CN116488144A - Electric power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guiding demand response - Google Patents
Electric power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guiding demand response Download PDFInfo
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Abstract
The invention discloses a double-layer optimization strategy of an electric power system based on node carbon intensity and time-of-use electricity price guiding demand response, and belongs to the field of low-carbon demand response of the electric power system. According to the invention, the carbon emission responsibility is transmitted to the load side in the form of node carbon intensity by using a carbon emission flow tracking model, the condition that the node carbon intensity changes along with the output of the generator is tracked from the time and space level, and the carbon emission quantity generated by the electricity consumption of the load node is more accurately calculated; and by adding the limiting conditions, the node carbon strength and the load quantity change result after the time-of-use electricity price is guided to respond are obtained, and after responding, compared with before responding, the flexible load under the jurisdiction of each load aggregator is transferred from a period with higher carbon potential to a period with lower carbon potential, so that the total cost is reduced, the carbon emission is obviously reduced, and the electricity consumption pressure of a period with high carbon potential is slowed down.
Description
Technical Field
The invention relates to a double-layer optimization strategy of an electric power system based on node carbon intensity and time-of-use electricity price guiding demand response, and belongs to the field of low-carbon demand response of the electric power system.
Background
The carbon emission is gradually increased in the world, and energy conservation, emission reduction and green low carbon development become the consensus of all countries. In the electric power system, the traditional carbon emission metering mode starts from the source side, but the characteristic of 'source follow-up' of the electric power system is that the contradiction of 'separation of rights' of a carbon emission responsibility main body is gradually highlighted, and the load side electricity consumption requirement is the driving force of carbon emission generation although the source side is the main body for generating carbon emission. In addition, the direct carbon emission of the system is reduced by the optimization method, the flow direction and the size of carbon cannot be reflected, and the influence of load side demand response on low carbon emission reduction of the power system is not considered. Therefore, it is necessary to consider the scientific calculation of the node carbon intensity through the carbon emission flow tracking, and guide the load aggregator to call the adjustable load to perform the demand response by using the node carbon intensity, so as to reduce the total cost of the load aggregator and improve the low carbon property of the system.
Disclosure of Invention
The invention provides a double-layer optimization strategy and a double-layer optimization system for an electric power system based on node carbon intensity and time-of-use electricity price guiding demand response, which utilize a carbon emission flow tracking model to transmit carbon emission responsibility to a load side in the form of node carbon intensity, track the condition that the node carbon intensity changes along with the output of a generator from a time-space layer, and more accurately calculate the carbon emission quantity generated by the electricity consumption of a load node.
The technical scheme of the invention is as follows:
according to an aspect of the invention, there is provided a power system double-layer optimization strategy based on node carbon strength and time-of-use electricity price guidance demand response, comprising: step S1, determining the node carbon intensity of each load aggregator according to the established carbon emission flow tracking model; s2, establishing a flexible load demand response model in jurisdiction of a load aggregator; the flexible load comprises an electric automobile, a load which can be reduced and a load which can be transferred; step S3, determining the actual carbon emission of each load aggregator according to the node carbon intensity, distributing the initial carbon emission quota of each load aggregator according to the purchase electricity quantity, and establishing a carbon transaction model; s4, establishing a double-layer optimized scheduling model based on node carbon intensity and time-of-use electricity price guiding demand response; the upper layer of the double-layer optimization scheduling model is a power grid operator, the lower layer of the double-layer optimization scheduling model is a load aggregator, and the total cost is minimum as an optimization target.
The step S1 includes: s1.1: calculating a distribution matrix in the node system; s1.2: and constructing a carbon emission flow tracking model based on a proportion sharing principle, and calculating the carbon intensity of each node.
The step S2 includes: s2.1: according to an excitation contract signed by an electric automobile and a load aggregator, aiming at the charge and discharge characteristics of the electric automobile, solving the carbon emission generated after the electric automobile is charged and discharged based on the node carbon intensity; s2.2: according to the load-reducible excitation contract and response characteristics thereof, obtaining carbon emission after load-reducible demand response based on node carbon strength; s2.3: and according to the transferable load excitation contract and the response characteristic thereof, solving the carbon emission amount after demand response of the transferable load based on the node carbon intensity.
Carbon emission generated after charging and discharging of electric automobileThe expression is:
wherein:the node carbon intensity is accessed to the node m for the load aggregator at the moment t; p (P) t EV Charging for all electric vehicles accessed at t momentSum of discharge amounts; n (N) ev The number of the electric automobiles; />Respectively charging and discharging power of the nth electric automobile at the t moment;
carbon emissions after load-reducible demand responseThe expression is:
wherein: p (P) t cut The load amount after the reduction at the time t is calculated;
carbon emissions after demand response of the transferable loadThe expression is:
wherein: p (P) t tra And transferring the load to participate in demand response after the moment t.
The step S3 includes: s3.1, solving the actual carbon emission of each load polymerizer through the node carbon intensity; wherein the actual carbon emission of each load aggregator comprises the carbon emission of the original load and the carbon emission of the flexible load after the demand response; s3.2, setting an initial carbon emission quota coefficient, and distributing the initial carbon emission quota to the magnitude of the electric quantity purchased by the upper power grid operator according to the load aggregator; s3.3, establishing a ladder carbon transaction model, wherein if the actual carbon emission is larger than the initial carbon emission quota, the load aggregator needs to pay the excessive part of the cost, otherwise, if the actual carbon emission is smaller than the initial carbon emission quota, the load aggregator can sell the excessive carbon emission quota to obtain benefits.
The actual carbon emission amount is expressed as:
wherein:actual carbon emissions for the load polymerizer; p (P) t load Is the initial load amount; />Carbon emission generated after the electric automobile is charged and discharged; />The node carbon intensity is accessed to the node m for the load aggregator at the moment t; />Carbon emissions after demand response for load shedding; />Carbon emissions after demand response for transferable loads.
The step S4 includes:
the upper-layer power grid operator uses the minimum total cost of the power grid operator as an objective function, and establishes power grid operator constraint conditions based on thermal power unit output constraint, thermal power unit climbing constraint, thermal power unit start-stop constraint, wind power output constraint, line transmission capacity constraint, balance node constraint, node power balance constraint and line tide equation constraint; the total cost of the power grid operators comprises thermal power generation cost, wind power generation cost and carbon transaction cost of the power grid operators;
the lower layer load aggregator uses the minimum total cost of the load aggregator as an objective function, and establishes constraint conditions of the load aggregator based on power balance constraint, electric vehicle charge and discharge constraint and electric vehicle battery power constraint, wherein the total cost of the load aggregator comprises upper-level electricity purchasing cost, carbon transaction cost of the load aggregator, load-reducible and load-transferable demand response subsidy cost and electric vehicle discharge subsidy cost.
The grid operator objective function F1 is:
min F1＝min(C G +C W +C C1 )
wherein: c (C) G The coal consumption cost is; c (C) W The wind power generation cost is the wind power generation cost; c (C) C1 Carbon trade costs for grid operators; n (N) G The number of the thermal power generating units is; n (N) t Is a scheduling period; a, a i 、b i 、c i The coal consumption cost coefficients of the ith thermal power generating unit are respectively; q W The wind power generation cost is the wind power generation cost; q Wq A wind cost coefficient is generated for wind power generation and wind abandonment;the output is output at t time of the ith thermal power generating unit; p (P) t W The actual output of wind power is obtained; />Predicting output for a u-th wind power scene; u is the total number of wind power scenes; epsilon u The probability of the wind power scene is the u th wind power scene; e, e w Representing the carbon emission coefficient of the wind turbine generator; e, e quote Carbon quota corresponding to unit power generation capacity of the generator set; />And (5) representing the carbon emission intensity of the ith thermal power generating unit.
The load aggregator objective function F2 is:
minF2＝min(C buy +C c +C CT +C evd )
wherein: c (C) buy C for higher electricity purchasing cost c For load aggregation merchant carbon transaction cost, C CT C, response subsidy cost for load reduction and load transferability requirement evd The electric vehicle discharge patch cost is increased; n (N) t Is a scheduling period; p (P) t buy The electricity quantity purchased from the power grid operator at the moment of t is shown as LA; q t 、q cut 、q tra,in And q tra,out The method comprises the steps of purchasing electricity price, reducing load unit compensation cost, transferring load into unit compensation cost and transferring load out of unit compensation cost; p (P) t cut The load amount after the reduction at the time t is calculated; p (P) t tra,in 、P t tra,out Load transfer in and load transfer out power can be transferred at the moment t respectively; n (N) ev The number of the electric automobiles; phi is the electric automobile discharge patch coefficient;and the discharge power of the nth electric automobile at the time t is shown.
According to another aspect of the present invention, there is provided a power system double-layer optimization system for guiding demand response based on node carbon intensity and time-of-use electricity price, comprising: the determining module is used for determining the node carbon intensity of each load aggregator according to the established carbon emission flow tracking model; the first building module is used for building a flexible load demand response model in the jurisdiction of the load aggregator; the flexible load comprises an electric automobile, a load which can be reduced and a load which can be transferred; the second building module is used for determining the actual carbon emission of each load aggregator according to the node carbon intensity, distributing the initial carbon emission quota of each load aggregator according to the purchase electricity quantity and building a carbon transaction model; the third building module is used for building a double-layer optimized scheduling model based on node carbon intensity and time-of-use electricity price guiding demand response; the upper layer of the double-layer optimization scheduling model is a power grid operator, the lower layer of the double-layer optimization scheduling model is a load aggregator, and the total cost is minimum as an optimization target.
The beneficial effects of the invention are as follows: according to the invention, the carbon emission responsibility is transmitted to the load side in the form of node carbon intensity by using a carbon emission flow tracking model, the condition that the node carbon intensity changes along with the output of the generator is tracked from the time and space level, and the carbon emission quantity generated by the electricity consumption of the load node is more accurately calculated; and by adding the limiting conditions, the node carbon strength and the load quantity change result after the time-of-use electricity price is guided to respond are obtained, and after responding, compared with before responding, the flexible load under the jurisdiction of each load aggregator is transferred from a period with higher carbon potential to a period with lower carbon potential, so that the total cost is reduced, the carbon emission is obviously reduced, and the electricity consumption pressure of a period with high carbon potential is slowed down.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph illustrating carbon emission flow characteristics;
FIG. 3 is a diagram of a modified IEEE-30 node topology of the present invention;
FIG. 4 is a response of a resident load aggregator invoking an electric vehicle, load reducible and load transferable;
FIG. 5 is a response of a commercial load aggregator invoking an electric vehicle, load reducible and load transferable;
FIG. 6 is a response of an industrial load aggregator invoking an electric vehicle, load reducible and load transferable;
FIG. 7 is a graph of load before and after demand response by a residential load aggregator;
FIG. 8 is a graph of load before and after a business load aggregator demand response;
FIG. 9 is a graph of load before and after an industrial load aggregator demand response.
Detailed Description
The invention will be further described with reference to the drawings and examples, but the invention is not limited to the scope.
Example 1: 1-9, according to an aspect of the embodiment of the present invention, there is provided a power system double-layer optimization strategy based on node carbon strength and time-of-use electricity price guiding demand response, including: step S1, determining the node carbon intensity of each load aggregator according to the established carbon emission flow tracking model; s2, establishing a flexible load demand response model in jurisdiction of a load aggregator; the flexible load comprises an electric automobile, a load which can be reduced and a load which can be transferred; step S3, determining the actual carbon emission of each load aggregator according to the node carbon intensity, distributing the initial carbon emission quota of each load aggregator according to the purchase electricity quantity, and establishing a carbon transaction model; and S4, establishing a double-layer optimization scheduling model based on node carbon intensity and time-of-use electricity price guiding demand response, wherein the upper layer is a power grid operator, the lower layer is a load aggregator, and the total cost is minimum as an optimization target.
Further, the step S1 includes: s1.1: calculating a distribution matrix in the node system; s1.2: and constructing a carbon emission flow tracking model based on a proportion sharing principle, and calculating the carbon intensity of each node.
Further, the node system distribution matrix is:
in an electric power system with M nodes, the V nodes have loads, the number of generator set access is K, the generator set injection distribution matrix is K multiplied by M order matrix, and P G ＝(P Gkj ) K×M ，k＝1,2,…,K，j∈[1,M]And is a positive integer, if the power of the injection node j of the kth generator set is p 1 P is then Gkj ＝p 1 Otherwise P Gkj ＝0；
The load distribution matrix is a V multiplied by M matrix, P L ＝(P Lvj ) V×M V=1, 2, …, V, if node j is the V-th node with load and the load amount is p 2 P is then Lvj ＝p 2 Otherwise P Lvj ＝0；
The branch power flow distribution matrix is an M-order square matrix, P B ＝(P Bmj ) M×M M=1, 2, …, M, if a forward active power flow p flows between node M and node j 3 P is then Bmj ＝p 3 Otherwise P Bmj =0, and diagonal element P Bmm ＝0；
The flow direction and the size of the carbon emission flow of the power system are quantitatively determined according to the trend tracking, and the carbon emission flow is closely related to the trend distribution of the power system. In the carbon emission flow calculation, the node carbon strength is only affected by the inflow power flow and is not affected by the outflow power flow.
As shown in fig. 2, the sum P of the active power flows of node m m Inflow flow for the leg to which the node is connected:
wherein: p (P) m Is the sum of the incoming active power flows of node m;active power for branch s; s is S + A branch set representing a flow of power into the node m; />To switch in the output of the generator k; obtaining an active flux matrix P of the system node M ：
P M ＝diag(ξ M+K [P B P G ] T )(2)
Wherein: zeta type toy M+K The vector is an M+K order row vector, and all elements in the vector are 1; p (P) B For the branch flow distribution matrix, P G Injecting a distribution matrix for the generator set;
wherein:the M-dimensional unit row vector is M-dimensional unit row vector, and the M-th element is 1; p (P) Bsm Elements representing the(s) th row and (m) th column in the branch flow distribution matrix, P Gkm Elements representing the kth row and the mth column in the generator set injection distribution matrix;
node carbon intensity of node m based on proportion sharing principleCarbon bank generated by the connected generator and carbon bank flown into by the rest nodes are determined by:
wherein: ρ s Carbon flow density for branch s;the carbon emission intensity of the kth generator set is; will ρ s The carbon intensity of the node at the starting end of the branch is replaced by the carbon intensity of the node at the starting end of the branch, and the carbon intensity of the node is written as follows:
wherein: e (E) M As a vector of the intensity of the node carbon,E G for the carbon number vector of the generator set, respectively represent P B 、P G Is a transpose of (2);
further, the step S2 includes: s2.1: according to an excitation contract signed by an electric automobile and a load aggregator, aiming at the charge and discharge characteristics of the electric automobile, solving the carbon emission generated after the electric automobile is charged and discharged based on the node carbon intensity; s2.2: according to the load-reducible excitation contract and response characteristics thereof, obtaining carbon emission after load-reducible demand response based on node carbon strength; s2.3: and according to the transferable load excitation contract and the response characteristic thereof, solving the carbon emission amount after demand response of the transferable load based on the node carbon intensity.
Specifically, after the electric vehicle and the load aggregator enter into an excitation contract, the load aggregator has the right to call the electric vehicle to charge and discharge in the electric vehicle access time, but when the electric vehicle leaves, the electric vehicle must meet the requirementAnd (5) electric quantity requirement. Carbon emission of electric automobileThe amount of carbon emissions generated for charging minus the amount of carbon emissions reduced by discharging.
Wherein:the node carbon intensity is accessed to the node m for the load aggregator at the moment t; />The sum of the charge and discharge amounts of all the connected electric vehicles at the time t; n (N) ev The number of the electric automobiles; />Respectively charging and discharging power of the nth electric automobile at the t moment;
the load aggregator makes an incentive contract with the power consumer, and the contract agrees with a load-reducible cut-down time period, a maximum limit cut-down time period, a cut-down coefficient, a load-transferable transfer time period, a transfer coefficient and the like in the consumer.
The load reduction means that a certain proportion of load can be reduced in a stipulated time:
wherein:the load amount after the reduction at the time t is calculated; />Is the initial load amount; />0-1 state variable for load shedding at time t, when shedding occurs +.>Otherwise->β cut To cut down coefficients;
wherein: n (N) t For a scheduling period;upper and lower limit values for reducing load; />Starting and ending time periods, respectively, for which load response time can be reduced,>to cut the upper limit of the load response time period, < > for>To cut down the coefficient maximum.
Load-shedding carbon emission after demand responseThe method comprises the following steps:
wherein:is the loadNode carbon intensity at time t after the aggregation quotient accesses the node m;
transferable loads are loads that allow for interruption and are flexible in operating periods:
P t tra ＝P t tra,in -P t tra,out (10)
wherein:the load can be transferred at the moment t to participate in the load after the demand response; p (P) t tra,in 、P t tra,out The load transfer in and the load transfer out power can be respectively carried out at the moment t.
In consideration of the actual use condition of the equipment, the equipment cannot be started and stopped frequently, certain constraint is carried out on the load power and the transfer time in each transfer, and the total amount of load in-and out-of-the-way is kept unchanged.
In the method, in the process of the invention,load transfer-in and load transfer-out response states at the time t are respectively represented by 0-1 variable, and when transfer occurs +.>Otherwise->In the event of a roll-out +.>Otherwise->Respectively an upper limit coefficient and a lower limit coefficient of a transferable load; />Start and end time periods of the transferable load in-out time period, respectively; Δt is the time interval, N t For the scheduling period. In the embodiment of the invention, N t Take 24, time interval Δt take 1, t=1, 2,..24.
Carbon emissions after demand response by load transferThe method comprises the following steps:
further, the step S3 includes: s3.1, solving the actual carbon emission of each load polymerizer through the node carbon intensity; wherein the actual carbon emission of each load aggregator comprises the carbon emission of the original load and the carbon emission of the flexible load after the demand response; s3.2, setting an initial carbon emission quota coefficient, and distributing the initial carbon emission quota to the magnitude of the electric quantity purchased by the upper power grid operator according to the load aggregator; s3.3, establishing a ladder carbon transaction model, wherein if the actual carbon emission is larger than the initial carbon emission quota, the load aggregator needs to pay the excessive part of the cost, otherwise, if the actual carbon emission is smaller than the initial carbon emission quota, the load aggregator can sell the excessive carbon emission quota to obtain benefits.
Specifically:
solving the actual carbon emission of each load aggregator through the node carbon intensity, wherein the actual carbon emission comprises the original load carbon emission and the carbon emission after the flexible load is subjected to demand response;
wherein:actual carbon emissions are for the load polymerizer.
time tActual carbon emission trading volume D with load aggregator LA participating in carbon trading market t T The method comprises the following steps:
wherein: d (D) t Q Initial carbon allocation quota amount obtained for load aggregator LA at time t; e (E) quote The carbon emission quota coefficient is the unit electricity purchase quantity.
Setting the length of a carbon emission interval, establishing a stepped carbon transaction model, and if the actual carbon emission amount isIs greater than the initial carbon emission allowance->The load aggregator needs to pay the excess portion of the fee, otherwise, if the actual carbon emission is less than the initial carbon emission allowance, the load aggregator may sell the excess carbon emission allowance to obtain the benefit.
Wherein: c (C) c The price is the ladder-type carbon transaction cost, and lambda is the carbon transaction reference price; alpha is a price increase coefficient; l is the carbon emission interval length.
Further, the step S4 includes:
the upper-layer power grid operator uses the minimum total cost of the power grid operator as an objective function, and establishes power grid operator constraint conditions based on thermal power unit output constraint, thermal power unit climbing constraint, thermal power unit start-stop constraint, wind power output constraint, line transmission capacity constraint, balance node constraint, node power balance constraint and line tide equation constraint; the total cost of the power grid operators comprises thermal power generation cost, wind power generation cost and carbon transaction cost of the power grid operators;
the lower layer load aggregator uses the minimum total cost of the load aggregator as an objective function, and establishes constraint conditions of the load aggregator based on power balance constraint, electric vehicle charge and discharge constraint and electric vehicle battery power constraint, wherein the total cost of the load aggregator comprises upper-level electricity purchasing cost, carbon transaction cost of the load aggregator, load-reducible and load-transferable demand response subsidy cost and electric vehicle discharge subsidy cost.
Further, a double-layer optimized scheduling model for guiding demand response based on node carbon intensity:
and the upper-layer power grid operator aims at the minimum total cost, adjusts the output of the generator set, calculates the carbon emission to the load side based on the carbon emission flow tracking model, and calculates the node carbon intensity of the load side. The optimization goal of the grid operator is to reduce the overall cost, including thermal power generation costs, wind power generation costs, and grid operator carbon trade costs. The objective function is:
min F1＝min(C G +C W +C C1 ) (16)
wherein: c (C) G The coal consumption cost is; c (C) W The wind power generation cost is the wind power generation cost; c (C) C1 Carbon trade costs for grid operators; n (N) G The number of the thermal power generating units is; a, a i 、b i 、c i The coal consumption cost coefficients of the ith thermal power generating unit are respectively; q W The wind power generation cost is the wind power generation cost; q Wq A wind cost coefficient is generated for wind power generation and wind abandonment;the output is output at t time of the ith thermal power generating unit; p (P) t W The actual output of wind power is obtained; />Predicting output for a u-th wind power scene; epsilon u The probability of the wind power scene is the u th wind power scene; e, e w Representing the carbon emission coefficient of the wind turbine generator; e, e quote Carbon quota corresponding to unit power generation capacity of the generator set; />Representing the carbon emission intensity of the ith thermal power unit;
constraint conditions
Thermal power generating unit output constraint
Wherein:the output force of the ith thermal power generating unit is minimum and maximum respectively;
climbing constraint of thermal power generating unit
Wherein:the power is output at the moment t-1 of the ith thermal power generating unit; r is R i U 、R i D The maximum ramp rate and the maximum ramp rate of the ith thermal power generating unit are respectively; Δt is the time interval;
thermal power generating unit start-stop constraint
Wherein:the operation and the shutdown time of the ith thermal power generating unit t-1 time period are respectively; />Respectively the shortest operation and the shutdown time of the ith thermal power generating unit; u (u) i,t 、u i,t-1 Respectively isThe start-stop state of the ith thermal power generating unit at the time t and t-1 is the value of 1 when the thermal power generating unit is started, and otherwise, the value of 0 is the value of 1;
wind power output constraint
Wherein:is the maximum value of wind power output;
line transmission capacity constraints
P f,min ≤P f,t ≤P f,max (22)
Wherein: p (P) f,t The active power flow of the line f at the moment t; p (P) f,max 、P f,min The upper limit and the lower limit of transmission power between lines are respectively;
balancing node constraints
Wherein:balancing node voltage phase angles for the time t;
node power balancing constraints
Wherein:the inflow power and the outflow power of the node m at the moment t; />The power load of the node m at the moment t;
line flow equation constraint
Wherein: beta mj Reactance for line mj; θ m,t 、θ j,t The voltage phase angles of the node m and the node j are respectively.
And the lower layer LA responds to the node carbon intensity signal of the upper layer by taking the minimum total cost as a target, and the flexible load power utilization strategy is adjusted, so that the carbon emission and the total cost of the LA are reduced. The LA guides the user to participate in low-carbon response through price incentive based on the electricity demand of the user, and the LA gives the user a certain economic subsidy. The total cost of LA includes the upper level electricity purchase cost C buy LA carbon trade cost C c Load reducible and load transferable demand response subsidy cost C CT Electric automobile discharge patch cost C evd The objective function is:
minF2＝min(C buy +C c +C CT +C evd )(26)
wherein:the electricity quantity purchased from the power grid operator at the moment of t is shown as LA; q t 、q cut 、q tra,in And q tra,out The method comprises the steps of purchasing electricity price, reducing load unit compensation cost, transferring load into unit compensation cost and transferring load out of unit compensation cost; phi is the electric automobile discharge patch coefficient; />The discharge power of the nth electric automobile at the moment t is represented; p (P) t tra,in 、P t tra ,out Load transfer in and load transfer out power can be transferred at the moment t respectively;
constraint conditions
Power balance constraint
Respectively charging and discharging power of the nth electric automobile at the moment t; />Is the initial load amount; />The load amount after the reduction at the time t is calculated;
charging and discharging constraint of electric automobile
Wherein:charging and discharging states of the nth electric automobile EV at the time t, and when the EV is charged,/-is carried out>1 is shown in the specification; during EV discharge, javaScript>1 is shown in the specification; />EV maximum charge-discharge power, respectively.
Battery power constraint of electric automobile
Wherein:the method comprises the steps that the initial electric energy reaching the nth electric automobile, the target electric energy and the rated capacity of a battery are respectively; e (E) n,t The battery capacity and the SOC of the nth electric automobile at the t moment n,t 、SOC n,t-1 The states of charge of the batteries of the nth electric automobile at the time t and the time t-1 are respectively; />The upper limit value and the lower limit value of the charge state of the battery of the nth electric automobile at the time t and the time t-1 are respectively; t is t in 、t out The access time and the departure time of the electric automobile are respectively; η (eta) c 、η d Indicating charge and discharge efficiency.
According to another aspect of the present invention, there is provided a power system double-layer optimization system for guiding demand response based on node carbon intensity and time-of-use electricity price, comprising: the determining module is used for determining the node carbon intensity of each load aggregator according to the established carbon emission flow tracking model; the first building module is used for building a flexible load demand response model in the jurisdiction of the load aggregator; the flexible load comprises an electric automobile, a load which can be reduced and a load which can be transferred; the second building module is used for determining the actual carbon emission of each load aggregator according to the node carbon intensity, distributing the initial carbon emission quota of each load aggregator according to the purchase electricity quantity and building a carbon transaction model; the third building module is used for building a double-layer optimized scheduling model based on node carbon intensity and time-of-use electricity price guiding demand response; the upper layer of the double-layer optimization scheduling model is a power grid operator, the lower layer of the double-layer optimization scheduling model is a load aggregator, and the total cost is minimum as an optimization target. For portions of the foregoing that are not detailed for each module, reference may be made to the relevant description of the embodiments.
In order to demonstrate the implementation effect of the present invention, the steps S1 to S4 of the present invention are sequentially executed based on the improved IEEE-30 node development simulation analysis shown in fig. 3. The detailed parameter setting information of the simulation analysis is as follows:
(1) The resident load aggregator, the commercial load aggregator and the industrial load aggregator are respectively connected into the node 24, the node 26 and the node 29, the wind power plant with the capacity of 600MW replaces the thermal power unit of the node 13, the relevant parameters of the thermal power unit are shown in the following table 1, and the time-of-use electricity price is shown in the table 2.
(2) Cost q of wind power generation W Penalty factor q for waste wind of 60 yuan/MW Wq Carbon emission coefficient e of wind turbine generator set is 250 yuan/MW w Is 0.043tCO 2 /MW。
(3) LA initial carbon emission quota coefficient E quote Is 0.728tCO 2 The reference price lambda of the carbon transaction is 252 yuan/t, the price increase coefficient alpha is 0.25, and the carbon emission interval length of each load polymerizer is 1t, 25t and 90t respectively.
(4) For the convenience of calculation, assuming that all electric vehicles are of the same model, relevant parameters of the electric vehicles are shown in table 3 below, and the number of electric vehicles in each load aggregator is set to be 800, 300 and 500 respectively. The transferable load and reducible load contract parameters are shown in tables 4 and 5 below.
TABLE 1 thermal power generating unit parameters
Table 2 time-of-use electricity price meter
| Time period of | Time-of-use electricity price/(Yuan. KW. H) -1 ) | |
| Peak time period | 08:00-11:00、18:00-23:00 | 0.9164 |
| Flat time period | 12:00-17:00 | 0.6164 |
| Valley period | 00:00-07:00、23:00-24:00 | 0.3113 |
Table 3 electric vehicle scheduling related parameters
TABLE 4 transferable load incentive contract parameters
TABLE 5 load shedding incentive contract parameters
Taking the electric vehicle, load-reducible and load-transferable demand response results shown in fig. 4-6 as an example, the results before and after the demand response of each load aggregator are shown in fig. 7-9.
As can be seen, each load aggregator shifts the transferable load out during the high carbon potential period, shifts the load in during the low carbon potential period, and cuts the load during the high carbon potential period; in the time of electric automobile access, electric automobile charges and concentrates in the relatively lower period of carbon potential, and the discharge concentrates in the relatively higher period of carbon potential, and when the carbon potential risees, load polymer guide electric automobile begins discharging, improves the discharge and reduces regional electricity purchasing quantity. After the flexible load is excited and scheduled, the electric automobile is orderly charged and discharged and scheduled, the load is cut down and transferred, the load in the electricity consumption peak period is transferred to the electricity consumption valley period, the electricity consumption pressure in the load peak period is reduced, and the peak-valley difference of resident load aggregators is reduced from 28.61MW to 23.16MW. Commercial load aggregation Shang Feng valley rises from 43.10MW to 46.83MW and commercial load aggregation LA3 rises from 62.40MW to 78.01MW. Although it is. The peak-to-valley difference of commercial load aggregators and industrial load aggregators increases, but the total load decreases at the time of electricity consumption peak period 16 to 21, and the forward effect of relieving the electricity consumption tension of the peak period is achieved.
In order to verify the effectiveness of the model and method provided herein, the conditions of time-of-use electricity prices and node carbon strength guiding each LA flexible load to perform demand response are analyzed, and the following 5 scenes are established:
scene one: considering that the fixed electricity price and the load do not participate in the optimal scheduling, and not considering the carbon transaction;
scene II: a double-layer optimization scheduling strategy for guiding flexible load adjustment by considering time-of-use electricity price is not considered;
scene III: considering the time-of-use electricity price and the ladder-type carbon transaction, the flexible load does not participate in the optimal scheduling;
scene four: double-layer optimization scheduling strategies for guiding flexible load adjustment by considering fixed electricity price, ladder-type carbon transaction and node carbon intensity;
scene five: and a double-layer optimized scheduling strategy for guiding flexible load adjustment by considering time-of-use electricity price, step-type carbon transaction and node carbon intensity.
The carbon emissions and costs for each load polymerizer are shown in table 6 below:
TABLE 6 carbon Displacement and cost before and after load aggregator demand response
In combination with table 6, it is known that the first and third scenes do not consider the demand response, and the carbon emission is highest, wherein the third scene considers the time-of-use electricity price, and the total cost is highest. And the second scene is used for guiding the flexible load to perform demand response through time-sharing electricity price, the fourth scene is used for guiding the load side to perform demand response through node carbon intensity, and the carbon emission is reduced after the two scenes are responded. And in the fifth scene, the second scene and the fourth scene are combined, the load aggregator is guided to respond to demands by considering the time-of-use electricity price and the node carbon intensity, and after responding, compared with the first scene, the total cost of each LA is increased, the added part of the total cost is mainly carbon transaction cost, but the electricity purchasing cost of each LA is reduced, the carbon emission of each LA is respectively reduced by 19.99t, 12.56t and 31.21t, and the low carbon property of the LA is improved.
After the requirement response is brought into the scene five on the basis of the scene two, under the influence of the time-of-use electricity price and the node carbon intensity, the transaction cost and the electricity purchasing cost of each LA carbon are reduced, the carbon emission is obviously reduced, and 3.04t, 0.16t and 11.03t are respectively reduced, so that the system low-carbon property and the economy can be effectively improved by adopting the time-of-use electricity price and the node carbon intensity to guide the flexible load. In addition, comparing the fourth and fifth scenes, it is obvious that, compared with the fixed electricity price, after the time-sharing electricity price is considered in the fifth scene, the responsiveness of the flexible load to participate in the demand response is improved, the carbon emission of each LA is respectively reduced by 3.16t, 1.62t and 12.16t compared with the fourth scene, and the carbon transaction cost is also reduced. However, due to the limitation of the access time and the departure time of the electric vehicle, in order to meet the charging requirement of the electric vehicle, the electric vehicle may be charged in the time-of-use electricity price peak period, which results in an increase in electricity purchasing cost of the fifth scenario compared with that of the fourth scenario. Thus, the model presented herein, while increasing some of the costs to some extent, reduces the dependence on the power generation side by increasing the response of the flexible load, reducing more carbon emissions, and improving the low-carbon nature of the system.
After the network operators consider carbon trade, the unit output plans are adjusted, so that the cost and the carbon emission are influenced. Analysis of carbon emissions and costs before and after participation of the grid operators in the carbon trade was performed on scenario five, as shown in table 7.
TABLE 7 comparison of carbon emissions and costs before and after participation of grid operators in carbon transactions
As can be seen from table 7, the carbon emission is reduced after the grid operator participates in the carbon trade, the total cost is increased compared with the total cost before the carbon trade is participated, the main increase is the carbon trade cost, and the coal consumption cost is slightly increased, because the grid operator selects the unit output with higher coal consumption cost but lower carbon emission coefficient in certain time periods in order to reduce the carbon emission and minimize the carbon trade cost when comprehensively considering the total cost.
While the present invention has been described in detail with reference to the drawings, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (10)
1. The utility model provides a power system double-deck optimization strategy based on node carbon intensity and timesharing price of electricity guide demand response which characterized in that includes:
step S1, determining the node carbon intensity of each load aggregator according to the established carbon emission flow tracking model;
s2, establishing a flexible load demand response model in jurisdiction of a load aggregator; the flexible load comprises an electric automobile, a load which can be reduced and a load which can be transferred;
step S3, determining the actual carbon emission of each load aggregator according to the node carbon intensity, distributing the initial carbon emission quota of each load aggregator according to the purchase electricity quantity, and establishing a carbon transaction model;
s4, establishing a double-layer optimized scheduling model based on node carbon intensity and time-of-use electricity price guiding demand response; the upper layer of the double-layer optimization scheduling model is a power grid operator, the lower layer of the double-layer optimization scheduling model is a load aggregator, and the total cost is minimum as an optimization target.
2. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 1, wherein the step S1 comprises:
s1.1: calculating a distribution matrix in the node system;
s1.2: and constructing a carbon emission flow tracking model based on a proportion sharing principle, and calculating the carbon intensity of each node.
3. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 1, wherein the step S2 comprises:
s2.1: according to an excitation contract signed by an electric automobile and a load aggregator, aiming at the charge and discharge characteristics of the electric automobile, solving the carbon emission generated after the electric automobile is charged and discharged based on the node carbon intensity;
s2.2: according to the load-reducible excitation contract and response characteristics thereof, obtaining carbon emission after load-reducible demand response based on node carbon strength;
s2.3: and according to the transferable load excitation contract and the response characteristic thereof, solving the carbon emission amount after demand response of the transferable load based on the node carbon intensity.
4. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 3, wherein,
carbon emission generated after charging and discharging of electric automobileThe expression is:
wherein:the node carbon intensity is accessed to the node m for the load aggregator at the moment t; p (P) t EV The sum of the charge and discharge amounts of all the connected electric vehicles at the time t; n (N) ev The number of the electric automobiles; />Respectively charging and discharging power of the nth electric automobile at the t moment;
carbon emissions after load-reducible demand responseThe expression is:
wherein: p (P) t cut The load amount after the reduction at the time t is calculated;
carbon emissions after demand response of the transferable loadThe expression is:
wherein: p (P) t tra And transferring the load to participate in demand response after the moment t.
5. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 1, wherein the step S3 comprises:
s3.1, solving the actual carbon emission of each load polymerizer through the node carbon intensity; wherein the actual carbon emission of each load aggregator comprises the carbon emission of the original load and the carbon emission of the flexible load after the demand response;
s3.2, setting an initial carbon emission quota coefficient, and distributing the initial carbon emission quota to the magnitude of the electric quantity purchased by the upper power grid operator according to the load aggregator;
s3.3, establishing a ladder carbon transaction model, wherein if the actual carbon emission is larger than the initial carbon emission quota, the load aggregator needs to pay the excessive part of the cost, otherwise, if the actual carbon emission is smaller than the initial carbon emission quota, the load aggregator can sell the excessive carbon emission quota to obtain benefits.
6. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 5, wherein the actual carbon emission is expressed as follows:
wherein:actual carbon emissions for the load polymerizer; p (P) t load Is the initial load amount; />Carbon emission generated after the electric automobile is charged and discharged; />The node carbon intensity is accessed to the node m for the load aggregator at the moment t; />Carbon emissions after demand response for load shedding; />Carbon emissions after demand response for transferable loads.
7. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 1, wherein the step S4 comprises:
the upper-layer power grid operator uses the minimum total cost of the power grid operator as an objective function, and establishes power grid operator constraint conditions based on thermal power unit output constraint, thermal power unit climbing constraint, thermal power unit start-stop constraint, wind power output constraint, line transmission capacity constraint, balance node constraint, node power balance constraint and line tide equation constraint; the total cost of the power grid operators comprises thermal power generation cost, wind power generation cost and carbon transaction cost of the power grid operators;
the lower layer load aggregator uses the minimum total cost of the load aggregator as an objective function, and establishes constraint conditions of the load aggregator based on power balance constraint, electric vehicle charge and discharge constraint and electric vehicle battery power constraint, wherein the total cost of the load aggregator comprises upper-level electricity purchasing cost, carbon transaction cost of the load aggregator, load-reducible and load-transferable demand response subsidy cost and electric vehicle discharge subsidy cost.
8. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 7, wherein the grid operator objective function F1 is:
minF1＝min(C G +C W +C C1 )
wherein: c (C) G The coal consumption cost is; c (C) W The wind power generation cost is the wind power generation cost; c (C) C1 Carbon trade costs for grid operators; n (N) G The number of the thermal power generating units is; n (N) t Is a scheduling period; a, a i 、b i 、c i The coal consumption cost coefficients of the ith thermal power generating unit are respectively; q W The wind power generation cost is the wind power generation cost; q Wq A wind cost coefficient is generated for wind power generation and wind abandonment;the output is output at t time of the ith thermal power generating unit; p (P) t W The actual output of wind power is obtained; />Predicting output for a u-th wind power scene; u is the total number of wind power scenes; epsilon u The probability of the wind power scene is the u th wind power scene; e, e w Representing the carbon emission coefficient of the wind turbine generator; e, e quote Carbon quota corresponding to unit power generation capacity of the generator set; />And (5) representing the carbon emission intensity of the ith thermal power generating unit.
9. The power system double-layer optimization strategy based on node carbon intensity and time-of-use electricity price guidance demand response according to claim 7, wherein the load aggregator objective function F2 is:
minF2＝min(C buy +C c +C CT +C evd )
wherein: c (C) buy C for higher electricity purchasing cost c For load aggregation merchant carbon transaction cost, C CT C, response subsidy cost for load reduction and load transferability requirement evd The electric vehicle discharge patch cost is increased; n (N) t Is a scheduling period; p (P) t buy The electricity quantity purchased from the power grid operator at the moment of t is shown as LA; q t 、q cut 、q tra,in And q tra,out The method comprises the steps of purchasing electricity price, reducing load unit compensation cost, transferring load into unit compensation cost and transferring load out of unit compensation cost; p (P) t cut The load amount after the reduction at the time t is calculated; p (P) t tra,in 、P t tra,out Load transfer in and load transfer out power can be transferred at the moment t respectively; n (N) ev The number of the electric automobiles; phi is the electric automobile discharge patch coefficient;and the discharge power of the nth electric automobile at the time t is shown.
10. The utility model provides a double-deck optimizing system of electric power system based on node carbon intensity and timesharing price of electricity guide demand response which characterized in that includes:
the determining module is used for determining the node carbon intensity of each load aggregator according to the established carbon emission flow tracking model;
the first building module is used for building a flexible load demand response model in the jurisdiction of the load aggregator; the flexible load comprises an electric automobile, a load which can be reduced and a load which can be transferred;
the second building module is used for determining the actual carbon emission of each load aggregator according to the node carbon intensity, distributing the initial carbon emission quota of each load aggregator according to the purchase electricity quantity and building a carbon transaction model;
the third building module is used for building a double-layer optimized scheduling model based on node carbon intensity and time-of-use electricity price guiding demand response; the upper layer of the double-layer optimization scheduling model is a power grid operator, the lower layer of the double-layer optimization scheduling model is a load aggregator, and the total cost is minimum as an optimization target.
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| CN117239844B (en) * | 2023-11-15 | 2024-04-05 | 广东电网有限责任公司广州供电局 | Power system scheduling method, device and storage medium based on carbon emission responsibility |
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