CN110516855B - Load aggregator-oriented distributed energy storage control right optimized scheduling method - Google Patents

Load aggregator-oriented distributed energy storage control right optimized scheduling method Download PDF

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CN110516855B
CN110516855B CN201910730979.0A CN201910730979A CN110516855B CN 110516855 B CN110516855 B CN 110516855B CN 201910730979 A CN201910730979 A CN 201910730979A CN 110516855 B CN110516855 B CN 110516855B
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王建学
朱宇超
雍维桢
张耀
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Xian Jiaotong University
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Abstract

The invention discloses a distributed energy storage control right optimized scheduling method facing a load aggregator, which is used for obtaining system historical data to predict load and output of new energy; establishing a user operation self-scheduling objective function and a user self-scheduling constraint condition with distributed energy storage; establishing a distributed energy storage control right optimal purchase strategy and operation scheduling objective function and a distributed energy storage control right optimal purchase strategy and operation scheduling constraint condition; determining an acquisition energy storage strategy and an operation scheduling method of an aggregator in a distributed energy storage control right transaction mode, and optimizing an acquisition scheme of an energy storage control right; a scheduling scheme of the aggregator for user load; the cost of the users participating in the transaction reduces the situation and the economic benefits of the aggregator. The invention improves the utilization rate of distributed energy storage, reduces the peak-valley difference of the power system and contributes to social benefits to a certain extent.

Description

Load aggregator-oriented distributed energy storage control right optimized scheduling method
Technical Field
The invention belongs to the technical field of distributed energy storage scheduling, and particularly relates to a load aggregator-oriented distributed energy storage control right optimal scheduling method.
Background
The popularization of the distributed new energy system cannot be separated from the distributed energy storage system. Taking distributed photovoltaic as an example, when a large number of users in a power grid install a certain amount of distributed photovoltaic systems, due to the fluctuation and randomness of photovoltaic output, the photovoltaic systems can bring challenges and hazards to power grid dispatching and even power grid safety. When a certain amount of distributed energy storage is configured for distributed photovoltaic users to form a set of light storage system, the problems and challenges caused by large-scale access and rapid increase of loads of distributed power supplies can be solved by reasonably scheduling the distributed energy storage. The distributed energy storage users refer to users who are installed with distributed photovoltaic and distributed energy storage and have a certain amount of rigid electricity loads. The existing scheduling method for user-side distributed energy storage can be divided into 2 types:
1) the user can satisfy self-scheduling through moving the light storage system, when self power consumption demand, through utilizing the fluctuation of price of electricity along with time, reduces self running cost, acquires certain benefit. However, there are time and technical difficulties in implementing self-scheduling by users, and when a large number of distributed users perform independent self-scheduling in a certain area, due to large difference of power load curves of the users, a large number of disordered energy storage charging and discharging operations may be caused, which may cause certain impact on the power grid. In addition, because the capacity of a single user is small, the peak clipping and valley filling effects possibly generated by self-scheduling of the user are limited, and the compensation of a power grid cannot be obtained.
2) The load aggregation business purchases the energy storage control right of part of users, signs a monthly purchase contract, pays a certain fee to the users, uniformly controls the energy storage of the signed users and simultaneously undertakes the power supply work of the part of users. For distributed energy storage installed on a user side, a load aggregator can stabilize fluctuation and randomness of distributed photovoltaic output and a small amount of fluctuation in a short period by formulating a reasonable distributed energy storage load group scheduling method and utilizing the characteristic of quick response of the load aggregator and reasonably performing charge and discharge scheduling, so that the output of the load aggregator can be scheduled according to a longer time scale. After a large amount of distributed energy storage user loads are aggregated to participate in peak clipping and valley filling, the benefits of the users, the aggregators and the power grid can be realized.
Most of the existing researches are carried out aiming at the transaction of the stored energy power and electricity quantity, and the researches about the transaction of the stored energy control right are rare; most of the energy storage systems aim at a centralized large-capacity energy storage system, and the research on distributed energy storage at the user side is relatively less; the benefit of the user side is taken into consideration. Therefore, the invention develops two-stage scheduling problem research for the distributed energy storage optimal acquisition and scheduling method under the energy storage control right transaction mode. In the stage 1, the user self-scheduling problem optimization is carried out by taking the minimized user cost as an optimization target; and in the 2 nd stage, performing optimal acquisition of the energy storage control right and energy storage scheduling optimization by taking the maximum income of the aggregator as a target, implementing energy storage optimization scheduling on the basis of selecting a proper user for energy storage control right acquisition, and ensuring that the operating cost of the signed user is lower than that of the user in the 1 st stage. The user can damage the benefit of the user when the price for selling the energy storage control right is too low, and the aggregator can not obtain the benefit when the price is too high; and due to the energy storage and load parameters, the operation cost of each individual user after the individual user participates in the optimal scheduling of the energy storage load group may be increased or the benefit of the aggregator will be damaged, so that the optimal acquisition problem of the energy storage control right exists.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a load aggregator-oriented distributed energy storage control right optimized scheduling method, which can provide a strong reference for economic operation of a distributed energy storage aggregator on the basis of ensuring that user benefits are not infringed.
The invention adopts the following technical scheme:
a distributed energy storage control right optimized scheduling method facing a load aggregator comprises the following steps:
s1, obtaining system historical data, obtaining equipment state and information from a user side through a remote communication system, and performing load prediction and new energy output prediction;
s2, establishing a user operation self-scheduling objective function with distributed energy storage, wherein the user operation self-scheduling optimization objective with distributed energy storage is to minimize the total operation cost of a user, and the income and expenditure of the user comprise the income of selling electricity to a power grid, the cost of purchasing electricity from the power grid, the cost of charging at valley time and the loss cost of energy storage charging and discharging;
s3, establishing a user self-scheduling constraint condition according to the step S2;
s4, in the interaction with the power grid, the aggregator pays the electricity purchasing cost and obtains electricity selling income; in the interaction with the user, the aggregator charges the power supply for the user and pays the charging fee for the energy storage of the user at night to the power grid; in the interaction with the power grid dispatching side, an aggregator obtains peak clipping benefits and establishes a distributed energy storage control right optimal purchase strategy and an operation dispatching objective function;
s5, establishing an optimal purchase strategy and an operation scheduling constraint condition of the distributed energy storage control right according to the step S4;
s6, solving the two-stage optimization model formed in the steps S2 and S4, determining an energy storage purchasing strategy and an operation scheduling method of the aggregator in a distributed energy storage control right trading mode, and optimizing an energy storage control right purchasing scheme; a scheduling scheme of the aggregator for user load; the cost of the users participating in the transaction reduces the situation and the economic benefits of the aggregator.
Specifically, in step S2, the user operating the self-scheduling objective function with distributed energy storage is:
Figure BDA0002160566820000031
wherein I ∈ IESSThe subscript i represents the ith user with distributed energy storage, the subscript t represents the tth time period, and the subscript d represents the d typical day; dESS、TESS、IESSRespectively representing typical days, control periods and user sets with distributed energy storage;
Figure BDA0002160566820000032
representing the energy storage capacity configured by the ith user;
Figure BDA0002160566820000033
respectively equivalent electricity purchasing and selling loads of the ith energy storage unit in the t-th time period of the d typical day;
Figure BDA0002160566820000035
respectively purchasing electricity and selling electricity prices for users from the power grid; Δ T is a single period duration;
Figure BDA0002160566820000034
represents the SOC of the stored energy at the beginning of the 1 st period; lambda [ alpha ]user,valleyIndicating the electricity purchase price of the user at night;
Figure BDA0002160566820000041
respectively charge power and discharge power; lambda [ alpha ]ESS,costTo representThe power consumption cost of energy storage charging and discharging; lambda [ alpha ]ESS,costAnd the kilowatt-hour cost of energy storage charging and discharging is shown.
Specifically, in step S3, the constraints include satisfying the power demand of the user and physical constraints of the energy storage battery, including energy storage unit equivalent power constraint, energy storage unit power balance constraint, energy storage unit SOC constraint, charge/discharge constraint, and maximum charge/discharge frequency constraint.
Further, the energy storage unit equivalent power constraint expression is as follows:
Figure BDA0002160566820000042
wherein I ∈ IESS,t∈TESS,d∈DESS，
Figure BDA0002160566820000043
Is the total equivalent load;
Figure BDA0002160566820000044
respectively predicting the load and the photovoltaic of the ith energy storage unit in the tth period of the tth typical day;
the energy storage unit power balance constraint is as follows:
Figure BDA0002160566820000045
Figure BDA0002160566820000046
wherein I ∈ IESS,t∈TESS,d∈DESS；
The energy storage unit SOC is constrained as follows:
Figure BDA0002160566820000047
in which I ∈ IESS,d∈DESS，
Figure BDA0002160566820000048
SOC, eta of i-th stored energy for t-th period of d-th typical daycha、ηdisRespectively the charge and discharge efficiency of the stored energy; setting the lower limit and the upper limit of the energy storage SOC:
Figure BDA0002160566820000049
therein, SOCmin、SOCmaxRespectively an upper limit and a lower limit of the SOC;
charge and discharge constraints
Figure BDA00021605668200000410
ai,t,d+bi,t,d≤1
Wherein, ai,t,d,bi,t,d∈{0,1}，Pcha,max、Pdis,maxMaximum charging and discharging power respectively; 0-1 variable ai,t,d、bi,t,dRespectively are charging and discharging marks, and both are 0 to indicate that the device is in an idle state;
the maximum charge-discharge times constraint is as follows:
Figure BDA0002160566820000051
wherein I ∈ IESS，
Figure BDA0002160566820000052
Respectively the maximum charging and discharging times within one month of energy storage, DTypicalNumber of typical days selected, DMonthThe total days of the month.
Specifically, in step S4, the optimization objective is to maximize the average daily gain of the aggregator in one month, where the average daily gain of the aggregator is equal to the average daily operating gain minus the average daily acquisition cost of the energy storage control right in a typical day, a first term of the objective function represents the average daily operating gain of the typical daily load aggregator, and a second term represents the average daily acquisition cost of the energy storage control right, specifically:
Figure BDA0002160566820000053
wherein, IESSIs a set of users with distributed energy storage,
Figure BDA0002160566820000057
purchase price per capacity, alpha, for energy storage control of the ith useriA decision variable of 0-1 is set, wherein 1 means that the aggregator purchases the energy storage control right of the ith user, and vice versa means that the aggregator does not purchase the energy storage control right; f3,dRepresenting the operation economic benefit of the load aggregation provider distributed user service on the d typical day; dTypicalIs the number of days of a typical day, DMonthIs the total number of days in the month.
Specifically, in step S4, in the interaction with the grid, the operation F of the aggregator is performed during the d-th typical day3,dThe expression of (a) is as follows:
Figure BDA0002160566820000054
wherein the content of the first and second substances,
Figure BDA0002160566820000055
respectively the actual electricity purchasing power and the actual electricity selling power of the load aggregator,
Figure BDA0002160566820000058
respectively the purchase price and the selling price of the electricity from the load aggregation businessman to the power grid;
Figure BDA0002160566820000056
respectively the load peak clipping amount and the unit load peak clipping benefit in the t-th period of the d-th typical day.
Further, load peak clipping
Figure BDA0002160566820000061
The correction quantity of the original load curve after the energy storage is installed is defined as:
Figure BDA0002160566820000062
wherein T ∈ TESS，d∈DESS。
Specifically, in step S5, the constraint conditions include a system-level power constraint, a storage group charging/discharging constraint, a technical constraint of the energy storage unit, and a constraint for guaranteeing user benefits.
Further, the total power balance constraint is:
Figure BDA0002160566820000063
Figure BDA0002160566820000064
Figure BDA0002160566820000065
wherein T ∈ TESS,d∈DESS，
Figure BDA0002160566820000068
For the total load under the adjustment of the aggregate quotient,
Figure BDA0002160566820000066
respectively predicting the load and the photovoltaic of the ith energy storage unit in the tth period of the tth typical day;
the charge and discharge constraints of the energy storage group are as follows:
ai,t,d+bj,t,d≤1
wherein I, j ∈ IESS,t∈TESS,d∈DESSThe subscript j also indicates the energy storage number, and energy storage in a charged state and energy storage in a discharged state cannot exist in the energy storage group at the same time.
The energy storage control authority constraint is as follows:
Figure BDA0002160566820000067
wherein I ∈ IESS，t∈TESS,DESS；
The user benefit constraint is guaranteed to be:
Figure BDA0002160566820000071
wherein I ∈ IESS，
Figure BDA0002160566820000074
Selling electricity to the user for the aggregator's price of electricity,
Figure BDA0002160566820000072
lower than the electricity price of the user directly purchasing electricity from the power grid. Lambda [ alpha ]ESS,costThe kilowatt-hour cost of energy storage charging and discharging can be calculated according to the total investment cost of energy storage, the depth of discharge, the maximum charging and discharging times, the capacity and other parametersESS,cost。
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a load aggregator-oriented distributed energy storage control right optimization scheduling method, which is characterized in that a user self-scheduling optimization model is established from the perspective of a user with distributed energy storage and with the aim of minimizing the user running cost, and the optimization result of the model provides a basis for the next analysis; then, from the perspective of a load aggregator, based on the parameters and the electricity utilization characteristics of the users who can be signed, a scheduling decision model which takes the benefit of the users as the core constraint and takes the benefit of the load aggregator to be the maximum target is established, so that the users who are suitable and equipped with distributed energy storage are selected for signing, and reasonable day-ahead scheduling arrangement is worked out; by the method, the load aggregator can optimally purchase the distributed energy storage control right and the scheduling optimization model, select reasonable users with distributed energy storage for signing, and reasonably schedule and arrange the energy storage at the user side. For the aggregator, the aggregator can gain a profit by aggregating distributed energy storage users; for the user, compared with a user-side self-scheduling model, the operation cost of the user is reduced; for the power grid, the peak clipping effect of self load reduction can be achieved through effective management and scheduling of the aggregator, and the commissioning of a new power plant is effectively delayed. The results show that the huge schedulable potential of the user side energy storage type flexible load exists, and the load aggregator converges the distributed resources of the user side to participate in the market, so that considerable benefits can be brought, and the multi-win effect is achieved.
Further, the users with distributed energy storage operate the self-scheduling objective function to minimize the total operating cost of the user i
Figure BDA0002160566820000073
The user's income and expenditure include the income of selling electricity to the grid, the cost of purchasing electricity from the grid, the cost of charging at valley time, and the loss cost of energy storage charging and discharging. The objective function is mainly used in the aspect of users, the cost of the users is reduced as much as possible, the income obtained by selling electricity to a power grid is mainly considered, and the cost is mainly considered from the cost of purchasing electricity from the power grid, the cost of charging in valley time and the loss cost of energy storage charging and discharging. The objective function shows that users tend to reduce the purchase of electricity, the charging at the valley time and the charging and discharging of stored energy to the power grid, and increase the sale of electricity to the power grid, which is consistent with the actual situation of engineering.
Furthermore, the user operation self-scheduling constraint condition with distributed energy storage mainly meets the user power consumption requirement and the physical constraint of an energy storage battery, and specifically comprises energy storage unit equivalent power constraint, energy storage unit power balance constraint, energy storage unit SOC constraint, charge and discharge constraint and maximum charge and discharge times constraint. The constraint conditions fully reflect the operating characteristics of the distributed energy storage, and the self-scheduling process of the operation of the user with the distributed energy storage can be fully reflected by matching with the objective function.
Further, the aggregator distributed energy storage control right optimal purchase strategy and the operation scheduling objective function maximize the aggregator daily average profit within one month, and the aggregator daily average profit is equal to the typical daily average operation profit minus the daily average purchase cost of the energy storage control right. The objective function is divided into two parts, one part is daily average operation income, and the other part is daily average energy storage control right acquisition cost. The objective function is mainly positioned at the angle of a load aggregator, and the income of the load aggregator is improved as much as possible so as to encourage the load aggregator to purchase energy storage control power to participate in load scheduling.
Furthermore, the aggregation business daily average operation income target mainly comprises three parts, wherein the first part is a part interacting with the power grid, the second part is a part interacting with a user, and the third part is a part interacting with the dispatching side. Specifically, the gains for the goal include the gains from selling electricity to the grid, the gains from powering the consumer load, and the peak clipping gains, with the costs including the cost of purchasing electricity from the grid, and the cost of charging during the off-peak hours. The goal fully reflects the main source of the aggregator profit and embodies the superiority of the distributed energy storage control right trading mode.
Further, the aggregator distributed energy storage control right optimal acquisition strategy and operation scheduling constraint conditions comprise system-level power constraint, energy storage group charging and discharging constraint, energy storage unit technical constraint and constraint for guaranteeing user benefits. These constraints represent, on the one hand, the physical characteristics of the power system and the distributed energy storage, and, on the other hand, ensure that both the users and the load aggregators can profit therefrom. Through the mode of the optimal acquisition strategy and the operation scheduling of the distributed energy storage control right of the aggregator, the benefits of the user and the benefits of the load aggregator are improved, and pareto improvement is achieved.
In summary, the invention provides a load aggregator-oriented distributed energy storage control right optimized scheduling method, which takes the benefits of both the user and the load aggregator into account, can reduce the energy use cost of the user, can increase the income of the load aggregator, and simultaneously improves the utilization rate of distributed energy storage, reduces the peak-valley difference of a power system, and contributes to social benefits to a certain extent.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a diagram of a user self-optimizing scheduling result for a typical day;
FIG. 2 shows the aggregate quotient dispatch results and load curve improvement over a typical day;
FIG. 3 is a cost-benefit analysis of an aggregator run over a typical day;
FIG. 4 is a comparison of user costs before and after participation in aggregator scheduling;
fig. 5 is a two-stage optimization problem of optimal purchase and scheduling of distributed energy storage in the energy storage control right transaction mode.
Detailed Description
The invention provides a load aggregator-oriented distributed energy storage control right optimal scheduling method. Then, from the perspective of the load aggregator, based on the parameters and electricity utilization characteristics of the users who can be signed, a scheduling decision model which takes the benefit of the users as the core constraint and takes the benefit of the load aggregator as the maximum target is established, so that the users who are suitable for being equipped with the distributed energy storage are selected for signing, and reasonable day-ahead scheduling arrangement is worked out.
The invention relates to a distributed energy storage control right optimal scheduling method facing a load aggregator, which comprises the following steps:
s1, obtaining system historical data from related departments, obtaining equipment states and information from a user side through a remote communication system, and performing load prediction and new energy output prediction based on an advanced prediction technology;
when the model provided by the invention is applied, required data needs to be acquired from related departments at first. The input data of the calculation model obtained from the related department comprises the following data, the system historical data is obtained from the related department, the equipment state and information are obtained from the user side through a remote communication system, and the load prediction and the new energy output prediction are carried out based on the advanced prediction technology.
S2, establishing a user operation self-scheduling objective function with distributed energy storage;
user operation self-scheduling optimization target with distributed energy storage to minimize total operation cost of user i
Figure BDA0002160566820000101
The user's income and expenditure include the income of selling electricity to the grid, the cost of purchasing electricity from the grid, the cost of charging at valley time, and the loss cost of energy storage charging and discharging.
Figure BDA0002160566820000102
In which I ∈ IESSThe subscript i indicates the ith user with distributed energy storage, the subscript t indicates the tth time period, and the subscript d indicates the d typical day. DESS、TESS、IESSRespectively representing a typical day, a control period and a set of users equipped with distributed energy storage.
Figure BDA0002160566820000106
Indicating the energy storage capacity, kWh, configured by the ith user.
Figure BDA0002160566820000103
Respectively is the equivalent electricity purchasing load of the ith energy storage unit in the tth period of the tth typical day.
Figure BDA0002160566820000107
The electricity prices of the electricity purchased and sold from the power grid are respectively provided for the users. Δ T is a single period duration.
Figure BDA0002160566820000104
Represents the SOC of the energy stored at the beginning of the 1 st period. Lambda [ alpha ]user,valleyAnd indicating the electricity purchase price of the user at night.
Figure BDA0002160566820000105
Respectively charging and dischargingAnd (4) power. Lambda [ alpha ]ESS,costAnd the kilowatt-hour cost of energy storage charging and discharging is shown. The kilowatt-hour cost lambda of energy storage charging and discharging can be converted from the parameters of total investment cost, depth of discharge, maximum charging and discharging times, capacity and the like of energy storageESS,cost。
S3, establishing a self-scheduling constraint condition of the user;
the constraint mainly comprises the steps of meeting the power consumption requirement of a user and the physical constraint of an energy storage battery, including the equivalent power constraint of an energy storage unit, the power balance constraint of the energy storage unit, the SOC constraint of the energy storage unit, the charge-discharge constraint and the maximum charge-discharge frequency constraint;
1) energy storage unit equivalent power constraint
The energy storage unit has rigid electricity load, distributed photovoltaic output, energy storage charging and discharging load, and the overall power expression is as follows:
Figure BDA0002160566820000111
in which I ∈ IESS,t∈TESS,d∈DESS，
Figure BDA0002160566820000112
Is the total equivalent load.
Figure BDA0002160566820000113
Respectively the predicted load and the photovoltaic of the ith energy storage unit in the tth period of the tth typical day.
2) Energy storage unit power balance constraint
Figure BDA0002160566820000114
Wherein I ∈ IESS,t∈TESS,d∈DESSThe equation (3) shows that if the total equivalent load is applied
Figure BDA0002160566820000115
If the value is positive, the energy storage unit purchases electricity from the power grid, including
Figure BDA0002160566820000116
If total equivalent load
Figure BDA0002160566820000117
If the value is negative, the energy storage unit sells electricity to the power grid, including
Figure BDA0002160566820000118
3) Energy storage unit SOC constraint
The SOC of the energy storage at the end of the t-th time period is related to the SOC at the end of the previous time period, the charge and discharge power of the current time period, the charge and discharge efficiency and other factors, and the expression of the SOC along with the time is as follows:
Figure BDA0002160566820000119
in which I ∈ IESS，d∈DESS，
Figure BDA00021605668200001110
SOC, eta of i-th stored energy for t-th period of d-th typical daycha、ηdisRespectively the charge-discharge efficiency of stored energy.
Further, in order to prevent the battery from aging (capacity fade) due to excessive charge and discharge, the lower limit and the upper limit of the energy storage SOC are set:
Figure BDA0002160566820000121
in which I ∈ IESS,t∈TESS,d∈DESS，SOCmin、SOCmaxRespectively, the upper and lower limits of the SOC.
4) Charge and discharge constraints
Figure BDA0002160566820000122
ai,t,d+bi,t,d≤1 (7)
Wherein, ai,t,d,bi,t,d∈{0,1}，i∈IESS,t∈TESS,d∈DESS，Pcha,max、Pdis,maxThe maximum charge and discharge power is respectively. 0-1 variable ai,t,d、bi,t,dRespectively, charge and discharge flags, each of which is 0, indicate an idle state. Equation (7) is a charge-discharge mutual exclusion constraint, which indicates that the stored energy cannot be charged and discharged simultaneously.
5) Maximum charge-discharge frequency constraint
In order to reduce the energy storage start and stop loss, set up the maximum charge-discharge number of times restraint of energy storage this month, guarantee that the energy storage does not carry out frequent charge-discharge operation to reduce the loss cost of energy storage:
Figure BDA0002160566820000123
in which I ∈ IESS，
Figure BDA0002160566820000124
Respectively the maximum charging and discharging times within one month of energy storage, DTypicalNumber of typical days selected, DMonthThe total days of the month.
S4, establishing a distributed energy storage control right optimal purchase strategy and an operation scheduling objective function;
in the interaction with the power grid, the aggregator pays the electricity purchasing cost and obtains electricity selling benefits; in the interaction with the user, the aggregator charges the power supply for the user and pays the charging fee for the energy storage of the user at night to the power grid; in the interaction with the power grid dispatching side, an aggregator obtains peak clipping benefits;
the optimization goal is to maximize the average daily gain of the aggregator within one month, wherein the average daily gain of the aggregator is equal to the average daily operating gain minus the average daily acquisition cost of the energy storage control right within a typical day:
Figure BDA0002160566820000131
in the formula IESSIs a set of users with distributed energy storage,
Figure BDA0002160566820000136
purchase price per capacity, alpha, for energy storage control of the ith useriA decision variable of 0-1, with 1 indicating that the aggregator purchased energy storage control for the ith user, and vice versa indicating no purchase. F3,dRepresenting the operating economic benefit of the load aggregator distributed user service on the day d of typical day. DTypicalIs the number of days of a typical day, DMonthIs the total number of days in the month. The first term of the objective function represents the average daily operation income of a typical daily load aggregator, and the second term represents the average daily acquisition cost of the energy storage control right.
In the interaction with the power grid, the aggregator pays the electricity purchasing cost and obtains electricity selling benefits; in the interaction with the user, the aggregator charges the power supply for the user and pays the charging fee for the energy storage of the user at night to the power grid; in the interaction with the power grid dispatching side, the aggregator obtains peak clipping benefits. Thus, the aggregator's run F during the d's typical day3,dThe expression of (a) is as follows:
Figure BDA0002160566820000132
in the formula (I), the compound is shown in the specification,
Figure BDA0002160566820000133
respectively the actual electricity purchasing power and the actual electricity selling power of the load aggregator,
Figure BDA0002160566820000137
the prices of electricity purchase and electricity sale from the load aggregation company to the power grid are respectively.
Figure BDA0002160566820000134
Respectively the load peak clipping amount and unit load peak clipping benefit (only peak clipping income exists in peak period, and the peak clipping income in other periods is0). The expression (10) gives an expression of the operation profit of the aggregator, and the operation profit is-the cost of buying electricity from the power grid + selling electricity to the power grid profit + selling electricity to the user profit-the cost of charging the stored energy at valley time + peak clipping profit.
At F3,dIn the expression of (1), load peak clipping amount
Figure BDA0002160566820000135
The correction quantity of the original load curve after the energy storage is installed is defined as:
Figure BDA0002160566820000141
wherein T ∈ TESS，d∈DESS。
S5, establishing a distributed energy storage control right optimal purchase strategy and an operation scheduling constraint condition;
the constraint conditions in the distributed energy storage control right optimal purchase strategy and the operation scheduling comprise system-level power constraint, technical constraint of the energy storage unit and constraint for ensuring user benefits;
1) total power balance constraint
Figure BDA0002160566820000142
Figure BDA0002160566820000143
In the formula, T is equal to TESS，d∈DESS，
Figure BDA0002160566820000144
For the total load under the adjustment of the aggregate quotient,
Figure BDA0002160566820000145
respectively the predicted load and the photovoltaic of the ith energy storage unit in the tth period of the tth typical day. Formulas (12) and (13) give the aggregator the purchase and sale of electricity to the external gridAnd (5) expressing.
2) The SOC constraint, the charge-discharge constraint and the maximum charge-discharge frequency constraint of the energy storage unit are the same as those of the user under the self-scheduling condition and are expressed by formulas (4) - (6) and (8).
Rewriting the single energy storage charge-discharge constraint of equation (7) into an energy storage group charge-discharge constraint:
ai,t,d+bj,t,d≤1 (14)
wherein I, j ∈ IESS,t∈TESS,d∈DESSThe subscript j also represents the energy storage number, and formula (16) represents ai,tAnd bj,tAt least one of the energy storage groups is 0, and the energy storage in a charging state and the energy storage in a discharging state cannot exist in the energy storage group at the same time.
3) Energy storage control authority constraint
Figure BDA0002160566820000146
Wherein I ∈ IESS,t∈TESS,DESSThe formula (15) shows that only when α isiAnd 1, the energy storage control right of the user is purchased and then the charging and discharging operation can be carried out.
4) Securing user interest constraints
In a typical day, the total cost of the user under the model framework is not higher than the total cost of the user under the self-optimization operation
Figure BDA0002160566820000152
And obtaining the scheduling model by the previous section of the user self-optimization. The user cost and the profit under the model framework comprise the cost of purchasing electricity from the aggregator by the user, the charging and discharging loss cost of energy storage of the user and the profit of selling the energy storage control right obtained from the aggregator.
Figure BDA0002160566820000151
In which I ∈ IESS，
Figure BDA0002160566820000153
Selling electricity to the user for the aggregator's price of electricity,
Figure BDA0002160566820000154
lower than the electricity price of the user directly purchasing electricity from the power grid. Lambda [ alpha ]ESS,costThe kilowatt-hour cost of energy storage charging and discharging can be calculated according to the total investment cost of energy storage, the depth of discharge, the maximum charging and discharging times, the capacity and other parametersESS,cost. The formula (16) ensures that the benefit of the users participating in the contract signing is not damaged, the first item on the left side of the inequality is the total electricity cost paid to the aggregator by the user i on the d day under the model framework, the second item is the energy storage charging and discharging cost, the third item is the income obtained by selling the energy storage control right, and the income is converted into the average daily income in the current month from the total income in the typical day.
And S6, solving the two-stage optimization model formed in the previous step. The energy storage purchasing strategy and the operation scheduling method of the aggregator in the distributed energy storage control right transaction mode can be determined.
The optimization result comprises the following steps:
1. a purchase scheme of the energy storage control right;
2. a scheduling scheme of the aggregator for user load;
3. (ii) a user cost reduction for participating in the transaction;
4. economic benefits to the aggregator.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in typical day 3, the distributed energy storage is charged in a period of 16: 00-18: 00 when the electricity price is low, and partial power supply of the load of the user is maintained through discharging in a period of 12: 00-15: 00 and 18: 00-21: 00 when the electricity price is high, so that the electric quantity purchased from the power grid in a peak period is reduced, and the electricity consumption cost of the user is effectively reduced.
Referring to fig. 2 and fig. 3, the scheduling result of the typical day 1 is selected from the scheduling results for display. In the legend, "sell electricity" means that the aggregator sells electricity to the power grid, "buy electricity" means that the aggregator purchases electricity from the power grid, "discharge" means energy storage discharge, "charge" means energy storage charge, "and" corrected load "means that the load value presented by the user unit to the outside is the sum of the distributed energy storage charge and discharge amount and the rigid load amount.
In general, although the electricity utilization of each user lacks regularity, the total net load curve of the user group does not change greatly, so the four typical daily scheduling results are similar. The overall guiding effect of the electrovalence curve on the charging and discharging of the stored energy is obvious:
storing energy for charging in a 16: 00-18: 00 time period with lower electricity price;
and the stored energy is discharged in the periods of 12: 00-15: 00 and 18: 00-21: 00 with high electricity price and peak clipping subsidies.
The load before correction is different on four typical days, the corrected load has a certain reduction amplitude in the time period of 12: 00-15: 00 and 18: 00-21: 00, and the load is increased in the time period of 16: 00-18: 00, and the change trend is caused by the peak shifting and valley filling effect of energy storage.
In the peak load period of the power grid of 12: 00-15: 00 and 18: 00-21: 00, the aggregator reduces about 15% of the self load through optimized scheduling.
As can be seen from fig. 2 to 3, due to the limited capacity of the distributed energy storage, the discharged amount of the stored energy is fully used for supplying the electricity demand of the users in the dispatching area of the aggregation provider, and no surplus amount of electricity is used for feeding the power grid. The load aggregator cannot profit from selling electricity to the grid. In the period of 16: 00-18: 00, the net income of the load aggregators is low or even negative, because the load aggregators obtain certain income by supplying power to the users, but the users have low power consumption and low electricity price and need to charge the stored energy in advance. And in the time periods of 12: 00-15: 00 and 18: 00-21: 00, the net income of the load aggregator is higher, because the user has large electricity consumption and high electricity price, the aggregator can supply part of electricity loads through energy storage and discharge, and the peak clipping subsidy of the power grid can be obtained in the peak time period.
Referring to fig. 4, after model optimization decision, the aggregator selects 16 users for subscription control. The benefit analysis of all users participating in the market is shown in fig. 4, where the cost of the non-subscribing user is 0. It can be seen from the figure that the user cost can be saved by more than 15% after the user participates in the scheduling of the aggregator, because the electricity price of the user is reduced after the user participates in the scheduling mode of the aggregator, and the user can obtain certain benefit for the aggregator by selling the energy storage control right. Wherein the cost savings for subscriber 6 is as high as 46% due to the high peak and average power usage levels of the electrical load of subscriber 6 and the high peak distributed energy storage to power usage ratio for the subscriber. This shows that in the long term, the user installs a certain amount of distributed energy storage and participates in the aggregator scheduling, which is beneficial to the user to save cost.
The numbers of the class 4 users without the contract are 1, 12, 14 and 15, which are due to the reasons that the class 4 users have low load peak value, low energy storage capacity and load peak value ratio, high price quotation of the energy storage control right and the like, the cost of the users cannot be reduced after the users participate in the energy storage control right transaction mode, or the aggregators cannot obtain economic benefits by purchasing the energy storage control right of the users.
Referring to fig. 5, fig. 5 shows a two-stage scheduling process of the distributed energy storage optimal purchasing and scheduling method in the energy storage control right transaction mode. In the stage 1, the user self-scheduling problem optimization is carried out by taking the minimized user cost as an optimization target; and in the 2 nd stage, performing optimal acquisition of the energy storage control right and energy storage scheduling optimization by taking the maximum income of the aggregator as a target, implementing energy storage optimization scheduling on the basis of selecting a proper user for energy storage control right acquisition, and ensuring that the operating cost of the signed user is lower than that of the user in the 1 st stage. The user can damage the benefit of the user when the price for selling the energy storage control right is too low, and the aggregator can not obtain the benefit when the price is too high; and due to the energy storage and load parameters, the operation cost of each individual user after the individual user participates in the optimal scheduling of the energy storage load group may be increased or the benefit of the aggregator will be damaged, so that the optimal acquisition problem of the energy storage control right exists.
In summary, by the method provided by the present invention, the load aggregator can optimally purchase the distributed energy storage control right and the scheduling optimization model to select a reasonable user with distributed energy storage for subscription, and perform reasonable scheduling arrangement on the user-side energy storage. For the aggregator, the aggregator can gain a profit by aggregating distributed energy storage users; for the user, compared with a user-side self-scheduling model, the operation cost of the user is reduced; for the power grid, the peak clipping effect of self load reduction can be achieved through effective management and scheduling of the aggregator, and the commissioning of a new power plant is effectively delayed. The results show that the huge schedulable potential of the user side energy storage type flexible load exists, and the load aggregator converges the distributed resources of the user side to participate in the market, so that considerable benefits can be brought, and the multi-win effect is achieved.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (7)

1. A distributed energy storage control right optimization scheduling method facing a load aggregator is characterized by comprising the following steps:
s1, obtaining system historical data, obtaining equipment state and information from a user side, and performing load prediction and new energy output prediction;
s2, establishing a self-scheduling objective function of user operation with distributed energy storage, wherein the optimization objective of the user operation self-scheduling objective function is to minimize the total operation cost of the user, the user income comprises the income of selling electricity to a power grid, the user expenditure comprises the cost of purchasing electricity from the power grid, the cost of charging at valley time and the loss cost of energy storage charging and discharging, and the user operation self-scheduling objective function with distributed energy storage is as follows:
Figure FDA0003508364460000011
wherein the content of the first and second substances,
Figure FDA0003508364460000012
for the total running cost of the ith user, I belongs to IESSThe subscript i represents the ith user with distributed energy storage, the subscript t represents the tth time period, and the subscript d represents the d typical day; dESS、TESS、IESSRespectively representing a user set of a typical day, a user set of a control period and a user set with distributed energy storage;
Figure FDA0003508364460000013
representing the energy storage capacity configured by the ith user;
Figure FDA0003508364460000014
respectively equivalent electricity purchasing load and electricity selling load of the ith energy storage unit in the tth period of the tth typical day;
Figure FDA0003508364460000015
respectively purchasing electricity and selling electricity prices for users from the power grid; Δ T is a single period duration;
Figure FDA0003508364460000016
representing the SOC of the stored energy at the beginning of the ith period; lambda [ alpha ]user,valleyIndicating the electricity purchase price of the user at night;
Figure FDA0003508364460000017
respectively charge power and discharge power; lambda [ alpha ]ESS,costRepresenting the power consumption cost of energy storage charging and discharging;
s3, establishing user self-scheduling constraint conditions according to the step S2, wherein the constraint conditions comprise the requirement for power utilization of the user and physical constraints of an energy storage battery, including equivalent power constraint of an energy storage unit, power balance constraint of the energy storage unit, SOC constraint of the energy storage unit, charging and discharging constraint and maximum charging and discharging times constraint;
s4, in the interaction with the power grid, the aggregator pays the electricity purchasing cost and obtains electricity selling income; in the interaction with the user, the aggregator charges the power supply for the user and pays the charging fee for the energy storage of the user at night to the power grid; in the interaction with the power grid dispatching side, an aggregator obtains peak clipping benefits and establishes a distributed energy storage control right optimal purchase strategy and an operation dispatching objective function;
s5, establishing an optimal purchase strategy and an operation scheduling constraint condition of the distributed energy storage control right according to the step S4;
and S6, solving the two-stage optimization model of the steps S2 and S4, and finishing user energy storage dispatching and power grid interaction optimization by the load aggregators.
2. The load aggregator-oriented distributed energy storage control right optimization scheduling method according to claim 1, wherein in step S3, the energy storage unit equivalent power constraint expression is as follows:
Figure FDA0003508364460000021
wherein I ∈ IESS,t∈TESS,d∈DESS，
Figure FDA0003508364460000022
Is the total equivalent load;
Figure FDA0003508364460000023
respectively the ith of the t-th period of the d-th typical dayThe predicted load and the photovoltaic of the energy storage unit;
the energy storage unit power balance constraint is as follows:
Figure FDA0003508364460000024
Figure FDA0003508364460000025
wherein I ∈ IESS,t∈TESS,d∈DESS；
The energy storage unit SOC is constrained as follows:
Figure FDA0003508364460000026
in the formula (I), the compound is shown in the specification,
Figure FDA0003508364460000027
for the ith SOC storing energy in the t-1 th period on the d-th typical day, I ∈ IESS,d∈DESS，
Figure FDA0003508364460000028
SOC, eta of i-th stored energy for t-th period of d-th typical daycha、ηdisRespectively the charge and discharge efficiency of the stored energy; setting the lower limit and the upper limit of the energy storage SOC:
Figure FDA0003508364460000029
therein, SOCmin、SOCmaxRespectively an upper limit and a lower limit of the SOC;
charge and discharge constraints
Figure FDA00035083644600000210
ai,t,d+bi,t,d≤1
Wherein, ai,t,d,bi,t,d∈{0,1}，Pcha,max、Pdis,maxMaximum charging and discharging power respectively; 0-1 variable ai,t,d、bi,t,dRespectively, charge and discharge marks, 0 indicates that the device is in an idle state;
the maximum charge-discharge times constraint is as follows:
Figure FDA0003508364460000031
wherein I ∈ IESS，
Figure FDA0003508364460000032
Respectively the maximum charging and discharging times within one month of energy storage, DTypicalNumber of days of typical day, DMonthIs the total number of days in the month.
3. The load aggregator-oriented distributed energy storage control right optimization scheduling method according to claim 1, wherein in step S4, the optimization objective is to maximize the aggregator daily average profit within one month, the aggregator daily average profit is equal to the daily average operating profit minus the daily average purchase cost of the energy storage control right in a typical day, a first term of the objective function represents the daily average operating profit of the typical day load aggregator, and a second term represents the daily average purchase cost of the energy storage control right, specifically:
Figure FDA0003508364460000033
wherein, IESSIs a set of users with distributed energy storage,
Figure FDA0003508364460000034
purchase price per capacity, alpha, for energy storage control of the ith useriIs a 0-1 decision variableA value of 1 indicates that the aggregator purchased energy storage control rights of the ith user, and vice versa indicates that no purchase was made; f3,dRepresenting the operation economic benefit of the load aggregation provider distributed user service on the d typical day; dTypicalIs the number of days of a typical day, DMonthIs the total number of days in the month.
4. The load aggregator-oriented distributed energy storage control right optimized scheduling method as claimed in claim 1, wherein in step S4, in the interaction with the grid, the aggregator operates in the d typical day, i.e. operation F3,dThe expression of (a) is as follows:
Figure FDA0003508364460000035
d∈DESS
wherein, the Delta T is the time length of a single time interval,
Figure FDA0003508364460000036
price of electricity, alpha, for the aggregator selling electricity to the customeriIs a decision variable from 0 to 1 and is,
Figure FDA0003508364460000037
Figure FDA0003508364460000038
respectively predicting the load and the photovoltaic of the ith energy storage unit in the tth period of the tth typical day; lambda [ alpha ]valleyFor the aggregator to purchase electricity at night,
Figure FDA0003508364460000039
the energy storage capacity configured for the ith user,
Figure FDA00035083644600000310
the SOC for the initial energy storage of the 1 st period,
Figure FDA00035083644600000311
Figure FDA00035083644600000312
respectively the actual electricity purchasing power and the actual electricity selling power of the load aggregator,
Figure FDA00035083644600000313
respectively the purchase price and the selling price of the electricity from the load aggregation businessman to the power grid;
Figure FDA0003508364460000041
respectively the load peak clipping amount and the unit load peak clipping benefit in the t-th period of the d-th typical day.
5. The load aggregator-oriented distributed energy storage control right optimal scheduling method according to claim 4, wherein load peak clipping amount
Figure FDA0003508364460000042
The correction quantity of the original load curve after the energy storage is installed is defined as:
Figure FDA0003508364460000043
wherein the content of the first and second substances,
Figure FDA0003508364460000044
charging and discharging power of stored energy respectively, T ∈ TESS，d∈DESS。
6. The load aggregator-oriented distributed energy storage control right optimization scheduling method according to claim 1, wherein in step S5, the constraint conditions include system-level power constraint, energy storage group charging and discharging constraint, energy storage unit technical constraint and constraint for guaranteeing user benefits.
7. The load aggregator-oriented distributed energy storage control right optimal scheduling method according to claim 6, wherein the total power balance constraint is:
Figure FDA0003508364460000045
Figure FDA0003508364460000046
Figure FDA0003508364460000047
wherein alpha isiIs a decision variable from 0 to 1 and is,
Figure FDA0003508364460000048
respectively the charging power and the discharging power of the stored energy,
Figure FDA0003508364460000049
respectively the actual electricity purchasing power and the actual electricity selling power of the load aggregator,
Figure FDA00035083644600000410
for the total load under the regulation of the polymerization quotient, T ∈ TESS,d∈DESS，
Figure FDA00035083644600000411
Respectively predicting the load and the photovoltaic of the ith energy storage unit in the tth period of the tth typical day;
the charge and discharge constraints of the energy storage group are as follows:
ai,t,d+bj,t,d≤1
wherein, ai,t,d、bj,t,dRespectively charge and discharge marks, I, j ∈ IESS,t∈TESS,d∈DESSThe subscript j also indicates the number of stored energy, and the stored energy in the charged state and the stored energy in the stored energy group cannot exist simultaneouslyAn energy storage in a discharge state;
the energy storage control authority constraint is as follows:
Figure FDA0003508364460000051
wherein I ∈ IESS，t∈TESS,DESS；
The user benefit constraint is guaranteed to be:
Figure FDA0003508364460000052
wherein the content of the first and second substances,
Figure FDA0003508364460000053
energy storage capacity configured for the ith user, DTypicalNumber of days of typical day, DMonthThe total number of days in the month is,
Figure FDA0003508364460000054
for the total running cost of the ith user, I belongs to IESS，
Figure FDA0003508364460000055
Selling electricity to the user for the aggregator's price of electricity,
Figure FDA0003508364460000056
lower than the electricity price, lambda, of the user purchasing electricity directly from the gridESS,costThe kilowatt-hour cost of energy storage charging and discharging can be calculated according to the total investment cost of energy storage, the depth of discharge, the maximum charging and discharging times, the capacity and other parametersESS,cost。
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