CN116402223A - Cooperative scheduling method, system and equipment for power distribution network - Google Patents
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Abstract
The invention discloses a power distribution network cooperative scheduling method, a system and equipment, and relates to the field of power distribution network scheduling optimization of a power system, wherein the method comprises the following steps: performing game balance analysis on the constructed multi-element emerging-load participation electric power market transaction model, wherein the multi-element emerging-load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model; converting objective functions of the multiple emerging loads participating in the electric power market transaction model according to game equilibrium analysis results, and respectively solving a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model; and constructing a multi-element emerging load aggregator market settlement model, including a 5G base station aggregator settlement model and an EV aggregator settlement model. The invention solves the bidding problem of participation of emerging load multi-main body in electric power market transaction and the market clearing problem of considering the safety and blocking of the distribution network.
Description
Technical Field
The invention relates to the technical field of power system distribution network scheduling optimization, in particular to a power distribution network cooperative scheduling method, a system and equipment.
Background
Along with the acceleration construction of the related industry of the new foundation, the emerging load represented by the 5G base station and EV is developed vigorously, so that the new growth point of the electric power and the electric quantity is increasingly formed, and the difficult problem is brought to the safe operation of the electric power system and the green development of the industry. Therefore, starting from the side of power demand, the decisive role of power grid companies and markets in resource allocation is fully played, the new load resources still falling asleep are awakened, the active participation and efficient utilization of power flexible resources are promoted, and the power system operation is converted from a 'source follow-up' mode to a 'source load interaction' mode.
At present, the bidding problem of the emerging load multi-subject participating in the electric power market transaction and the market clearing problem considering the safety and blocking of the distribution network are to be solved.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a cooperative scheduling method, a cooperative scheduling system and cooperative scheduling equipment for a power distribution network, which solve the bidding problem of participation of emerging load multi-main bodies in electric power market transaction and the market clearing problem of considering safety and blocking of the power distribution network.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
in a first aspect, a power distribution network cooperative scheduling method is provided, including:
performing game balance analysis on the constructed multi-element emerging load participation electric power market transaction model, wherein the multi-element emerging load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model;
converting objective functions of the multiple emerging loads participating in the electric power market transaction model according to game equilibrium analysis results, and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model and the electric power transaction center clearing model;
establishing a multi-element emerging load aggregator market settlement model, wherein the multi-element emerging load aggregator market settlement model comprises a 5G base station aggregator settlement model and an EV (evolution-data) aggregator settlement model;
and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model, the power transaction center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model, and verifying the effectiveness of the scheduling strategy through a solving result.
Preferably, the 5G base station aggregator bid model is constructed as follows:
in bidding, the bidding calculation of the 5G base station aggregator is performed with the goal of minimizing electricity costs, i.e., maximizing the total market revenue before day:
wherein: f (f) i 5G Bidding targets for the ith 5G base station aggregator;
representing the offer of the ith base station aggregator at time t;
The power output and clearing price of the node n is represented; n (N) ic A set of base station clusters managed by base station aggregator i 5G;
the power of the winning bid charge and discharge of the 5G base station cluster energy storage resource at the node n of the medium-voltage distribution network is represented, and i is less than or equal to n;
Reserving electric quantity of backup energy storage at time t for a 5G base station cluster at a node n of the medium-voltage distribution network;
The method comprises the steps of providing a basic power load for a 5G base station cluster at a node n of a medium-voltage distribution network; Δt is the bidding time interval, 1h in the market before day; t is the bidding period, 24 hours in the market before day;
when bidding, 5G base station aggregators meet quotation constraint and schedulable domain constraint:
quotation constraints:
wherein: pi max 、π min Maximum and minimum quotes to protect benign competition of the market; in a bidding model
Is a decision variable;
5G base station cluster schedulable domain constraints:
wherein:
a charging and discharging sign of the 5G base station cluster indicates that the net power of the cluster can only be in one state at each moment, and charging and discharging can be carried out in the actual cluster; η (eta) 5G,ch 、η 5G,dis The charge and discharge efficiency of the clusters is improved;
domain parameters may be scheduled for a cluster of base stations.
Preferably, the EV aggregator bid model is constructed as follows:
when quoting, taking the node of the power distribution network where the EV charging station is located as a unit, and the daily bidding target is characterized by the node of the power distribution network:
wherein: f (f) n ev Bidding targets of EV aggregators at a node n of the medium-voltage distribution network;
representing the offer of the EV aggregator at node n at time t;
The winning charge and discharge power of the EV aggregator at the node n of the medium-voltage distribution network is represented;
a schedulable electric quantity state at a time t for a charging station at node n;
the EV aggregator bids satisfying the bid constraint and the schedulable domain constraint:
quotation constraints:
wherein:
quotation for EV aggregator j, in Bid model +.>
Is a decision variable;
EV charging station schedulable domain constraints:
wherein:
the EV cluster charge-discharge sign indicates that the net power of the cluster can only be in one state at each moment, and the inside of the cluster can be charged and discharged; η (eta) EV,ch 、η EV,dis Charging and discharging efficiency for EV clusters;
schedulable domain parameters for EV clusters; delta tau is the scheduling interval.
Preferably, the construction of the clearing model of the electric power transaction center specifically comprises:
for the clearing of the electric power trading center, the aim is to maximize social benefits of the market in the future (namely consumer surplus), and for facilitating solving, the aim is to change the social benefits into minimized solving
Wherein: f (f) ISO The method comprises the steps of taking a daily clearing target, wherein the first item of the target represents the electricity purchasing cost to an upper power grid, the second item is the electricity purchasing cost to a photovoltaic power generator, the third item is the electricity purchasing cost willing to be paid by all 5G base station aggregators, and the fourth item is the charging cost willing to be paid by all EV aggregators;
step electricity price for the generator, g is step label; n (N) step Ladder set for ladder quotation +.>
Purchasing clear power of each step for the upper power grid at time t; pi PV For photovoltaic electricity purchase price, N pv For the number of nodes of the photovoltaic system, +.>
The power output and clearing of the photovoltaic at the node n at the time t are achieved; n (N) i A set of base station aggregators; n (N) j Aggregate for EV aggregators; it should be noted that +.>
And->
In order to offer a price to be offered,
is a decision variable.
When the photovoltaic power generation system is out of order, the power flow constraint, the voltage safety constraint, the line capacity constraint, the superior electricity purchasing constraint, the emerging load constraint and the photovoltaic power generation constraint of the power distribution network are met:
linearization power flow constraint of power distribution network:
wherein: the first to fourth, fifth to sixth and seventh formulas are respectively active balance constraint, reactive balance constraint and voltage balance constraint, and for convenience of expression, the active power balance constraint and the reactive power balance constraint are expressed in a single mode; n (N) M 、N i 、N pv Respectively a medium-voltage distribution network node set, a base station aggregation and quotient node set and a photovoltaic node set; is P mn,t 、P nk,t The active flows of the branches mn, nk at the time t are respectively given, v (n) represents the set of all child nodes when node n is the parent node,
the basic active load at t moment is the node n of the power distribution network; q (Q) mn,t 、Q nk,t Reactive flow of branches nm, mk at time t, < >>
For the basic reactive load of the distribution network node m at time t,/->
Reactive power output of photovoltaic at a node n of the power distribution network at a time t is obtained;
Respectively representing the square value of the voltage of the child node n and the father node m at the time t, R mn 、X mn The resistance value and the reactance value of the branch mn are respectively;
distribution network safety constraints:
wherein:
the maximum and minimum values of the square of the node voltage are respectively.
Blocking management of a power distribution network:
wherein:
maximum load capacity for branch mn; n (N) ML Collecting all branches of the medium-voltage distribution network;
superior electricity purchase constraint:
wherein: node 1 represents a root node; v (1) represents a node set connected to the root node; p (P) g G,max Maximum power when segment g is quoted;
emerging load power constraints:
photovoltaic output constraint:
wherein:
the photovoltaic active and reactive maximum regulating quantity at the node n is obtained.
Preferably, the game balance analysis is performed on the constructed multi-element emerging load participation electric power market transaction model, which specifically comprises the following steps:
nash games are formed among emerging load aggregators, all emerging load aggregators and a power transaction center form a Stackelberg game, the problem of the Stackelberg game is that BMINLP is converted into a single-layer mixed integer linear programming model by adopting a KKT reconstruction method, a large M method and a strong dual theorem, and the single-layer linear programming model is solved by using a commercial solver, and generalized Nash games are formed between the emerging load aggregators due to electric quantity coupling constraint of a power distribution network.
Preferably, the objective function of the multiple emerging loads participating in the electric power market transaction model is converted according to the game equilibrium analysis result, and the objective function is specifically as follows:
and (3) establishing a KKT system:
the formula to be solved is as follows:
wherein: f (x) is; h is a a (x) =0 is an equality constraint; a is the number of equality constraints; g b (x) Less than or equal to 0 is inequality constraint; b is the number of inequality constraints;
the conversion KKT system is as follows:
wherein: l (x, α, β) is in the lagrangian form; t is a complementary symbol, i.e. the left and right symbol have and only one term is 0;
the complementary constraint in the KKT system is a nonlinear constraint, and is linearized by a large M method, which is converted as follows:
wherein: d, d b Is an increased boolean variable; m is a very large constant;
conversion objective function:
the 5G base station aggregator bid solving goal is finally converted into:
wherein:
a dual variable that is an equality constraint;
A dual variable that is an inequality constraint;
the EV aggregator bid resolution objective is ultimately translated into:
preferably, the construction of the 5G base station aggregator settlement model specifically includes: .
After the electricity is discharged, the 5G base station polymerizer obtains the winning total charge and discharge power and the discharge electricity price, and the final energy consumption cost of the 5G base station polymerizer is settled by the following formula.
Wherein:
total energy costs for the aggregate i.
In order to respond to the system requirement, the energy storage charging and discharging power of each 5G base station needs to be managed. And optimizing the output of each base station by taking the minimum deviation of the total output of the 5G base stations as a target. Since linear power flow calculation is adopted, the objective function has a non-unique solution. In order to ensure the consistency of the power supply reliability of different base stations, a base station power distribution algorithm based on the consistency of the scheduling capacity and the schedulable capacity is provided. The model is as follows:
wherein: f (f) i plan The target is optimized for the base station aggregator i,
for planning charge-discharge power of base station bs in aggregate quotient i, N bs A base station set in an aggregation quotient i;
To ensure a weakly consistent auxiliary variable, +.>
Is a bias constant that ensures weak consistency; the first constraint ensures that the base station power supply reliability is consistent; other constraints are the 5G base station itself regulatory constraints.
Preferably, the construction of the settlement model of the EV aggregator specifically comprises the following steps.
The formula of the EV aggregator settlement after clearing is as follows:
wherein:
total electricity costs for EV aggregators;
in order to respond to the system demand, each EV charge-discharge power is managed; optimizing the output of each EV by taking the minimum deviation of the total output of the EVs as a target, and simultaneously increasing the weak consistency constraint of the discharge charge quantity for balancing the loss of EV batteries participating in interaction; the model is as follows:
wherein:
optimizing the goal for EV aggregator j, +.>
For charging and discharging plans of vehicles ev at time t in aggregator j, N ev EV set within aggregator j;
To ensure a weakly consistent auxiliary variable, +.>
Is a bias constant that ensures weak consistency; the first constraint ensures vehicle discharge consistency; other constraints are EV self-regulating constraints;
the planned charging cost per EV is the product of the purge price and the planned charging and discharging power:
wherein:
the electricity cost is ev; η (eta) agg And the system also comprises a profit coefficient set for the aggregator for profit, the aggregator predicts the profit of the aggregator when bidding and transmits the coefficient, and the aggregator also attracts the EV to participate in grid interaction by adjusting the product of the coefficient and the predicted electricity clearing price.
In a second aspect, a power distribution network cooperative scheduling system is provided, including:
the analysis module is used for carrying out game balance analysis on the constructed multi-element emerging load participation electric power market transaction model, wherein the multi-element emerging load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model;
the conversion module is used for converting the objective function of the multi-element emerging load participating in the electric power market transaction model according to the game equilibrium analysis result, and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model and the electric power transaction center clearing model;
the settlement model construction module is used for constructing a multi-element emerging load aggregator market settlement model, including a 5G base station aggregator settlement model and an EV aggregator settlement model;
and the solving module is used for respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model, the power transaction center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model, and verifying the effectiveness of the scheduling strategy through a solving result.
In a third aspect, a computing device is provided, comprising:
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
(III) beneficial effects
(1) The method, the system and the equipment for collaborative scheduling of the power distribution network solve the bidding problem of participation of emerging load multi-main body in electric power market transaction and the market clearing problem considering safety and blocking of the power distribution network
(2) According to the collaborative scheduling method, the collaborative scheduling system and the collaborative scheduling equipment for the power distribution network, voltage management is carried out by using a linearization tide equation, blocking management is carried out by using linear outer approximate constraint, and the operation safety of the power distribution network during power transaction is ensured
(3) According to the collaborative scheduling method, system and equipment for the power distribution network, disclosed by the invention, the utilization rate of emerging load resources is improved by utilizing the bid model for minimizing the emerging load aggregate commercial power cost and the clear model for maximizing the social benefit power market, so that the energy consumption cost of the emerging load is reduced, and the power distribution network operator is not required to provide additional subsidies.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a diagram of an exemplary grid of a method according to an embodiment of the present invention;
FIG. 3 is a graph showing a voltage distribution of a distribution network after use according to an embodiment of the present invention;
FIG. 4 is a diagram of a circuit load condition of a distribution network after use according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a base station aggregator clear architecture provided in an embodiment of the present invention;
FIG. 6 is a graph of the results of EV polymerization provided by the example of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 1, an embodiment of the present invention provides a power distribution network cooperative scheduling method, including:
performing game balance analysis on the constructed multi-element emerging load participation electric power market transaction model, wherein the multi-element emerging load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model;
converting objective functions of the multiple emerging loads participating in the electric power market transaction model according to game equilibrium analysis results, and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model and the electric power transaction center clearing model;
establishing a multi-element emerging load aggregator market settlement model, wherein the multi-element emerging load aggregator market settlement model comprises a 5G base station aggregator settlement model and an EV (evolution-data) aggregator settlement model;
and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model, the power transaction center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model, and verifying the effectiveness of the scheduling strategy through a solving result.
Referring to fig. 2-6, the specific implementation steps are as follows:
(1) Establishing a model of participation of a multiple emerging load in electric power market transaction, wherein the model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model;
(2) Performing game balance analysis on the constructed transaction model;
(3) The constructed transaction model is subjected to model transformation, so that the solution is convenient;
(4) Establishing a multi-element emerging load aggregator market settlement model, wherein the multi-element emerging load aggregator market settlement model comprises a 5G base station aggregator settlement model and an EV (evolution-data) aggregator settlement model;
(5) And verifying the effectiveness of a power distribution network cooperative scheduling strategy considering that the emerging load multiple bodies participate in the electric market transaction by adopting an actual radiation type net rack.
The following provides an explanation of the implementation method of the present invention:
step one: the method comprises the following main steps of constructing a model of participation of an emerging load multi-subject in electric power market transaction:
(1) And constructing a 5G base station aggregator bidding model.
In bidding, each emerging load aggregator would like to have its own minimum electricity costs, and therefore bid calculations for 5G base station aggregators are performed with the goal of minimizing electricity costs, i.e., maximizing the total market revenue in the day before market. The targets for the 5G base station aggregator are as follows:
wherein: f (f) i 5G Bidding targets for the ith 5G base station aggregator;
representing the offer of the ith base station aggregator at time t;
The power output and clearing price of the node n is represented; n (N) ic A set of base station clusters managed by base station aggregator i 5G;
the bid-winning charge-discharge power of the 5G base station cluster energy storage resource at the node n of the medium-voltage distribution network is represented, and it is noted that the 5G base station is mainly built by three major operators in China, so that the node of the distribution network is not taken as a quotation unit when bidding, i is less than or equal to n;
Backup energy storage of electric quantity at time t for 5G base station cluster at node n of medium-voltage distribution networkStoring;
The method comprises the steps of providing a basic power load for a 5G base station cluster at a node n of a medium-voltage distribution network; Δt is the bidding time interval, 1h in the market before day; t is the bidding period, 24 hours in the day-ahead market.
The 5G base station aggregator should meet bid constraints and schedulable domain constraints when bidding.
a) Quotation constraints:
wherein: pi max 、π min Maximum and minimum quotes to protect benign competition of the market; in a bidding model
Is a decision variable.
b) 5G base station cluster schedulable domain constraints:
wherein:
a charging and discharging sign of the 5G base station cluster indicates that the net power of the cluster can only be in one state at each moment, and charging and discharging can be carried out in the actual cluster; η (eta) 5G,ch 、η 5G,dis The charge and discharge efficiency of the clusters is improved;
domain parameters may be scheduled for a cluster of base stations.
(2) And constructing an EV aggregator bidding model.
Like the 5G base station aggregator, EV aggregators bid day-ahead with the goal of minimizing energy costs, except that EV energy costs are already included in the charging power. In addition, the EV clusters are aggregated in the form of charging stations, so that bidding is performed in units of distribution network nodes where the EV charging stations are located, and the bidding targets before date can be characterized by the distribution network nodes:
wherein:
bidding targets of EV aggregators at a node n of the medium-voltage distribution network;
Representing the offer of the EV aggregator at node n at time t;
The winning charge and discharge power of the EV aggregator at the node n of the medium-voltage distribution network is represented;
The state of charge may be scheduled at time t for the charging station at node n.
EV aggregators should bid to meet bid constraints and schedulable domain constraints.
a) Quotation constraints:
wherein:
quotation for EV aggregator j, in Bid model +.>
Is a decision variable.
b) EV charging station schedulable domain constraints:
wherein:
the EV cluster charge-discharge sign indicates that the net power of the cluster can only be in one state at each moment, and the inside of the cluster can be charged and discharged; η (eta) EV,ch 、η EV,dis Charging and discharging efficiency for EV clusters;
schedulable domain parameters for EV clusters; delta tau is the scheduling interval.
(3) And constructing a power trading center clearing model.
For the clearing of the electric power trading center, the aim is to maximize the social welfare of the market (namely consumer surplus) before the day, and for facilitating the solving, the method is changed into the method of minimizing the solving.
Wherein: f (f) ISO The method comprises the steps of taking a daily clearing target, wherein the first item of the target represents the electricity purchasing cost to an upper power grid, the second item is the electricity purchasing cost to a photovoltaic power generator, the third item is the electricity purchasing cost willing to be paid by all 5G base station aggregators, and the fourth item is the charging cost willing to be paid by all EV aggregators;
step electricity price for the generator, g is step label; n (N) step Ladder set for ladder quotation +.>
Purchasing clear power of each step for the upper power grid at time t; pi PV For photovoltaic electricity purchase price, N pv For the number of nodes of the photovoltaic system, +.>
The power output and clearing of the photovoltaic at the node n at the time t are achieved; n (N) i A set of base station aggregators; n (N) j Aggregate for EV aggregators. It should be noted that +.>
And->
In order to offer a price to be offered,
is a decision variable.
When the photovoltaic power distribution system is out of order, the power flow constraint, the voltage safety constraint, the line capacity constraint, the superior electricity purchasing constraint, the emerging load constraint and the photovoltaic power output constraint of the power distribution network should be met.
a) Linearization power flow constraint of power distribution network:
in the electric power market transaction model solving, the grid power flow constraint based on the Distflow theory is a group of non-convex and nonlinear equation sets, so that the market clearing solving is inconvenient, and the electric power market transaction model approximately represents the electric power through a linearization Distflow model:
wherein: the first to fourth, fifth to sixth and seventh formulas are respectively active balance constraint, reactive balance constraint and voltage balance constraint, and for convenience of expression, the active power balance constraint and the reactive power balance constraint are expressed in a single mode; n (N) M 、N i 、N pv Respectively a medium-voltage distribution network node set, a base station aggregation and quotient node set and a photovoltaic node set; is P mn,t 、P nk,t The active flows of the branches mn, nk at the time t are respectively given, v (n) represents the set of all child nodes when node n is the parent node,
the basic active load at t moment is the node n of the power distribution network; q (Q) mn,t 、Q nk,t Reactive flow of branches nm, mk at time t, < >>
For the basic reactive load of the distribution network node m at time t,/->
Reactive power output of photovoltaic at a node n of the power distribution network at a time t is obtained;
Respectively representing the square value of the voltage of the child node n and the father node m at the time t, R mn 、X mn The resistance and reactance of branch mn are respectively. />
b) Distribution network safety constraints:
wherein:
the maximum and minimum values of the square of the node voltage are respectively.
c) Blocking management of a power distribution network:
wherein:
maximum load capacity for branch mn; n (N) ML And collecting all branches of the medium-voltage distribution network. The line capacity constraint is a convex quadratic constraint, but because the line capacity constraint has strong nonlinearity and non-convexity when the KKT condition is used in the clearing problem, the blocking management can be performed by adopting the linear outer approximation constraint, and the clearing calculation is simplified.
d) Superior electricity purchase constraint:
wherein: node 1 represents a root node; v (1) represents a node set connected to the root node;
for the segmentation g of quotationsMaximum power.
e) Emerging load power constraints:
f) Photovoltaic output constraint:
wherein:
the photovoltaic active and reactive maximum regulating quantity at the node n is obtained.
Step two: performing game balance analysis on the transaction model constructed in the step one, wherein the method mainly comprises the following steps:
from the clear model of the bidding between the emerging load aggregators and the power trading center, the Nash game is formed among the emerging load aggregators (5G base station aggregators 1, …,5G base station aggregators k, …, EV aggregators 1, … and EV aggregators n), and the Stackelberg game is formed by all the emerging load aggregators and the power trading center. The essence of the Stackelberg game problem is to solve a double-layer mixed integer nonlinear programming (BMINLP) model, and the BMINLP can not be directly solved by using a commercial solver, so that the BMINLP is converted into a single-layer Mixed Integer Linear Programming (MILP) model by adopting a KKT reconstruction method, a large M method and a strong dual theorem, and then the MILP model is solved by using the commercial solver, and at the moment, the generalized Nash game is formed due to the fact that electric quantity coupling constraint exists between the single-layer models of the bidding of emerging load polymers. Since the balance of the power retail market is ubiquitous, there is a balance solution to the problem to be solved.
Step three: model conversion is carried out on the constructed transaction model, and the method mainly comprises the following steps:
(1) And (5) establishing a KKT system.
To facilitate the set up of the KKT system, the basic form of KKT condition conversion will now be described. The following formula is to be solved:
wherein: f (x) is; h is a a (x) =0 is an equality constraint; a is the number of equality constraints; g b (x) Less than or equal to 0 is inequality constraint; b is the number of inequality constraints.
The converted KKT system is of the formula:
wherein: l (x, α, β) is in the lagrangian form; and t is a complementary symbol, i.e. the left and right symbol have and only one term is 0.
The complementary constraint in the KKT system is a nonlinear constraint, so that linearization is performed by using a large M method, and the large M method is converted as follows:
wherein: d, d b Is an increased boolean variable; m is a very large constant.
(2) And converting the objective function.
The 5G base station aggregator bid solving goal is finally converted into:
wherein:
a dual variable that is an equality constraint;
Dual variables constrained by inequality。
The EV aggregator bid resolution objective is ultimately translated into:
step four: constructing a multi-element emerging load aggregator settlement model, wherein the main steps of the method are as follows:
(1) And constructing a 5G base station aggregator settlement model.
After the electricity is discharged, the 5G base station polymerizer obtains the winning total charge and discharge power and the discharge electricity price, and the final energy consumption cost of the 5G base station polymerizer is settled by the following formula.
Wherein:
total energy costs for the aggregate i.
In order to respond to the system requirement, the energy storage charging and discharging power of each 5G base station needs to be managed. And optimizing the output of each base station by taking the minimum deviation of the total output of the 5G base stations as a target. Since linear power flow calculation is adopted, the objective function has a non-unique solution. In order to ensure the consistency of the power supply reliability of different base stations, a base station power distribution algorithm based on the consistency of the scheduling capacity and the schedulable capacity is provided. The model is as follows:
wherein: f (f) i plan The target is optimized for the base station aggregator i,
for planning charge-discharge power of base station bs in aggregate quotient i, N bs A base station set in an aggregation quotient i;
To ensure weak consistencyAuxiliary variable of->
Is a bias constant that ensures weak consistency; the first constraint ensures that the base station power supply reliability is consistent; other constraints are the 5G base station itself regulatory constraints.
(2) And constructing an EV aggregator settlement model.
Similar to the 5G base station aggregator, the EV aggregator settles after the purge as follows.
Wherein:
total electricity costs for EV aggregators.
In response to system demand, each EV charge-discharge power is managed. And optimizing the output of each EV by taking the minimum deviation of the total output of the EVs as a target, and simultaneously increasing the weak consistency constraint of the discharge charge and the electric quantity for balancing the loss of the EV batteries participating in interaction. The model is as follows:
wherein:
optimizing the goal for EV aggregator j, +.>
For charging and discharging plans of vehicles ev at time t in aggregator j, N ev EV set within aggregator j;
To ensure a weakly consistent auxiliary variable, +.>
Is a bias constant that ensures weak consistency; the first constraint ensures vehicle discharge consistency; which is a kind ofHe constraints are EV self-regulatory constraints.
The planned charging cost per EV is the product of the purge price and the planned charging and discharging power:
wherein:
the electricity cost is ev; η (eta) agg And the system also comprises a profit coefficient set for the aggregator for profit, the aggregator predicts the profit of the aggregator when bidding and transmits the coefficient, and the aggregator also attracts the EV to participate in grid interaction by adjusting the product of the coefficient and the predicted electricity clearing price.
Step five: based on the first, second, third and fourth steps, the effectiveness of the cooperative scheduling strategy of the power distribution network taking the emerging load multi-main body into account into consideration is verified by adopting an actual radiation type net rack, and the method mainly comprises the following steps:
(1) And calculating bidding models of 5G base station aggregators and EV aggregators.
(2) And calculating an electric power market clearing model.
(3) And calculating a 5G base station aggregator settlement model.
(4) And calculating an EV aggregator settlement model.
Table I Convergence expected and actual costs of electricity
An embodiment of the present invention provides a power distribution network cooperative scheduling system, including:
the analysis module is used for carrying out game balance analysis on the constructed multi-element emerging load participation electric power market transaction model, wherein the multi-element emerging load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model;
the conversion module is used for converting the objective function of the multi-element emerging load participating in the electric power market transaction model according to the game equilibrium analysis result, and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model and the electric power transaction center clearing model;
the settlement model construction module is used for constructing a multi-element emerging load aggregator market settlement model, including a 5G base station aggregator settlement model and an EV aggregator settlement model;
and the solving module is used for respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model, the power transaction center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model, and verifying the effectiveness of the scheduling strategy through a solving result.
Embodiments of the present application may be provided as a method or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. The power distribution network cooperative scheduling method is characterized by comprising the following steps of:
performing game balance analysis on the constructed multi-element emerging load participation electric power market transaction model, wherein the multi-element emerging load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model;
converting objective functions of the multiple emerging loads participating in the electric power market transaction model according to game equilibrium analysis results, and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model and the electric power transaction center clearing model;
establishing a multi-element emerging load aggregator market settlement model, wherein the multi-element emerging load aggregator market settlement model comprises a 5G base station aggregator settlement model and an EV (evolution-data) aggregator settlement model;
and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model, the power transaction center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model, and verifying the effectiveness of the scheduling strategy through a solving result.
2. The power distribution network cooperative scheduling method according to claim 1, wherein: the 5G base station aggregator bid model is constructed as follows:
in bidding, the bidding calculation of the 5G base station aggregator is performed with the goal of minimizing electricity costs, i.e., maximizing the total market revenue before day:
wherein: f (f) i 5G Bidding targets for the ith 5G base station aggregator;
representing the offer of the ith base station aggregator at time t;
The power output and clearing price of the node n is represented; n (N) ic A set of base station clusters managed by base station aggregator i 5G;
representing medium voltage power distributionThe power of the winning charge and discharge of the 5G base station cluster energy storage resource at the network node n is i less than or equal to n;
Reserving electric quantity of backup energy storage at time t for a 5G base station cluster at a node n of the medium-voltage distribution network;
The method comprises the steps of providing a basic power load for a 5G base station cluster at a node n of a medium-voltage distribution network; Δt is the bidding time interval, 1h in the market before day; t is the bidding period, 24 hours in the market before day;
when bidding, 5G base station aggregators meet quotation constraint and schedulable domain constraint:
quotation constraints:
wherein: pi max 、π min Maximum and minimum quotes to protect benign competition of the market; in a bidding model
Is a decision variable;
5G base station cluster schedulable domain constraints:
wherein:
a charging and discharging sign of the 5G base station cluster indicates that the net power of the cluster can only be in one state at each moment, and charging and discharging can be carried out in the actual cluster; η (eta) 5G,ch 、η 5G,dis The charge and discharge efficiency of the clusters is improved;
domain parameters may be scheduled for a cluster of base stations.
3. The power distribution network cooperative scheduling method according to claim 2, wherein: the EV aggregator bidding model is constructed as follows:
when quoting, taking the node of the power distribution network where the EV charging station is located as a unit, and the daily bidding target is characterized by the node of the power distribution network:
wherein:
bidding targets of EV aggregators at a node n of the medium-voltage distribution network;
Representing the offer of the EV aggregator at node n at time t;
The winning charge and discharge power of the EV aggregator at the node n of the medium-voltage distribution network is represented;
A schedulable electric quantity state at a time t for a charging station at node n;
the EV aggregator bids satisfying the bid constraint and the schedulable domain constraint:
quotation constraints:
wherein:
quotation for EV aggregator j, in Bid model +.>
Is a decision variable;
EV charging station schedulable domain constraints:
wherein:
the EV cluster charge-discharge sign indicates that the net power of the cluster can only be in one state at each moment, and the inside of the cluster can be charged and discharged; η (eta) EV,ch 、η EV,dis Charging and discharging efficiency for EV clusters;
schedulable domain parameters for EV clusters; delta tau is the scheduling interval.
4. A power distribution network collaborative scheduling method according to claim 3, characterized in that: the construction of the clearing model of the electric power transaction center specifically comprises the following steps:
for the clearing of the electric power trading center, the aim is to maximize social benefits of the market in the future (namely consumer surplus), and for facilitating solving, the aim is to change the social benefits into minimized solving
Wherein: f (f) ISO The method comprises the steps of taking a daily clearing target, wherein the first item of the target represents the electricity purchasing cost to an upper power grid, the second item is the electricity purchasing cost to a photovoltaic power generator, the third item is the electricity purchasing cost willing to be paid by all 5G base station aggregators, and the fourth item is the charging cost willing to be paid by all EV aggregators;
step electricity price for the generator, g is step label; n (N) step Ladder set for ladder quotation +.>
Purchasing clear power of each step for the upper power grid at time t; pi PV For photovoltaic electricity purchase price, N pv For the number of nodes of the photovoltaic system, +.>
The power output and clearing of the photovoltaic at the node n at the time t are achieved; n (N) i A set of base station aggregators; n (N) j Aggregate for EV aggregators; it should be noted that +.>
And->
For quotation->
Is a decision variable.
When the photovoltaic power generation system is out of order, the power flow constraint, the voltage safety constraint, the line capacity constraint, the superior electricity purchasing constraint, the emerging load constraint and the photovoltaic power generation constraint of the power distribution network are met:
linearization power flow constraint of power distribution network:
wherein: the first to fourth, fifth to sixth and seventh formulas are active balance constraint, reactive balance constraint and voltage level respectivelyThe balance constraint, for convenience of expression, active power balance constraint and reactive power balance constraint are expressed in a unitary mode in the following; n (N) M 、N i 、N pv Respectively a medium-voltage distribution network node set, a base station aggregation and quotient node set and a photovoltaic node set; is P mn,t 、P nk,t The active flows of the branches mn, nk at the time t are respectively given, v (n) represents the set of all child nodes when node n is the parent node,
the basic active load at t moment is the node n of the power distribution network; q (Q) mn,t 、Q nk,t Reactive flow of branches nm, mk at time t, < >>
For the basic reactive load of the distribution network node m at time t,/->
Reactive power output of photovoltaic at a node n of the power distribution network at a time t is obtained;
Respectively representing the square value of the voltage of the child node n and the father node m at the time t, R mn 、X mn The resistance value and the reactance value of the branch mn are respectively;
distribution network safety constraints:
wherein:
the maximum and minimum values of the square of the node voltage are respectively.
Blocking management of a power distribution network:
wherein:
maximum load capacity for branch mn; n (N) ML Collecting all branches of the medium-voltage distribution network;
superior electricity purchase constraint:
wherein: node 1 represents a root node; v (1) represents a node set connected to the root node;
maximum power when segment g is quoted;
emerging load power constraints:
photovoltaic output constraint:
wherein:
the photovoltaic active and reactive maximum regulating quantity at the node n is obtained.
5. The cooperative scheduling method for power distribution networks according to claim 4, wherein: the game balance analysis is carried out on the constructed multi-element emerging load participating in the electric power market transaction model, and the game balance analysis specifically comprises the following steps:
nash games are formed among emerging load aggregators, all emerging load aggregators and a power transaction center form a Stackelberg game, the problem of the Stackelberg game is that BMINLP is converted into a single-layer mixed integer linear programming model by adopting a KKT reconstruction method, a large M method and a strong dual theorem, and the single-layer linear programming model is solved by using a commercial solver, and generalized Nash games are formed between the emerging load aggregators due to electric quantity coupling constraint of a power distribution network.
6. The cooperative scheduling method for power distribution networks according to claim 5, wherein: and converting the objective function of the multi-element emerging load participating in the electric power market transaction model according to the game equilibrium analysis result, wherein the objective function is as follows:
and (3) establishing a KKT system:
the formula to be solved is as follows:
wherein: f (x) is; h is a a (x) =0 is an equality constraint; a is the number of equality constraints; g b (x) Less than or equal to 0 is inequality constraint; b is the number of inequality constraints;
the conversion KKT system is as follows:
wherein: l (x, α, β) is in the lagrangian form; t is a complementary symbol, i.e. the left and right symbol have and only one term is 0;
the complementary constraint in the KKT system is a nonlinear constraint, and is linearized by a large M method, which is converted as follows:
wherein: d, d b Is an increased boolean variable; m is a very large constant;
conversion objective function:
the 5G base station aggregator bid solving goal is finally converted into:
wherein:
a dual variable that is an equality constraint;
A dual variable that is an inequality constraint;
the EV aggregator bid resolution objective is ultimately translated into:
7. the power distribution network cooperative scheduling method according to claim 6, wherein: the construction of the 5G base station aggregator settlement model specifically comprises the following steps: .
After the electricity is discharged, the 5G base station polymerizer obtains the winning total charge and discharge power and the discharge electricity price, and the final energy consumption cost of the 5G base station polymerizer is settled by the following formula.
Wherein:
total energy costs for the aggregate i.
In order to respond to the system requirement, the energy storage charging and discharging power of each 5G base station needs to be managed. And optimizing the output of each base station by taking the minimum deviation of the total output of the 5G base stations as a target. Since linear power flow calculation is adopted, the objective function has a non-unique solution. In order to ensure the consistency of the power supply reliability of different base stations, a base station power distribution algorithm based on the consistency of the scheduling capacity and the schedulable capacity is provided. The model is as follows:
wherein: f (f) i plan The target is optimized for the base station aggregator i,
for planning charge-discharge power of base station bs in aggregate quotient i, N bs A base station set in an aggregation quotient i;
To ensure a weakly consistent auxiliary variable, +.>
Is a bias constant that ensures weak consistency; the first constraint ensures that the base station power supply reliability is consistent; other constraints are the 5G base station itself regulatory constraints.
8. The cooperative scheduling method for power distribution networks according to claim 7, wherein: the construction of the EV aggregator settlement model specifically comprises the following steps.
The formula of the EV aggregator settlement after clearing is as follows:
wherein:
total electricity costs for EV aggregators;
in order to respond to the system demand, each EV charge-discharge power is managed; optimizing the output of each EV by taking the minimum deviation of the total output of the EVs as a target, and simultaneously increasing the weak consistency constraint of the discharge charge quantity for balancing the loss of EV batteries participating in interaction; the model is as follows:
wherein: f (f) j plan The objective is optimized for the EV-aggregator j,
for charging and discharging plans of vehicles ev at time t in aggregator j, N ev EV set within aggregator j;
To ensure a weakly consistent auxiliary variable, +.>
Is a bias constant that ensures weak consistency; the first constraint ensures vehicle discharge consistency; other constraints are EV self-regulating constraints;
the planned charging cost per EV is the product of the purge price and the planned charging and discharging power:
wherein:
the electricity cost is ev; η (eta) agg And the system also comprises a profit coefficient set for the aggregator for profit, the aggregator predicts the profit of the aggregator when bidding and transmits the coefficient, and the aggregator also attracts the EV to participate in grid interaction by adjusting the product of the coefficient and the predicted electricity clearing price.
9. A power distribution network cooperative scheduling system, comprising:
the analysis module is used for carrying out game balance analysis on the constructed multi-element emerging load participation electric power market transaction model, wherein the multi-element emerging load participation electric power market transaction model comprises a 5G base station aggregator bidding model, an EV aggregator bidding model and an electric power transaction center clearing model;
the conversion module is used for converting the objective function of the multi-element emerging load participating in the electric power market transaction model according to the game equilibrium analysis result, and respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model and the electric power transaction center clearing model;
the settlement model construction module is used for constructing a multi-element emerging load aggregator market settlement model, including a 5G base station aggregator settlement model and an EV aggregator settlement model;
and the solving module is used for respectively solving the 5G base station aggregator bidding model, the EV aggregator bidding model, the power transaction center clearing model, the 5G base station aggregator settlement model and the EV aggregator settlement model, and verifying the effectiveness of the scheduling strategy through a solving result.
10. A computing device, comprising:
one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117077946A (en) * | 2023-08-16 | 2023-11-17 | 国网山东省电力公司东营供电公司 | Novel market subject identification method and system suitable for participating in power grid aggregation scheduling |
| CN117933600A (en) * | 2023-12-15 | 2024-04-26 | 天津大学 | Distributed algorithm-based distributed resource aggregation regulation and control method and system for power distribution network |
| CN118647044A (en) * | 2024-08-14 | 2024-09-13 | 国网浙江省电力有限公司丽水供电公司 | Communication base station aggregation method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117077946A (en) * | 2023-08-16 | 2023-11-17 | 国网山东省电力公司东营供电公司 | Novel market subject identification method and system suitable for participating in power grid aggregation scheduling |
| CN117077946B (en) * | 2023-08-16 | 2024-04-16 | 国网山东省电力公司东营供电公司 | Novel market subject identification method and system suitable for participating in power grid aggregation scheduling |
| CN117933600A (en) * | 2023-12-15 | 2024-04-26 | 天津大学 | Distributed algorithm-based distributed resource aggregation regulation and control method and system for power distribution network |
| CN118647044A (en) * | 2024-08-14 | 2024-09-13 | 国网浙江省电力有限公司丽水供电公司 | Communication base station aggregation method, system and storage medium |
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