CN113807554A - Load aggregator energy optimization method and device based on spot mode - Google Patents
Load aggregator energy optimization method and device based on spot mode Download PDFInfo
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Abstract
The invention discloses a load aggregator energy optimization method and device based on a spot mode, which comprises the following steps: step 1: constructing an energy distribution model; acquiring historical operating data in the power transaction process of the energy distribution model, and predicting the charging load of a load aggregator; step 2: establishing an objective function and monthly purchased and sold electric quantity balance, energy storage terminal load aggregation in each time period, energy storage discharge capacity and energy storage operation constraint conditions in a pre-monthly energy distribution model, and solving the objective function by taking the acquired monthly highest profit as an objective; and step 3: and constructing an objective function and time dimension purchase and sale electric quantity, energy storage and discharge quantity, energy storage capacity, charging terminal aggregate load in each time interval and energy storage operation constraint conditions in a day-ahead energy distribution model, and solving the objective function with the highest daily profit as an objective. The load aggregator reduces the influence of medium-long term random fluctuation on day-ahead optimization by adopting month-day-ahead rolling optimization.
Description
Technical Field
The invention belongs to the field of electric power spot transaction, and particularly relates to a load aggregator energy optimization method and device based on a spot mode.
Background
By 6 months in 2018, the installed wind power capacity in China exceeds 1.7 multiplied by 108And kW is high in randomness of wind power, and power of a power grid fluctuates after power generation and grid connection. The load aggregator can aggregate medium and small-sized users with certain load response capacity, provide better load response characteristics than single users, and particularly have superiority in the electric power spot market. Meanwhile, the charging behavior of the electric automobile has certain randomness and dispersity, and the disordered access of a large number of electric automobiles can aggravate the peak-valley difference of the load of the power grid and is not beneficial to the stable operation of the power grid. How to scale up the polymerization of electric vehicles and energy storage,the method is a problem to be solved urgently in coordination with wind power consumption and electric power market transaction.
In addition, the load aggregator is an independently operated economic entity, the operation target is necessarily the maximization of the benefit of the load aggregator, part of the profit is derived from the charge cost paid by the electric vehicle user and the price difference of the load aggregator for participating in market-oriented transaction, and the other part of the profit is the arbitrage of the price difference of the stored energy in different periods. At present, various flexible loads operate according to respective behavior characteristics, not only are the various flexible loads built lack of cooperative control, but also the various flexible loads do not cooperate with the market transaction in the electric power spot market environment for electricity purchasing and selling, and the resource value of the various flexible loads is not exerted. The patent researches the operation profit of the load aggregator from multiple areas and multiple time scales, carries out optimization control on aggregate load and energy storage of each area of the electric automobile, and realizes power purchasing and selling cooperation and energy optimization distribution of various loads. The method can be applied to energy distribution work after the electricity purchase transaction of the electric power market of the load aggregator, reduces electricity consumption deviation, and improves the operation characteristics of the power grid and the operation profit of the load aggregator.
2 techniques similar to the present invention:
(1) li chun swallow et al, university of Chongqing, published a load aggregator economic dispatch method that accounts for demand response flexibility and uncertainty (201910985269.2). The method mainly relates to the following modules:
firstly, a multi-period scheduling method is adopted to arrange a scheduling plan so as to realize the power purchasing and selling balance.
Secondly, the benefits of the day-ahead scheduling of the aggregator come from the profit of selling the flexibility of the load to the power grid after the penalty of incapable of accepting new energy is deducted;
and thirdly, considering the demand response flexibility and uncertainty of the load aggregator economic dispatching method, and establishing an aggregator-oriented non-cooperative game day-ahead economic dispatching model.
Fourthly, considering the influence of the default of the user on the real-time scheduling, specifically: and (5) according to default electric quantity xi of the user, the electric quantity xi is subjected to truncation normal distribution.
Fifth, the aggregator schedules with a maximum revenue target based on accounting for user violations.
(2) Forest, et al, at north china power university, discloses a load scheduling model (201810013993.4) in a load aggregator-wind farm collaborative operation mode. The method mainly relates to the following modules:
first, the flexibility of the load is determined for the power load governed by the load aggregator.
And secondly, establishing a cooperative operation mode of the load aggregators and the wind power plant.
And thirdly, establishing a day-ahead optimization scheduling model by the load aggregator according to the wind power plant consumption requirement and the maximum profit target.
Disclosure of Invention
Aiming at the problems, the invention provides a load aggregator energy optimization method and device based on a spot mode.
The technical purpose is achieved, the technical effect is achieved, and the invention is realized through the following technical scheme:
a load aggregator energy optimization method based on an off-the-shelf mode comprises the following steps:
step 1: constructing an energy distribution model; acquiring historical operating data in the power transaction process of the energy distribution model, and predicting the charging load of a load aggregator;
step 2: constructing an objective function in a pre-month energy distribution model, and solving the objective function by taking the monthly highest profit as an objective, wherein the objective function is balanced with monthly purchased and sold electric quantity, monthly charging load and stored energy discharge capacity are not transmitted in a power distribution network, and the constraint conditions of stored energy running characteristics are established;
and step 3: and constructing an objective function and time dimension purchase and sale electric quantity, energy storage and discharge quantity, energy storage capacity, day-time charging load and energy storage operating characteristic constraint conditions in a day-ahead energy distribution model, and solving the objective function with the highest daily profit as an objective.
As a further improvement of the present invention, step 1 predicts the charging load of the load aggregator based on the constructed platform charging terminal load aggregation model, and the constructed model formula is as follows:
in the formula, PtjRepresents the charging load of the charging terminal j at time t;
indicating the charging state of the charging terminal, 0 is uncharged or charged, and 1 is charging. PEtiRepresenting the aggregate charging load of the charging terminals in the platform charging station i; pEtRepresenting the charging load of the load aggregator at time t; n represents the number of the station areas.
As a further improvement of the invention, the energy storage operation characteristic is predicted by constructing an energy storage load model, and the constructed energy storage load model comprises the following formula:
soct,i＝soc(t-1),i+Pt,i
-Pt,i≤Pt,i≤Pt,i
wherein the formula is as follows: soct，iAnd soc(t-1)，iRespectively representing the residual capacity of the stored energy i at the time t and the time t-1; pt，iThe charging and discharging power of the energy storage i is represented, the charging is represented when the value is positive, and the discharging is represented when the value is negative;
and
representing the upper and lower capacity limits of the stored energy i.
As a further improvement of the invention, the energy distribution model before the month in the step 2 is as follows:
the load aggregator obtains profit in medium and long term transaction, and the objective function is as follows:
the monthly power purchase and sale balance constraint is as follows:
the monthly charging load constraints are:
the discharge capacity of the stored energy does not transmit constraints in the distribution network:
-PDit-PEit＜0；
and energy storage operating characteristic constraints.
As a further improvement of the present invention, the day-ahead energy distribution model constructed in step 3 is as follows:
profit for the day-ahead transaction, the objective function is:
the time dimension purchasing and selling electric quantity balance constraint is as follows:
PEti(zcq)+PEti(rq)+PDti(zcq)+PDti(rq)≥0；
the energy storage discharge capacity constraint is as follows:
PEti(zcq)+PEti(rq)+PDti(zcq)+PDti(rq)≥0；
the energy storage capacity constraint is:
PDimin≤PDti(zcq)+PDti(rq)≤PDimax
the charge load constraint in the day is:
and energy storage operating characteristic constraints.
As a further improvement of the invention, a linear programming method is adopted for the profit objective of medium and long term transaction or the profit objective function of the prior transaction, and a linear function in MATLAB is utilized for programming solution.
As a further refinement of the present invention, the determining of the monthly charge load threshold range includes obtaining a maximum value and a minimum value of the monthly charge load from the historical data and predicting the threshold range in the monthly charge load constraint using an average growth rate algorithm based on the data obtained on a daily basis.
As a further improvement of the method, the method also comprises the step of compounding the optimization result obtained by the pre-menstruation energy distribution model analysis and the optimization result obtained by the day-ahead energy distribution model to obtain a final optimization distribution scheme.
As a further improvement of the invention, the constructed energy distribution model comprises the load aggregators and the energy storage terminals and the load terminals which are used for carrying out electric power transaction with the load aggregators.
The invention also provides a load aggregator energy optimization device based on the spot mode, which comprises:
the energy distribution model is configured to obtain historical operation data in the electric power transaction process of the energy distribution model and predict the charging load of the load aggregator;
the system comprises a monthly energy distribution model, a monthly energy distribution model and a monthly operation characteristic constraint condition, wherein the monthly energy distribution model is configured to construct a monthly energy distribution objective function, monthly purchase and sale electric quantity balance, monthly charging load, energy storage and discharge capacity are not transmitted in a power distribution network, and the energy storage and operation characteristic constraint condition is solved to obtain monthly maximum profit as an objective solution objective function;
a day-ahead energy distribution model configured to construct constraint conditions including a day-ahead energy distribution objective function and time dimension purchase and sale electric quantity, energy storage and discharge quantity, energy storage capacity, day-time charging load and energy storage operation characteristic, and solve the objective function with the highest daily profit as the target
The invention has the beneficial effects that: the method provided by the invention optimizes the load characteristics of the power grid and reduces the peak-valley difference by optimizing and distributing the operation loads of the charging stations and the stored energy of the distribution area in a large scale and cooperating with the electric power market transaction. And establishing a cost model, and carrying out economic optimization distribution on various loads by a load aggregator under the condition of ensuring various constraint conditions. The load aggregation business before month-before day rolling optimization reduces the influence of medium-long term random fluctuation on the day-before optimization.
Drawings
Fig. 1 is a schematic structural diagram of an energy distribution module;
FIG. 2 is a diagram of a spot settlement model;
FIG. 3 is a flow chart of the energy optimization method of the present invention;
FIG. 4 is an optimized curve of an electric vehicle and stored energy before month;
FIG. 5 is an optimized curve of a day-ahead electric vehicle and stored energy.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Construction energy distribution module
The energy distribution module constructed by the invention comprises a load aggregator, and each energy storage terminal and each load terminal transacting with the load aggregator, wherein each terminal is connected through network communication equipment to transact electric energy and transmit data as shown in figure 1. In an embodiment shown in fig. 1, the energy storage terminal comprises various green electric energy sources participating in a transaction, and the energy storage terminal is exemplified by an electric pile arranged in a platform area and used for charging an electric vehicle, and a vehicle network system established based on the energy storage terminal.
Two hypothesis Condition
The market main body forms a next-month power utilization plan through power medium-term and long-term trading according to market trading participated by each province. When the spot market is completely released, the spot price fluctuates due to the supply and demand conditions, the power grid structure and the blocking condition of each node. In the current spot market, the deviation between the actual power load at a certain time interval and the spot traffic load before the day is checked according to the real-time spot price in the day, so that the technical scheme of the invention is completed on the premise of the following three assumed conditions.
2.1 spot trade clear price
The daily clear spot goods are cleared at the user side in a unified way, the clear electricity price is determined by the supply and demand capacity of the power grid of the whole province, and the transaction electricity price converted to the load aggregator side is higher than the catalog electricity price in part of time.
2.2 Power utilization deviation and load distribution
The deviation between the current spot-shipment traffic electricity quantity and the actual electricity consumption of the load aggregator is assumed, and the current spot-shipment transaction price in the day is checked. Considering that the predicted value of the day-ahead aggregated load is close to the actual power utilization condition, and the day-ahead load predicted power is all bid in the day-ahead spot market, it is necessary to optimize and decompose the bid power in the day-ahead spot market, operate the day-ahead load according to the day-ahead load, and realize that the day-ahead examination power fee is 0.
2.3 optimized Range and Green Power trade periods
The load aggregator performs optimal distribution on aggregated loads, energy storage loads and the like of the electric automobiles in all the transformer substations in the region, the load aggregator participates in green energy trading mainly based on wind power, and the minimum contract period of the green energy trading is monthly.
Modeling of three-spot settlement model and various flexible load models
3.1 spot Settlement model
As shown in fig. 2, the spot settlement model is used for direct medium and long term electric power trading under a spot trading framework, and the trading contract electric quantity of the market subject is decomposed according to the form of an electric power curve of each time period every day. The spot settlement adopts the day-clearing and month-clearing settlement, the actual electricity consumption is sequentially split during the day clearing, the deviation electricity quantity of the day-ahead successful bid curve and the medium-long term curve is settled, and different electricity prices are matched for calculation. The formula for settlement is as follows:
C＝Pzcq×ρzcq+(Prq-Pzcq)×ρrq+(Pss-Prq)×ρss (1)
in formula (1): c represents daily clearing cost; pzcq、Prq、PssRespectively representing the medium and long term contract decomposed electricity quantity, the current spot traffic electricity quantity before the day and the actual electricity consumption quantity of the load aggregator; rhozcq、ρrq、ρssRespectively representing medium and long term contract electricity prices, day-ahead market transaction electricity prices and day-in-time transaction electricity prices.
3.2 various flexible load models in spot mode
3.2.1 platform charging terminal load aggregation model
The charging terminal takes an electric automobile as an example, and the electric automobile does not consider the V2G mode in a multi-time and multi-space dimension collaborative scheduling model. When the charging state of the electric vehicle is lower than the safety threshold, and otherwise, the electric vehicle stops charging when the battery charging state of the electric vehicle is higher than the preset value, so that the charging state, the battery charging state and the aggregated charging upper limit of the electric vehicle meet the model constraint when the charging load of the electric vehicle is aggregated. The electric automobile aggregation model of the platform area and the load aggregator is as follows:
in formulae (2) to (3): ptjRepresents the charging load of the electric vehicle j at the time t;
indicating the charging state of the electric vehicle, 0 is uncharged or charged, and 1 is charging. PEtiTo representThe aggregated charging load of the electric automobile in the platform charging station i; pEtRepresenting the charging load of the load aggregator at time t; n represents the number of the station areas.
3.2.2 energy storage load model
The energy storage is located the district charging station, and the action of charging with the car that charges carries out unified dispatch, does not consider the electric energy of energy storage and conveys in the distribution network.
socDti＝socD(t-1)i+PDti (4)
-PDi≤PDti≤PDi (6)
In formulae (4) to (6): socDtiAnd socD(t-1)iRespectively representing the residual capacity of the stored energy i at the time t and the time t-1; pDtiThe charging and discharging power of the energy storage i at the time t is represented, the charging is represented when the value is positive, and the discharging is represented when the value is negative; pDiRepresenting the maximum charge and discharge power of the stored energy i;
and
representing the upper and lower capacity limits of the stored energy i.
Four-load aggregation business multi-region and multi-time scale energy distribution model
The operation profit of the load aggregators is researched, the long-term trade and the electricity spot trade in the green electricity trade are considered from the time dimension, and different catalog electricity prices are executed in different regions of the different transformer areas from the region dimension. The coordination operation of the aggregated load and the energy storage load of the platform charging station is realized through the method of the present profit, so that the load aggregators obtain the maximum profit.
4.1 month ago energy distribution model
4.1.1 objective function
The marketing settlement work of the trading center is carried out according to the monthly degree, and a model is established by taking the maximization of the monthly profit of the load aggregators as a target. Assuming 24 periods per day for 30 days per month, 720 periods, the objective function is shown below (7):
formula (7) represents the profit obtained by medium and long term transaction with the wind power plant, and n represents the number of charging stations in the transformer area in the model; pEti(zcq)And PDti(zcq)Respectively representing the aggregated load of the electric automobile in the station area i in the time period t and the medium and long term decomposition electric quantity of charge and discharge of stored energy; rhozcqiRepresenting the trade electricity prices, rho, of different green plants participating in medium and long term trading during a period tmltiIndicating the directory electricity prices of the i station areas during the t period.
4.1.2 constraints
(1) Monthly electricity purchasing and selling balance
Formula (8) represents that the medium and long-term green energy power purchase load is balanced with each charging load; pzcqtAnd representing the medium and long term decomposition electric quantity of the transaction with the green power plant in the period t.
(2) Electric automobile load aggregation electric quantity constraint of monthly charging station
Equation (9) represents the maximum value of the convergence of electric vehicles between similar days during the t period
And minimum value
In the meantime.
(3) Discharge capacity for energy storage without transmitting constraints in a power distribution network
-PDti-PEti＜0 (10)
Formula (10) represents that the discharge capacity of the energy stored in the charging station in the district is less than the aggregate charge capacity of the electric vehicles in the district during the t period; pDti、PEtiAnd the energy storage charging and discharging power and the electric automobile aggregated load of the station area i in the period t are shown.
(4) Operating characteristic constraints of stored energy
See section 3.2.2
4.2 day-ahead energy distribution model
4.2.1 objective function
The load prediction before the day is closer to the actual power consumption than before the month, a day-ahead energy distribution model is established, the prediction deviation is reasonably scheduled and distributed, and the profit maximization of day-ahead transactions is realized.
In formula (11): crqRepresenting the profit of the load aggregator; pEti(rq)And PEti(rq)Respectively representing the prediction deviation of the aggregated load and the stored energy of the electric automobile; pzcqtRepresenting the medium and long term transaction electric quantity of the load aggregator in the t period; rhorqtThe day ahead transaction representing the t period represents the price of the clear electricity.
4.2.2 constraints
(1) Balance of purchased and sold electric quantity in each time dimension
Formula (12) represents the deviation electricity quantity balance between the electricity purchase and sale in the day and the medium and long term, PrqtRepresenting the amount of transaction power in stock before the day for the t period.
(2) And after the optimal distribution in the day, the energy storage and discharge amount does not circulate in the power grid.
PEti(zcq)+PEti(rq)+PDti(zcq)+PDti(rq)≥0 (13)
(3) Each energy storage SOC is between the lowest capacity and the rated capacity
PDimin≤PDti(zcq)+PDti(rq)≤PDimax (14)
In equation (14): pDmaxRepresenting a rated capacity of stored energy; pDminRepresenting the lower capacity limit of the stored energy.
(4) Electric vehicle aggregate load restraint in the day
Formula (15) represents that the day-ahead optimal distribution power of the electric automobile aggregate load is between the highest value and the lowest value of the similar time period;
and
respectively representing the lowest value and the highest value of the electric vehicle aggregate load of the charging station i at the moment t on a similar day. (5) Constraint of aggregated load and energy storage load of electric automobile in each region
PEit＝PEit(zcq)+PEit(rq)(16)
PDit＝PDit(zcq)+PDit(rq)(17)
Equations (16) to (17) represent that the distributed power of the load in each period is the sum of the medium-long term and the day-ahead optimal distributed electric quantity.
(6) Operating characteristic constraints of stored energy
See section 3.2.2
4.3 simulation energy optimization method flow
As shown in fig. 3, the process of simulating energy optimization includes the following steps:
(one), data preparation
1. Obtain platform district from the car networking and fill electric pile information, for example: station area information, charging pile numbers, charging loads and the like; obtaining basic information of stored energy, for example; the station area, the energy storage number, the upper and lower capacity limits and the charging load of the energy storage;
2. inputting the directory electricity price information of each area; inputting monthly history electricity consumption and monthly electricity price information of the load aggregator participating in the medium and long term electricity trading according to the actual conditions of the load aggregator participating in the medium and long term and spot-goods electricity markets; and inputting historical data of spot transactions, wherein the historical data comprises the electric quantity and the electricity price of the market in the past and the electric quantity and the electricity price information of the real-time market in the day.
(II) Pre-monthly optimization
3. Acquiring the charging load P of the electric automobile in each district in one year on the basis of the charging load information of the districts and the electric automobiles in the step 1tjThe aggregate load P of each region is obtained by taking each region as a main bodyEtiAggregating the loads of all the districts to obtain the charging loads P of all the electric vehicles in the month by the load aggregatorEt. Dividing one year into 12 months by using working day and rest day as the division basis of similar days, and aggregating the charging load P of the provider according to each monthEtAnd obtaining the maximum and minimum aggregation loads of the load aggregators in each month. And (4) respectively adopting an average growth rate algorithm on a working day and a rest day to simulate the maximum and minimum aggregated loads of the future transformer area.
4. And (3) taking the formula (7) as an objective function, taking the formulas (4) - (6) and (8) - (10) as constraint conditions, calling a linear function of MATLAB by adopting a linear programming method to perform programming solution, and acquiring a running curve of energy storage in next 30 days and the aggregated load of the electric automobiles in each district when a load aggregator acquires the monthly maximum profit. The obtained optimization curve before month is shown in fig. 4 (note that the optimization curve of one of 30 days is cut out in the figure).
(III) optimization before day
5. Based on the charging load information of the districts and the electric vehicles in the step 1, the charging load P of the electric vehicles in each district before one day is obtainedtjReferring to the load aggregation model of the charging terminal of the distribution area established in section 3.2.1, the aggregation load P of each distribution area is obtained by taking each distribution area as a main bodyEtiAggregating the loads of all the districts to obtain the charging load P of all the electric vehicles by the load aggregator on the dayEt. With the daily load PEtSimulating the random fluctuation of daily load by using the rand function of MATLAB as a basis, and simulating the fluctuation condition of the electric vehicle polymerization load in the next day, which is predicted before a month;
6. taking the spot transaction before the day in the step 2 as basic data, and acquiring the transaction clear electricity price rho of each time period of the spot market before the dayrqtThe price ρ of the electricity is obtained in 24 hours of the dayrqtOn the basis, random fluctuation of daily transaction clear electricity price is simulated by using a rand function of MATLAB, so that transaction clear electricity price rho of each time period of the next day is obtainedrqt. The price rho of the clear electricity is obtained by trading in each time period of the next dayrqtSubtracting the medium and long term market trade electricity price rhozcqiObtaining: fluctuating electricity prices in spot markets and medium and long-term markets at various time intervals.
7. And (3) taking the formula (12) as an objective function, taking the formulas (13) to (17) as constraint conditions, adopting a linear programming method, and utilizing a linear function of MATLAB to perform programming solution to obtain a distribution curve of the fluctuating load built in each area of the electric automobile and the energy storage when the fluctuating load obtains the maximum profit in the spot goods in the day ahead. The resulting optimized dispensing curve is shown in fig. 5.
(IV) Final optimization
8. And overlapping the load curve optimized before the month and the load fluctuation curve optimized before the day to obtain a final optimization curve before the day.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A load aggregator energy optimization method based on an off-the-shelf mode is characterized by comprising the following steps:
step 1: constructing an energy distribution model; acquiring historical operating data in the power transaction process of the energy distribution model, and predicting the charging load of a load aggregator;
step 2: constructing an objective function in a pre-month energy distribution model, and solving the objective function by taking the monthly highest profit as an objective, wherein the objective function is balanced with monthly purchased and sold electric quantity, monthly charging load and stored energy discharge capacity are not transmitted in a power distribution network, and the constraint conditions of stored energy running characteristics are established;
and step 3: and constructing an objective function and time dimension purchase and sale electric quantity, energy storage and discharge quantity, energy storage capacity, day-time charging load and energy storage operating characteristic constraint conditions in a day-ahead energy distribution model, and solving the objective function with the highest daily profit as an objective.
2. The off-the-shelf based load aggregator energy optimization method of claim 1, wherein: step 1, predicting the charging load of a load aggregator based on a constructed platform charging terminal load aggregation model, wherein the constructed model formula is as follows:
in the formula, PtjRepresents the charging load of the charging terminal j at time t;
indicating the charging state of the charging terminal, 0 being uncharged or charged, 1 being charging; pEtiRepresenting the aggregate charging load of the charging terminals in the platform charging station i; pEtRepresenting the charging load of the load aggregator at time t; n represents the number of the station areas.
3. The off-the-shelf based load aggregator energy optimization method of claim 2, wherein: predicting the energy storage operating characteristics by constructing an energy storage load model, wherein the constructed energy storage load model comprises the following formula:
soct,i＝soc(t-1),i+Pt,i
-Pt,i≤Pt,i≤Pt,i
wherein the formula is as follows: soct，iAnd soc(t-1)，iRespectively representing the residual capacity of the stored energy i at the time t and the time t-1; pt，iThe charging and discharging power of the energy storage i is represented, the charging is represented when the value is positive, and the discharging is represented when the value is negative;
and
representing the upper and lower capacity limits of the stored energy i.
4. The off-the-shelf based load aggregator energy optimization method of claim 3, wherein: the energy distribution model before the month in the step 2 is as follows:
the load aggregator obtains profit in medium and long term transaction, and the objective function is as follows:
in the formula, n represents the number of charging stations in the transformer area in the model; pEti(zcq)And PDti(zcq)Respectively representing the aggregation load of a charging terminal and the medium-and-long-term decomposition electric quantity of charging and discharging of stored energy in the station area i in the time period t; rhozcqiRepresenting the trade electricity prices, p, of different power plants participating in medium and long term trading during a period tmltiThe catalog electricity price of the station zone i in the time period t is represented;
the monthly power purchase and sale balance constraint is as follows:
in the formula, PzcqtRepresenting the medium and long term decomposition electric quantity of the transaction with the power plant in the period t;
the monthly charging load constraints are:
the discharge capacity of the stored energy does not transmit constraints in the distribution network:
-PDit-PEit＜0
PDti、PEtirepresenting the energy storage charging and discharging power of the station area i and the aggregation load of the charging terminal in the t period;
and energy storage operating characteristic constraints.
5. The off-the-shelf based load aggregator energy optimization method of claim 3, wherein: the day-ahead energy distribution model constructed in step 3 is as follows:
profit for the day-ahead transaction, the objective function is:
in the formula, CrqRepresenting the profit of the load aggregator; pEti(rq)And PEti(rq)Respectively representing the prediction deviation of the aggregated load and the stored energy of the charging terminal; pzcqtRepresenting the medium and long term transaction electric quantity of the load aggregator in the t period; rhorqtThe day-ahead trading of the clear price in the period t is represented;
the time dimension purchasing and selling electric quantity balance constraint is as follows:
in the formula, PrqtThe transaction electric quantity of spot goods before the t period day is represented;
the energy storage discharge capacity constraint is as follows:
PEti(zcq)+PEti(rq)+PDti(zcq)+PDti(rq)≥0；
the energy storage capacity constraint is:
PDimin≤PDti(zcq)+PDti(rq)≤PDimax
in the formula (I), the compound is shown in the specification,
and
respectively representing the lowest value and the highest value of the charging load aggregate load of the charging station i at the moment t on a similar day;
the charge load constraint in the day is:
and energy storage operating characteristic constraints.
6. The off-the-shelf based load aggregator energy optimization method according to claim 4 or 5, wherein: and programming and solving the profit objective of the medium and long term transaction or the profit objective function of the prior transaction by using a linear programming method and using a linear function in MATLAB.
7. The off-the-shelf based load aggregator energy optimization method of claim 4, wherein: the determination of the monthly charge load threshold range includes obtaining a maximum value and a minimum value of the monthly charge load from historical data and predicting the threshold range in the monthly charge load constraint condition using an average growth rate algorithm based on data obtained on a daily basis.
8. The off-the-shelf based load aggregator energy optimization method of claim 1, wherein: and overlapping the optimization result obtained by the pre-menstruation energy distribution model analysis and the optimization result obtained by the day-ahead energy distribution model to obtain a final optimization distribution scheme.
9. The off-the-shelf based load aggregator energy optimization method of claim 1, wherein: the constructed energy distribution model comprises a load aggregator, and each energy storage terminal and each load terminal which perform power transaction with the load aggregator.
10. Load aggregator energy optimization device based on under the spot-shipment mode, its characterized in that: comprises that
The energy distribution model is configured to obtain historical operation data in the electric power transaction process of the energy distribution model and predict the charging load of the load aggregator;
the system comprises a monthly energy distribution model, a monthly energy distribution model and a monthly operation characteristic constraint condition, wherein the monthly energy distribution model is configured to construct a monthly energy distribution objective function, monthly purchase and sale electric quantity balance, monthly charging load, energy storage and discharge capacity are not transmitted in a power distribution network, and the energy storage and operation characteristic constraint condition is solved to obtain monthly maximum profit as an objective solution objective function;
and the day-ahead energy distribution model is configured to construct constraint conditions including a day-ahead energy distribution objective function and time dimension purchase and sale electric quantity, energy storage and discharge quantity, energy storage capacity, day-time charging load and energy storage operation characteristic so as to obtain the highest daily profit and solve the objective function as the target.
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