CN111899122A - User decentralized clearing method based on energy storage control - Google Patents
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Abstract
The invention discloses a user decentralized clearing method based on energy storage control, which comprises the steps of firstly modeling an electricity utilization scene of a user, carrying out scene modeling according to an electricity generation and utilization plan and an electricity utilization behavior habit of the user, and carrying out short-term electricity utilization behavior prediction based on the electricity utilization scene and the user plan; secondly, performing energy storage control by analyzing and predicting market behaviors of the scattered markets in the day ahead; through the charging and discharging of the stored energy, the power demand is translated along the time axis direction, and the optimized energy storage control decision is carried out through the energy storage value model, so that the power based on the energy storage control is realized. The invention optimizes the load fluctuation condition of the power grid by utilizing the stored energy and improves the benefit of users.
Description
Technical Field
The invention relates to an energy technology of an electric power market, in particular to a user decentralized clearing method based on energy storage control.
Background
With the continuous promotion of electric power marketization, users are more and more willing to actively participate in electric power market trading. On one hand, market trading can enable users to further master price initiative, and electric power trading experience and enthusiasm of the users are improved; on the other hand, the user can promote the trading income of the user by means of market through judgment and technical forms of the market of the user in the process of participating in trading.
However, with diversified user conditions, the existing trading method has poor trading flexibility, poor user response capability and relative passivity, and meanwhile, unpredictable energy storage participation is not beneficial to stable operation of a power grid.
Disclosure of Invention
In order to overcome the disadvantages of the prior art, it is an object of the present invention to provide a user decentralization method based on energy storage control, which solves the above problems.
The purpose of the invention is realized by adopting the following technical scheme:
a user scatter-clear transaction method based on energy storage control comprises the following steps:
step 1: modeling the value of the energy storage device: and establishing an energy storage value model according to the energy storage characteristics of the energy storage equipment and the heterogeneity of the energy storage value of the energy storage equipment in different periods of the market, and using the energy storage value model for clearing calculation.
Step 2: and (3) predicting the quotation of the user: and for the quotation characteristics and behavior habits of different electricity vendors, forecasting the quotation curve at a future moment by using a BP network model and a historical clearing curve.
And step 3: calculating a dispersion clearing strategy: and performing double-layer planning operation according to the previously established value model and related constraints of the energy storage equipment, and solving a quotation decision for maximizing the benefits of the energy storage equipment.
Preferably, in the step 1, the relevant energy storage characteristics include a maximum capacity of energy storage, a charge-discharge efficiency, a charge-discharge power, and the like, and the value of the energy storage in the market is heterologously expressed in the value of the energy storage variation electric quantity in the current time period of the market.
Preferably, in step 2, each time period of the user is calculated separately considering 96 time periods of one point every 15 minutes of the day. The quotation matching strategy adopted in the market is high-low matching, and the following steps are dispersed: the user reports the related quotation and the report quantity on the electricity purchasing side and the electricity selling side respectively, all the reported quotation information is collected on the platform side, two quotation curves of electricity purchasing and selling are formed according to the price sequence, the user at the front end in the curves is matched, the matched price is the arithmetic average value of the quotation of the two sides, and the clearing is finished until the two curves are crossed. In the user quotation prediction, a quotation curve of a user in the past 7 days and a quotation curve of the user in the past two years in the present day are used as training set input, and a data-driven analysis mode is used for calculating the quotation curve of the user in the present day.
Preferably, in step 3, a quotation mode and an energy storage control mode under the condition of maximizing user profits are calculated by adding a double-layer plan of an energy storage device value model, the upper layer is an energy storage decision and quotation calculation for maximizing user profits, and the lower layer is a market clearing model. Under this calculation, several basic assumptions are considered:
(1) the power generation amount of the user in each time interval participates in the control and market process, namely the power only comprises two parts, namely the power participating in market trading and the power participating in energy storage.
(2) The market consideration links are the single-stage day-ahead market mentioned above, and the market clearing form is high-low matching and dispersed clearing.
(3) Market users all participate independently in the market trading process without forming a federation or aggregator.
(4) The user participates in the decision making in an intelligent mode, namely, the price quoted is determined by the marginal electric quantity of the user.
(5) The effect of the output resistor plug is not considered.
Compared with the prior art, the invention has the beneficial effects that: compared with the traditional electric power trading form in the market, the electric power trading method based on the energy storage control considers diversified user conditions, enables users to have certain demand side response capability, and realizes active regulation of the electric network load involved in energy storage through the marketization form, so that on one hand, flexible trading requirements of the users are met, trading benefits and trading enthusiasm of the users are improved, on the other hand, the electric power trading method is beneficial to relieving the energy flow pressure of the electric network, improving the operation stability of the electric network, and helping the electric network to realize peak clipping and valley filling to a certain degree.
Drawings
FIG. 1 is a flow chart of market matching liquidation
Fig. 2 is a flow chart of user energy storage control and market quotation.
Detailed Description
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. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
A user scatter-clear transaction method based on energy storage control comprises the following steps:
step 1: firstly, a user models a value model of energy storage equipment, for the user, the value of energy storage of the user mainly comes from the value of the stored electric quantity of the user at the moment of energy storage, in brief, when the user i participates in market trading, if the market trading price in a k time period is p, when the electric energy with v capacity is stored to the energy storage, the energy storage value of the part of the stored energy should be p
Wherein the content of the first and second substances,
and the energy storage value of the user i in the k time period, p is the market trading price in the k time period, and v is the stored electric energy capacity.
When the electric quantity is stored in the form of energy storage, the value of the electric quantity is directly hooked with the storage quantity, and the electric quantity does not change along with the change of time and market electricity price and is only influenced by the attenuation of the energy storage.
Therefore, for the user group N {1,2, …, i, … N } the energy storage value model is defined as:
in the formula, the energy storage variation of the user group in the time period k is integrated into
When the stored energy is discharged, the value is positive; when the stored energy is charged, the value is negative; dBThe attenuation coefficient of the stored energy is the attenuation residual proportion of the stored energy in each time interval, and the attenuation coefficient mainly depends on the energy storage material; v. ofi,k-1The reserve capacity of the previous time interval is stored, and the reserve capacity change and the transaction electric quantity value of the current time interval jointly form the electricity generation and utilization quantity of the user in the time interval;
for the user's transaction benefits over the period of time,
the transaction electric quantity of the user in the time period is represented, and the absolute number of the quotient of the transaction electric quantity and the transaction electric quantity represents the electric energy value of the user in the time period; eta represents the charge-discharge efficiency of the stored energy, and the charge efficiency and the discharge efficiency are different, and eta is defined as follows:
wherein etacRepresents the charging efficiency, ηdRepresents the discharge efficiency;
the efficiency depends on the energy storage material and the energy storage using environment, and the charge-discharge efficiency is 0.85-0.97 in a normal state;
according to the formula, when energy storage is discharged, firstly, the energy storage value when the energy storage is not discharged in the period is obtained by considering the attenuation of electric energy, then, the discharging is carried out, because the discharging efficiency exists, the actual discharging amount is larger than the obtained energy storage variable amount, and the residual energy storage value after the discharging is obtained through the proportional change of the energy storage electric quantity before and after the discharging;
when the energy storage is charged, the stored energy is multiplied by the average price of the user transaction in the current time period after the original energy storage attenuation is calculated, and the energy storage value of the stored electric quantity in the current time period can be obtained.
Step 2: in this market, the matching clearance mode is a high-low matching, and the dispersion clearance: the specific flow is shown in fig. 1, a user reports the quoted price and the report quantity based on the power utilization plan and the electric quantity prediction at the power purchasing or power selling side, all the quoted price information is collected on a trading platform, two quoted price curves of purchasing and selling power are formed according to the price sequence, the user at the front end in the curves is matched, the matched price is the arithmetic mean value of the quoted prices of both sides, and the clearing is finished until the two curves are crossed.
In the user quotation prediction, a BP neural network is used, a quotation curve of a user in the past 7 days and a quotation curve of the user in the past two years in the present day are used as training set input, and a data-driven analysis mode is used for calculating the quotation curve of the user in the present day.
After the completion, the user utilizes the quotation prediction information to plan the energy storage control behavior and the quotation behavior through the optimal quotation strategy, and accordingly, the optimization benefit is obtained.
And step 3: and performing energy storage control and quotation strategy calculation considering the user income maximization, wherein the user income maximization is planned to be a double-layer model, and the income calculation model of the user i under the time period k is calculated as follows:
wherein the content of the first and second substances,
vmin≤vi,k≤vmaxformula 10
subject to：
pmin≤pi,k≤pmaxFormula 14
pmin≤pj,k≤pmaxFormula 15
The upper model is a profit maximization model of the user, in which ui,kIndicating that the user is at time kThe yield of (a) to (b) is,
for the benefit of the transaction at that moment,
and adding the energy storage value change of the moment compared with the previous moment to obtain the total income of the user at the moment. p is a radical ofi,kThe transaction offer at time k on behalf of user i.
And
representing the energy storage minimum and maximum power constraints.
The formula is a calculation formula of the trading income of the user i, and the trading price is the average value of the quoted prices of the matched users due to the adoption of a scattered clearing trading mode,
a bottom-of-pocket sales revenue representing an unmatched portion of the user, having a value of:
i.e. the product of the electric quantity of the bottom part of the bag and the price of the bottom part of the bag.
The formula is the energy storage value, and the formula is the electric quantity constraint, which represents that the electric quantity of the user must trade in the market or perform energy storage conversion in a time period. The formula is the energy storage reserve of the user in the current time period. The formula is energy storage capacity constraint, and the formula is energy storage power constraint.
The lower model is a market clearing model and is used for calculating trading users and trading prices matched with the decision users. The objective function is the maximization of social welfare, the user group I is divided into two groups according to the identity of the user group I in the transaction, and the electricity purchasing group IbAnd electricity selling group Is。
Match the electricity purchased and sold on behalf of the user with the transaction amount of electricity, ci,kThe power generation cost corresponding to the electricity selling user.
The formula is trade electric quantity balance constraint, and the formula is trade price constraint.
From this, the user is through comparing energy storage value and trade income, and the point will deposit the electric quantity in the energy storage at the price low point, takes out at the price high point and sells, can learn user's income maximize decision-making.
Taking a certain city in Jiangsu as an example, a daily power generation and power utilization curve and a quotation curve of 24 photovoltaic users under a 38KV transformer are simulated, the average daily power generation peak value of the users is 4KW, the daily regional power generation total amount and the daily regional power utilization total amount are 651MWh and 613MWh respectively, and through calculation, regional users participate in transactions through energy storage control, and the income is improved by 8% -15%.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A user decentralized clearing method based on energy storage control is characterized in that decentralized clearing control is participated in through energy storage, and the method comprises the following steps:
step 1, modeling the value of energy storage equipment: according to the energy storage characteristics of the energy storage device and the heterogeneity of the energy storage value of the energy storage device in different periods of the market, the energy storage value of the user i in the k period is as follows:
wherein the content of the first and second substances,
the energy storage value of the user i in the k time period is shown, p is the market trading price in the k time period, and v is the stored electric energy capacity;
for user group
The energy storage value model is defined as:
in the formula, the energy storage variation of the user group in the time period k is integrated into
When the stored energy is discharged, the value is positive; when the stored energy is charged, the value is negative; dBThe attenuation coefficient of the stored energy is the attenuation residual proportion of the stored energy in each time interval, and the attenuation coefficient mainly depends on the energy storage material; v. ofi,k-1The reserve capacity of the previous time interval is stored, and the reserve capacity change and the transaction electric quantity value of the current time interval jointly form the electricity generation and utilization quantity of the user in the time interval;
for the user's transaction benefits over the period of time,
the transaction electric quantity of the user in the time period is represented, and the absolute number of the quotient of the transaction electric quantity and the transaction electric quantity represents the electric energy value of the user in the time period; eta represents the charge-discharge efficiency of the stored energy, and the charge efficiency and the discharge efficiency are different, and eta is defined as follows:
wherein etacRepresents the charging efficiency, ηdRepresents the discharge efficiency;
the efficiency depends on the energy storage material and the energy storage using environment, and the charge-discharge efficiency is 0.85-0.97 in a normal state;
according to the formula, when energy storage is discharged, firstly, the energy storage value when the energy storage is not discharged in the period is obtained by considering the attenuation of electric energy, then, the discharging is carried out, because the discharging efficiency exists, the actual discharging amount is larger than the obtained energy storage variable amount, and the residual energy storage value after the discharging is obtained through the proportional change of the energy storage electric quantity before and after the discharging;
when the energy is stored and charged, the stored energy is multiplied by the average price of the user transaction in the current time period after the original energy storage attenuation is calculated, and the energy storage value of the stored electric quantity in the current time period can be obtained;
step 2, predicting the user quotation: for the quotation characteristics and behavior habits of different electricity vendors, forecasting the quotation curve at a future moment by utilizing a BP network model and a historical clearing curve recorded by a database;
and step 3: calculating a dispersion clearing strategy: and performing double-layer planning operation according to the previously established value model and related constraints of the energy storage equipment, and solving a quotation decision for maximizing the benefits of the energy storage equipment.
2. The energy storage control-based user decentralization clearing method according to claim 1, wherein: in the step 1, the energy storage characteristics comprise the maximum capacity of energy storage, the charge-discharge efficiency and the charge-discharge power; the value of the stored energy in the market is heterologously expressed in the value of the stored energy variable electric quantity in the current time period of the market.
3. The energy storage control-based user decentralization clearing method according to claim 1, wherein: in step 2, equally spaced time periods are set, with each time period for the user being calculated separately.
4. The energy storage control-based user decentralization clearing method according to claim 3, wherein: the 96 periods of one point every 15 minutes are set at equal intervals.
5. The energy storage control-based user decentralization clearing method according to claim 3, wherein: in step 3, a price quotation matching strategy of high-low matching is adopted in the market, and the following steps are dispersed: the user reports the quotation and the report amount on the electricity purchasing side and the electricity selling side respectively, the platform side collects all the reported quotation information, two quotation curves of electricity purchasing and selling are formed according to the price sequence, the user at the front end in the curves is matched, the matched price is the arithmetic mean value of the quotation of the two sides, and the clearing is finished until the two curves are crossed.
6. The energy storage control-based user decentralization clearing method according to claim 5, wherein: in the user quotation prediction, a quotation curve of a user in the past 7 days and a quotation curve of the user in the past two years in the present day are used as training set input, and a data-driven analysis mode is used for calculating the quotation curve of the user in the present day.
7. The energy storage control-based user decentralization clearing method according to claim 1, wherein: in the step 3, a quotation mode and an energy storage control mode under the condition of maximizing user profits are calculated by adding a double-layer plan of an energy storage equipment value model, the upper layer is an energy storage decision and quotation calculation for maximizing user benefits, and the lower layer is a market clearing model.
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