular to a scheduling optimization method and device for an electric-carbon fusion power distribution system.
Background
The need of building a novel power system and achieving the double-carbon strategic objective stimulates the enthusiasm of academics and industries for researching the carbon emission flow of the power system, is different from the traditional power system trend that the power system has a complete and mature research system, the research method and system of the carbon emission flow are still in further exploration, and numerous experts and scholars are dedicated to researching the distribution condition of the carbon emission flow and how to achieve the optimization objective of energy saving and carbon reduction through an effective scheduling strategy. The novel power system who uses the new forms of energy as the main part can promote the fan by a wide margin, the shared proportion of renewable clean energy such as photovoltaic in the energy supply structure, however because wind energy, renewable energy such as solar energy itself is inherent randomness and volatility, its uncertainty of exerting oneself will certainly lead to the fact huge challenge to power system's safe and stable operation, consequently, for dealing with renewable energy generating set's the volatility problem of exerting oneself, the installation capacity of the distributed power system who matches the use with this kind of the big distributed power source of volatility also can increase by a wide margin in the novel power system, can these distributed resources of full play in the energy-saving carbon reduction through the effective cooperation use of distributed power system and distributed renewable energy, promote the low carbonization of energy structure, the potential in the aspect of clean transformation.
The existing power system carbon emission flow calculation method is mainly based on the steady-state active power flow distribution of a power system to determine the distribution condition of the carbon emission flow, but few documents consider the influence of the carbon potential of an energy storage system on the distribution of the carbon emission flow, and because the energy storage system has the characteristic of bidirectional power flow, the calculation methods of the carbon potential in a charging state and a discharging state are different, and along with the continuous increase of the installation capacity of the energy storage system, the influence of the energy storage system on the carbon emission flow cannot be ignored, so that the calculation of the influence of the energy storage system on the carbon emission flow has great research significance; meanwhile, with the large-scale distributed resource access of the power distribution system side, the problem of effective management and control and scheduling of a large amount of distributed resources becomes a key technical problem to be solved urgently, the existing research idea of dividing and treating can reduce the management and control difficulty of mass resources to a certain extent, but how to aggregate the mass distributed resources in the most effective way to be used as a whole for unified management is still a key technical bottleneck.
Disclosure of Invention
The invention aims to solve the technical problem of effective management and control scheduling for optimizing a large number of distributed resources, and provides a scheduling optimization method and equipment for an electric-carbon fusion power distribution system.
The technical problem of the invention is solved by the following technical scheme:
a scheduling optimization method for an electric-carbon fusion power distribution system comprises the following steps:
s1: generating a random scene set;
s2: constructing a linear power distribution system power flow model, and calculating to obtain optimal power flow distribution;
s3: solving a distribution system branch carbon flow rate distribution matrix according to the power flow distribution;
s4: taking the electrical connection degree among the nodes of the power distribution system and the branch carbon flow rate distribution matrix as indexes for dividing the carbon emission area of the power distribution system, and constructing a linear power distribution system carbon emission area division model to obtain a power distribution system carbon emission area division result;
s5: and optimizing the dispatching of the power distribution system according to the carbon emission region division result of the power distribution system.
In some embodiments, step S1 further includes: and performing feature extraction clustering and reduction on the randomness scene set by using a synchronous back-substitution elimination method to obtain a reduced randomness scene set.
In some embodiments, in step S2, an optimal power flow distribution of the power distribution system is determined according to a network topology architecture and physical transmission line parameters of the power distribution system, generator set parameters, a node load power prediction curve, and a distributed energy output prediction curve, in combination with a network transmission loss of the power distribution system.
In some embodiments, in step S2, the expression for constructing the linearized power distribution system power flow model is as follows:
in the formula
Wherein r is ij ,x ij The per unit resistance value and the reactance value of the branch between the connection nodes i and j are respectively; n is a radical of Bus ,S Base Respectively, the set of all nodes of the power distribution system and the system reference capacity; p i,t ,Q i,t ,V i,t ,θ i,t Are respectively node iThe active power injection value, the reactive power injection value, the voltage amplitude and the voltage phase which are subjected to per unit processing; p j,t ，Q j,t ，V j,t ，θ j,t Respectively an active power injection value, a reactive power injection value, a voltage amplitude value and a voltage phase which are subjected to per unit processing at a node j; v i The voltage amplitude at node i; p ij,t ,Q ij,t Respectively the active power and the reactive power of the branch between the connection nodes i and j after per unit processing;
Respectively the active power and the reactive power injected by the distributed generator at the node i after per unit processing;
Active power injected into the wind driven generator at the node i after per unit processing;
The load requirements of active power and reactive power at the node i after per unit processing are respectively, and t is a time index and indicates the scheduling time corresponding to the variable; n is a radical of T Is a total set of scheduling periods;
the lower limit and the upper limit of active power, the lower limit and the upper limit of reactive power and the lower limit and the upper limit of voltage amplitude at the node i of the branch circuit between the connecting nodes i and j are respectively.
In some embodiments, the objective function of the optimal power flow distribution is:
wherein, C Grid (P Grid ) Cost function for purchasing power from distribution system to higher-level power system, C k (P k ) Cost of the kth generator set, C e (P e ) Charge-discharge degradation cost of the e-th energy storage system, C w (P w ) Cost of abandoned wind for the w-th wind-driven generator set, C l (P l ) The load deflection cost for the l-th load.
In some embodiments, in step S3, the distribution system branch carbon flow rate distribution matrix is expressed as follows:
R B ＝P B diag(E N )
wherein E is N As a distribution system node carbon potential vector, P B Constructing a system branch power flow distribution matrix, P, for the optimal power flow distribution B E, N is the number of all nodes in the power distribution system;
the distribution system node carbon potential vector calculation formula is
Wherein, P N Injecting an active power matrix, P, for a node G Injecting a power distribution matrix, P, for the generator E Accessing the energy storage system with a power distribution matrix, P W Injecting a power distribution matrix for the wind turbine, E G As a vector of the carbon emission intensity of the generator set, E E Is the carbon potential vector of the energy storage system, E W And the vector is the carbon emission intensity vector of the wind generating set.
In some embodiments, step S4 comprises the steps of:
s41: normalizing the electrical distance matrix and the branch carbon flow rate distribution matrix;
s42: formulating an area division optimization index;
s43: calculating the region modularity;
s44: and constructing a power distribution system carbon emission region division model based on a linearized power flow equation.
In some embodiments, the expression of the power distribution system carbon emission region division model is:
wherein ρ ij For edge weights connecting nodes i, j, m = Σ i Σ j (ρ ij ) K 2 is the sum of the weights of all edges in the system i ,k j Respectively representing the sum of the edge weights directly connected with the nodes i and j, wherein delta (i and j) is a variable 0-1 for representing whether the nodes i and j belong to the same region, and the constraint condition is a linearized power flow equation constraint condition.
In some embodiments, in step S44, the expression of the power flow equation is as follows:
wherein, theta and V are respectively N Bus X 1 node voltage phase angle and voltage amplitude column vector, P Br ,Q Br Are respectively N Branch Branch active and reactive power column vector of x 1, P' In ，Q' In Respectively an injected active power column vector and a reactive power column vector, S, of nodes except the balance node θ ，S V ，S P ，S Q ，K θ ，K V ，K P ，K Q For the auxiliary matrix in the calculation process, the constituent elements of each matrix are shown on the right side of the equation corresponding to the matrix.
The invention also provides an electrical-carbon fusion power distribution system scheduling optimization device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the electrical-carbon fusion power distribution system scheduling optimization method when executing the computer program.
The invention further provides a storage medium which comprises a stored computer program, wherein when the computer program runs, the device where the storage medium is located is controlled to execute the scheduling optimization method for the electric-carbon fusion power distribution system.
Compared with the prior art, the invention has the advantages that:
the scheduling optimization method of the electric-carbon fusion power distribution system is based on a linearized power distribution system flow model, the electric connection degree among the power distribution system nodes and the branch carbon flow rate distribution matrix are used as indexes for carbon emission area division of the power distribution system, the linearized power distribution system carbon emission area division model is constructed, the problem of carbon emission area division of the power distribution system can be constructed into a linearized planning model, and compared with a heuristic algorithm, the method has the advantages of high solving efficiency, optimal solution and the like, the division of the carbon emission area of the power distribution system is optimized, and carbon reduction scheduling strategies can be formulated and controlled by a power distribution system operator according to the carbon emission characteristics of the area, so that the power distribution system scheduling is optimized, and the efficiency of controlling resource equipment in the area and the effect of saving energy and reducing emission of the power distribution system operator are improved.
In some embodiments of the invention, the following benefits are also achieved:
according to the method, the obtained original randomness scene set is reduced, so that the problem solving burden can be reduced while the randomness problem is fully considered;
according to the method, the problem of carbon emission region division of the power distribution system is constructed into the linear programming model for solving by carrying out linear processing on the region division constraint, and the division index of the expanded region modularity can be flexibly changed according to different requirements of users, so that the method has good universality and expansibility.
Other advantages of embodiments of the present invention are further described below.
Drawings
FIG. 1 is a flow chart of a method for partitioning carbon emission zones of a power distribution system in an embodiment of the present invention;
FIG. 2 is a schematic diagram of an improved IEEE 33 node power distribution system in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system fan, photovoltaic output power and load power curve according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a set of random scenarios of output power of a wind turbine in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a set of photovoltaic output power stochastic scenes in an embodiment of the invention;
fig. 6 is a schematic diagram of a random scenario set of the power curve of the electrical load in the embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and preferred embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms of orientation such as left, right, up, down, top and bottom in the present embodiment are only relative concepts to each other or are referred to the normal use state of the product, and should not be considered as limiting.
In the technical problem of how to optimize the carbon emission area division of the power distribution system, the most important thing to determine the distribution condition of the carbon emission flow is to determine the specific source of the carbon emission source. The accurate quantitative analysis of the carbon emission characteristics of all components in the power system is crucial to the research of carbon emission flow, a traditional generator set can calculate definite carbon emission intensity according to generator parameters, the carbon emission intensity of a renewable energy source unit can be considered to be approximate to 0, but in order to deal with the problems of output randomness and fluctuation of renewable energy sources such as fans, photovoltaic and the like, the power system must be provided with an energy storage system matched with the installed capacity of the renewable energy sources, the energy storage system has two working modes of charging and discharging, the charging mode can be considered as a load connected to a node of the power system, and the discharging mode can be considered as a generator set connected to the node, so the carbon potential of the energy storage system is not fixed and is changed according to a scheduling plan executed by the energy storage system, and the consideration of the time-varying carbon potential of the energy storage system is crucial to the research of carbon emission flow.
Secondly, the inherent randomness problem of renewable energy sources such as fans and photovoltaic can bring great challenges to the scheduling decision of the power system, so how to effectively deal with the randomness problem becomes an important factor for guaranteeing the safe and stable operation of the power system. The traditional scheduling mode lacks consideration on randomness problems, but the safety and the stability of the system are guaranteed by an over-conservative strategy, and the potential of massive distributed resources for improving the resource utilization efficiency of the power system is sacrificed; further, the problem of the area division method is another important problem of dividing the carbon emission area. The result of the region division directly affects the management and control efficiency of a system operator on resource equipment in a region and the effect of a scheduling strategy on energy conservation and emission reduction, however, most of the existing region division methods are heuristic algorithms, have the problems of low convergence rate, easy falling into local optimum and the like, and how to improve the effectiveness of the region division method becomes a key technical problem which needs to be solved urgently.
In order to solve the technical problem, the embodiment of the invention provides a method for dividing a carbon emission area of a power distribution system, which adopts a typical scene simulation method, and the content of the method comprises the following steps: aiming at the inherent randomness problem of mass diversified heterogeneous distributed resources (DERs) such as wind driven generators, photovoltaic arrays and the like accessed to the side of a power distribution system, a synchronous back substitution elimination method is adopted to perform feature extraction clustering on a random scene set obtained based on a Monte Carlo method to obtain a typical scene set with a reduced number, so that the calculation solving burden of an optimization problem is reduced; the electrical distance between nodes of the power system and the carbon flow rate of branches are comprehensively considered as division indexes of a high-carbon emission area and a low-carbon emission area of the power distribution system, the area division problem is converted into a linear programming model to be solved, and an optimal division scheme of the carbon emission area is formulated.
The following is a summary of embodiments of the invention:
as shown in fig. 1, an embodiment of the present invention provides a scheduling optimization method for an electrical-carbon fusion power distribution system, including the following steps:
s1: generating a random scene set;
the randomness scene set is obtained by performing feature extraction clustering and reduction on the randomness scene set by using a synchronous back-substitution elimination method.
S2: constructing a linear power distribution system power flow model, and calculating to obtain optimal power flow distribution;
the optimal power flow distribution of the power distribution system is determined according to a network topology framework of the power distribution system, physical transmission line parameters, generator set parameters, a node load power prediction curve and a distributed energy output prediction curve and by combining network transmission loss of the power distribution system.
The expression for constructing the power flow model of the linearized power distribution system is as follows:
in the formula
Wherein r is ij ,x ij The per unit resistance value and the reactance value of the branch between the connection nodes i and j are respectively; n is a radical of Bus ,S Base Respectively, the set of all nodes of the power distribution system and the system reference capacity; p i,t ,Q i,t ,V i,t ,θ i,t Respectively an active power injection value, a reactive power injection value, a voltage amplitude and a voltage phase which are subjected to per unit processing at a node i; p j,t ，Q j,t ，V j,t ，θ j,t Respectively an active power injection value, a reactive power injection value, a voltage amplitude and a voltage phase which are subjected to per unit processing at a node j; v i The voltage amplitude at node i; p ij,t ,Q ij,t Respectively the active power and the reactive power of the branch between the connection nodes i and j after per unit processing;
Respectively the active power and the reactive power injected by the distributed generator at the node i after per unit processing;
Injecting active power into the wind driven generator at the node i subjected to per unit processing;
The load requirements of active power and reactive power at the node i after per unit processing are respectively, and t is a time index and indicates the scheduling time corresponding to the variable; n is a radical of T Is a total set of scheduling periods;
the lower limit and the upper limit of active power, the lower limit and the upper limit of reactive power and the lower limit and the upper limit of voltage amplitude at the node i of the branch circuit between the connecting nodes i and j are respectively.
The objective function of the optimal power flow distribution is as follows:
wherein, C Grid (P Grid ) Cost function for purchasing electricity from distribution system to higher-level power system, C k (P k ) Cost of the kth generator set, C e (P e ) Charge and discharge degradation cost for the e-th energy storage system, C w (P w ) Cost of abandoned wind for the w-th wind-driven generator set, C l (P l ) The load deflection cost for the l-th load.
S3: solving a distribution system branch carbon flow rate distribution matrix according to the power flow distribution;
the expression of the distribution system branch carbon flow rate distribution matrix is as follows:
R B ＝P B diag(E N )
wherein E is N As a distribution system node carbon potential vector, P B Constructing a system branch power flow distribution matrix, P, for optimal power flow distribution B And e.N, wherein N is the number of all nodes in the power distribution system.
The carbon potential vector calculation formula of the nodes of the power distribution system is
Wherein, P N Injecting an active power matrix, P, for a node G Injecting a power distribution matrix, P, into the generator E Accessing the energy storage system with a power distribution matrix, P W Injecting a power distribution matrix for the wind turbine, E G As a vector of the carbon emission intensity of the generator set, E E To the carbon potential direction of the energy storage systemAmount, E W And the vector is the carbon emission intensity vector of the wind generating set.
S4: taking the electrical connection degree among the nodes of the power distribution system and the branch carbon flow rate distribution matrix as indexes for dividing the carbon emission area of the power distribution system, and constructing a linear power distribution system carbon emission area division model to obtain a power distribution system carbon emission area division result;
step S4 includes the following steps:
s41: normalizing the electrical distance matrix and the branch carbon flow rate distribution matrix;
s42: formulating an area division optimization index;
s43: calculating the region modularity;
s44: and constructing a power distribution system carbon emission region division model based on a linearized power flow equation.
The expression of the power distribution system carbon emission region division model is as follows:
where ρ is ij For the edge weights of the connecting nodes i, j, m = ∑ Σ i ∑ j (ρ ij ) K 2 is the sum of the weights of all edges in the system i ,k j Respectively representing the sum of the edge weights directly connected with the nodes i and j, wherein delta (i and j) is a variable 0-1 for representing whether the nodes i and j belong to the same region, and the constraint condition is a linearized power flow equation constraint condition. The expression of the power flow equation is as follows:
wherein, theta and V are respectively N Bus X 1 node voltage phase angle and voltage amplitude column vector, P Br ,Q Br Are respectively N Branch Branch active and reactive power column vector of x 1, P' In ，Q' In Respectively an injected active power column vector and a reactive power column vector, S, of nodes except the balance node θ ，S V ，S P ，S Q ，K θ ，K V ，K P ，K Q For the computation of the auxiliary matrices, the constituent elements of each matrix are shown on the right side of the equation for that matrix.
S5: and optimizing the distribution system scheduling according to the distribution system carbon emission region division result.
The embodiment of the invention also provides scheduling optimization equipment for the electric-carbon fusion power distribution system, which comprises a processor, a memory and a computer program which is stored in the memory and configured to be executed by the processor, wherein the processor realizes the scheduling optimization method for the electric-carbon fusion power distribution system when executing the computer program.
The embodiment of the invention also provides a storage medium, which comprises a stored computer program, wherein when the computer program runs, the device where the storage medium is located is controlled to execute the scheduling optimization method for the electrical-carbon fusion power distribution system.
Example (b):
the embodiment of the invention provides a scheduling optimization method for an electric-carbon fusion power distribution system, which comprises the following steps:
s1: reducing an initial randomness scene, and generating a reduced randomness scene set;
s2: constructing a linear power distribution system load flow model, and calculating to obtain optimal load flow distribution;
s3: solving a distribution system branch carbon flow rate distribution matrix according to the power flow distribution;
s4: taking the electrical connection degree among the nodes of the power distribution system and the branch carbon flow rate distribution matrix as indexes for dividing the carbon emission area of the power distribution system, and constructing a linear power distribution system carbon emission area division model to obtain a power distribution system carbon emission area division result;
s5: and optimizing the dispatching of the power distribution system according to the carbon emission region division result of the power distribution system.
Wherein the step S1 specifically comprises the following steps:
s11: obtaining an initial power distribution system side distributed resource power curve scene set by using a Monte Carlo simulation scene generation method; under the conditions of large random variable quantity and large scene quantity, large calculation solving burden is caused to the optimization problem, the clustering idea is introduced, the random scenes are analyzed and reduced, the random scene quantity is reduced, and the random scene set obtained through reduction still has the characteristics of the original random scene set.
The feature extraction clustering and reduction are two steps of the adopted synchronous back-substitution elimination method, and specifically comprise the following steps: firstly, feature extraction clustering is carried out on an original random scene set, and then scene reduction is carried out.
S12: performing feature extraction clustering and reduction on a random scene set by adopting a synchronous back-substitution elimination method, setting the number of the reduced scenes as M, and using S reduce Representing a random scene set obtained after scene reduction, initially setting the random scene set as an empty set, and specifically setting the synchronous back-substitution elimination method as follows:
(1-1) firstly, generating a random scene set with a scene number of N based on a random scene generation method:
in which ξ i ,ω i Predicting a power curve for DER corresponding to the ith scene and the occurrence probability of the scene respectively,
S ori for the generated original random scene set, the probability values of all scenes are initially set to be the same, namely the probability of each scene is
(1-2) calculating the Euclidean distance between any two scenes as follows:
(1-3) for the ith scene
The scene closest to the Euclidean distance is calculated and judged>
And combines the distance and the corresponding scene>
Probability of occurrence ω i The multiplication results in a probability-distance product->
(1-4) finding a minimum probability of occurrence scenario S d So that
Cut down the scene and make the scene->
The probability of occurrence is added to the scene closest in euclidean distance thereto->
Above, i.e. ω j ＝ω j +ω i 。
(1-5) reducing minimum probability of occurrence scenario S d Updating the original scene set and the reduced scene set as follows:
wherein
And S reduce For two different sets, according to the scene reduction method, the original random scene set generated firstly is combined into ^ er>
Reducing the minimum probability of occurrence scenario S during each step of the iteration process d Updating the original scene set and the reduced scene set, wherein the updating process is carried out in the mode of combining the random scene set and the reserved scene set>
In take out scene S d And will S d Put into the reduced scene set S reduce And iterating until the number of the scenes in the reduced scene set reaches a set value.
(1-6) repeating the steps (1-1) - (1-5) until the number of the scenes after the reduction reaches the target set scene number value, S reduce Namely a random scene set obtained by cutting.
The following is a detailed description of step S2 of the method for dividing the carbon emission area of the power distribution system according to the embodiment of the present invention:
the method comprises the following steps of establishing a linear power distribution system power flow model according to linearization, solving an optimal power flow distribution objective function according to a network topology framework, physical transmission line parameters, generating set parameters, a node load power prediction curve and a distributed energy output prediction curve of a power distribution system by considering network transmission loss of the power distribution system, and determining optimal power flow distribution of the power distribution system:
(2-1) the power purchase cost parameter of the power distribution system from the superior power grid is as follows:
the power purchasing power vector of the power distribution system from a superior power system at a grid-connected point (namely, a No. 1 node) is P Grid The electricity price vector of the superior power system is c Grid The cost function of the power distribution system to purchase power from the superior power system can be expressed as
(2-2) load allocation conditions and parameters in the power distribution system:
the load L in the power distribution system belongs to L, and L is the set of all loads;
the predetermined electric power vector of the l-th load is P l L belongs to L, and the offset power consumption vector of the L-th load in the actual operation process is set as delta P l ，l∈L；
The load power vector of the l load access node i is P il I belongs to N, L belongs to L, N is the set of all nodes of the distribution system, if the L-th load has an access node i, P is il ＝P l +ΔP l Else P il ＝0；
The original constant reactive power vector corresponding to the l load is Q l Setting the offset power consumption reactive power vector of the first load in the actual operation process as delta Q l ，l∈L；
The load power vector of the ith load access node i is
N is the set of all nodes of the power distribution system, and is greater than or equal to>
For the power factor of the l-th load, if the l-th load has an access node i, then
Otherwise Q il ＝0；
The load deflection cost of the first load is
Wherein C l (ΔP l ) Load deflection cost for the l-th load, a l ，b l Load deflection cost factor (a) for the l-th load l Is the coefficient of the quadratic term, b l Is the first order coefficient. ),
The lower limit and the upper limit of the electric power of the first load are respectively;
(2-3) configuration conditions and parameters of a Distributed Generator (DG) in the power distribution system:
the generator set K belonging to the power transmission system belongs to K, and K is the set of all the generator sets;
the output power vector of the kth generating set is P k ，k∈K；
The output power vector of the injection node i of the generator set k is P ik I belongs to N, K belongs to K, N is the set of all nodes of the power transmission system, if the node i has an access generator set K, P is ik ＝P k Otherwise P ik ＝0；
The cost of the kth power generating unit is
K belongs to K; wherein C k (P k ) For the cost of electricity generation of the kth power generating set, a k ，b k ，c k For the cost factor (a) of the generation of the kth power generating set k Is the coefficient of the quadratic term, b k Is the coefficient of a first order term, c k Is a constant. ), k P，
the lower limit and the upper limit of the output of the kth generator set are respectively;
(2-4) the configuration condition and parameters of the wind generating set in the power distribution system are as follows:
the wind generating set W allocated to the power distribution system belongs to W, and W is a set of all wind generating sets;
the output power vector of the w-th wind generating set is P w W belongs to W, and the abandoned wind power vector of the W-th wind generating set in the actual operation process is set as delta P w ,w∈W,ΔP w ≥0；
The output power vector of the w-th wind generating set injection node i is P iw I belongs to N, W belongs to W, N is the set of all nodes of the power distribution system, if the node i has access to the wind generating set W, P is iw ＝P w -ΔP w Else P iw ＝0；
The waste wind cost of the w-th wind generating set is
Wherein C is w (ΔP w ) Cost of abandoned wind for the w-th wind turbine generator system, a w ,b w A factor (a secondary term and a primary term respectively) for the wind abandon cost of the w-th wind generating set, and a method for combining the coefficients of the secondary term and the primary term>
Respectively setting a predicted output lower limit and a predicted output upper limit of the w-th wind generating set;
(2-5) Energy Storage System (ESS) allocation conditions and parameters in the power distribution system:
a generator set E arranged in the power transmission system belongs to E, and E is a set of all energy storage systems;
the charge-discharge power vector of the e-th energy storage system is
P e,d ,P e,c The discharge power and the charge power of the e-th energy storage system respectively,
lower limit and upper limit, respectively, of the discharge power of the e-th energy storage system>
Lower limit and upper limit, eta, of charging power of the e-th energy storage system, respectively e,d ,η e,c The discharge energy conversion efficiency and the charge energy conversion efficiency of the e-th energy storage system are respectively;
the output power vector of the energy storage system e injected into the node i is P ie I belongs to N, E belongs to E, N is the set of all nodes of the power transmission system, if the node i has an access energy storage system E, P is ie ＝P e Else P ie ＝0；
The charge-discharge degradation cost of the e-th energy storage system is
Wherein C is e (P e ) Is the charge-discharge degradation cost of the e-th energy storage system, a e The charge-discharge degradation cost coefficient of the e-th energy storage system;
(2-6) constructing a DG (distributed generator set) operation model as shown in the following formula:
in the formula of i DG Is the power factor of DG;
active power and reactive power respectively injected into the ith node of the power distribution system at the tth moment DG, and the active power and the reactive power are combined in the preset mode>
The lower limit and the upper limit of active power injected into the ith node of the power distribution system for DGs respectively are combined together>
Respectively injecting the lower limit and the upper limit of reactive power into the ith node of the power distribution system for the DGs;
(2-7) constructing an ESS (energy storage System) operation model as shown in the following formula:
in the formula
Charging power and discharging power of the ESS accessed at the ith node at the tth moment respectively, <' >>
For a 0-1 variable that characterizes the charging status of an ESS accessed at the ith node at the tth time, a @>
When ^ 0-1 variable indicating that the ESS accessed at the ith node is in a discharged state at the tth time>
When the state of the ESS accessed at the ith node is in a charging state at the tth moment, if->
Indicates that the ESS accessed at the ith node is not in a charging state at the tth time, and when->
The time indicates that the ESS accessed to the ith node is in a discharge state at the t moment, if the ESS is in the discharge state
When it is said that the ESS accessed at the ith node is not in a discharging state at the tth moment, and is in a on/off state>
Constraint guarantees that the ESS accessed at the ith node is not in a charging state and a discharging state at the same time at the tth moment; s. the i,t For the state of charge (SOC) of the ESS accessed at the ith node at the tth time, the status of charge (SOC) of the ESS is changed>
Respectively the lower limit and the upper limit of the state of charge, C, of the ESS accessed at the ith node i ESS For the capacity of the ESS accessed at the ith node, Δ t is an optimized scheduling time interval;
(2-8) carrying out physical modeling on the power distribution system by using the linearized power flow model, wherein the obtained linearized power flow model of the power distribution system is as follows:
in the formula
Wherein r is ij ,x ij The per unit resistance value and the reactance value of the branch between the connection nodes i and j are respectively; n is a radical of Bus ,S Base Respectively, the set of all nodes of the power distribution system and the system reference capacity; p is i,t ,Q i,t ,V i,t ,θ i,t Respectively an active power injection value, a reactive power injection value, a voltage amplitude value and a voltage phase which are subjected to per unit processing at a node i; p is j,t ，Q j,t ，V j,t ，θ j,t Respectively an active power injection value, a reactive power injection value, a voltage amplitude and a voltage phase which are subjected to per unit processing at a node j; v i The voltage amplitude at node i;
P ij,t ,Q ij,t respectively the active power and the reactive power of the branch between the connection nodes i and j after per unit processing;
Where t is a time index indicating the scheduling time, N, corresponding to the variable T Is the total set of scheduling periods, including all scheduling instants.
(2-9) constructing an optimal power flow distribution objective function of the power distribution system:
and solving the optimal power flow distribution objective function according to the linearized power distribution system power flow model to obtain the optimal power flow distribution of the power distribution system, thereby providing a required calculation basis for the calculation of the carbon emission flow.
The following is a detailed description of step S3 of the method for dividing the carbon emission area of the power distribution system according to the embodiment of the present invention:
the method comprises the following steps of solving and calculating branch carbon flow rate of a power distribution system and a distribution matrix of the branch carbon flow rate of the power distribution system based on power flow distribution of the power distribution system, and specifically comprises the following steps:
(3-1) formulating a system branch power flow distribution matrix according to the optimal power flow distribution:
the number of all nodes in the system is N, the number of all generator sets is K, the number of all loads is L, and a system branch tide distribution matrix is P B E.n.n, for transmissionA node i, j belongs to N directly connected with the transmission line, if the forward direction of the power flow of the line ij is from i to j, and the active power flow passing through the line is P, then P is Bij ＝P,P Bji =0, whereas if the reverse active power flow through the line is P, then P is Bij ＝0,P Bji ＝P；
Wherein P represents the active power flowing through the line ij, the active power is the absolute value of the active power flow power of the line, the forward direction of the power flow needs to be determined by the branch power flow distribution matrix, therefore, the power flow direction of the branch ij is determined in the actual power flow direction, the power flow power which is specified after the power flow direction and is the same as the forward direction is the absolute value of the power flow of the line, and the power flow power which is opposite to the power flow direction is set to be 0, and the power flow of one section of the line can only flow from the power flow outflow node to the power flow inflow node through the processing.
(3-2) formulating a generator injection power distribution matrix according to the output of the generator:
let the generator injection power distribution matrix be P G E, K is left, if the node i is connected with a generator set K, and the output vector of the generator is P k Then P is Gki ＝P k Else P Gki ＝0；
(3-3) establishing an injection power distribution matrix of the wind driven generator according to the actual use power of the wind driven generator:
let the wind power generator injection power distribution matrix be P W E W N, if the node i has access to the wind driven generator W, and the output vector of the generator is P w Then P is Wwi ＝P w Else P Wwi ＝0；
(3-4) formulating a charging and discharging power distribution matrix of the energy storage system according to the access power condition of the energy storage system:
setting the access power distribution matrix of the energy storage system as P E E is left to the N, if the node i has an access energy storage system E, and the access power vector of the energy storage system is P e Then P is Eei ＝P e Else P Eei ＝0；
(3-5) formulating a load power distribution matrix according to the electricity utilization condition of the user:
let the load power distribution matrix be P L ∈L* N, if the node i has an access load l, and the power consumption vector of the load is P l Then P is Lli ＝P l Otherwise P Lli ＝0；
(3-6) establishing a node injection active power matrix:
for the node i, all lines provided with the power flow injection node i are aggregated into
Is provided with a line>
The power flowing through is P Bs If a generator set k is connected to the node i, and the output vector of the generator set is P Gki If a wind generating set w is connected into the node i, and the output vector of the wind generating set is P Wwi If an energy storage system e is accessed to the node i, and the access power vector of the energy storage system is P Eei Defining diagonal elements of the node injection active power matrix
The non-diagonal elements of the matrix are all 0, i.e., P Nij =0,i ≠ j; then P is Z ＝[P B P G P W P E ] T ,P N ＝diag(ξ N+K+W+E P Z ) (ii) a In which ξ N+K+W+E A row vector in which all elements of order N + K + W + E are 1; wherein Pz is an auxiliary matrix introduced in the calculation process
(3-7) formulating a generator set carbon emission intensity vector:
let the vector of carbon emission intensity of the generator set be E G E K1, wherein the carbon emission intensity of the kth generating set is e Gk Then E is G ＝[e G1 e G2 …e GK ] T ；
(3-8) formulating a carbon emission intensity vector of the wind generating set:
setting the carbon emission intensity vector of the wind generating set as E W E W1, wherein the carbon emission intensity of the W wind generating set is e Ww Then E is W ＝[e w1 e w2 …e WW ] T The carbon emission intensity of the wind generating set is approximate to 0;
(3-9) formulating a carbon potential vector of the energy storage system:
let the carbon potential vector of the energy storage system be E E E is E1, wherein the carbon emission intensity of the E-th energy storage system is E Ee Then E is E ＝[e E1 e E2 …e EE ] T ；
The calculation method of the carbon emission intensity of the energy storage system is specifically described as follows:
for the e-th energy storage system, if the energy storage system is in a discharging state from the moment t-1 to the moment t, the carbon potential calculation expression is as follows:
wherein S e,t-1 For the state of charge of the e-th energy storage system at time t-1, e Ee,t For the carbon potential, P, of the e-th energy storage system at time t e,d,t The discharge power of the e energy storage system at the moment t;
for the e-th energy storage system, if the energy storage system is in the charging state from the moment t-1 to the moment t, the carbon potential calculation expression is as follows:
wherein P is e,c,t Charging Power for the e-th energy storage System at time t, e Ni,t-1 And injecting the carbon potential of the node i accessed to the e-th energy storage system at the moment t-1, namely the charging power of the e-th energy storage system from the node i.
From the above formula, it can be seen that the intensity of carbon emission of the energy storage system is time-varying, and the intensity of carbon emission of the energy storage system at each time depends on the state of charge of the energy storage system at the previous time and the charging and discharging power conditions performed at the previous time.
(3-10) establishing a system node carbon potential vector:
setting the carbon potential vector of the system node as E N E N1, wherein the carbon potential of the ith node is e Ni Then E is N ＝[e N1 e N2 …e NN ] T ；
(3-11) establishing a system branch carbon flow rate distribution matrix:
let the distribution matrix of the carbon flow rates of the system branches be R B E N x N, for a node i, j e N with a direct connection of transmission lines, R if the forward flow direction of line ij is from i to j and the carbon flow rate through this line is R Bij ＝R,R Bji =0, whereas if the reverse carbon flow rate through the line is R, then R Bij ＝0,R Bji ＝R；
R B ＝P B diag(E N )
In which the carbon emission flow is attached to the power flow, therefore the branch carbon flow rate distribution matrix definition is similar to the branch flow distribution matrix definition, and both the definitions need to specify the positive direction, so as to ensure that only the flow from the outgoing node to the incoming node (including the power flow and the carbon emission flow attached thereto) is an absolute value, and the flow from the incoming node to the outgoing node is 0.
(3-12) establishing a system branch load carbon flow rate vector:
let the distribution matrix of the carbon flow rate of the system branch load be R L E L1, for the L-th load, the loaded carbon flow rate is R Ll ，
Then R is L ＝[R L1 R L2 …R LL ] T ；
(3-13) the method for calculating the carbon potential of the nodes of the power system and the branch carbon flow rate comprises the following steps:
the node carbon potential of the computing system node i is
Wherein e Gi ，e Ei ，e wi The carbon emission intensity rho of the generator, the energy storage system and the wind driven generator with index i s For the carbon flow density of the transmission line s, the line carbon flow density can be used according to the distribution property of the carbon emission flowThe node carbon potential at the beginning of the line is represented, and the above formula is rewritten as follows:
wherein E G As a vector of the carbon emission intensity of the generator set, E E Is a carbon potential vector of the energy storage system,
except the ith element is 1, the other elements are unit row vectors of 0, and the formula is arranged according to the definition to obtain the carbon potential calculation formula of the system node which is greater than or equal to->
Wherein P is N Is the node carbon potential vector to be calculated.
Thereby obtaining the distribution matrix of the branch carbon flow rate of the power distribution system as R B ＝P B diag(E N )。
The following is a description of step S4 of the method for dividing the carbon emission region of the power distribution system according to the embodiment of the present invention
S41: will be an electrical distance matrix A ij And branch carbon flow rate distribution matrix R Bij Carrying out normalization processing;
wherein the elements of the electrical distance matrix are used for measuring the electrical connection degree;
s42: formulating an area division optimization index as follows:
where ρ is ij Edge weights, ω, for connecting nodes i, j 1 、ω 2 Influence weight factors of each part of the optimization index on the region division target;
s43: calculating the regional modularity:
the cluster modularity index is defined as follows:
wherein m = ∑ Σ i ∑ j (ρ ij ) K 2 is the sum of the weights of all edges in the system i ,k j Respectively representing the sum of the edge weights directly connected with the nodes i and j, wherein delta (i and j) is a variable 0-1 for representing whether the nodes i and j belong to the same region;
wherein constraint conditions are added
Is the active power flow of the branch ij, and satisfies t at any time
The condition that reverse power flow occurs between divided regions can be avoided, and the constraint condition that reverse power flow does not occur between the divided regions is as follows:
s44: establishing a distribution system carbon emission region division model based on a linearized power flow equation, wherein the distribution system carbon emission region division model comprises the following steps:
wherein the constraint condition is a linearized power flow equation constraint condition.
The following is specifically described for step S4:
and (3) comprehensively considering the electrical connection degree (namely an electrical distance matrix) between the system nodes and the branch carbon flow rate distribution matrix as indexes for the carbon emission area division of the power distribution system, and constructing a linear carbon emission area division model of the power distribution system to obtain the carbon emission area division result of the power distribution system.
A more specific description of step S4 is as follows:
(4-1) calculating the electrical distance between each node of the power distribution system according to a Power Transfer Distribution Factor (PTDF):
the PTDF is used to define the branch power flow variation caused when the transmission power between the node pairs changes, and as shown in the above formula, the transmission power variation between the node pairs (i, j) is Δ P ij When the active power changes at the nodes i, j are respectively + delta P ij ,-ΔP ij Firstly, the linearized power flow equation is rewritten into a matrix form as follows:
H 1 (l i-j ,i)＝-B 1 (i,j),H 1 (l i-j ,j)＝B 1 (i,j)
H 2 (l i-j ,i)＝-B 2 (i,j),H 2 (l i-j ,j)＝B 2 (i,j)
wherein P is In ,Q In Are respectively N Bus X 1 node injection active and reactive power column vectors, N Bus Is the number of nodes of the system, N Branch Is the number of branches of the system, P Br ,Q Br Are each N Branch Active power and reactive power column vectors of x 1 branch, theta and V are N respectively Bus X 1 node voltage phase angle and voltage amplitude column vector, B 1 ,B 2 Are respectively N Bus ×N Bus Coefficient matrix of (H) 1 ,H 2 Are respectively N Branch ×N Bus Coefficient matrix of l i-j Representing the direction of the current as a branch from i to j, r ij ,x ij Representing the branch resistance and reactance of ij, respectively.
Further rewriting the above formula:
wherein B is E To calculate the auxiliary matrix in the process of voltage phase angle and amplitude, θ ', V' are the node voltage phase angle and voltage amplitude column vectors, P ', respectively, except for the balance node' In ，Q' In Respectively, the injected active and reactive power column vectors, B, of nodes other than the balancing node E For the auxiliary matrix in the calculation process, the constituent elements are shown on the right side of the equation corresponding to the matrix,
B' 1 ,B' 2 are respectively B 1 ，B 2 Removing the submatrix of the first row and the first column with dimension (N) Bus -1)×(N Bus -1)，
P In ，Q In injecting active and reactive power column vectors, S, respectively, of a node θ ，S V For the auxiliary matrix in the calculation process, eachThe constituent elements of the matrix are shown on the right side of the equation for that matrix,
S' θ ，S' V are respectively (B) E ) -1 Before (N) Bus -1) lines and postlines (N) Bus -1) a matrix of rows, K θ ，K V For the auxiliary matrices in the calculation process, the constituent elements of each matrix are shown to the right of the equation corresponding to that matrix, K' θ ，K' V Respectively K' (-) before Bus -1) lines and postlines (N) Bus -1) a matrix of rows.
And finishing again to obtain:
S P ＝H 2 S θ +H 1 S V ,S Q ＝-H 1 S θ +H 2 S V
K P ＝H 2 K θ +H 1 K V ,K Q ＝-H 1 K θ +H 2 K V
wherein S is P ,S Q ,K P ,K Q For the computation of the auxiliary matrices, the constituent elements of each matrix are shown on the right side of the equation for that matrix.
The final power flow equation is obtained as follows:
setting the variable quantity influence factors of I power flow of branch caused by power change of nodes i and j as
Obtaining the power transmission variable quantity between nodes i, j to cause branch circuitThe variable influence factor of the/tidal current is ^>
Is S P The ith row vector of the power transmission distribution factor can be obtained according to a relational expression of branch power flow and node injection power flow, when the active power injected into a node changes, if the variable quantity of corresponding node injection reactive power is considered to be small and ignored, the influence factor of the node injection active power change on the branch active power change can be obtained, and the power transmission distribution factor can be used for obtaining the influence factor
Thus, in an electric power system with a total of l branches, the amount of change in power flow, which causes the overall system when power interaction occurs between nodes i and j, is defined as follows:
the premise condition of the regional division of the power distribution system is that the influence on the power flow of the system is as small as possible when resource nodes in the same cluster perform power interaction, so that the network loss in the actual operation process of the system is reduced, and the edge weight of the system network is defined as
(4-2) constructing a linearized distribution system carbon emission region division model
And formulating a region division index based on the electrical distance matrix and the system branch carbon flow rate distribution matrix.
Firstly, carrying out normalization processing on a weight matrix:
for the matrix of electrical distances it is necessary to,
wherein +>
The maximum value and the minimum value of the elements of the electrical distance matrix are respectively;
for the bypass carbon flow rate distribution matrix,
wherein +>
Respectively the maximum value and the minimum value of the elements of the branch carbon flow rate distribution matrix;
and taking the modularity of the electrical connection and the modularity of the expanded area of the branch carbon flow rate distribution matrix as an optimization index of area division.
Wherein ω is 1 ,ω 2 And optimizing the influence weight factors of each part of the index on the region partition target.
The calculation of the regional modularity is specifically explained as follows:
the regional modularity index is used for measuring the quality of cluster division results, the large cluster modularity indicates that the connection degree of all nodes in the same cluster is tight, and otherwise, the cluster structure is loose.
The cluster modularity index is defined as follows:
where rho ij For the edge weights of the connection nodes i, j, the edge weights of the power system are calculated by comprehensively considering the modularity of the expansion areas of the electrical distance matrix and the branch carbon flow rate distribution matrix, and m = ∑ Σ i Σ j (ρ ij ) K 2 is the sum of the weights of all edges in the system i ,k j Respectively representing the sum of the edge weights directly connected with the nodes i and j, wherein delta (i and j) is a variable 0-1 for representing whether the nodes i and j belong to the same region, if delta (i and j) is 0, the nodes i and j do not belong to the same region, otherwise, the nodes i and j belong to the same region.
The decision variable delta (i, j) of the area division is a 0-1 variable, the area division needs to accord with a physical network topological structure, a node adjacent matrix of the power distribution system is defined as Adj, and when the nodes i, j are connected, the element Adj ij =1, otherwise 0;
the constraint condition of delta (i, j) is more than or equal to 0 and less than or equal to delta (i, j) and less than or equal to min (1,adj) can be obtained ij )。
In a power distribution system, the situation of reverse power flow needs to be avoided, that is, the power flow direction on a branch is opposite to the original forward power flow direction, and in order to ensure that the situation of reverse power flow does not occur between divided areas, constraint conditions need to be added
For the active power flow of branch ij, for any time t satisfies->
The constraint condition that no backward power flow occurs between the written-out areas is as follows:
the linear treatment is carried out on the formula by adopting a large M method, and a sufficiently large positive number M and an auxiliary variable are introduced
The power flow equation based on linearization obtains a region division model:
the constraint conditions are the constraint conditions of a linearized power flow equation, the constraint conditions of decision variables delta (i, j) of region division and the constraint conditions that no reverse power flow occurs between regions subjected to linearization processing.
The method for dividing the carbon emission area of the power distribution system based on the scene simulation and considering the electrical distance and the branch carbon flow rate has the following characteristics:
considering that the scale of distributed resources accessed by a power distribution system side under a novel power system is continuously increased, massive diversified heterogeneous distributed resources present different operation characteristics, wherein operation power curves of a renewable energy generating set with strong randomness, such as a fan, a photovoltaic set and the like, and user loads and the like can generate larger fluctuation due to wind power change, illumination intensity change and change of user power utilization preference, the power distribution system needs to properly process the randomness problem during decision making so as to ensure the safe and stable operation of the power system while exerting the flexibility potential of massive distributed resources as much as possible,
firstly, a distributed resource randomness scene set is generated based on a Monte Carlo method, and the solution efficiency is reduced in consideration of huge calculation load caused by a large amount of randomness scene sets, so that the clustering idea is introduced, scenes with similar characteristics are merged and classified, the original randomness scene set is reduced based on a back-substitution elimination method, the obtained reduced scene set still has the characteristics of the original scene set, and the problem solution load can be reduced while the randomness problem is fully considered;
considering the large-scale use of the energy storage system in the power system, considering the influence of the carbon potential of the energy storage system changed along with the scheduling strategy and the time executed by the energy storage system on the overall carbon emission flow distribution of the system, realizing the quantitative analysis of the carbon emission flow distribution condition of the power distribution system comprising the energy storage system, the distributed generator set and the wind turbine generator set, and obtaining a system branch carbon flow rate distribution matrix;
the method comprises the steps that a specific relation between line power flow and node injection power is deduced in a matrix form based on a linearized power distribution system power flow model, a power transmission distribution factor matrix under the linearized power distribution system power flow model is obtained through calculation in combination with definition of power transmission distribution factors of a power system, compared with the method that the result obtained through deduction according to a direct current power flow model is more accurate and more agrees with the power distribution system, the electrical distance between nodes in the power distribution system is further obtained according to the power transmission distribution factors;
the method is characterized in that the method comprises the steps of obtaining the modularity of an expansion area as a dividing index of a carbon emission area of the power distribution system based on the definition of the area modularity and comprehensively considering the electrical connection degree between nodes inside the area and the carbon flow rate of branches, and constructing the problem of carbon emission area division of the power distribution system into a linear programming model based on a linear power distribution side system power flow model and linear processing on area division constraint.
According to the carbon emission region division scheme obtained according to the embodiment, the electrical connection tightness among all nodes in the region can be considered, and the nodes and branches with similar carbon emission characteristics can be divided into the same region, so that a system operator can make and manage a carbon reduction scheduling strategy according to the regional carbon emission characteristics, the energy saving and emission reduction efficiency of the system is improved, and a double-carbon target is achieved by assistance.
Experimental example:
the improved IEEE 33 node power distribution system in this experimental example is shown in fig. 2. The total active load of the power distribution system is 3.715MWh, the total reactive load is 3.29MVar, and the reference voltage of the system is 12.66kV. No. 7 and No. 33 nodes are connected with photovoltaic power generation equipment with 350kW of capacity, no. 4 and No. 10 nodes are connected with photovoltaic power generation equipment with 300kW of capacity, no. 24 nodes are connected with photovoltaic power generation equipment with 400kW of capacity, and No. 20 and No. 27 nodes are respectively connected with photovoltaic power generation equipment with 300kW and 200kW of capacity; 13. no. 18 node is connected with fan power generation equipment with the capacity of 600kW, and No. 22 and No. 30 node are connected with fan power generation equipment with the capacity of 500 kW.
The daily fan, photovoltaic output power and load power curves of the power distribution system are shown in fig. 3. The ordinate of fig. 3 represents power level, the abscissa represents time, and the curves in the graph represent fan, photovoltaic output power (i.e., generated electric power) and load power (consumed electric power).
The random scene set of the fan, the photovoltaic and the load obtained by the method for generating the random scene based on the back-substitution elimination method is shown in fig. 4-6.
Wherein the ordinate of fig. 4 represents power magnitude, the abscissa represents time, the curve cluster in fig. 4 represents an obtained fan output power scene set, and each curve represents a stochastic scene; the ordinate of fig. 5 represents power magnitude, the abscissa represents time, the cluster of curves in fig. 5 represents the resulting set of photovoltaic output power scenes, each curve representing a stochastic scene; the ordinate of fig. 6 represents power magnitude and the abscissa represents time, and the cluster of curves in fig. 6 represents the resulting set of load power scenarios, each curve representing a stochastic scenario.
The method for dividing the area of the electric-carbon coordinated power distribution system is adopted to divide the area of carbon emission of the system, and an area division result and a modularity index of each area are obtained by combining with a figure 2, wherein the area division result is shown in a table 1, and the modularity index of each area is shown in a table 2.
TABLE 1
| Serial number | Node numbering of |
| 1 | 1，2，19，20，21，22 |
| 2 | 3，23，24，25 |
| 3 | 4，5，6，7，8 |
| 4 | 26，27，28，29，30，31，32，33 |
| 5 | 9，10，11，12，13，14，15，16，17，18 |
TABLE 2
| Serial number | Modularity of electrical connections | Branch carbon |
| 1 | 0.9012 | 0.8265 |
| 2 | 0.8634 | 0.6641 |
| 3 | 0.8400 | 0.7976 |
| 4 | 0.8125 | 0 |
| 5 | 0.7927 | 0.6480 |
According to the regional division method of the electric-carbon cooperative power distribution system provided by the embodiment of the invention, the carbon emission intensity and the electrical connection degree of the carbon emission region of the power distribution system can be comprehensively considered, and the result can be used for drawing a conclusion that the regional branch carbon flow rate supplied by the distributed energy source unit with the carbon emission intensity of a fan, a photovoltaic and the like approximate to 0 is small, the regional branch carbon flow rate supplied by the distributed energy source unit with the carbon emission intensity of the fan, the photovoltaic and the like approximate to 0 can be reduced to 0, the clean energy replaces the traditional energy with high carbon emission intensity to reduce the overall carbon emission intensity of the system, and the method provided by the embodiment can enable a system operator to evaluate and figure the carbon emission situation of each part of the system, so that a basis is provided for the operator to make a carbon reduction and emission reduction scheduling strategy.
The following is a summary of the method for dividing the area of the electric-carbon fusion power distribution system based on the scene simulation provided by the embodiment of the invention:
aiming at the randomness problem of mass distributed resources accessed at the side of a power distribution system in the actual operation process, the region division method of the power distribution system carries out simplification processing on an original randomness scene set obtained by adopting a Monte Carlo scene simulation method based on a back-substitution elimination method, so that the number of randomness scenes is reduced while the randomness scene set still has the characteristics of the original scene set after reduction, and the problem calculation burden is reduced;
the distribution condition of the carbon emission flow of the power distribution system is determined based on the carbon emission flow calculation theory on the basis of the time-varying characteristic of the carbon potential of the energy storage system on the basis of obtaining the optimal power flow distribution according to a linearized power distribution system side power flow model, and the distribution condition of the carbon emission intensity of the system is described;
in addition, in the dividing process of the carbon emission area, the electrical connection tightness of the nodes in the area and the distribution condition of branch carbon flow rate are taken into comprehensive consideration as indexes of the carbon emission area division, so that the obtained area division result can take two indexes into consideration, the effectiveness of the carbon reduction scheduling strategy formulated by a system operator is improved, the carbon emission area division problem of the power distribution system is constructed into a linear programming model for solving by carrying out linearization treatment on the area division constraint, the problems that a common heuristic algorithm is easy to fall into local optimization and the convergence speed is low in the area division problem are avoided, the division indexes can be flexibly changed according to different requirements of users according to the division indexes of the modularity of the expanded area, and the carbon emission area division method has good universality and expansibility.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several equivalent substitutions or obvious modifications can be made without departing from the spirit of the invention, and all the properties or uses are considered to be within the scope of the invention.
Claims (10)
1. The scheduling optimization method of the electric-carbon fusion power distribution system is characterized by comprising the following steps of:
s1: generating a random scene set;
s2: constructing a linear power distribution system load flow model, and calculating to obtain optimal load flow distribution;
s3: solving a distribution system branch carbon flow rate distribution matrix according to the power flow distribution;
s4: taking the electrical connection degree among the nodes of the power distribution system and the branch carbon flow rate distribution matrix as indexes for dividing the carbon emission area of the power distribution system, and constructing a linear power distribution system carbon emission area division model to obtain a power distribution system carbon emission area division result;
s5: and optimizing the distribution system scheduling according to the distribution system carbon emission region division result.
2. The scheduling optimization method for the electrical-carbon fusion power distribution system according to claim 1, wherein the step S1 further comprises: and performing feature extraction clustering and reduction on the randomness scene set by using a synchronous back-substitution elimination method to obtain a reduced randomness scene set.
3. The scheduling optimization method for the electric-carbon fusion power distribution system according to claim 1, wherein in step S2, the optimal power flow distribution of the power distribution system is determined according to the network topology architecture and the physical transmission line parameters of the power distribution system, the generator set parameters, the node load power prediction curve and the distributed energy output prediction curve, in combination with the network transmission loss of the power distribution system.
4. The scheduling optimization method for the electric-carbon fusion power distribution system according to claim 3, wherein in the step S2, the expression for constructing the power flow model of the linearized power distribution system is as follows:
in the formula
Wherein r is ij ,x ij The per unit resistance value and the reactance value of the branch between the connection nodes i and j are respectively; n is a radical of hydrogen Bus ,S Base Respectively, the set of all nodes of the power distribution system and the system reference capacity; p is i,t ,Q i,t ,V i,t ,θ i,t Respectively an active power injection value, a reactive power injection value, a voltage amplitude and a voltage phase which are subjected to per unit processing at a node i; p j,t ，Q j,t ，V j,t ，θ j,t Respectively as a passing mark at node jThe active power injection value, the reactive power injection value, the voltage amplitude and the voltage phase of the unitary processing; v i The voltage magnitude at node i; p ij,t ,Q ij,t Respectively the active power and the reactive power of the branch between the connection nodes i and j which are subjected to per-unit processing;
Respectively the active power and the reactive power injected by the distributed generator at the node i after per unit processing;
Active power injected into the wind driven generator at the node i after per unit processing;
The load requirements of active power and reactive power at the node i after per unit processing are respectively, and t is a time index and indicates the scheduling time corresponding to the variable; n is a radical of hydrogen T Is a total set of scheduling periods;P ij ,
Q ij ,
V i ,
the lower limit and the upper limit of active power, the lower limit and the upper limit of reactive power and the lower limit and the upper limit of voltage amplitude at the node i of the branch circuit between the connecting nodes i and j are respectively.
5. The electric-carbon fusion power distribution system scheduling optimization method according to claim 4, wherein the objective function of the optimal power flow distribution is as follows:
wherein, C Grid (P Grid ) Cost function for purchasing power from distribution system to higher-level power system, C k (P k ) Cost of the kth generator set, C e (P e ) Charge-discharge degradation cost of the e-th energy storage system, C w (P w ) Cost of abandoned wind for the w-th wind-driven generator set, C l (P l ) The load offset cost for the l-th load.
6. The scheduling optimization method for the electrical-carbon fusion power distribution system according to claim 1, wherein in step S3, the expression of the distribution system branch carbon flow rate distribution matrix is as follows:
R B ＝P B diag(E N )
wherein E is N As a distribution system node carbon potential vector, P B Constructing a system branch power flow distribution matrix, P, for the optimal power flow distribution B E, N is the number of all nodes in the power distribution system;
the distribution system node carbon potential vector calculation formula is
Wherein, P N Injecting an active power matrix, P, for a node G Injecting a power distribution matrix, P, into the generator E Accessing the energy storage system with a power distribution matrix, P W Injecting a power distribution matrix for the wind turbine, E G As a vector of the carbon emission intensity of the generator set, E E Is the carbon potential vector of the energy storage system, E W And the vector is the carbon emission intensity vector of the wind generating set.
7. The electric-carbon fusion power distribution system scheduling optimization method according to claim 1, wherein the step S4 comprises the following steps:
s41: normalizing the electrical distance matrix and the branch carbon flow rate distribution matrix;
s42: formulating an area division optimization index;
s43: calculating the region modularity;
s44: and constructing a power distribution system carbon emission region division model based on a linearized power flow equation.
8. The scheduling optimization method for the electrical-carbon fusion power distribution system according to claim 7, wherein the expression of the power distribution system carbon emission region division model is as follows:
where ρ is ij For the edge weights of the connecting nodes i, j, m = ∑ Σ i ∑ j (ρ ij ) K 2 is the sum of the weights of all edges in the system i ,k j Respectively representing the sum of edge weights directly connected with the nodes i and j, wherein delta (i and j) is a 0-1 variable for representing whether the nodes i and j belong to the same region, and the constraint condition is a linearized power flow equation constraint condition;
the expression of the trend equation is as follows:
wherein, theta and V are respectively N Bus Node voltage phase angle of x 1 and voltage amplitude column vector, P Br ,Q Br Are respectively N Branch Active power and reactive power column vector, P 'of branch circuit x 1' In ，Q' In Are respectively in balanceActive and reactive power column vectors, S, injected into the node external nodes θ ，S V ，S P ，S Q ，K θ ，K V ，K P ，K Q For the auxiliary matrix in the calculation process, the constituent elements of each matrix are shown on the right side of the equation corresponding to the matrix.
9. An electrical carbon fusion power distribution system dispatch optimization device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor when executing the computer program implementing the electrical carbon fusion power distribution system dispatch optimization method of any one of claims 1 to 8.
10. A storage medium comprising a stored computer program, wherein when the computer program is executed, the apparatus on which the storage medium is located is controlled to perform the method for optimizing scheduling of an electrical-carbon fusion power distribution system according to any one of claims 1 to 8.
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| CN117057659A (en) * | 2023-08-25 | 2023-11-14 | 国网湖北省电力有限公司电力科学研究院 | Regional power grid electricity-carbon joint risk assessment method |
| CN117117876A (en) * | 2023-10-25 | 2023-11-24 | 国网浙江省电力有限公司宁波供电公司 | Power grid full-element resource coordination control method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117057659A (en) * | 2023-08-25 | 2023-11-14 | 国网湖北省电力有限公司电力科学研究院 | Regional power grid electricity-carbon joint risk assessment method |
| CN117057659B (en) * | 2023-08-25 | 2024-05-24 | 国网湖北省电力有限公司电力科学研究院 | Regional power grid electricity-carbon joint risk assessment method |
| CN117117876A (en) * | 2023-10-25 | 2023-11-24 | 国网浙江省电力有限公司宁波供电公司 | Power grid full-element resource coordination control method and system |
| CN117117876B (en) * | 2023-10-25 | 2024-01-09 | 国网浙江省电力有限公司宁波供电公司 | Power grid full-element resource coordination control method and system |
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