CN117833329A - Distribution network optimization scheduling-oriented distributed resource dynamic aggregation regulation and control method - Google Patents
Distribution network optimization scheduling-oriented distributed resource dynamic aggregation regulation and control method Download PDFInfo
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Abstract
The invention discloses a distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network, which comprises the following steps: (1) Extracting data such as adjusting power, response time, response speed and the like of the distributed resources according to historical energy supply data of the regional distribution network, and establishing potential evaluation indexes of the distributed resources; (2) Defining index weight of a potential evaluation model based on the potential evaluation index, and establishing a distributed resource dynamic response potential evaluation model; (3) Based on a complex network theory, establishing a dynamic response characteristic similarity network, and dynamically aggregating distributed resources; (4) Based on a response characteristic similarity network aggregation result, defining a distributed cluster, establishing a distribution network optimization model considering dynamic aggregation of distributed resources by taking the minimum economic cost and the highest user satisfaction as optimization targets, and optimally solving a distribution network optimization scheduling scheme considering dynamic aggregation regulation of the distributed resources on a MATLAB simulation platform by adopting a Gurobi solver.
Description
Technical Field
The invention relates to the field of power distribution optimal scheduling, in particular to a distributed resource dynamic aggregation regulation method for power distribution network optimal scheduling.
Background
With the development of the energy internet, the country increasingly pays attention to the regulation and management of distributed resources on the power demand side. Modern power and energy systems are transitioning from vertically structured power systems to smart grids. The transformation is characterized by the wide application of the distributed renewable energy sources and energy storage equipment on the demand side, and the bidirectional communication between the energy load and the power grid is realized through a demand response mechanism.
Because the user side contains a large amount of distributed resources with the potential adjusting function, such as an electric automobile, an energy storage device, a distributed power supply, an air conditioner, electric heating and other adjustable loads, the power grid adjusting device has the characteristics of wide points and multiple surfaces, various types, small capacity and the like, and if the power grid adjusting device is brought into the conventional power grid adjusting category, the power grid adjusting capability is greatly improved. But at the same time, how to adaptively regulate and control according to the energy supply and utilization characteristics of various distributed resources is a challenge facing the regulation and control of the distributed resources at present and is also an important research direction.
Because the energy supply characteristics of the distributed resources change with time sequence, dynamic aggregation forming cluster regulation is a regulation strategy of the distributed resources. Dynamic aggregation regulation needs to fully consider the energy supply characteristics of users and distribution networks, and the satisfaction degree of the users is also considered while the benefits are considered.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a distributed resource dynamic aggregation regulation method oriented to the optimal scheduling of a distribution network.
The invention aims at realizing the following technical scheme:
a distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network comprises the following steps:
(1) According to historical energy supply data of the regional distribution network, extracting data of adjustment power, response time and response speed of the distributed resources, and establishing potential evaluation indexes of the distributed resources, wherein the potential evaluation indexes are used for establishing a dynamic response potential evaluation model of the distributed resources, and the distributed resources comprise photovoltaic, wind power, electric vehicles, energy storage and user adjustable load resources;
(2) Defining index weight of a potential evaluation model based on potential evaluation indexes, establishing a distributed resource dynamic response potential evaluation model, and fitting model parameters according to historical data;
(3) Based on a complex network theory, a dynamic response feature similarity network is established, user nodes are constructed, connection is carried out according to the similarity among the nodes under different time sequences, wherein the feature similarity is obtained by evaluating and calculating the dynamic response potential of the distributed resources, and then the distributed resources are dynamically aggregated according to the distance between the node connection under different time sequences;
(4) Based on a response characteristic similarity network aggregation result, defining a distributed cluster, taking the minimum economic cost and the highest user satisfaction as optimization targets, taking a power model, an upper limit and a lower limit of power output and regional energy balance of each distributed resource cluster as constraint conditions, establishing a distribution network optimization model considering dynamic aggregation of distributed resources, and adopting a Gurobi solver to optimally solve a distribution network optimization scheduling scheme considering dynamic aggregation regulation of the distributed resources on a MATLAB simulation platform.
Further, the potential evaluation index of the distributed resource specifically includes:
(1) Maximum regulated power ΔP R : after receiving the regulation response signal, the distributed resource has the difference between the maximum or minimum stable output or load and the output or load at the moment of receiving the regulation response signal, wherein the difference is as follows:
ΔP R ＝|P 1 -P 2 |
wherein: ΔP R Representing maximum regulated power, P 1 Output or load at the moment of receiving regulation response signal for distributed resource, P 2 To regulate the output or load in steady state;
(2) Reaction time: the time from the issuing of the response signal to the initiation of participation in the response by the distributed resource is:
ΔT R ＝T 2 -T 1
wherein: delta T R Representing a distributed resource reaction time; t (T) 1 To issue the control command, T 2 The method comprises the steps of receiving a regulation response signal for distributed resources and stabilizing output or load moment;
(3) Response duration: the time from the start of the response of the distributed resource to the departure of the response state is:
T D ＝T 3 -T 2
wherein: t (T) D Representing a duration of the distributed resource response; t (T) 3 The moment of response regulation is ended for the distributed resource;
(4) Response rate: after the distributed resource receives the response signal, the adjusting power per unit time is as follows:
further, the distributed resource dynamic response potential evaluation model specifically comprises the following steps:
wherein: α, β, γ, λ represent the decision maker's distributed resource response preferences, respectively; η is the dynamic response potential of the distributed resource.
Further, the specific process of dynamically aggregating the distributed resources is as follows:
(1) Construction node and connection line
The response feature similarity network is composed of resource nodes and node connecting lines, and the network is a topological structure composed of the resource nodes and the node connecting lines and reflects the aggregation and group states of distributed resources;
the resource node is a basic constitution unit of a network and represents a certain distributed resource, including distributed photovoltaic, wind power, electric automobile, energy storage or user adjustable load resource;
the node connection lines are the connection lines of different resource nodes in the network to form edges in the network, and the node connection lines exist between different nodes to represent the similarity of electricity utilization characteristics between two nodes;
(2) Calculating feature similarity and constructing network
Feature similarity S of resource node x and node y im The (x, y) calculation formula is:
wherein: s is S im (x t ,y t ) The characteristic similarity of the resource node x and the node y at the moment t is represented;and->Respectively representing the average response potential of the x node and the y node at the time t; />Respectively representing the maximum value and the minimum value of response potential of the resource node x and the node y at the time t;
setting a connection threshold, if the feature similarity S im (x t ,y t ) If the response characteristics of the two resource nodes are similar and higher than the connection threshold, a connecting line is set and adjacent to the element a in the matrix A xy 1 is shown in the specification; otherwise, no connection is set and the element a in the adjacent matrix xy 0, expressed as:
S im (x t ,y t )＞S im_min a xy ＝1
S im (x t ,y t )＜S im_min a xy ＝0
wherein: s is S im_min Representing a connection threshold; a, a xy An element in a collarband matrix A for representing the connection relationship between the nodes xy;
(3) Distributed resource aggregation
According to the constructed response characteristic similarity network topology, the effect of distributed resource aggregation is measured by adopting a module degree:
wherein: q represents modularity, and the larger the numerical value is, the better the aggregation cluster effect is; m is the number of edges in the network; k (k) x And k y Representation and nodeThe number of edges where x, y are directly connected; delta (c) x ,c y ) The method is used for judging whether the node x and the node y are in a cluster or not, if yes, the node x and the node y are 1, and if not, the node y is 0;
(4) Iterative polymerization process is repeated until the optimal polymerization is reached
By adjusting the connection threshold S im_min And calculating the modularity Q of each step until the aggregated cluster network reaches the maximum modularity, and stopping and outputting an aggregation optimization result.
Furthermore, the construction process of the distribution network optimization model considering the dynamic aggregation of the distributed resources comprises the following steps:
(1) The distribution network optimization model comprises an upper layer optimization model and a lower layer optimization model, wherein the upper layer optimization model aims at maximizing the distribution network operator profit, and the total profit mainly comprises the sales energy profitSubsidy expenditure for purchasing distributed energy response->Energy storage expenditure and operation and maintenance cost are called>And cost of purchasing electricity to distributed new energy ∈ ->The objective function of the upper layer optimization model is:
wherein: f (F) up Representing the benefits of the distribution network for the objective function of the upper-layer optimization model; t is the optimization time;the electricity selling cost of the distribution network at the time t is expressed as +.>Wherein (1)>Price for electricity selling of distribution network at t moment, < ->The power supplied at the moment t of the distribution network is supplied; />Subsidized expenditure for purchasing distributed energy response at time t, denoted +.> The power variation quantity of the demand response is participated in for the distributed resource at the moment t; />The subsidy price is responded for the unit demand at the time t; />For the invocation of the stored energy at time t, the operation and maintenance costs are expressed as +.>Wherein->And->For the charge and discharge power at the time t of energy storage, R ES The operation and maintenance cost of the energy storage unit power is; />The cost of purchasing electricity from a power grid to a distributed new energy user at the time t is expressed as +.> Output +.f. for distributed new energy t moment>The purchase price of distributed new energy power is distributed for the distribution network at the time t;
(2) The lower optimization model is multi-objective optimization, the cost of energy consumption of each distributed cluster is lowest by an objective function 1, the satisfaction of users is highest by an objective function 2, and the two are expressed as follows:
wherein:an objective function 1 of the lower optimization model; n is the aggregation number of the distributed clusters; />Is the rigid load of the ith cluster at the moment t; />The demand response power at the moment t of the ith cluster;
wherein:an objective function 2 of the lower optimization model; />Satisfaction of the power utilization mode of the user at the moment t;satisfaction of electricity charge expenditure of the user at time t; alpha and beta are weight coefficients; />And paying for the electricity fee at the moment t of the ith cluster.
Further, the constraints of the upper optimization model include energy balance constraints and energy storage operation constraints, expressed as:
wherein:the user rigidity load at the moment t; />The energy storage charge capacity at the time t; />And->Is 0-1 variable, which represents the charge and discharge state of the stored energy at time t,/and>when charging, 1, the rest are 0, < >>1 when discharging, the rest is 0; p (P) ES_max Representing the maximum charge-discharge power of the stored energy; SOC (State of Charge) ES_min And SOC (System on chip) ES_max Is the minimum and maximum state of charge of the stored energy.
Still further, the constraints of the underlying optimization model include demand response constraints expressed as:
wherein P is DR_min Indicating a lower limit of the demand response amount,representing the distributed resource aggregation demand response quantity at the time t and P DR_max Indicating an upper limit for demand response.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the invention decomposes and extracts the power consumption behaviors of different time sequences of the distributed resource, and can decompose and analyze the relation which is difficult to distinguish among all influencing factors, thereby distinguishing the difference of response characteristics of all time nodes.
2. The invention considers the time sequence response characteristic under the uncertainty of the adjustable resources, is beneficial to obtaining the probability distribution of the response potential of the adjustable resource clusters under different time nodes, and is beneficial to the hierarchical classification regulation and control of the multi-type adjustable resources in the power assisting area. The method can provide support for price decisions of participation of adjustable resource managers such as VPPs and the like in response, but does not consider the influence of market environment on response potentials of various adjustable resource clusters, and when the response business is actually participated in, response adjustment potentials provided at different time scales before and during the day are needed to be distinguished. In subsequent studies, probability distributions of response potential at different time scales can be further studied.
3. The planning scheme of the invention can establish a distributed resource dynamic response potential evaluation model considering maximum adjusting power, response time, response duration and response speed according to the characteristic change of the power supply and the power consumption aiming at the response time sequence characteristics of the distributed resource, and the hierarchical classification regulation and control of the multi-type distributed adjustable resource in the power assisting area. And the response time and the response capacity of different distributed resources are considered to be different, and the response time and the response capacity can be complemented through reasonable regulation and control, so that the economic effect of maximum scheduling is realized, and the energy consumption satisfaction degree and the energy consumption economical efficiency of users are balanced.
4. The invention considers the similarity of the distributed resource response and performs aggregation to form a regulation and control cluster. Compared with the regulation and control of a distributed monomer, the method can respond to a larger range of demands, obviously promote the on-site consumption of distributed new energy, and make up for the defects of non-positive response of distributed resources and insufficient economy. Meanwhile, the similar distributed response clusters can be better subjected to unified regulation and control, and the energy balance requirement of the area is met.
5. The invention integrates various distributed resources in the area, such as distributed photovoltaic, wind power, energy storage, electric automobiles, air conditioners, flexible adjustable loads and the like, and can carry out scientific regulation and control scheduling scheme by matching with a distribution network through a distribution network optimization scheduling model. Thereby playing the roles of improving energy efficiency, saving energy and reducing emission.
Drawings
FIG. 1 is a graph of regional load prediction and response;
FIG. 2 is a diagram of a distributed resource distribution scenario;
FIG. 3 is a schematic diagram of a distributed resource dynamic aggregation cluster;
FIG. 4 is a schematic diagram of response potential after dynamic aggregation of distributed resources;
FIG. 5 is a charge-discharge power plot of battery energy storage and pumped-hydro energy storage;
FIG. 6 is a power usage diagram of a business load cluster;
FIG. 7 is a comparison of user energy costs;
FIG. 8 is a schematic diagram of a distributed cluster day-ahead purchase;
FIG. 9 is a schematic diagram of the distribution network operation yield.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
1. Distributed resource modeling
The user distributed resources comprise distributed photovoltaics, wind power, temperature control loads (air conditioner, water heater, electric heating and the like), interruptible loads (household appliances such as illumination, televisions and the like), electric automobiles and the like.
(1) Distributed photovoltaic output model:
wherein:the output power of the photovoltaic output at the moment t; />Irradiance at time t; g 0 Irradiance for standard state; />Maximum output power in a standard state; />The temperature of the photovoltaic module at the moment t; n (N) es The temperature of the battery for normal operation of the photovoltaic; t (T) 0 Component temperature for standard conditions.
(2) Distributed wind power output model:
wherein:the power is output for the moment t of the fan; />The wind speed at time t; />Wind speed for cut-in and cut-out; p (P) WT And->Is rated power and wind speed at rated power.
(3) Temperature control load equivalent model
Wherein: p (P) t r The thermal power is the temperature control load; t (T) t The user temperature at time t; t (T) t 0 The external environment temperature at the time t; t is t u Indicating the working time of the heating equipment; k represents an equivalent parameter of the relation between the working time and the temperature change; Δt represents a state update time interval; r, C the equivalent thermal resistance and heat capacity; s is(s) t The switch state of the temperature control load is represented; lambda represents a thermodynamic equivalent parameter.
(4) Interruptible load model
Wherein:the load value can be interrupted at the moment t; p is p max Power representing transitions; t is t s Indicating the load opening time; t is t f Indicating the moment of load closure.
(5) Industrial and commercial flexible load model
Wherein: p (P) I t The industrial and commercial flexible load value is t time;the upper limit of the adjustable load at the time t is represented; />The lower limit of the adjustable load at time t is indicated.
(5) Electric automobile model
Wherein: s is S ne (i) The method comprises the steps of (1) requiring output power for the charging electric quantity of the electric automobile of a user i; c (C) 0 Is the battery capacity; s is S ex (i) The expected state of charge of the electric automobile is the user i; s is S 0 (i) The state of charge when the user i accesses the electric automobile is set; s (i, t) is the state of charge of the electric automobile at the moment t of the user i; η is the charging efficiency; p (P) ev (i, t) is the charging power of the electric automobile at the moment t of the user i; Δt is the time interval;rated charging power of the electric automobile; s is S min 、S max The upper and lower limits of the SOC of the electric automobile; the charging time and the cancelling charging time of the electric automobile are set for the user i.
The power variation of the distributed resource participation demand response is equal to the sum of the power of the electric automobile, the temperature control load, the industrial and commercial flexible load and the interruptible load.
2. Potential evaluation index and dynamic response potential evaluation model of distributed resources
(1) Maximum regulated power ΔP R : after the distributed resources such as the electric automobile, the energy storage, the user adjustable load resources and the like receive the regulation response signals, the difference between the maximum or minimum stable output or load and the output or load at the moment when the regulation signal is currently received is calculated:
ΔP R ＝|P 1 -P 2 |
wherein: ΔP R Representing a maximum regulated power; p (P) 1 Output or load when receiving regulation signal for distributed resource, P 2 To regulate the force or load at steady state.
(2) Reaction time: after issuing the response signal, the distributed resource starts to participate in the response.
ΔT R ＝T 2 -T 1
Wherein: delta T R Representing a distributed resource reaction time; t (T) 1 To issue the control command, T 2 And receiving the control signal for the distributed resource and stabilizing the moment of output or load.
(3) Response duration: the time from the start of the response of the distributed resource to the departure of the response state.
T D ＝T 3 -T 2
Wherein: t (T) D Representing a duration of the distributed resource response; t (T) 3 The moment of response regulation is ended for the distributed resource.
(4) Response rate: representing the regulated power per unit time of the distributed resource after receiving the response signal.
The method comprises the following steps of:
wherein: α, β, γ, λ represent the decision maker's distributed resource response preferences, respectively; η is the dynamic response potential of the distributed resource.
3. Dynamic aggregation distributed resource method
(1) Construction node and connection line
The response characteristic similarity network is composed of resource nodes and node connecting lines. The network is a topological structure formed by resource nodes and node connection lines, and reflects the aggregation and group states of distributed resources.
The resource node is a basic building unit of the network, and represents a certain distributed resource, such as a distributed photovoltaic, wind power, electric automobile, energy storage, user-adjustable load resource and the like.
The node connection lines are the connection lines of different resource nodes in the network and form edges in the network. The existence of node links between different nodes indicates that the electricity utilization characteristics between two nodes are similar.
(2) Calculating feature similarity and constructing network
Feature similarity S of resource node x and node y im The (x, y) calculation formula is:
wherein: s is S im (x t ,y t ) The characteristic similarity of the resource node x and the node y at the moment t is represented;and->Respectively representing the average response potential of the x node and the y node at the time t; />And->The maximum value and the minimum value of response potential of the resource node x and the node y at the time t are respectively represented.
Setting a connection threshold S im_min If (3)Feature similarity S im (x t ,y t ) If the response characteristics of the two resource nodes are similar, a connecting line should be arranged to adjoin the element a in the matrix A xy 1 is shown in the specification; otherwise, no connection is set, adjacent element a in matrix xy Is 0.
S im (x t ,y t )＞S im_min a xy ＝1
S im (x t ,y t )＜S im_min a xy ＝0
Wherein: s is S im_min Representing a connection threshold; a, a xy Is an element in the collarband matrix a representing the connection relationship between the nodes xy.
(3) Distributed resource aggregation
And according to the constructed response characteristic similarity network topology, measuring the effect of distributed resource aggregation by adopting a modularity Q.
Wherein: q represents modularity, and the larger the numerical value is, the better the aggregation cluster effect is; m is the number of edges in the network; k (k) x And k y Representing the number of edges directly connected to nodes x, y; delta (c) x ,c y ) Judging whether the x node and the y node are in a cluster, if so, judging that the x node and the y node are 1, otherwise, judging that the y node is 0.
(4) Iterative repeat polymerization process to realize optimal polymerization
And calculating the modularity of each step by adjusting the connection threshold value until the aggregated cluster network reaches the maximum modularity, stopping and outputting an aggregation optimization result.
4. Distribution network optimized scheduling considering distributed resource dynamic aggregation
(1) The distribution network optimization comprises an upper layer optimization model and a lower layer optimization model, wherein the upper layer model aims at maximizing the benefits of distribution network operators. The total income mainly comprises sellingEnergy benefitSubsidy expenditure for purchasing distributed energy response->Energy storage expenditure and operation and maintenance cost are called>Cost of purchasing electricity for distributed new energy sources ∈>The specific objective function is:
wherein: f (F) up Representing distribution network benefits for an upper layer objective function; t is the optimization time;the electricity selling cost of the distribution network at the moment t is calculated; />Subsidy expenditure for purchasing distributed energy response at time t; />The energy storage at the time t is called and the operation and maintenance cost is realized; />And the cost of purchasing electricity from the power grid to the distributed new energy users at the time t is obtained.
Wherein:the power supplied at the moment t of the distribution network is supplied; />And (5) selling electricity for the distribution network at the moment t.
Wherein:the power variation quantity of the demand response is participated in for the distributed resource at the moment t; />And responding to the subsidy price for the unit demand at the time t.
Wherein:and->The charging and discharging power is stored at the time t; r is R ES And the operation and maintenance cost of the energy storage unit power is realized.
Wherein:the output at the moment t is the output of the distributed new energy; />And (5) acquiring price of distributed new energy power for the distribution network at the time t.
Constraints of the upper model include energy balance constraints, energy storage operation constraints, and the like.
Wherein:the user rigidity load at the moment t; />The energy storage charge capacity at the time t; />And->Is 0-1 variable, which represents the charge and discharge state of the stored energy at time t,/and>when charging, 1, the rest are 0, < >>1 when discharging, the rest is 0; p (P) ES_max Representing the maximum charge-discharge power of the stored energy; SOC (State of Charge) ES_min And SOC (System on chip) ES_max Is the minimum and maximum state of charge of the stored energy.
(2) The lower model is multi-objective optimization, the cost of energy consumption of each distributed cluster is lowest by the objective function 1, and the satisfaction degree of users is highest by the objective function 2.
Wherein:an objective function 1 of the lower optimization model; n is the aggregation number of the distributed clusters; />Is the rigid load of the ith cluster at the moment t; />And responding to the power for the demand of the ith cluster at the moment t.
Wherein:an objective function 2 of the lower optimization model; />Satisfaction of the power utilization mode of the user at the moment t;satisfaction of electricity charge expenditure of the user at time t; alpha and beta are weight coefficients; />And paying for the electricity fee at the moment t of the ith cluster.
Constraints of the underlying model include demand response constraints, etc., expressed as:p in the formula DR_min Represents a lower limit of demand response, +.>Representing the distributed resource aggregation demand response quantity at the time t and P DR_max Indicating an upper limit for demand response.
The model adopts a Gurobi solver to optimally solve a distribution network optimization scheduling scheme considering distributed resource dynamic aggregation regulation on an MATLAB simulation platform.
The following is a detailed description of a distributed resource dynamic aggregation regulation strategy for optimal scheduling of a distribution network in combination with an example:
the data used in this example is simulated data of 500 distributed resource electricity loads including distributed photovoltaic, wind power, electric automobile, temperature control load, interruptible load and the like, and the data are distributed in three areas A, B, C, as shown in fig. 1 and 2. The energy storage comprises 5MWh/800kW pumped storage and 800kWh/600kW battery energy storage. And the temperature data adopts the daily highest temperature and the daily lowest temperature to analyze the influence of the temperature on the power utilization characteristics of each time sequence component of the distributed resource. In order to analyze the influence of non-parametric data such as holidays, meteorological data and the like on each component of the distributed resource electricity utilization time sequence, boolean variables are used for carrying out parameterization on non-parametric influence factors.
According to the dynamic aggregation distributed resource method, clustering is performed based on time sequence response characteristics and indexes of distributed resources, and a classification result of clustering clusters at the time t=3h, t=11h, t=14h and t=21h is shown in fig. 3. The clustering results at the moments t=11h and t=21h are analyzed in detail.
When t=11h, the cluster I contains electric vehicles, battery energy storage systems, small temperature control loads, interruptible loads and the like, and is mainly a distributed resource capable of fast response, so that the response time involved in the response is short, but because the distributed resource comprises battery energy storage, the regulation capacity is usually small, and the response duration is short, as shown in fig. 4, the area of a graph expected to be formed by each index of the cluster I is narrow. Although the partial index of the cluster I is poor, the cluster I can provide partial adjustable capacity under the scene with higher reaction time requirement due to the excellent reaction capability when the cluster participates in response, and is suitable for some emergency response scenes. The clusters II and III contain temperature control load and interruptible load, mainly are distributed adjustable resources of resident users, and because 11 noon, the resident has less interruptible load and limited adjustment range of the temperature control load because of a part of rigidity requirements for energy consumption. As shown in fig. 4, the response time potential of distributed resources is longer, but the duration is longer, due to the temperature controlled load. The cluster III is more in load quantity, the maximum adjustable capacity is larger, so that the area of the lower half area is larger, the cluster III is characterized by being more suitable for medium-long-term adjustment response scenes, and the clusters II and III are suitable for some peak regulation and frequency modulation scenes. The cluster IV has the maximum adjustment potential because of containing industrial and commercial flexible loads with larger capacity and pumped storage, and the characteristics of the industrial and commercial adjustable loads and the pumped storage contained in the cluster IV are difficult to realize quick response, so that the response reaction time is longer, but the industrial and commercial adjustable resource adjustment capacity is larger, and the cluster IV has larger adjustment potential under some business scenes with lower requirements on the reaction time and can be suitable for long-term new energy consumption scenes. Each cluster at time t=11h contains the resource type and characteristics as shown in table 1.
Table 1t=11h time each cluster contains a resource type and a characteristic
When t=21h is the rigid electricity consumption peak of the resident user, so that the characteristics of the user energy consumption are similar, the cluster I comprises the electric automobile, the temperature control load and the interruptible load of the resident user and the battery energy storage, the response regulation scenes of short term and medium term are considered, and the response capacity is larger because more distributed resources are integrated. The cluster II is mainly a large-capacity industrial and commercial flexible load and pumped storage, and is also provided with a large-capacity temperature control load, so that the characteristic of large capacity and long-term capacity adjustability is shown. Each cluster at time t=11h contains the resource type and characteristics as shown in table 2.
Table 2t=21h time each cluster contains resource types and characteristics
Fig. 5 shows charge and discharge power of battery energy storage and pumped storage, and it can be seen from fig. 5 that after a dynamic aggregation regulation strategy is adopted, response potential of the distributed resources is mobilized, and response frequency of the battery energy storage is faster than that of the pumped storage, because the distributed resource cluster where the battery energy storage is located mainly corresponds to rapid response requirement, supply energy has large change, but energy storage capacity of the battery is limited. Pumped storage is therefore often used as a responsive backup for battery energy storage by virtue of its large capacity.
As the characteristics of various distributed resources of the resident user clusters are changed, the cluster aggregation types are also changed continuously, and the industrial and commercial flexible load clusters mainly take industrial and commercial flexible loads and pumped storage as main, so that the types of the contained distributed resources are less changed. Fig. 6 shows the energy consumption of the industrial and commercial load clusters, and it can be seen that compared with the traditional response regulation and control strategy, the dynamic aggregation regulation and control strategy is adopted, so that the flexible load responds more actively under the support of pumped storage, the energy consumption in the daytime is generally reduced, the energy consumption of electricity used in the low valley at night is increased, the peak clipping and valley filling effects are achieved, and the dispatching pressure of the distribution network is reduced.
Fig. 7 is a comparison of energy consumption costs of users, and it can be seen that the energy consumption time period and willingness of the users are reasonably distributed by mining the distributed resource potential of the users by adopting the distributed dynamic aggregation regulation, so that the energy consumption cost of the users is reduced to a certain extent.
Fig. 8 shows that the distributed cluster purchase electricity before day, compared with the traditional optimized scheduling method, the dynamic aggregation strategy translates the energy consumption at the midday and at the night at the peak time, and ensures the local balance of regional energy sources through the transition of energy storage at the valley price, thereby reducing the waste of distributed new energy sources, improving the energy supply benefit of the distribution network and increasing the response subsidy benefit of users.
Fig. 9 is a comparison of distribution network operational benefits. Compared with the traditional optimal scheduling method, the operation income of the distribution network is improved by about 28% by means of energy storage allocation and full in-situ consumption of distributed new energy.
Therefore, in terms of regulation and control effects, the distributed resource dynamic aggregation regulation and control method for the optimal scheduling of the distribution network can mobilize the response potential of the distributed resources to a certain extent, and the response scene matching of the distributed resources is enhanced through dynamic clustering, so that an optimal scheduling scheme with higher economical efficiency is realized by matching with the optimal scheduling of the distribution network.
The invention is not limited to the embodiments described above. The above description of specific embodiments is intended to describe and illustrate the technical aspects of the present invention, and is intended to be illustrative only and not limiting. Numerous specific modifications can be made by those skilled in the art without departing from the spirit of the invention and scope of the claims, which are within the scope of the invention.
Claims (7)
1. A distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network is characterized by comprising the following steps:
(1) According to historical energy supply data of the regional distribution network, extracting data of adjustment power, response time and response speed of the distributed resources, and establishing potential evaluation indexes of the distributed resources, wherein the potential evaluation indexes are used for establishing a dynamic response potential evaluation model of the distributed resources, and the distributed resources comprise photovoltaic, wind power, electric vehicles, energy storage and user adjustable load resources;
(2) Defining index weight of a potential evaluation model based on potential evaluation indexes, establishing a distributed resource dynamic response potential evaluation model, and fitting model parameters according to historical data;
(3) Based on a complex network theory, a dynamic response feature similarity network is established, user nodes are constructed, connection is carried out according to the similarity among the nodes under different time sequences, wherein the feature similarity is obtained by evaluating and calculating the dynamic response potential of the distributed resources, and then the distributed resources are dynamically aggregated according to the distance between the node connection under different time sequences;
(4) Based on a response characteristic similarity network aggregation result, defining a distributed cluster, taking the minimum economic cost and the highest user satisfaction as optimization targets, taking a power model, an upper limit and a lower limit of power output and regional energy balance of each distributed resource cluster as constraint conditions, establishing a distribution network optimization model considering dynamic aggregation of distributed resources, and adopting a Gurobi solver to optimally solve a distribution network optimization scheduling scheme considering dynamic aggregation regulation of the distributed resources on a MATLAB simulation platform.
2. The distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network according to claim 1, wherein the potential evaluation index of the distributed resource specifically comprises:
(1) Maximum regulated power ΔP R : after receiving the regulation response signal, the distributed resource has the difference between the maximum or minimum stable output or load and the output or load at the moment of receiving the regulation response signal, wherein the difference is as follows:
ΔP R ＝|P 1 -P 2 |
wherein: ΔP R Representing maximum regulated power, P 1 Output or load at the moment of receiving regulation response signal for distributed resource, P 2 To regulate the output or load in steady state;
(2) Reaction time: the time from the issuing of the response signal to the initiation of participation in the response by the distributed resource is:
ΔT R ＝T 2 -T 1
wherein: delta T R Representing a distributed resource reaction time; t (T) 1 To issue the control command, T 2 The method comprises the steps of receiving a regulation response signal for distributed resources and stabilizing output or load moment;
(3) Response duration: the time from the start of the response of the distributed resource to the departure of the response state is:
T D ＝T 3 -T 2
wherein: t (T) D Representing a duration of the distributed resource response; t (T) 3 The moment of response regulation is ended for the distributed resource;
(4) Response rate: after the distributed resource receives the response signal, the adjusting power per unit time is as follows:
3. the distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network according to claim 1, wherein the distributed resource dynamic response potential evaluation model is specifically:
wherein: α, β, γ, λ represent the decision maker's distributed resource response preferences, respectively; η is the dynamic response potential of the distributed resource.
4. The distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network according to claim 1, wherein the specific process of dynamically aggregating the distributed resources is as follows:
(1) Construction node and connection line
The response feature similarity network is composed of resource nodes and node connecting lines, and the network is a topological structure composed of the resource nodes and the node connecting lines and reflects the aggregation and group states of distributed resources;
the resource node is a basic constitution unit of a network and represents a certain distributed resource, including distributed photovoltaic, wind power, electric automobile, energy storage or user adjustable load resource;
the node connection lines are the connection lines of different resource nodes in the network to form edges in the network, and the node connection lines exist between different nodes to represent the similarity of electricity utilization characteristics between two nodes;
(2) Calculating feature similarity and constructing network
Feature similarity S of resource node x and node y im The (x, y) calculation formula is:
wherein: s is S im (x t ,y t ) The characteristic similarity of the resource node x and the node y at the moment t is represented;and->Respectively representing the average response potential of the x node and the y node at the time t; />Respectively representing the maximum value and the minimum value of response potential of the resource node x and the node y at the time t;
setting a connection threshold, if the feature similarity S im (x t ,y t ) If the response characteristics of the two resource nodes are similar and higher than the connection threshold, a connecting line is set and adjacent to the element a in the matrix A xy 1 is shown in the specification; otherwise, no connection is set and the element a in the adjacent matrix xy 0, expressed as:
S im (x t ,y t )＞S im_min a xy ＝1
S im (x t ,y t )＜S im_min a xy ＝0
wherein: s is S im_min Representing a connection threshold; a, a xy An element in a collarband matrix A for representing the connection relationship between the nodes xy;
(3) Distributed resource aggregation
According to the constructed response characteristic similarity network topology, the effect of distributed resource aggregation is measured by adopting a module degree:
wherein: q represents modularity, and the larger the numerical value is, the better the aggregation cluster effect is; m is the number of edges in the network; k (k) x And k y Representing the number of edges directly connected to nodes x, y; delta (c) x ,c y ) The method is used for judging whether the node x and the node y are in a cluster or not, if yes, the node x and the node y are 1, and if not, the node y is 0;
(4) Iterative polymerization process is repeated until the optimal polymerization is reached
By adjusting the connection threshold S im_min And calculating the modularity Q of each step until the aggregated cluster network reaches the maximum modularity, and stopping and outputting an aggregation optimization result.
5. The method for dynamic aggregation regulation and control of distributed resources for optimal scheduling of a distribution network according to claim 1, wherein the construction process of the distribution network optimization model considering dynamic aggregation of the distributed resources is as follows:
(1) The distribution network optimization model comprises an upper layer optimization model and a lower layer optimization model, wherein the upper layer optimization model aims at maximizing the distribution network operator profit, and the total profit mainly comprises the sales energy profitSubsidy expenditure for purchasing distributed energy response->Energy storage expenditure and operation and maintenance cost are called>And cost of purchasing electricity to distributed new energy ∈ ->The objective function of the upper layer optimization model is:
wherein: f (F) up Representing the benefits of the distribution network for the objective function of the upper-layer optimization model; t is the optimization time;the electricity selling cost of the distribution network at the time t is expressed as +.>Wherein (1)>Price for electricity selling of distribution network at t moment, < ->The power supplied at the moment t of the distribution network is supplied; />Subsidized expenditure for purchasing distributed energy response at time t, denoted +.> The power variation quantity of the demand response is participated in for the distributed resource at the moment t; />The subsidy price is responded for the unit demand at the time t; />For the invocation of the stored energy at time t, the operation and maintenance costs are expressed as +.>Wherein->Andfor the charge and discharge power at the time t of energy storage, R ES The operation and maintenance cost of the energy storage unit power is; />The cost of purchasing electricity from a power grid to a distributed new energy user at the time t is expressed as +.> Is the output of the distributed new energy source at the moment t, < >>The purchase price of distributed new energy power is distributed for the distribution network at the time t;
(2) The lower optimization model is multi-objective optimization, the cost of energy consumption of each distributed cluster is lowest by an objective function 1, the satisfaction of users is highest by an objective function 2, and the two are expressed as follows:
wherein:an objective function 1 of the lower optimization model; n is the aggregation number of the distributed clusters; />Is the rigid load of the ith cluster at the moment t; />The demand response power at the moment t of the ith cluster;
wherein:an objective function 2 of the lower optimization model; />Satisfaction of the power utilization mode of the user at the moment t; />Satisfaction of electricity charge expenditure of the user at time t; alpha and beta are weight coefficients; />And paying for the electricity fee at the moment t of the ith cluster.
6. The distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network according to claim 5, wherein the constraints of the upper layer optimization model include energy balance constraints and energy storage operation constraints, expressed as:
wherein:the user rigidity load at the moment t; />The energy storage charge capacity at the time t; />And->Is 0-1 variable, which represents the charge and discharge state of the stored energy at time t,/and>when charging, 1, the rest are 0, < >>1 when discharging, the rest is 0; p (P) ES_max Representing the maximum charge-discharge power of the stored energy; SOC (State of Charge) ES_min And SOC (System on chip) ES_max Is the minimum and maximum state of charge of the stored energy.
7. The distributed resource dynamic aggregation regulation method for optimal scheduling of a distribution network according to claim 5, wherein the constraints of the lower layer optimization model include demand response constraints expressed as:
wherein P is DR_min Indicating a lower limit of the demand response amount,representing the distributed resource aggregation demand response quantity at the time t and P DR_max Indicating an upper limit for demand response.
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